Antibiotic exposure is often inadequate in critically ill patients with severe sepsis or septic shock and this is associated with worse outcomes. Despite markedly altered and rapidly changing pharmacokinetics in these patients, guidelines and clinicians continue to rely on standard dosing schemes. To address this challenge, we developed AutoKinetics, a clinical decision support system for antibiotic dosing. By feeding large amounts of electronic health record patient data into pharmacokinetic models, patient-specific predicted future plasma concentrations are displayed graphically. In addition, a tailored dosing advice is provided at the bedside in real time. To evaluate the effect of AutoKinetics on pharmacometric and clinical endpoints, we are conducting the Right Dose Right Now multicenter, randomized controlled, two-arm, parallel-group, non-blinded, superiority trial.All adult intensive care patients with a suspected or proven infection and having either lactatemia or receiving vasopressor support are eligible for inclusion. Randomization to the AutoKinetics or control group is initiated at the bedside when prescribing at least one of four commonly administered antibiotics: ceftriaxone, ciprofloxacin, meropenem and vancomycin. Dosing advice is available for patients in the AutoKinetics group, whereas patients in the control group receive standard dosing.The primary outcome of the study is pharmacometric target attainment during the first 24 h. Power analysis revealed the need for inclusion of 42 patients per group per antibiotic. Thus, a total of 336 patients will be included, 168 in each group. Secondary pharmacometric endpoints include time to target attainment and fraction of target attainment during an entire antibiotic course. Secondary clinical endpoints include mortality, clinical cure and days free from organ support. Several other exploratory and subgroup analyses are planned.This is the first randomized controlled trial to assess the effectiveness and safety of bedside data-driven automated antibiotic dosing advice. This is important as adequate antibiotic exposure may be crucial to treat severe sepsis and septic shock. In addition, the trial could prove to be a significant contribution to clinical pharmacometrics and serve as a stepping stone for the use of big data and artificial intelligence in the field.NL6501/NTR6689. Registered on 25 August 2017.Netherlands Trial Register (NTR), European Clinical Trials Database (EudraCT), 2017-002478-37. Registered on 6 November 2017. Antibiotics are a cornerstone in the treatment of sepsis and septic shock . Their eInadequate antibiotic dosing may be an important modifiable cause for the morbidity and mortality of sepsis. Indeed, there is a strong rationale for optimization of antibiotic exposure in septic patients given the robust relationship between antibiotic exposure and bacterial killing . HoweverDespite these considerations, intensive care physicians continue to rely on standard dosing schemes for prescribing antibiotics. This may be caused by the fact that clinically relevant pharmacokinetic knowledge on antibiotic dosing among intensive care professionals is insufficient . For exaExisting solutions that address these challenges include therapeutic drug monitoring (TDM) and dosing nomograms. However, both have significant drawbacks . NomograTo address these challenges, we now propose an innovative solution that extracts electronic health record data in real time to personalize antibiotic dosing. This is useful as these electronic health records have now become rich data warehouses, especially in the context of intensive care medicine. Large amounts of precise patient information that determines their pharmacometric profile are constantly and instantly available. Therefore, we set out to develop AutoKinetics software that connects with every modern electronic health record. AutoKinetics uses all available data at the time of prediction to forecast future drug plasma levels using pharmacometric models. These typically represent individual patient characteristics including parameters from connected monitors, life support devices as well as laboratory values.AutoKinetics is reminiscent of TDM, albeit without its drawbacks and with two distinct advantages. First, the direct and automated connection between AutoKinetics and the electronic health records circumvents manual data entry, which reduces error rates and may improve the accuracy of plasma concentration predictions. Second, through its integration with the electronic patient record, dosing guidance is always available immediately and enables prompt response to the patient’s clinical course. As such, AutoKinetics may be regarded as an expert system and a stepping stone for the use of artificial intelligence with big data in the domain of clinical pharmacometrics.As illustrated in Fig. It is our objective to evaluate the effect of providing bedside data science-driven decision support by AutoKinetics to healthcare professionals treating critically ill septic patients. To evaluate our hypothesis that bedside dosing guidance by AutoKinetics will improve antibiotic plasma-level target attainment and patient-relevant outcome measures, we designed the Right Dose Right Now study, a randomized controlled superiority trial.The Right Dose, Right Now trial is a multicenter, randomized controlled, two-arm, parallel-group, non-blinded, superiority trial. It is designed to investigate the effect of providing intensive care professionals with bedside, data-driven, individualized decision support from our AutoKinetics software on antibiotic plasma-level target attainment and patient-relevant clinical outcome measures.The trial is being conducted at the Department of Intensive Care Medicine of each of two hospitals in Amsterdam, The Netherlands: Amsterdam UMC, location VUmc, a university medical center with 22 intensive care beds; and OLVG, location Oost, a large teaching hospital with 18 intensive care beds. Both are tertiary referral centers for intensive care medicine. Figure Age 18 years or olderIntensive care treatmentSuspected or confirmed infectionClinical decision to start antibiotic therapy○ Suspected or confirmed serum lactate greater than 2 mmol/L○ Requirement for any vasopressor support in any doseOne or both of the following indices of disease severity:This study will recruit critically ill septic patients who require antibiotic treatment. All patients meeting the following criteria will be eligible for inclusion:In this study we focus on four of the most frequently prescribed antibiotics for treating sepsis in the participating centers. These are the β-lactam antibiotics ceftriaxone and meropenem; the fluoroquinolone ciprofloxacin; and the glycopeptide vancomycin. Inadequate antibiotic exposure occurs with a frequency of up to 60% for all of these antibiotics , 12 and An extensive literature review was conducted to find the most suitable pharmacometric models published for the study of antibiotics in intensive care. Candidate models were tested using retrospective and prospective antibiotic plasma-level data. A further prospective analysis was done to evaluate these models in our patient population using data from routine sampling. Finally, by means of a model validation study for each antibiotic, the most adequate model was chosen and parameters were calibrated prior to implementation. The results of model evaluations and calibrations will be reported separately.Dosing targets are based on results from clinical and preclinical studies, and focus on the prevention of underdosing as this may lead to excess mortality. For the β-lactam study antibiotics, the time that the concentration or free concentration remains above the minimum inhibitory concentration of the bacteria involved (fT > MIC) was selected. For vancomycin and ciprofloxacin, this index is the area under the time concentration curve divided by the minimum inhibitory concentration of the bacteria involved (fAUC / MIC) . Dosing Screening and inclusion will be performed by the attending physicians. For this, our AutoKinetics software features an inclusion and randomization module instantly available at the bedside at the click of a button. This is essential to ensure that the treatment team is able to follow dosing advice right from the start of antibiotic therapy.Patients are primarily included for one of the four study antibiotics, even if they are treated with more of them. If multiple eligible antibiotics are started at the same time, the choice of antibiotic for primary inclusion is left at the discretion of the physician. For example, if treatment with both ceftriaxone and ciprofloxacin is indicated, the patient may be included for either one of them.After inclusion, the patient will be allotted to the control group or intervention (AutoKinetics) group. In the AutoKinetics group, the treating healthcare professionals will receive personalized antibiotic dosing advice. Importantly, regardless of the antibiotic the patient is included for, the treatment team will receive advice for all study antibiotics. In the control group, no such advice is provided and the treating healthcare professionals are advised to prescribe standard dosing. The electronic randomization module, which is incorporated into the AutoKinetics software, uses a 1:1 allocation ratio with stratification by study center, gender, and age with a cutoff point of 65 years using minimization techniques .For feasibility reasons, the treatment team will not be blinded to the study intervention. Patients are also not blinded to the intervention, although it is unlikely that these critically ill and frequently unconscious patients will be aware of which dose is administered. Plasma-level determinations that will be used to assess the study effect will not be available to the treatment team. For vancomycin, routine therapeutic drug monitoring is used at both study sites. Those plasma levels are determined using a separate workflow in addition to the study samples and will be available in both groups as per standard practice. To control for ascertainment bias, the outcome assessors and data analysts will be blinded.Patients treated in the ICU are included as soon as they meet the inclusion criteria. They stay included for the duration of their hospital admission. If a patient is readmitted to the ICU, they stay within their allocated treatment arm. In the rare event that patients who are first included in the ICU of one participating hospital are then readmitted to the ICU of the other participating hospital as part of a new hospital admission, these patients may theoretically be included in the study again and allocated to a possibly different treatment arm again if they meet the inclusion criteria.For patients who are included in the AutoKinetics study arm, it is at the physician’s discretion to follow the advice. They have to manually enter the dose order into the electronic medical record. Physicians are recommended to check AutoKinetics at least once per shift, so at least three times per day. Physicians are asked to provide