We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area  Such a crop offers the global agriculture and bioeconomy sector possibilities to fabricate food, fuel, and feed  under harsher conditions. however, it can significantly accumulate photo-assimilates only at maturity, which occurs between 10 and 18 months after planting, depending on genotype-environment interactions . A maturChlorophylls are primary light-harvesting pigments. They can provide reliable indicators of the physiological conditions of a crop, such as sugarcane . TherefoBy reviewing the literature on remotely sensing sugarcane, the system-level study by 2< 0.85). They could work better on processing data from a tolerant-drought field. However, they could not predict °Brix and Pol upon imagery data on a flood-tolerant area as accurately as those functions containing SRPI.2< 0.9; RMSE = 2.8°Brix). Therefore, UAV and ML could be enablers in soluble solids (SS) as indicators of maturity in sugarcane. However, Purity could offer a more reliable marker than °Brix in mapping and monitoring saccharification. It describes the proportion of sucrose the juice contains and is an indicator of raw material degradation during the cut-to-crush time and industrial processing efficiency.In a more recent publication, Therefore, we analyzed whether ML algorithms could predict °Brix and Purity upon multispectral UAV imagery data for precision mechanical harvesting of material with higher quality.22.1We carried out our study in a sugarcane field located near the city of Jaboticabal, São Paulo, Brazil Figure 12.2A multirotor UAV  was used as the remote sensing platform in this study. The UAV is equipped with a multispectral camera that has five spectral bands, namely Blue (450 nm ± 16 nm), Green (560 nm ± 16 nm), Red (650 nm ± 16 nm), RedEdge (730 nm ± 16 nm), and NIR (840 nm ± 26 nm). The UAV has a sunlight sensor on top to compensate for incident solar radiation during flight and ensure that spectral data are consistent. In addition, it is equipped with a multi-frequency GNSS receiver  able to receiver signals from constellation namely GPS, GLONASS, BeiDou, and Galileo, ensuring centimeter positional accuracy, making it possible to acquire temporal data from the same point. The flight missions were performed automatically by application . Flight settings and parameters are described in The images were stitched using Structure from Motion (SfM) software  to generate 8 multispectral orthomosaics. To extract the spectral information and calculate the vegetation indices Table 2 2.3After the collection of stalks in the field, they were properly identified and taken to the laboratory to determine the quality parameters °Brix and Purity. Initially, the stalks from each sample point were processed individually in a hydraulic press for juice extraction. We used the juice to measure the °Brix content by digital refractometer  and recorded the value corrected to a temperature of 20°C. To measure Purity, we followed the methodology proposed by LPb is the polarimetric reading equivalent to lead subacetate, LAI is the polarimetric reading with aluminum chloride, and %Pol is the apparent sucrose content.Where, 2.42.4.1best subsets regression function from the open-source package “olsrr”  and mean squared error of prediction (MSEP).A total of 15 independent variables  were used as input to the °Brix and Purity prediction models. For the data to faithfully represent the field truth, we applied the interquartile range method to remove outliers from the dataset. Thus, the length of our dataset was reduced from 240 to 223. Then the dataset was randomly divided into subsets with 70% (156) and 30% (67) for train and test, respectively. Since we constructed our dataset with 15 predictor variables for °Brix and Purity, we decided to apply the  “olsrr” , in the 2.4.2To model the contents of °Brix and Purity we chose 4 ML regression algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT) and support vector machine (SVM). These algorithms are widely used because they produce high accuracy results, solve problems on relatively small database sizes and handle a large number of input features. All analyses were performed in the programming language R (version 4.1.0) using the packages “stats” , “random2.4.3The fit of the models was evaluated according to the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE) applied to the test dataset. The closer the R² value is to 1, the more precise. In contrast, the closer the RMSE and MAE values are to 0, the more accurate the model.33.1nd evaluation. Additionally, we measured 14.4 ± 2.1°Brix from samples of the 3rd collection, supporting a field at early maturity and still unsuitable for cost-effective harvesting. As the elongation occurred, however, the °Brix increased significantly. Therefore, its values for the 4th and 5th survey-level evaluations were 16.9 ± 2.1 and 17.9 ± 1.6, respectively. Summarily, sugarcane developed the highest °Brix of 19.7 ± 0.9 at the 7th evaluation.We mapped the dynamic ripening on biometric data 3. As thth evaluation. Later, in the 7th evaluation, however, the measure for this technological feature exceeded 90%, supporting an optimal Pol/SS ratio for high-quality harvesting. More importantly, its distribution throughout the field was homogeneous, further supporting the suitability of such a phenological stage for standard operation and precision crop management. A decreasing proportion of the area with the highest °Brix and Purity at the 8th evaluation could make the recovery of adequate raw material (sucrose) for industrial processing difficult, driving the need to determine the most reliable time to intervene in the