feedback through the user interface of AutoKinetics, especially if they choose not to follow the advice. AutoKinetics provides advice on dose and dose interval; the duration of antibiotic treatment and choice of antibiotics are left to the physician. In both groups, all other treatments are also left to the treatment team.For all patients, blood sampling will be timed right after dosing, halfway through the dosing interval and right before the next dose for the first dosing interval; and daily before a next dose for the following dosing intervals. In the case of continuous infusions, sampling will be performed 1 h after a loading dose and at least daily thereafter. At one of the study sites , antibiotic plasma concentrations are also monitored as part of a continuous quality control project. Their results are not available to the treating physicians. We will use these results in addition to the samples specifically taken in the context of the present study.For patients in the AutoKinetics study arm, antibiotic dosing is changed back to dosing according to clinical guidelines at ICU discharge. Dosing according to AutoKinetics is reinstated in the case of readmission to the ICU within the same hospital admission.At hospital discharge, and at 6 months after hospital admission, surveys assessing quality of life and societal costs will be carried out as further specified in the following. Therefore, the maximum window in which a patient is included in the study is the duration of hospital admission plus 6 months.In the DALI study , up to 6To avoid any treatment delay, the ethics committee has approved initial inclusion under deferred consent as critically ill patients are often incapacitated. However, informed consent will be sought from the patient’s legal representative by the research team within 48 h following inclusion. The consent form explicitly describes that the study may have already commenced under deferred consent and that the patient will be withdrawn from the study should consent not be given. In select cases, patients may be able to provide informed consent themselves. If not, additional informed consent will be sought from the patient after discharge from the intensive care department. To avoid selection bias, all patients who die within 48 h following inclusion and before informed consent has been obtained remain in the study. All research team members have obtained Good Clinical Practice certification or its equivalent.The primary endpoint is pharmacometric target attainment during the first 24 h following randomization for the antibiotic that patients are randomized for. This target has been defined as 75%-T0–24 > 4 × MIC for ceftriaxone and meropenem, where 75%T0–24 denotes 75% of the time ; and as AUC0–24/MIC > 300 for vancomycin or AUC0–24/MIC > 94 for ciprofloxacin, which is also 75% of the target used for dosing recommendations. The area under the time–concentration curve is calculated by the trapezoidal rule for 1-min time intervals. We assume a concentration of 1 mg/ml for the MIC based on the consistent pattern of low antimicrobial resistance in the Netherlands unless the MIC has been specifically determined and electronically transferred to AutoKinetics.AutoKinetics is designed to reach desired antibiotic plasma levels faster as well as to better maintain desired antibiotic plasma levels during the course of antibiotic therapy. We therefore define our two pharmacometric secondary endpoints as the fraction of plasma-level target attainment during an antibiotic therapy course (%-TA) and the time to attainment of plasma target levels (time to TA).The %-TA is the fraction of days of the antibiotic therapy course for which the patient achieves the pharmacometric targets as defined in the primary endpoint. The start of antibiotic therapy is defined as the time of randomization. The end of therapy is defined as either: the moment of the last dose plus the dosing interval; the moment the physician stops the antibiotic order; the moment the antibiotic order expires without restarting within 48 h; or the end of ICU admission.For ceftriaxone and meropenem, the time to TA is defined as the time until a concentration of 4 × MIC or greater has been reached for 75% of a 24-h time period For vancomycin and ciprofloxacin, the time to TA is defined as the time until 75% of the targeted AUC/MIC has been reached during 24 h. As the targeted AUC0–24/MIC is 400 h for vancomycin and 125 h for ciprofloxacin, 75% of those targets amount to 300 h and 94 h respectively.X × MIC where % is predefined as 25%, 50%, 75% or 100% and X is specified as 1, 2, 3 or 4. Note that 75% > 4 × MIC overlaps with the primary pharmacometric endpoint. These predefined secondary endpoints are part of an exploratory analysis into the relationship between pharmacometric target attainment and clinical endpoints. A post-hoc analysis will be performed to investigate the relationship between clinical endpoints, in particular clinical cure, and pharmacometric target attainment.We will report actual pharmacometric targets that are reached in the first 24 h and over the course of antibiotic therapy, either as a percentage of time above MIC for ceftriaxone and meropenem or as the AUC / MIC for ciprofloxacin and vancomycin. In addition, we will explore target attainment using different predefined percentages. These predefined percentages are 25%, 50% and 100%. For the β-lactams, we will also report the % of time above It is important to note that while patients are initially randomized for a single antibiotic, they often receive a combination of antibiotics for which they may receive personalized dosing using AutoKinetics. Therefore, the analysis of clinical endpoints will be based on group allocation (i.e. AutoKinetics versus control), instead of per antibiotic. Mortality was selected as the primary safety endpoint, and analyzed at hospital discharge, day 28 and 6 months. Further secondary clinical endpoints include the attainment of clinical cure , length of ICU and hospital stay, delta Sequential Organ Failure Assessment (SOFA) score at 96 h (between first SOFA score and SOFA score on that day depending on the date of randomization (at admission or at a later date)), days free of ventilator support, days free of prone positioning, days free of hemofiltration, days free of other organ days and days free of delirium (as assessed by routinely monitored CAM-ICU and/or DOS scores), all during ICU admission.Using the EuroQol EQ-5D-5L, the iMTA Medical Consumption Questionnaire (iMTA MCQ) and the iMTA Productivity Cost Questionnaire (iMTA PCQ) tools, the quality of life, calculated in quality-adjusted life years (QALY), and societal costs, based on the questionnaire results, will be assessed at hospital discharge and after 6 months for all included patients and compared between groups –28.These values will be used for an economic evaluation. Incremental cost-effectiveness ratios (ICERs) will be calculated for the ratio of difference in costs between the study groups to the difference in clinical effects . Furthermore, cost-effectiveness acceptability curves will be used to determine the cost-effectiveness of AutoKinetics in comparison with usual care for a range of different ceiling rations.Finally, physician satisfaction, as well as their compliance with the decision support software, will be assessed using structured interviews and questionnaires. For quantification of physician satisfaction, a 5-point Likert scale will be used. For quantification of compliance, the log files of AutoKinetics will be used to calculate the percentage of AutoKinetics dose recommendations followed and relate this to potential explanatory factors including the antibiotic group and deviation from standard dosing.Target attainment will be evaluated based on the antibiotic plasma-level measurements at the various sampling time points. The same models that will be used by AutoKinetics to provide dosing advice will be used to generate the full course of antibiotic plasma levels over time, using a Bayesian approach. We will use dedicated pharmacometric software  to perform these estimations. We will primarily use the total concentration for analysis of pharmacometric endpoints. However, we will explore the effect of using the free fraction concentration of antibiotics for those endpoints by using the available measured free fraction concentrations.t tests, the Mann–Whitney U test or linear mixed-effect models where appropriate. As already explained, it is theoretically possible that patients are included in two different groups at two different sites. For these patients, only the pharmacometric endpoints will be evaluated.Statistical analysis will be performed using R statistical software. Primarily, an intention-to-treat analysis will be performed. A per-protocol analysis has also been planned, with group allocation determined by the actual dose regimen followed. Descriptive statistics of quantitative data  will include the mean, median, standard deviation and interquartile range where appropriate. Missing data will be handled using multiple imputation methods. Mixed-effects logistic models will be used for the analysis of binary endpoints. Mixed-effects linear models will be used for analysis of the log odds transformation of fraction-based outcomes. The between-group comparison for continuous outcomes will be assessed using unpaired U test or an unpaired t test. Differences between discharge and 6-month utility scores will be analyzed using the Friedman test. Similar analysis techniques will be used for iMTA MCQ and iMTA PCQ measurements.Time-to-event data will be analyzed by Kaplan–Meier/log-rank analysis and univariate and multivariate Cox proportional hazards regression analysis. The between-group comparison of the EQ-5D-5L scores at hospital discharge and after 6 months will be conducted using a Mann–Whitney n = 42 versus n = 42. These models will include the stratification factors  used in the randomization process and treatment allocation. As already outlined, we opted not to stratify randomization for other factors to avoid treatment delay. Therefore, we intend to also use a regression model to account for potentially modifying effects on other endpoints. A univariable regression analysis will be conducted on all dependent variables and p < 0.25 will be used for inclusion in a multivariable model. We predefine these variables as follows:Acute Physiology, Age, Chronic Health Evaluation (APACHE) classification, specifically the APACHE IV and or SOFA score as a measure of disease severityPrior antibiotic treatment (measured in hours)Time since onset of illness (based on documented history)For the primary endpoint a separate logistic regression model will be used for each antibiotic group, thus aiming for a comparison of If the sample size permits, we will perform subgroup analysis by adding these subgroups as terms in the mixed-effect models used for the whole group analysis. Endpoints for these subgroups will be target attainment in the first 24 h, %-TA, time to TA, length of stay and mortality endpoints. The subgroups are defined as follows: fulfillment of the Sepsis-3 criteria, stratification for severity of sepsis (APACHE IV score or SOFA score), type of sepsis , type of admission , presence of renal dysfunction during treatment as well as the presence of renal replacement therapy. Multiplicity will be accounted for using the Fallback procedure to control the overall probability of a type I error and keep the family-wise error rate (FWER) below 0.05.A predefined subgroup analysis will be performed to analyze the safety of personalized dosing. The endpoints of interest from a safety perspective are target attainment in the first 24 h, %-TA, clinical cure attainment, new occurrence of AKI, days free of CVVH and ICU mortality. Subgroups are made based on the cumulative dose received by the patient in the first 24 h after randomization in comparison to recommended clinical guidelines: underdosing (< 50% of recommended daily dose), similar dosing (dose within 50% and 200%) and overdosing (> 200%). We choose these categories as they coincide with the lower and upper boundaries of the dose recommendations for which AutoKinetics provides an additional safety warning and may prompt the physician to deviate from the dose recommendation by AutoKinetics. As these safety warnings may influence the physician’s decision-making, we will include physician adherence to the recommended dosing advice as a feature in the model. Furthermore, we include the exposure to each antibiotic as a fixed effect and interaction term in the mixed-effect model.An additional analysis will be performed for all treatment courses for each antibiotic, including those for which patients were not primarily randomized. An antibiotic treatment course is defined as first antibiotic administration up to the completion of antibiotic therapy course within the ICU without initiation of the same antibiotic within 48 h. For this secondary analysis we will report the primary endpoint as well as the secondary pharmacometric endpoints. Furthermore, for all antibiotic treatment courses, we will perform a subgroup analysis for which we will add the effect of physician adherence to AutoKinetics advice on the %-TA targets as an interaction term.Blood samples will be collected in standard ethylenediaminetetraacetic acid (EDTA) vials and stored at 4 °C for a maximum of 24 h. Following centrifugation and protein filtration of selected samples for determination of the free fraction, plasma will be stored at − 80 °C. Drug concentrations will be measured using a validated high-performance liquid chromatography (HPLC) column. We will determine both free and total drug concentrations to account for the effect of protein binding. Drug concentration results are stored in the electronic patient record but remain undisclosed for the duration of the trial. From the start of inclusion, relevant data will be collected automatically from each individual patient from the electronic patient record. Additionally, quality of life and societal costs assessment data are collected on paper using the EQ-5D-5L, iMTA MCQ and iMTA PCQ tools.Data will be entered into an Electronic Data Capture platform. The quality of life and societal costs will be assessed at hospital discharge and after 6 months among all included patients.Societal costs and quality of life survey data are collected on paper while all other data are collected directly from the electronic patient record. Patient data are coded. Data will not be directly traceable to individual patients. The decryption key will be kept separately by the primary investigators in Amsterdam UMC, location VUmc. Blood samples will be stored until final analysis and will be disposed of thereafter. All original data, including survey data and laboratory results, will be stored for 15 years following completion of the trial in accordance with national guidelines. Independent quality officers from the Clinical Research Bureau of Amsterdam UMC, location VUmc will monitor the study according to the regulations described under Good Clinical Practice (GCP).In this population and within this clinical context, events that can be considered serious adverse events (SAEs), suspected unexpected serious adverse reactions (SUSARs) or serious adverse device events (SADEs) within the context of this study population are death and severe new organ failure. We have defined the latter as a new start of renal replacement therapy, a new start of mechanical circulatory support and a new start of prone position ventilation. All SAEs, SUSARs and SADEs will be reported to the appropriate bodies within the time frames prescribed by national law.The sponsor/investigator has liability insurance which is in accordance with national law. The insurance applies to the damage that becomes apparent during the study or within 4 years after the end of the study.The risk qualification for this study is low risk, but this was upgraded to medium risk as vulnerable patients are involved. In accordance with national law, this implies that a formal Data Safety Monitoring Board is not needed. We will, however, conduct an interim analysis as part of our safety strategy, carried out by one of the authors (LFR) under the supervision of our statistician (TLTK). This interim analysis is focused on mortality, and the occurrence of serious adverse events after randomization will be carried out after inclusion of 50% of patients. Those conducting the interim analysis will be unblinded for those outcomes only. As this is the first trial of its kind, no stopping rules for the primary endpoint have been defined.p < 0.01, we will immediately halt the trial and consult the appropriate authorities. The level of significance should allow for detection of a mortality difference of 25% versus 39% with 1 – β = 0.8.Analysis will be performed per treatment group and per antibiotic. Between-group comparison of these clinical outcomes will be assessed using the two-group chi-square test or Fisher exact test where applicable. If the between-group difference is large enough to yield The authors declare that they have no competing interests. Both positive and negative study results will be published, in accordance with scientific integrity standards. All study data will be made available guided by the FAIR principles if reasonable and within the context of relevant laws and privacy regulations. Authorship eligibility follows accepted academic standards. We do not intend to use professional writers.The trial design follows the Standard Protocol Items Recommendations for Interventional Trials (SPIRIT). The SPIRIT Checklist is presented in Additional file Inclusion started in February 2018. Currently, over 200 patients have been enrolled in the study. The current version of the study protocol is version 5, dated 21 December 2017. We expect recruitment completion before the end of summer 2020.This is the first study to clinically evaluate bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock. This is important because better antibiotic dosing may lead to better antibiotic plasma-level target attainment, which may improve patient-relevant outcome measures.Our AutoKinetics software has been designed to optimize antibiotic exposure by reducing the time to adequate antibiotic target and to maintain target attainment for the duration of antibiotic therapy. The live connection with the electronic patient records greatly facilitates real-time and precise dosing decision support as large amounts of data may briskly find their way into the underlying pharmacometric models. This brings therapeutic drug monitoring to the next level, which is that of a stepping stone toward the use of big data and artificial intelligence in the field.It should be remembered that clinical studies related to dosing decision support and feedback as well as clinical studies assessing the relationship between antibiotic exposure and clinical outcome are scarce. Even though the relationship between antibiotic exposure and bacterial killing is well established in preclinical and clinical studies , this paGiven the association between early antibiotic administration and survival , pharmacIt is important to emphasize that this study conceptually involves two targets; that is, the target that the decision support software uses to calculate its dosing recommendations and the target that is used for the evaluation of the pharmacometric endpoints. The importance of this distinction may best be illustrated by considering perfect physician compliance with dosing advice based on a well-validated pharmacometric model. Even under these ideal circumstances, a number of patients would miss their target, although they may not be far off. Therefore, we decided to set the target used for evaluation of the pharmacometric endpoints at 75% of the target used for calculating dose recommendations. As this represents an arbitrary cutoff point and because any cutoff point will remain a subject of debate –32, we dAn interesting aspect of the trial design is that the study resembles four trials being conducted simultaneously (i.e. one for each study antibiotic). However, patients who are primarily included for a certain antibiotic will also receive dosing advice for the other study antibiotics if prescribed. One advantage of this approach is that while the sample size has been calculated for the primary pharmacometric endpoint, it allows for the grouping of all patients for analysis of the clinical endpoints and thus improves power for those secondary outcomes. A downside of such a design is that it will be more difficult to determine the relative contribution of adequate exposure to individual antibiotics to the clinical endpoints. To address this, we will use mixed regression models to better understand the importance of all relevant contributing and confounding factors.Originally, the trial design allowed for one more study antibiotic, the β-lactam cefotaxime. However, due to a national shortage prior to the first inclusion and persevering delivery constraints, this antibiotic was not available at the time of writing. As the power analysis was calculated for each antibiotic separately, we chose not to change our intended inclusion rate. We will include patients for this antibiotic should it become structurally available again and if reasonable time remains to implement its model.In order to avoid any treatment delay, we chose gender and age as stratification factors rather than more sophisticated severity of illness scores, such as APACHE IV or SOFA, as these rely on measurements that are not always immediately available on admission.For pragmatic reasons, some of the inclusion criteria were based on clinical decisions, such as suspicion of infection and need for vasopressor support. Similarly, only one of either lactatemia or need for a vasopressor was required for enrollment, instead of both. Therefore, strictly speaking, some included patients may be considered severely septic as opposed to in septic shock as defined by the Sepsis-3 criteria .Additional file 1. SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents.