field. Therefore, by analyzing the spatio-temporal variability of such indicators of qualitative yield, we must plan to harvest the sugarcane at the 7th evaluation. However, we could act earlier since 50–70% of the area produced a raw material with 18–19°Brix and 85–90% purity at the 6th evaluation.We identified a similar trend to Purity. The crop produced 80–85% pure juice until the 43.22 of 0.85 into the biophysical modeling for Purity, while the adequacy for those above at predicting °Brix was 0.65, making them less accurate.We selected spectral features to predict °Brix and Purity by applying regression analysis to remote sensing data Figure 4By analyzing MSEP, however, we could recognize a lower predictive error from such a brix-fitting model, making it more precise. In addition, the more inputs, the higher the accuracy and precision of a polynomial function Figure 43.32 into the biophysical modeling for °Brix; hence, it qualified as the most accurate algorithm. In addition, SVM estimated such a measure of SS as accurately and precisely as RF, outperforming both MLR and DT. These approaches developed the least accuracy and precision as ten-input regressors.Machine-learning models effectively estimated °Brix Figure 5We obtained mathematical descriptions with higher precision for Purity since estimates of R², MAE, and RMSE were 0.85–0.9, 4.3–5.1%, and 5.9–6.7%, respectively; the ranges of these metrics for ML models predicting °Brix were 0.6–0.8, 1.1–1.4%, and 1.5–1.9%, respectively. Therefore, compared to Purity, such an indicator of the technological quality of juice added more systematic errors in the modeling, reducing exactness; however, it could not necessarily decrease correctness, which is another part of robustness. Random forest most accurately and precisely predicted not only °Brix but also Purity, further supporting its outperformance at learning on spatio-temporal data to map a series of spectral inputs to an agronomic output. Its predictive metrics were 0.9 R², 4.6% MAE, and 6% RMSE. Additionally, MLR and DT described the Purity at an equal level of adequacy. However, DT outperformed the MLR in supporting a seven-input model to predict Purity.44.1th evaluation; hence, they produced the purest juice. However, °Brix and Purity decreased as the field started flowering at the 8th evaluation. In addition, stalks became more fibrous, supporting “isoporization”  in a specific spectral band or fluorescence within a region . Moreove4.3Predictive data analytics can develop knowledge for advancing agriculture. However, conventional models can be statistically complex and demand considerable computational processing, making their implementation challenging. Even though fundamental approaches, such as correlational or regression analysis , can detWe can train an ML algorithm on a heterogenous and “messy” dataset to learn meaningful and non-duplicative patterns to solve a task automatically, accurately, and unbiasedly. Some applications of ML for sugarcane research and development available from earlier independent studies include predicting or forecasting chlorophyll content , standarDecision tree and MLR could be options for RF and SVM in predicting °Brix and Purity. However, they could develop a lower level of accuracy or precision, driving the need for improvement. The DT consists of an advanced problem-solving and computation-performing procedure. It splits a dataset into multiple branches to establish relationships hierarchically . However4.4We demonstrated the technical viability of ML algorithms in predicting °Brix and Purity upon UAV imagery data. Our approach is still at an early stage of research and development. However, it is consistent and can offer stakeholders possibilities to address precision harvesting for cost-effective production. Such an operation is costly  and deteAcquiring imagery data by a multispectral sensor onboard UAV allows the development of accurate and precise biophysical modeling of qualitative yield. Our predictive frameworks can be technically comparable with those functions available in independent studies by 5We predicted °Brix and Purity by applying machine learning to multispectral imagery data from a UAV. We optimized the biophysical modeling by implementing a random forest algorithm. The most accurate spectral predictors of °Brix were Red and NIR, while those of Purity Green and RedEdge. We, therefore, developed an AI-intensive solution to model qualitative yield, advancing the field of aerial remote sugarcane mapping and monitoring. Our approach offers the global sugar-energy sector a strategy to harvest high-quality feedstock for industrial processing while streamlining fieldwork and addressing a pressing prescriptive and analytical agriculture for sustainable development. Additionally, it provides knowledge to develop a resource-effective, self-evolving framework to select sugar-dense material objectively and non-invasively, which is not an assumption of conventional phenotyping.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.MBJ: conceptualization, methodology, validation, formal analysis, investigation, data curation, writing – original draft preparation, writing – review and editing, and visualization. BM: methodology, formal analysis, investigation, writing – original draft preparation, writing – review and editing, and visualization. RO: investigation, writing – review and editing, and visualization. LS: writing – review and editing, visualization, and supervision. RS: conceptualization, methodology, writing – review and editing, visualization, supervision, and project administration. All authors contributed to the article and approved the submitted version.