A simple empirical model that captures site-specific guest contributions to the framework expansion is derived, and displays excellent agreement with the observed lattice behaviour.Control of the thermomechanical properties of functional materials is of great fundamental and technological significance, with the achievement of zero or negative thermal expansion behavior being a key goal for various applications. A dynamic, reversible mode of control is demonstrated for the first time in two Prussian blue derivative frameworks whose coefficients of thermal expansion are tuned continuously from negative to positive values by varying the concentration of adsorbed CO 2.Achieving control over the thermomechanical properties of functional materials is desirable, yet remains highly challenging. Here, the authors demonstrate continuous negative-to-positive tuning of thermal expansion in two Prussian blue analogues, by varying the concentration of adsorbed CO Among the most notable of these is an ability to selectively adsorb and release small-molecule guests over many operating cycles6. This functionality is critical to the ongoing quest for efficient gas storage and separation materials in numerous industrial contexts, such as post-combustion carbon dioxide capture and hydrogen fuel storage9. The inherent porosity and relative structural flexibility that facilitate guest sorption in many frameworks also contribute to the remarkable thermomechanical behaviours exhibited by some of these materials;11 for example, dynamic motions of framework structural components into the available void spaces give rise to negative thermal expansion (NTE) in a number of frameworks16. NTE and zero thermal expansion (ZTE) are considered desirable material properties for a variety of applications where the minimisation of strain during temperature cycling is important, and the ability to tune the rate of expansion of a device component to compensate for the positive expansion of another component is particularly advantageous. As the inclusion of guests or other modifiers in the voids of porous frameworks can directly influence the dynamic motions associated with NTE, the interplay between guest concentration and temperature introduces considerable complexity to the study of thermomechanical properties in host–guest framework systems19.Coordination frameworks have attracted sustained research interest in recent years due to their various properties of potential industrial and technological relevanceMy[M′(CN)6], M and M′ = transition metals) comprises one of the simplest coordination framework structure types and is a model system often suited to the study of phenomena common to more complicated systems. The archetypical Prussian blue framework is constructed of cyanido (CN−) ligands bridging the octahedrally coordinated M and M′ ions, which are arranged in a “rock salt” ordering pattern on a cubic lattice. M and M′ may be chosen with various combinations of valence states24, generating a series of structurally analogous frameworks known as Prussian blue analogues (PBAs). Many of the possible valence combinations result in well-defined concentrations of randomly distributed hexacyanidometallate vacancies in order to preserve charge balance  family26, in which [Fe(CN)5NO] is substituted for [M′(CN)6] and additional “metal vacancy pores” are introduced adjacent to every six non-bridging NO ligands.The Prussian blue family  of PBA and other NTE-active frameworks can be varied considerably, even from negative to positive values, by the substitution of different metal ions28. It has also been shown that NTE behaviour in certain frameworks is converted to positive thermal expansion when solvent molecules are present in the pores30 and that intermediate CTE values can be achieved by partial desolvation at high temperatures18. Substitution of different solvent guests has also been shown to modify CTEs in uniaxial and biaxial NTE materials32. The presence of extra-framework ionic species has additionally been shown to influence thermal expansion behaviour34. However, to our knowledge, no studies have yet shown how the CTE of one framework can be tuned continuously and reversibly over a wide range of positive and negative values using a dynamic stimulus. Here we demonstrate a new and facile method of negative-to-positive CTE tuning by altering the concentration of CO2 guest molecules adsorbed in the pores, as exhibited by the TMNP Co[Fe(CN)5NO] (hereafter Co-NP) and the PBA Fe3[Co(CN)6]2 (hereafter FeCo-PB). A simple empirical model for the modifying effects of guest concentration on the thermal expansion characteristics of the empty framework is also developed, allowing the observed behaviour to be understood in relation to the guest-binding locations and host–guest interactions within the host frameworks.Various PBA and derivative frameworks have been shown to exhibit isotropic NTE over a wide temperature range due to a mechanism in which transverse ligand vibrations into the adjacent void spaces cause the metal-centred octahedral units to be drawn closer together2 loadings were extracted by Le Bail analysis of variable-temperature neutron powder diffraction (NPD) data collected upon warming (da/dT)) calculated from linear regression fits in the low-temperature range display a continuous increase with increasing CO2 concentration, from strongly negative for the empty framework to positive for the highest CO2 concentrations measured (Table 21.The unit cell parameters of Co-NP and FeCo-PB at various COEs (αa = /a(da/dT)14. As the empty pore space of the framework becomes filled with guests, the transverse vibrations of the bridging cyanides, which ordinarily lead to concerted, volume-reducing distortions of the framework, are increasingly impeded by steric interactions with the guest molecules. This explanation has also been proposed for the suppression of NTE by the presence of solvent molecules in the M[Pt(CN)6] 17 and Cd(CN)218 frameworks.The tunability of thermal expansion in these frameworks is rationalised by considering the nature of low-energy, rigid-unit NTE modes operating in PBAs and their derivatives35 and MOFs36 and is characterised by contraction of the framework at low temperatures due to the attractive nature of the guest acting on multiple framework atoms; this may be termed the “static” effect. Simultaneously, the guests exert an expansive pressure on the framework with increasing thermal excitation, which may be called the “dynamic” or “thermal” effect. Contraction of the framework due to the static effect of CO2 is considered to be temperature independent and has the effect of decreasing the 0 K intercept of the thermal expansion trend with increasing guest concentration. By contrast, the temperature-dependent dynamic effect positively increases the rate of thermal expansion with increasing guest concentration. The following simple expression captures the static and dynamic effects on thermal expansion behaviour:x is the guest concentration, T is temperature, a0 is the extrapolated lattice parameter of the empty framework at 0 K, m0 is the thermal expansion gradient of the empty framework, S is the static parameter or contractive guest effect (Å), and D is the dynamic thermal parameter or expansive guest effect (Å K−1). It may be seen that when DT = −S, the concentration-dependent expansive and contractive effects are cancelled out; consequently, thermal expansion trends for all guest concentrations intersect at T = −S/D. Furthermore, zero thermal expansion (ZTE) behaviour is predicted when the guest concentration x = −m0/D. In the case of Co-NP, the calculated crossover temperature for all curves is 145 K and ZTE is predicted to occur at a guest concentration of 0.72(2) CO2/formula unit, with the associated unit cell parameter a = 10.2424(3) Å. The values determined for S and D are reported in Table 2 desorption.In Co-NP, the intersection of all lattice parameter trends at a single point is suggestive of competition between two linear factors of opposite sign affecting thermal expansion simultaneously and negating each other at a certain temperature. We assign these competing effects to two different interactions known to occur between guest molecules and porous frameworks. The first has been reported in zeolites37 previously identified two distinct “bridged” binding sites for CO2 in FeCo-PB at 15 K: Site A in the large pore (maximum capacity 2 CO2 molecules per formula unit), and Site B located over the small-pore squares. A modified equation taking into account different static and dynamic contributions of guests bound at Sites A and B takes the formA and B denote guest concentrations and thermal expansion parameters corresponding to CO2-binding sites A and B, respectively. Assuming maximal occupation of Site A followed by Site B up to the nominal guest concentration , probably reflecting the weaker host–guest interaction at B that is associated with the strong guest preference for Site A. By contrast, the fact that Co-NP was satisfactorily modelled using the simpler Eq.  and K3Co(CN)6  were mixed dropwise with vigorous stirring. The solution was allowed to stand for 24 h before the precipitate was filtered and washed with water (100 mL), followed by ethanol (100 mL), and dried in air to yield Fe3[Co(CN)6]2·xH2O as a pale yellow powder . Desolvation was achieved by grinding the sample to a fine powder and heating it in a glass sample tube at 423 K under high vacuum (~10−5 mbar).Aqueous solutions of FeCl2[FeII(CN)5NO]·2H2O  in 200 mL deionised water was stirred until fully dissolved, followed by addition of Co(NO3)2·6H2O  to the stirred solution. After continuously stirring for 16 h, the reaction mixture was centrifuged at 4000 rpm and the supernatant discarded. The product was washed four times by adding fresh deionised water, agitating by hand and then repeating the centrifuge procedure. The resulting solid was air dried for 48 h and then stored in a desiccator for 24 h. The product was a pale reddish brown powder (yield: ~70%).A solution of Na38 at the Australian Nuclear Science and Technology Organisation (ANSTO). A 2.142 g sample was loaded into a 6-mm-diameter cylindrical vanadium can inside a helium-filled glovebox and attached to a custom-designed gas-delivery centrestick equipped with in situ gas delivery capabilities, which has been described elsewhere39. The incident neutron wavelength (~2.41 Å) was refined against an Al2O3 standard reference material (NIST SRM 676). NPD data were collected in the angular range 17° < 2θ < 137° at various temperature intervals upon heating, with the sample temperature stabilised within 1 K of the set point prior to data acquisition. A Hiden Isochema IMI manometric dosing system was then used to dose the sample cumulatively with aliquots of CO2 gas loaded into a dosing manifold of calibrated volume, allowing the quantity of gas in each dose to be determined from the manifold pressure. All gas doses were applied to the sample at 220 K followed by slow cooling to 15 K to avoid the deposition of solid CO2 in the sample chamber; the attainment of near-zero pressure measured inside the sample vessel was taken to indicate complete uptake of the dose by the sample. The entire series of variable-temperature NPD measurements was repeated after each CO2 dose.Variable-temperature NPD data were collected for Co-NP using the high-intensity neutron powder diffractometer WOMBAT40 and included a correction applied for the Debye–Scherrer ring curvature. Lattice parameters for the Co-NP phase were extracted using the Le Bail method as implemented in the program GSAS41, which was used in conjunction with the EXPGUI42 graphical interface. A pseudo-Voigt peak profile with axial divergence (GSAS Type II) and 16-term shifted Chebyshev background function were fitted to the data. Manual analysis was carried out for the first NPD pattern in each temperature series, followed by sequential (automated) Le Bail extractions over the rest of the series, using the final refined parameters from the previous pattern as a starting point for each subsequent extraction.Data reduction was performed using LAMP37. Examples of the Le Bail fit profiles obtained for FeCo-PB and Co-NP are shown in Supplementary Fig. Details of the NPD experiments and data treatments for FeCo-PB were very similar to those described here for Co-NP and can be found in Supplementary Information