Staphylococcus aureus and test how the subsequent evolution of the resistant bacterium is modulated by antibiotic treatment and oxygen levels, both of which are known to vary extensively at sites of infection. We show that acquiring tetracycline resistance was costly, reducing competitive growth against the isogenic strain without the resistance gene in the absence of the antibiotic, for S. aureus under hypoxic but not normoxic conditions. Treatment with tetracycline or doxycycline drove the emergence of enhanced resistance through mutations in an RluD-like protein-encoding gene and duplications of tetL, encoding the acquired tetracycline-specific efflux pump. In contrast, evolutionary adaptation by S. aureus to hypoxic conditions, which evolved in the absence of antibiotics through mutations affecting gyrB, was impeded by antibiotic treatment. Together, these data suggest that the horizontal acquisition of a new resistance mechanism is merely a starting point for the emergence of high-level resistance under antibiotic selection but that antibiotic treatment constrains pathogen adaptation to other important environmental selective forces such as hypoxia, which in turn could limit the survival of these highly resistant but poorly adapted genotypes after antibiotic treatment is ended.Bacterial pathogens are confronted with a range of challenges at the site of infection, including exposure to antibiotic treatment and harsh physiological conditions, that can alter the fitness benefits and costs of acquiring antibiotic resistance. Here, we develop an experimental system to recapitulate resistance gene acquisition by  Staphylococcus aureus is a Gram-positive commensal bacterium capable of causing a range of opportunistic infections  is recommended for the treatment of outpatients with community-acquired pneumonia  in many chronic infections, including the cystic fibrosis airway , regulating the production of virulence factors to resist oxidative stress and allowing metabolic adaptation  . To test the immediate fitness effect of acquiring tetL and how this varied with the oxygen level, we competed SH1000_TetR against SH1000 under hypoxia (0.8% O2 and 5% CO2 at 37°C) and normoxia (21% O2 and 5% CO2 at 37°C) for 24 h. SH1000_TetR showed significantly lower fitness relative to SH1000 in hypoxia than in normoxia (P = 0.0028), suggesting that tetL caused a large fitness cost but only under hypoxic conditions . Although the mechanism by which tetL causes this hypoxia-dependent fitness cost is unknown, the insertion of alternative resistance genes (erm or kan) at the same site did not cause differential fitness in hypoxia versus normoxia  , suggesting that the effect is specific to tetL and is not caused by disruption of lysA.To mimic the horizontal acquisition of a new antibiotic resistance gene, we used a reafter) . SH1000_reafter)  (normoxinormoxia  (P = 0.0Pr(>F) = 0.0055] . Correspondingly, antibiotic-treated populations evolved higher levels of antibiotic resistance such that populations treated with TET or DOX had higher levels of resistance against both antibiotics by the end of the experiment, whereas populations that evolved without antibiotics showed no change in resistance to either antibiotic  . The hypoxic but not the normoxic growth of populations that evolved without antibiotics was significantly higher than that of populations that evolved with TET or DOX treatment {antibiotic, Pr(>F) = 1.6 × 10−5 by the analysis of variance (ANOVA) model [normalized integral ~ (oxygen · antibiotic · condition)]}   and B. FPr(>F) = 0.326 by 2-way ANOVA]. However, treatments varied in the loci that had acquired mutations , with distinct sets of mutated loci associated with the response to antibiotic- or oxygen-mediated selection leading to genetic divergence between treatments .To investigate the genetic response to selection, we performed whole-genome sequencing on one randomly chosen clone per replicate population plus clones from control populations of SH1000 that had been evolved without antibiotics in either normoxia or hypoxia. A total of 115 mutations ; Fig. S4eatments   in the pMUTIN2 gene after the lysA insertion site but retained the entire tetL gene. Elevated (>2-fold) coverage of the tetL gene was observed in 15 clones that had evolved under TET or DOX selection, suggesting gene duplication . The maximum coverage of tetL was 29× in a clone that had evolved under TET selection in normoxia. The location of the gene duplication was inspected, and no additional insertion sites were identified, suggesting that the gene duplications are within the lysA insertion site. Multiple genes involved in transcription and translation processes gained mutations in evolved clones selected with antibiotics. These included the 30S ribosomal protein S10 , which is the target of tetracycline . The poorly characterized rluD-like protein is a pseudouridine synthase that carries out posttranscriptional modification of 23S rRNA  . Two such clones had no mutations elsewhere in their genomes, both of which evolved in DOX in either normoxia (15.6× coverage) or hypoxia (26.2× coverage). Competing evolved clones with tetL duplications but no other mutations showed no change in fitness compared to the SH1000_TetR ancestor with or without tetracycline, irrespective of oxygenation  , suggesting that the tetL duplications did not lead to a higher fitness cost. There was no clear association between the increase in the tetL coverage of clones and their tetracycline MICs ; 3/8 clones had the maximum TET MIC of 256 μg/mL without tetL duplication. The increased resistance of these clones was instead associated with a combination of mutations in genes encoding the RluD-like protein (2 clones), accessory gene regulator protein A (1 clone), RpoB (1 clone), 30S ribosomal protein S10 (1 clone), a hypothetical protein (1 clone), and a 7,659-bp deletion in the tetL insertion cassette (1 clone [resistance gene intact]). These achieve the same high-level tetracycline resistance, thus obscuring any benefit of tetL duplication and suggesting that chromosomal point mutations elsewhere in the genome contribute to the high-level resistance. In addition, two highly TET-resistant evolved clones from different treatments carried a parallel nucleotide mutation in a noncoding region between malK and lytM . However, an identical mutation was present in all of the replicate 2 sequenced SH1000_TetR evolved clones regardless of the treatment, suggesting that this mutation was present in the ancestral clone used to found these replicate lines .Another genetic variant favored by antibiotic selection was the gyrB showed increased growth in both normoxia (P = 0.021) and hypoxia (P = 0.0014) (P = 0.019) (gyrB mutations with the antibiotic-free environment was interrogated through competition assays performed with or without tetracycline (0.125 μg/mL). Clones with the DNA gyrase B A439S variant showed increased fitness under antibiotic-free hypoxic conditions compared to their ancestor (P = 0.005) but not under normoxia (P = 0.138) or in the presence of TET . These data support the increased fitness under antibiotic-free conditions in hypoxia driving the selection of DNA gyrase B mutations, with the addition of antibiotics in hypoxia eliminating this fitness advantage.Evolved clones with mutations in  0.0014) . DNA gyr 0.0014) . Overall= 0.019) . The strancestor   is in proximity to Tyr122 (subunit A), which forms a phosphotyrosine interaction with the DNA. The space between Ser439 and Tyr122 creates a space and appropriate bonding for an H2O molecule or ion  with all experiments matched in normoxia  and medium equilibrated overnight (280 rpm) before use. Cultures were grown overnight in normoxia before use with either oxygen tension.All strains used in this study are listed in Table S3 in the supplemental material. Bacteria were cultured in brain heart infusion (BHI) broth (280 rpm) or BHI agar. Hypoxia was maintained in a Ruskinn SCI-tive chamber (0.8% O5 CFU) of each competing strain was inoculated at a 1:1 ratio into 10 mL equilibrated BHI broth under normoxia or hypoxia before growth for 24 h, with or without 0.125 μg/mL tetracycline (TET). The starting and final bacterial densities were calculated by plating a serial dilution onto BHI agar with or without TET (5 μg/mL) or kanamycin (5 μg/mL), depending on the resistant cassette in the competing strain. Fitness (W) was calculated as W = ln[(A end CFU/mL)/(A start CFU/mL)]/ln[(B end CFU/mL)/(B start CFU/mL)].Strains were grown overnight in 10 mL BHI broth in normoxia to stationary phase. Fifty microliters  plus the controls (antibiotic-free SH1000), all matched in normoxia and hypoxia. Cultures for all treatments were grown in a final volume of 400 μL BHI broth in 1.2-mL 96-well plates sealed with gas permeable film to allow gas exchange while preventing contamination. Every 24 h for 34 days, each population had a 1:100 serial passage into fresh BHI broth, and the 600nm) was measured by destructive sampling with a separate plate at each time point. The growth of the evolved population or clone was compared to that of the ancestors grown simultaneously under identical conditions.Cultures were grown overnight directly from a 20% glycerol stock in normoxia prior to inoculation at a mean 1:100 dilution of the populations grown overnight into 400 μL preequilibrated BHI broth in deep-well 96-well plates with slitted plate seals in normoxia or hypoxia, including the ancestor controls. These cultures were grown at 280 rpm for 0, 2, 4, 6, or 24 h, at which time the optical density at 600 nm  using an Illumina MiSeq platform with 2× 250-bp paired-end reads. Sequence analysis was performed according to methods described previously by Wright et