This study analyzes the effect of using waste by-products generated in the process of granite cutting as part of the granular structure of Ultra High Performance Concrete (UHPC). The manufactured concrete has a compressive strength greater than 115 MPa. This study substitutes 35%, 70% and 100% of the volume of micronized quartz powder (<40 μm) with granite cutting waste. This is an innovative study where the feasibility of using waste from granite quarries as a replacement for micronized quartz in UHPC has been analyzed. The results show an improvement in the workability and compressive strength of UHPC, for all substitution ratios. The flexural strength and tensile strength increase when the substitution ratio is 35%, and even the values obtained for 100% substitution are acceptable. In view of the results obtained in this study, granite cutting waste, instead of the micronized quartz powder usually used, is a viable alternative for the manufacture of expectedly more sustainable UHPC. In recent decades, the overexploitation of natural resources has reached unsustainable levels. Overexploitation depletes resources, destroys natural habitats and pollutes the environment. The hole in the ozone layer and the greenhouse effect are two of the most serious results. For these reasons, the European Union (EU) is enforcing policies to reduce the impact of waste materials on the environment while improving the efficiency of resource management within the common territory. In 1973, the EU launched a corrective policy, called the Environment Action Program, currently in its seventh edition [The cement and concrete industries are among the main sources of greenhouse gas emissions. In order to improve the sustainability of the building sector with more environmentally friendly concrete, studies are being carried out using recycled materials or by-products from the building sector as partial or total replacements of natural aggregates in the manufacture of Ultra High Performance Concrete (UHPC).Soliman and Tagnit Hamou  studied Al-Jabri et al.  and AmbiUHPC is a promising building material with great compressive strength and excellent behavior against flexural stresses when short steel fibres are added. The Association Française de Génie Civil differentiates them from HPC, since they have a compressive strength higher than 150 MPa, although this limit is only indicative and when failure occurs it is not brittle due to the presence of different types of fibre . GeneralHowever, it is not an ecologically sustainable material as it requires high cement contents, resulting in high energy consumption. Accordingly, the development of more sustainable concrete, incorporating materials that require lower energy consumption in the manufacturing process, is currently being exploited. This can be achieved by reusing industrial by-products. In this regard, Pyo and Kim  studied Following the same line of research, Ghafari et al.  evaluateEdwin et al.  investig2 footprint due to the grinding process. It can also cause serious health problems, such as silicosis [The use of micronized quartz  in the manufacture of UHPC reduces the cement content and completes the size grading curve in the smallest sizes, thanks to the size of its particles (<40 μm). The packing density of the matrix is thus increased, achieving greater concrete compactness, stability and durability. However, the utilization of micronized quartz, (not unlike the use of silica fume), has a high energy cost and increases the COilicosis . Variousilicosis  analyzedilicosis  managed ilicosis  are in tFor all these reasons, and because the mining industry sector is second only to the building sector in the amount of waste it generates , this stThe cement used was CEM I 42.5 R/SR. 3.To carry out this study, a reference mix was designed that ensures a self-compacting fresh concrete with a compressive strength above 115 MPa. Once the characteristics of the reference concrete were verified, 35%, 70% and 100% of the micronized quartz was replaced by the same volume of granite powder. The experimental program includes four mixes. The first one is the reference UHPC. The rest of the mixes correspond to the UHPC with different contents of granite waste .The manufacturing procedure was as follows: First, the two silica sands were introduced into the mixer. Next, micronized quartz or the granite powder was added, then silica fume, and finally cement. Once all the dry materials were in the mixer, they were mixed for 30 s before the water was added. Mixing continued for two more minutes, after which the superplasticizer was added and, finally, the steel fibres. The final mixing lasted 25 min.12 specimens of each of the five mixes were manufactured according to the UNE-EN 12390-1 standard .The consistency of fresh UHPC , its denThe workability of UHPC was determined after the mixing process. The density of the hardened UHPC was measured after 28 days. The results shown in These slight increases, in the compressive strength of UHPC are in agreement with the results of Soliman  and VaitThe tests to determine the modulus of elasticity were carried out after 28 days. Three 15 cm × 30 cm cylindrical specimens were used per replacement level. Regarding this, variations between the results obtained in different studies have been observed. The results of Yazıcı  show a rThe results shown in The parameters necessary to determine the tensile strength of UHPC resulted from the stress-strain curves , obtaine1 is defined as the intersection between straight S75, with a slope equal to 0.75 of the slope of the elastic zone of the stress-strain curve passing through the origin, and the stress-strain curve. P1 is defined by parameters δ75 and σ75. P2 is defined as the intersection between straight S40, with a slope equal to 0.40 of the slope of the elastic zone of the stress-strain curve passing through the origin, and the stress-strain curve. P2 is defined by parameters δ40 and σ40. P3 is defined as the point of the ascending branch at which the equivalent stress is 97% of the maximum stress. P3 is defined by parameters δloc and σloc.Ph is the thickness of the specimen, in mm; E is the modulus of elasticity; tf is the crack resistance of the matrix reinforced with fibres; t,uf is the ultimate tensile strength; t,uε is the peak deformationOnce the key points of the stress-strain curve are defined, the model of tensile behaviour of the concrete is determined.2 content in silica fume compared to cement and palm oil fuel ash.These results are in line with those obtained by Aldahdooh et al. , who useThe previous sections indicate that the replacement of micronized quartz with granite waste improves the mechanical behavior, especially the compressive strength. This is probably due to physical issues related to an improvement in compactness due to better distribution of particle sizes. To validate this hypothesis, observations of the microstructure of the samples were made by SEM. It can be observed that in the reference UHPC a, cracksUltra-high-performance concrete (UHPC) has been manufactured using granite cutting waste and the results obtained open the possibility of their use as partial substitutes of the granular skeleton of the concrete.In some cases, the properties suffer slight increases relative to the reference concrete and in others the variation is so small than the waste incorporation does not influence the analyzed property. The most important conclusions are summarized next.The workability of UHPC increases as the granite powder waste content is increased. This improvement in the workability of concrete may be due to the lower demand for water by the granite fines, which increases the effective amount of water.The incorporation of granite cutting waste has no impact on the density of UHPC. The variations are very small, and they are due to the variability of the experimental results.The greater workability of UHPC as the granite fines content increases favours the compactness of concrete and increases the compressive strength of mixes with granite powder waste. The highest compressive strength is obtained in the 35-70% ratio, with an increment of 9%, although between 3% to 4% can be due to the scatter as result of the random distribution of the fibres.Regarding the modulus of elasticity, although the results were not as expected, the variability of the results fall within of the expected error and again this property is not clearly affected by the substitution ratio.


The application of gangue concrete can be an effective method to solve the massive gangue heap and shortage of raw materials of concrete by replacing the gravel and river sand with crushed gangue. An Acoustic emission (AE) is one of the non-destructive testing methods that can be used for damage detection of the gangue concrete structure. However, there are obvious mechanics differences between gangue and gravel/river sand, so the previous analysis methods of AE signal for concrete structure detection, mainly applied to ordinary concrete, are not suitable for gangue concrete. Based on this, the physical and mechanical characteristics of coal gangue were studied, and the uniaxial compressive test, along with AE monitoring of gangue concrete, was conducted in this paper. The differences in AE behavior between gangue concrete and ordinary concrete were also analyzed. The mechanical test result shows that the compressive strength of gangue concrete can reach 35–40 MPa. Comparing with ordinary concrete, gangue concrete has larger initial porosity and abrupt rupture. Additionally, the accumulative energy growth rate of gangue concrete has two peak values before the peak load, while ordinary concrete only has one. This difference can be used to forecast damage of gangue concrete structure by AE technology. This paper shows the possibility of making concrete by coal gangue, and the possibility of identifying its damage degree with the use of acoustic emission technology. Concrete, the most widely used building material at present, is cementing composites that use gravels and river sand as aggregate . ConcretAcoustic emission (AE) is a non-destructive testing method and has some advantages, such as real-time monitoring, reliability, and high sensitivity. In recent years, AE technology has become a frequently-used method to damage detection of concrete structures. It has been more than 30 years since some scientists began conducting the research into AE technology, which acquired many of achievements . At firsAs mentioned above, AE technology has become one of the most widely used non-destructive testing methods in the field of concrete engineering safety detection. AE signal behavior of concrete under different stress states is important to evaluate the damage level of the concrete structure. However, the previous analysis methods of AE signals about concrete structure detection can not apply to gangue concrete, as a result of obvious mechanical differences between gangue and gravel/river sand. Thus, it is important to investigate the AE behavior of gangue concrete. Base on this, the physical and mechanical characteristics of coal gangue was studied, the gangue concrete with higher strength was proposed in this paper, axial compressive tests along with AE detection of gangue concrete were conducted, and the AE signals in concrete damage processes were obtained in this paper. Additionally, the differences in AE behavior between gangue concrete and ordinary concrete were analyzed, and the variation laws of AE parameters in each stage of gangue concrete damage were obtained. This paper aims to study the change law of AE parameters with the increasing of external loading so as to apply AE non-destructive testing technology to gangue concrete in the future.The gangue used in this study comes from Kailuan (group) Co. LTD, which is a large coal enterprise in Tangshan City, China. Most of the gangue was excavated from dirt bands in the coal seam. The gangue was crushed by a small jaw crusher, and sieved by square hole sieve. And the crushed gangue was divided into four groups by their size, which includes 0–2.5 mm, 2.5–5 mm, 5–10 mm, and 10–15 mm . The proTwo types of concrete were chosen in this test, and the detailed parameters are listed in In order to obtain the standard strength of concrete, the specimen preparation refers to a national standard . The cubAt first, the crushed gangue was screened and classified as four groups, which have particle sizes of 0–2.5 mm, 2.5–5 mm, 5–10 mm, and 10–15 mm, respectively. According to the parameters in 0–106 time-varying gain [In this test, the SEMOS V1.0 seismic-electric-magnetic coupling effects observation system  was used . This sying gain . LimitedA hydraulic servo loading device  was used in this test. The maximum axial compressive force can reach 2000 kN, and the maximum piston stroke is 150 mm. The test equipment layout is shown in Acoustic emission measurement was conducted during the uniaxial compressive test of gangue concrete. The AE monitoring system is illustrated in The mineral composition of gangue was tested by an X-ray diffraction instrument . The step scan, one of the scan methods of quantitative analysis, was used in this test. The scanning speed and sample interval were set to be 0.25°/min and 0.01°, respectively. The test results are shown in 2 has the highest content, and the higher content of SiO2 is also often observed in the gravel. The high content of SiO2 shows that the coal gangue can be used as the concrete aggregate. However, the gangue contains few easily weathered and hydrolyzed chemical components, such as Al and CaO.The chemical component of gangue is shown as It can be seen by electron microscope scanning  that a sIn order to evaluate the strength of coal gangue, uniaxial compressive experiments were carried out with prepared cylindrical coal gangue specimens. The result  shows th(1) Compaction stage (OA). The intrinsic voids or cracks in concrete were compressed by the load. At this stage, the concrete’s macroscopic deformation is large, while the stress level is relatively low. The shape of the stress-strain curve is like a “concave”, which shows a non-elastic feature. (2) Linear elastic stage (AB). The intrinsic voids and cracks within the concrete have been compacted, and the structure that is comprised of mortar and aggregate bears most of the external loads, and the stress-strain curve is linear. Macro-cracks seldom appear at this stage. (3) Non-linear elastic stage (BC). Stress-strain curve changed to non-linear. This stage is still elastic, because no residual deformation occurs after unloading. However, there are few cracks that began to initiate and propagate at this stage. (4) Microcracking stage (CD). The elastic module obviously decreases at this stage, and bearing capacity of concrete weakens irreversibly. Some cracks in concrete propagate steadily and gradually coalesce with each other. (5) Unstable fracture stage (DE). The specimen fractures immediately, and the rapid stress drop occurs. It is due to the lower stiffness of the loading plate. The macro fractures are generated at this stage, and the dominating mode of failure is a shear failure, as shown in The stress-strain curve of ordinary concrete is also shown in When concrete is damaged by external loads, it will go through the process of micro deformation, expansion, fractures transfixion, and instability failure. This process is very complicated, and it shows the weakening of concrete bearing capacity and the ultimate instability failure at the macro level. The strain energy generated by deformation can be released when microcracks initiate and propagate, and partial strain energy is released in the form of stress waves, which can be collected as AE signal. The strain energy changes with different damage stages of concrete, which form different AE signals. Thus, the damage levels of concrete can be evaluated by the AE monitoring method.Among the characteristic parameters of AE, the hits and ring-down counts are often used to analyze the damage development process of the monitored specimen. A hit represents transmitting AE signals that exceed the threshold value and be counted by a channel. Thus, the cumulative hits reflect the quantity and frequency of the AE signal. Ring-down counts refer to the number of times that oscillating signals go over the threshold value, which can reflect the intensity of the AE signal. The highest signal-to-noise ratio AE signal was discussed in this paper. Cumulative ring-down counts & cumulative hits-stress curve of gangue concrete is shown as (1) Compaction stage (OA). At this stage, the AE accumulative ring-down counts and accumulative hits monotonously increase, while the growth rate keeps decreasing. It is because of the uneven and protruding surface of the specimen, as well as the damage at corners . BesidesThe accumulative hits of ordinary concrete changes with the uniaxial compressive load are also given in The amplitude of the AE signals is the maximum voltage value in a hit, which can be used to represent the AE events intensity. Accumulative energy is the area under the envelope line of AE signals, which is a frequently-used parameter to measure the magnitude of AE event. (1) OA section. The accumulative energy increases at first, and then gradually becomes stable. The hits have a large quantity and their amplitude varies greatly, which indicates the AE events at this stage are mainly caused by the uneven and protruding surface of the specimen, as well as the damage at the corners of the specimen. (2) AB section. A large number of hits with different amplitude appear near point A, which cause the accumulative energy to increase rapidly. These stress waves are released by the tip of pores that are compacted. After point A, the number of hits and the increment of accumulative energy is both smaller, which indicates the beginning of linear elastic. (3) BC section. The accumulative energy accelerated growth at this stage, and an abrupt increase appears near point B. It should be noted that the number of hits does not change significantly, and the abrupt increase is due to the appearance of some hits with a large amplitude. Macro fractures appear at this stage, and some gangue aggregates with large size ruptured, which release a lot of strain energy and cause a significant increase of accumulative energy. It should be noted that the stress of point C is 34.8 MPa, which is about 90.8% of peak stress. After point C, the number of hits meets significant growth, but their values are small, which causes the growth rate of the accumulative energy-stress curve to decrease. It indicates that, after the gangue aggregates rupturing, the macro fracture develops steadily. (4) CD section. The accumulative energy continues to increase at a higher rate. A large number of hits with variety amplitude magnitudes appear, which indicates there have both macro factures coalescence and microcracks appearing and developing. Finally, these fractures cause the failure of the gangue concrete specimen.The accumulative energy of ordinary concrete changes with the uniaxial compressive load is also given in The gangue concrete, where coarse and fine aggregates are replaced by crushed gangue, was cast in this study. The uniaxial compressive tests along with AE monitoring were conducted, and the mechanical and AE characteristic parameters were obtained. The differences of AE characteristic parameters between ordinary concrete and gangue concrete were also analyzed. This paper obtained the following main results.The mechanical properties of coal gangue are lower than gravel. This is due to the lithology, higher porosity, and microcracks of the coal gangue.The mechanical test result shows that the maximum bearing strength of gangue concrete can reach 35–40 MPa.Comparing with ordinary concrete, the AE signals of gangue concrete in the linear elastic stage is inactive. It is due to the mortar with angular gangue particles can not easily rotate, thus fewer microcracks appear and develop. The greater growth in AE signal strength is observed near the peak load as a result of the lower value of accumulative hits in the linear elastic stage.In the Acoustic Emission (AE) test, the accumulative energy of gangue concrete has two jumps before the peak load, while ordinary concrete has one. The first jump of the accumulative energy of gangue concrete is caused by the rupture of gangue aggregates. Therefore, this difference can be used to forecast damage to the gangue concrete structure.


Prediction of hydration temperature and induced stresses in mass foundation slabs, due to the hydration effects is a difficult task. The complexity of this issue is compounded by transient and non-linear thermo-mechanical phenomena as well by a significant number of contributing technological and material factors that affect the early-age volume changes. This is a probable reason for the limited number of simple analytical methods allowing for the estimation of these effects. This work presents a new proposal in the discussed field. The submitted analytical method for determining the hydration temperature rise, its differentials at a cross-section and induced thermal stresses in mass concrete foundation slabs considers the majority of important technological and material factors, such as the initial temperature of the concrete, the ambient temperature, the thermal properties of the concrete and the heat exchange conditions on the slab surfaces. In stress analysis, both self-balanced and restraint stresses are calculated. Finally, the method is validated in FE analysis conducted for the slabs with various heights and made of different types of cements, as well as by the thermal measurements from the construction site. Due to the limited number of methods allowing for the analytical estimation of the early age thermo-mechanical effects in slabs, this new proposal can be useful in the assessment of these effects. The main feature of a mass concrete foundation slab at an early age is its thermal volume change due to the hydration heat. These thermal volume changes, due to the hydration temperature rise and subsequent cooling, are accompanied by creep of maturing concrete, which gradually gains the strength and stiffness. Additionally, the described volume changes are restricted by external or internal restraints and, as a consequence, the stresses are induced. The external restraints stem from the bond or frictional forces between the foundation slab and the underlaying base. Excepting the foundation on the rock, the stiffness of the subsoil is low in comparison to the concrete and the degree of the external restraints is usually inconsiderable and the stress, due to this effect is not substantial . A signiThe key to control early age cracking of the described origin is the appropriate prediction of the thermal effects by the designer and transferring the recommendations to the contractor and concrete producer. The prediction of the hydration temperature rise and its gradient, stresses and cracking risk in mass foundation slabs presents particular problems both because of the properties of maturing concrete, as well as the conditions of the construction process. Many parameters are necessary for the analysis discussed above, such as heat evolved during cement hydration, thermal and mechanical properties of hardening concrete and its curing conditions ,6,7,8,9.The methods used for evaluating the early age thermal effects in mass foundation slabs range from simple analytical methods to complex finite element approaches. Beyond doubt, the finite element analysis provides comprehensive recognition of the problem, including temperature and stress development in the slab in the time of concrete hardening with simultaneous consideration of the material properties and technological conditions. Although not all commercially available software is suitable for this type of analysis, several advanced numerical tools are available to perform the FE analysis of concrete structures at an early age ,11,12. TTherefore, the simple analytical methods are still a continued need in this field because of their ease and the possibility of the application without using any specialized software. Despite that some guidelines dedicated to mass concrete recommend the application of numerical methods to design of discussed structures ,16, the Exemplary, British guidelines CIRIA C660  proposedThis work presents a new proposal in the discussed field. A simple analytical method for the determination of hydration temperature rise, temperature gradient and the induced thermal stresses in mass concrete foundation slabs are described. The method has been supported by the finite element analysis of the foundation slabs with various heights and made of different types of cements. In the experimental validation, the temperature measurements in the real foundation slabs have been used. The proposed method allows for the relatively simple determination of temperature rise, temperature differentials at the slab thickness and stresses in slabs both in the heating and cooling phases of concrete hardening. It should be mentioned that the version of the presented method for temperature evaluation in early age walls has been already presented . For mas3 of concrete, kg; 3; The adiabatic temperature