al. (CP000253). Larger genetic variations, including deletions of >100 bp and duplication events, were identified by analyzing changes in the coverage depth using R version 4.0.3, and all variants were verified visually using IGV  where at least 1 read aligns to the SH1000_TetR SPAdes assembly using the Burrows-Wheeler aligner  accession number 6RKW . Prior to statistical analysis, the normality of data was tested with a Shapiro-Wilk test. Statistical analysis of parametric data was carried out by ANOVA with Tukey’s multiple-comparison test, a paired t test for 2 groups of paired data, a t test for 2 groups of unpaired data, and an analysis of variance model with antibiotic and evolution oxygen tension as independent variables followed by Tukey’s multiple-comparison test of evolution phenotype data. Nonparametric data were analyzed by a Kruskal-Wallis rank sum test with Wilcoxon rank sum post hoc testing and Benjamini-Hochberg correction for multiple comparisons  was used on the data with population number as a random effect following Box-Cox transformation to meet the underlying test assumptions. Phylogenetic distances were calculated using the Jaccard index based on the presence or absence of mutations within a gene or parisons .PRJNA850453. All study data are included in the supplemental material.All sequencing data are available in the Sequence Read Archive under BioProject accession number


The water/binder (w/b) ratio was varied between 0.4 and 0.8. Concrete specimens were immersed in highly severe effective sodium sulfate solutions  for 180 days after standard curing for 28 days. The fresh concrete performance was evaluated through a slump test to attain proper workability. Concrete compressive strength and mass change at 28 days and 180 days were measured before and after immersion in the solution to evaluate the long-term effect of sulfate attack on the proposed concrete durability. Scanning electron microscopy (SEM) analysis was conducted to study the concrete microstructure and its deterioration stages. The obtained results revealed that BFC cement has the best resistance to aggressive sulfate attacks. The strength deterioration of BFC cement was 3.5% with w/b of 0.4 and it increased to about 7.8% when increasing the w/b ratio to 0.6, which are comparable to other types of cement used. The findings of this research confirmed that the quality of concrete, specifically its composition of low permeability, is the best and recommended protection against sulfate attack.Concrete sulfate attack is of great interest as it represents one of the main reasons of concrete deterioration and poor durability for concrete structures. In this research, the effect of different cement types on concrete sulfate resistance was investigated. This included three concrete classes, namely, low strength concrete, medium strength concrete, and high strength concrete. Blast furnace cement (BFC), sulfate resisting Portland cement (CEM I-SR5), and ordinary Portland cement (OPC) were used in a total of eighteen concrete mixes. Three binder contents of 250 kg/m The ett (C3AH6) . All of 2) reacts with sulfate ions (SO42−) in the pore solution, producing gypsum in the case of sodium sulfate (Na2SO4) attack, leading to strength deterioration of concrete. As a result of this reaction, ettringite is created as a secondary product that increases volume, resulting in expansion and cracking owing to the reaction with tricalcium aluminate (C3A), monosulfate (C4A¯SH12), and calcium aluminate hydrate (C4AH13) [2 reacts with magnesium sulfate in case of magnesium sulfate (MgSO4) attack to create brucite (Mg(OH)2) and gypsum. Calcium silicate hydrate (C-S-H) and ettringite are thus destabilized at low pH levels of brucite. Therefore, the reaction of C-S-H with MgSO4 is accelerated to produce MgSO4 and acts rapidly with C-S-H to silica gel (S2H) and gypsum. Additional amounts of free lime have existed in the system, which can respond to react with MgSO4 to produce more brucite; consequently, a higher content of brucite and gypsum is created in the microstructure of the concrete matrix. With such a high increase in brucite concentration, it reacts with S2H. As a result, C-S-H gradually decomposes, forfeits its lime, and transforms to another phase, non-cementitious M-S-H (magnesium silicate hydrate) [3A content, and minimizing CH content in the system [2 during the process of deterioration [Calcium hydroxide (Ca(OH)(C4AH13) . Howeverhydrate) . To dealhydrate)  reportedhydrate) . In othehydrate) . Blendede system ,21. Althe system . In addie system , decrease system . It is ae system ,25. Repl3A content, and the third one was by reducing the permeability of concrete to prevent the sulfate solution from penetrating the concrete [Recently, several studies have been carried out to reduce the harmful impact of sulfate attack on concrete using supplementary cementitious materials as well as nanomaterials ,29,30,31concrete . Incorpoconcrete ,27,28. H2SO4 solutions  for 180 days after 28 days of water curing. A Slump test was performed in order to control the workability of the produced concrete. Compressive strength of concrete mixes was determined at 28 days as an indicator for mechanical properties. Durability was assessed using compressive strength loss and mass change at 180 days. Scanning electron microscopy (SEM) was implemented to analyze the concrete microstructure and the formed phases due to severe sulfate attacks. The SEM imaging was carried out using a JSM 6510 lv microscope  at an acceleration voltage of 30 kV.Various publications discussed the performance of different types of concrete under sulfate attack; however, according to the authors’ best knowledge, there is no investigations comparing sulfate resistance of different types of cement and different concrete qualities. This research investigates the influence of concrete quality and three different types of cement on sulfate resistance of concrete with various strength levels. Three strength classes of concrete; low strength, medium strength, and high strength have been prepared and tested. In addition, the impact of SF and w/b ratio on concrete resistance to sulfate has been studied intensely and compared with other concrete components. Prepared mixes were subjected to very severe Na2) was used. The fineness, bulk density, and specific gravity of SF were 23,420 m2/kg, 8.0 t/m3, and 2.2, respectively. In this investigation, the performance of three different types of cement were examined under sever sulfate attack. Ordinary Portland cement grade 42.5 conforming to EN 197-1 (2011)  was usedIn this research, crushed dolomite stone was utilized as coarse aggregate with two nominal maximum sizes of 9.5 mm and 25 mm. In addition, natural siliceous sand was used as fine aggregate with a maximum particle size of 2.36 mm and fineness modulus of 2.57. 3, 350 kg/m3, and 450 kg/m3 for low strength concrete, medium strength concrete, and high strength concrete, respectively. SF content was constant at 10% by weight of binder in all mixes. The w/b ratio was varied between 0.4 and 0.8 depending on the strength class and the existence of high range water reducer (HRWR). The HRWR was used in some mixes with dosage of 1% by weight of cement in order to achieve proper workability. In those mixes, the w/b ratio decreased to 0.40–0.65. For low strength concrete, local crushed dolomite was used as coarse aggregate with a nominal maximum size of 25 mm. For medium and high strength concrete mixes, local crushed dolomite was used as coarse aggregate with a nominal maximum size of 9.5 mm. Normal sand with fineness modulus of 2.57 was used as fine aggregate. The water absorption of coarse aggregate was 0.5 % while it was 1.1% for fine aggregate. Additional amount of water equaling to aggregate water absorption has been added to the mixing water in order to prevent negative effects on concrete workability. Nine mixes were prepared and three mixes were considered as control mixes (with superplasticizer) with w/b ratio of 0.65 for low strength concrete, 0.45 for medium strength concrete, and 0.4 for high strength concrete. The other nine mixes had w/b of 0.80 for low strength concrete, and 0.60 for both medium and high strength concrete mixes. Slump test was utilized to measure the workability of fresh concrete. Cubical specimens of 100 mm³ were prepared from each mix and water cured for 28 days, then some of the cube specimens were kept in very severe sodium sulfate solutions  for 180 days according to ACI 318-19 [Eighteen different concrete mixes were designed, prepared, and tested. Concrete mixes with three strength classes were mixed and examined after sulfate attack. The binder contents were 250 kg/mI 318-19 . CompresThe performance of the fresh concrete was evaluated through a slump test to attain proper workability according to ASTM C-143 . This teIn this investigation, a compressive strength test was carried out at 28 days and 180 days conforming to BS EN 12390-3-19 . In addiW is the change in specimen mass, M0 is the initial mass of the concrete specimen before immersion, and tM is the specimen mass after immersion in a sulfate solution. In this study, the change in the specimen’s masses was determined at 28 days before the sulfate exposure as control value, and at 56, 90, and 180 days after the sulfate exposure.The change in concrete mass can be considered a good sign to assess the concrete resistance to sulfate attack. Mass loss expresses the deterioration degree of hydration products. It can be viewed as an indicator of the degree of spalling of the concrete sample during severe sulfate exposure. Mass change can be determined by measuring specimen mass, which was determined utilizing a digital scale with high precision of 0