rise of concrete Based on the results of own experimental tests  the foll3 of concrete, and Optionally, the specific heat  be used :(2)cb=∑iSimilarly, the total heat of hydration Folliard  is used.The temperature rise determined in Equation (1) applies to fully adiabatic conditions of concrete curing. In fact, concrete matures in conditions different from adiabatic and a portion of heat is transferred to the environment. The heat transfer to the environmental effects in a lower maximum temperature of concrete than determined in Equation (1), which is for the adiabatic conditions. The proposed two coefficients considering this effect have been derived from FE analysis of foundation slabs with different thickness and made of concrete with various cements. The coefficient The coefficient rd type boundary condition, which is known in general form as:2·°C); Temperatures at the surfaces of the slab are determined on the base of 3for the top surface of the slab:for the bottom surface of the slab:Available test results of temperature distribution at the thickness of massive members show that the temperature profile can be approximated by a parabola, as shown in Similarly as for the specific heat, considering the results of own experimental tests , the fol3 of concrete, and It is also possible to take thermal conductivity  formula :(10)λ=∑iCoefficients of heat transfer are crucial in the evaluation of temperature at slab surfaces. The coefficient i—thickness of the insulation layer, m. For the bottom surface of the slab, based on the performed numerical analysis, the recommended value is 2·°C).If any insulation layer is applied on the slab top surface, the coefficient  formula :(11)αptmFinally, the mean temperature has been calculated for the parabolic distribution of temperature at the slab thickness :(12)Tm=2The induced strains and stresses, due to the internal restraints (caused by the temperature difference between the core of the slab and its surfaces) have been determined using the compensation plane method . The ideIn the center of the slab:At the top surface of the slab:At the bottom surface of the slab:As the self-balanced stresses are caused by temperature gradients at the thickness of the slab, they have been determined on the basis of the difference between the actual strain and the strain on the compensation line . TherefoStrains on the compensation line:Strains in the center of the slab:Strains at the top surface of the slab:Strains at the bottom surface of the slab:The strains included in Equations (13)–(18) are determined using the following formulas:The recommended values of the coefficient of thermal expansion ken from  and listcm—a mean value of the modulus of elasticity of 28-day concrete in MPa, s—a coefficient dependent on the type of cement. For calculations of induced stresses described by Equations (13)–(18), a development of the modulus of elasticity during concrete hardening is required. In the proposed method, the recommendations of Model Code 90  are usedThe creep is considered by means of the effective modulus of elasticity:ysis and , where iThis part of the model requires special attention because of the concrete age corresponding to the occurrence of the maximum hardening temperature and the age at which the cooling of the slab will be completed. The age of concrete for which the slab will be completely cooled is relatively easy to determine—it can be assumed that it will be at least 28-day concrete. For the heating phase, the time of the maximum temperature occurrence is intractable to define. Thus, in the presented procedure, the time t depends on the slab thickness . GeneralAlthough internal restraints resulting from temperature gradients within the slab and induced self-balanced stresses are dominant, the stresses induced by the external restraints are also considered:The recommended value of the external restraint coefficient Numerical validation of the presented analytical model was performed with the use of TEMWIL and MAFEM original software ,25. This3 of cement, 150 l/m3 of water, 583 kg/m3 of 0–2 mm sand, 427 kg/m3 of 2–8 mm aggregate, 389 kg/m3 of 8–16 mm aggregate and finally 544 kg/m3 of 16–31.5 mm aggregate. Six types of cement were applied in the concrete mix. These cements included both ordinary Portland cement (CEM I), ground-granulated blast furnace slag cements (CEM II/BS and CEM III), siliceous fly ash cements (CEM II/BV), as well as cements with both slag and fly ash (CEM V and VLH). Compositions of all considered cements were listed in chapter 2.1. Detailed thermal and mechanical properties are listed in The slabs with the thicknesses of 1 m, 2 m, 3 m and 4 m were analyzed. The dimensions in the plan view were 20 m × 20 m. In all cases the same concrete mix composition was assumed, only the type of cement was diversified. Concrete mix composition was, as follows—300 kg/mThe remaining data necessary for analysis are presented in The results of the calculations are shown in In all cases, the numerical and analytical results obtained for temperature distribution are practically indistinguishable a. It wasSimilarly, the good agreement between the numerical and analytical results was obtained for stress distribution in the heating phase b. This cThe experimental validation of the proposed analytical model has been limited to the thermal analysis. This limitation was dictated by the unavailability of stress measurements in the available examples of the real slabs. Data from one foundation of the power station was used for thermal analysis . The dimPrediction of the temperature rise and induced stresses in mass foundation slabs, due to the hydration effects is a particularly difficult challenge. The complexity of this issue is compounded by the non-stationarity and non-linearity of the thermo-mechanical phenomena as well by a significant number of contributing technological and material factors influencing the early-age volume changes. This is the probable reason for a limited number of simple methods allowing for the analytical estimation of these effects. In the article, a new analytical method for prediction of thermo-mechanical behavior in mass foundation slab is presented. The detailed procedure with all necessary coefficients for the hydration temperature rise, its differentials at a cross-section of the slab, as well as for the induced thermal stresses are described. The method for determining thermal loads related to the hydration process considers the majority of important factors, such as the initial temperature of the concrete, the ambient temperature, the thermal properties of the concrete as specific heat and thermal conductivity, as well as the heat exchange conditions on the slab surfaces. Additionally, the method seems to be simpler than methods based on standard heat diffusion theory or Schmidt’s method. In the mechanical part of the model, both self-balanced and restraint stresses are calculated. The applied procedure proposed is based on the compensation plane method. Finally, the correctness of the proposed method is confirmed by the results of a finite element analysis of the exemplary slabs and the thermal measurements from the construction site. Nevertheless, no theoretical model is perfect and can always be improved. In this respect, the model coefficients and assumptions should be further tested and possibly clarified, especially in relation to the mechanical properties of concretes made of cements with non-clinker constituents ,28. It s


The research aims to explore the influences of lithology on the compaction behaviours of broken waste rocks. For this purpose, a WAW-1000D servo test machine and a self-made bidirectional loading test system for granular materials were used to conduct axial and lateral compaction tests on four typical types of broken waste rocks: sandstone, mudstone, limestone and shale. On this basis, we analysed the relationships between lateral and axial stress with the strain in, and porosity of, the four types of broken waste rocks. In addition, the relationship of axial stress with lateral stress and lateral pressure coefficient, and the changes in the particle size distribution of broken waste rocks before and after compaction were discussed. The test results demonstrated that the samples of higher strength were found to have low lateral and axial strains as well as a lower porosity in axial and lateral loading tests, while samples of lower strength showed low lateral stress and lateral pressure coefficient under axial load. After being compacted, the samples of the four types of broken waste rocks were found to have a higher proportion of small particles, indicating some particle crushing. Moreover, the samples of lower strength were broken to a greater extent. The pet al. [et al. [et al. [et al. [et al. [et al. [Broken waste rocks are commonly used as backfill materials in solid backfill mining, so their compaction behaviours affect strata movement and surface subsidence in backfill mining. Scholars have investigated the compaction behaviours of broken waste rocks to good effect, for example, combining the testing machine with the self-made compaction device, Li et al.  studied et al.  investig [et al. ,21 used  [et al.  performe [et al.  proposed [et al.  proposed [et al. ,26 testeConsidering this, the preparation method of samples of broken waste rocks was introduced at first. Then, by testing the compaction behaviours of the broken waste rocks using the self-developed bidirectional loading test system for granular materials, the influence of lithology on the compaction behaviours of the broken waste rocks was studied. Meanwhile, the effects of changes in lateral strain, axial strain, porosity, lateral stress and lateral pressure coefficient of the samples under lateral and axial load were analysed, and the relationship between the lithology and compaction behaviours of broken waste rocks was established.2.2.1.The test materials were waste rocks collected from a coal mine, comprising four typical types of rocks: sandstone, mudstone, limestone and shale. Before the test, these four types of rocks were broken. At first, they were broken to 50 mm down and then broken again to 30 mm down, then graded to 5, 10, 15, 20, 25 and 30 mm, respectively, as shown in The test schemes for compaction of samples of different rock types are listed in In the test schemes, the particle size distribution, lateral stress and number of cycles of lateral compaction were all kept unchanged to analyse the influences of lithology on the compaction behaviours of the broken waste rocks by taking different types of rocks, which was equivalent to indirectly adjusting the strength of samples. The particle size, lateral stress and number of cycles of lateral compaction were set to 0–30 mm, 2 MPa and 5, respectively. Meanwhile, to reduce the influence of test errors, three groups of tests were carried out for each scheme.Considering the influences of particle size of broken waste rocks and to realize a compromise between the mass of broken waste rocks and the simplicity of test, each group of the samples for the four types of waste rock was prepared by uniformly mixing particles in the mass ratio of 1 : 1 by particle size.2.2.A bidirectional loading test system for granular materials was developed. The system was composed of four parts: an axial loading system, a loading box, a lateral loading system and a data monitoring and acquisition system, as displayed in 2.2.1.The axial loading system is the foundation of the bidirectional loading test system and provides constant and stable axial stress to granular materials. In the test, a WAW-1000D electro-hydraulic servo-controlled universal testing machine was used, which is capable of providing axial load to 1000 kN over a travel of 250 mm. In addition, being equipped with a large test bench, the machine can meet the requirements for tests of the compaction behaviours of granular materials.2.2.2.The loading box forms a loading chamber for granular materials and the location for fixing the oil cylinder for loading. It consists of a base, a ribbed slab, an arc-shaped support, a front plate, the plates on the left and right, an upper cover plate, a side-push plate and M18 bolts.2.2.3.The lateral loading system provides lateral loading pressure on the samples of granular materials and includes a hydraulic pump unit, an oil cylinder for loading, a control box, a pressure gauge, a hydraulic pipe and an overflow valve.2.2.4.The data monitoring and acquisition system is able to monitor and acquire experimental data during loading. It is composed of a pull-rod displacement sensor, a spoke-structure pressure sensor, a pressure transmitter, FX2N-5a analogue input and output modules, an FX2N-32MR controller, MCGS configuration software and a laptop.3.To study the influences of lithology on the compaction behaviours of broken waste rocks, compaction tests were carried out on the four typical types of waste rock  using the developed bidirectional loading test system for granular materials. The influence of lithology on the compaction behaviours of broken waste rocks were obtained by following the test steps. The test method and procedure refer to the standard ‘Method of compaction testing of solid backfilling materials' issued by China's National Energy Administration .3.1.The preparation scheme for the samples is summarized in §2.1.3.2.The loading box was assembled before placing the samples in and the displacement and pressure sensors were reset. Afterwards, the prepared samples were put in the loading box in three to six layers. After loading each layer, the surface of each layer was not very smooth; thus, the samples needed to be pre-compacted. Pre-compaction can make the surface of each layer smooth, and the experimental error can be greatly reduced. The total height of the samples placed in the loading box was 200 mm and the mass of the loaded samples in each group was recorded. After filling the box, bolts were used to fix the upper cover plate to the box. Then, the loading box was put on the test bench of the WAW-1000D electro-hydraulic servo-controlled testing machine.3.3.a. In the lateral loading process, the lateral pressure and displacement were monitored and recorded in real time.The motor of the hydraulic pump unit, the sensors and the PLC controller were powered on and the MCGS software was run to monitor the pressure and displacement. The required oil pressure, which was calculated corresponding to the lateral stress set according to the test scheme, was adjusted on the pressure gauge. Afterwards, the lateral loading of the samples was performed through the control box, as shown in figure 33.4.b. The lateral pressure, axial pressure and axial displacement were monitored and recorded in real time.After lateral loading, the upper cover plate was removed and the height of the samples recovered to slightly higher than the loading height (200 mm) of the samples; therefore, load was applied to the samples to pre-compress the samples to a height of 200 mm again. Then, the pressure for axial loading was set, followed by axial loading as shown in figure 33.5.The samples were graded through square-aperture sieves after axial loading. The particle size distributions of the samples before and after loading were recorded .Figure 4.4.1.4.1.1.σo) and lateral stress (σh) isAh, Lh and hh represent the area, length and height of the side-push plate, respectively; Ao and ro refer to the cross-sectional area and the radius of the oil cylinder used for loading.The lateral loading on the samples of broken waste rocks is realized by virtue of oil pressure provided by the oil cylinder for loading. The lateral stress in the test scheme refers to the compressive stress borne by the samples. Therefore, it is necessary to equate the lateral stress to an oil pressure. Then, lateral loading is applied to samples of broken waste rocks under different lateral stresses by setting the oil pressure on the pressure gauge. The relationship between oil pressure  to find that the oil pressure should be 6.5 MPa corresponding to a lateral stress of 2 MPa.4.1.2.σh) on samples during lateral loading is the ratio of the lateral loading pressure to the area of the side-push plate, and is calculated thusPh denotes the lateral pressure on the samples.The lateral stress (ɛh) of samples during lateral loading can be expressed as the ratio of displacement resulting from lateral loading to the length of the loaded regionLh and Ls denote the displacement of samples due to lateral loading and the length of the loaded region, respectively.The lateral strain  on samples during axial loading is the ratio of the axial load to the area of the upper compression plate. The area of the upper compression plate can be determined depending on lateral displacement. The axial stress can be calculated as follows:Pv refers to the axial pressure applied to the samples. Av, Lv and lv are the area, length and width of the upper compression plate, respectively. In addition, Lv is determined by the displacement of samples due to lateral loading.The axial stress (ɛv) in samples under axial load is the ratio of displacement resulting from axial loading to the loading height, expressed as follows:hv represents the displacement of the samples under axial load.The axial strain –(4.4), the changes in strain and porosity of the samples of different types of broken waste rocks under lateral loading are obtained figures  and 6.F(1)After the first lateral loading cycle, the lateral strain in samples of broken waste rock increased the most, then, as the number of cycles of lateral loading increased, the increase in lateral strain decreased as did the lateral porosity. This indicates that lateral loading can reduce the porosity and densify such waste rock samples. While, with increasing cyclic lateral compaction, the influences of lateral loading on lateral strain and lateral porosity decreased.(2)The samples of the four types of broken waste rocks all showed elastic rebound after each cycle of lateral loading and unloading; however, the recovery only accounted for a low proportion of the total lateral deformation, while the lateral plastic deformation (irrecoverable deformation) was significant.(3)The samples of lower strength were found to have larger decrease in lateral porosity. For example, the lateral porosity of broken shale fell from 0.425 to 0.344. In comparison, the decreases in lateral porosity of broken sandstone, mudstone and limestone samples were 0.044, 0.063 and 0.057, respectively, which indicated that broken sandstone underwent less lateral deformation, while broken shale underwent more lateral deformation, during lateral loading.(4)The samples of higher strength underwent less lateral strain. The four types of broken waste rocks were listed, in descending order of lateral strain, as follows: shale, mudstone, limestone and sandstone. For instance, the lateral strain in broken sandstone samples was only 0.068, while that in broken shale samples was 0.122. This is because, due to the high strength of broken sandstone samples, the particles are stronger, stiffer and better able to resist slippage and rotation during lateral load; while particles in the broken shale samples were apt to be broken and slip, resulting in larger lateral deformation.Analysis of the data in figures 5.2.In accordance with the experimental results of the axial loading on the samples and data calculation using formulae (4.5)–4.7), the changes in strain and porosity of different types of broken waste rocks under axial loading are as shown in figures , the cha(1)The axial porosity of the stronger samples decreased least. For example, the axial porosity of broken sandstone samples fell from 0.422 to 0.205, while the decreases in the axial porosity of broken mudstone, limestone and shale samples reached 0.229, 0.225 and 0.237, respectively. The phenomenon indicates that the broken sandstone samples underwent the less axial deformation, while broken shale samples underwent the most axial deformation.(2)The weaker samples underwent more axial strain. The four types of broken waste rocks were listed in descending order as shale, mudstone, limestone and sandstone in terms of the axial strain. To be specific, the axial strain in broken sandstone samples was 0.239, while that of broken shale samples was 0.295. This is because the higher strength of the broken sandstone samples endowed the particles thereof with a higher capacity to resist crushing, slippage and rotation during axial loading, while the particles of broken shale samples were easily broken and slipped, thus undergoing more axial deformation.From figures 5.3.η) is the ratio of the horizontal stress to vertical stress, as shown in the formulaσmh and σmv represent the horizontal and axial loading stress of the real-time monitoring, respectively.Based on the experimental results of the samples during axial loading and the data arising from the use of formula (4.2), the changes in lateral stress and lateral pressure coefficient of the different types of broken waste rocks under axial loading are obtained figures  and 10. (1)As the axial stress increased gradually, the lateral stress also increased, which can be roughly divided into three stages: rapid increase, increase at a decreasing rate and steady increase. This is because, as the axial stress gradually increased, the stress transferred to the lateral direction also increased.(2)The lateral pressure coefficient first increased and then decreased under increasing axial stress. This is because, under low axial stresses unable to crush particles of the waste rocks used for backfill, most axial stress was transferred to the lateral direction; while under high axial stress, particles were broken, thus dissipating energy from the work done by the testing machine. Finally, the lateral pressure coefficient declined slightly.(3)The weaker samples presented a lower lateral stress. The four types of broken waste rocks are ranked, in descending order, as sandstone, limestone, mudstone and shale according to their lateral stress. To be specific, the lateral stress on the broken sandstone sample was as high as 4.24 MPa while that on the broken shale samples was only 3.31 MPa. This can be explained as follows: the broken sandstone samples were stronger, so their particles showed higher resistance to crushing, slippage and rotation during axial loading; in comparison, broken shale particles were apt to be broken and slip. As a result, compared with the broken shale samples, a higher stress was transferred to the lateral direction in broken sandstone samples.(4)The stronger samples had a larger lateral pressure coefficient. For example, the lateral pressure coefficient of broken sandstone samples was 0.214, while those of broken mudstone, limestone and shale samples were 0.182, 0.195 and 0.168, respectively. The result suggested that a larger stress was applied to the lateral plane of the broken sandstone samples, and less to that in shale samples.Figures 5.4.The samples undergoing the compaction test were graded and the particle size distributions of the samples before, and after, loading were computed .Figure As shown in 6.(1)The porosity of weaker samples fell during lateral and axial loading, indicating that the samples of stronger waste rocks showed more apparent deformation in lateral and axial loading. The four types of broken waste rocks were listed in a descending order as shale, mudstone, limestone and sandstone in terms of the decrease in porosity.(2)The stronger samples underwent smaller lateral and axial strains. Samples of the four types of broken waste rocks all exhibited a spring-back effect during cyclic loading under lateral stress. Therein, the elastic deformation was small, while plastic deformations were large.(3)During axial loading, the lateral stress on the four types of waste rocks changed in three stages: rapid increase, increase at a decreasing rate and steady increase, and the lateral pressure coefficient first increased and then decreased. Among the four types of waste rocks, stronger samples showed a higher lateral stress and lateral pressure coefficient.(4)After compaction testing, the proportions of small particles in the four types of waste rocks all increased. In addition, for stronger samples, their particles were less severely broken. The four types of waste rocks, in descending order of crushing were: shale, mudstone, limestone and sandstone.(5)By this paper's research, we can obtain the influence of lithology on the compaction behaviours of broken waste rock. In order to get the better backfill effects, the reasonable lithology can be optimized during backfill. The reasonable particle size can improve the support ability of backfill materials for strata and surface. Meanwhile, the waste rock dumps can be reduced, so the coal mine environment can be protected greatly.We investigated the influence of lithology on the compaction behaviours of broken waste rocks by using a WAW-1000D servo-controlled testing machine and a self-made bidirectional loading test system for granular materials. The following conclusions were obtained: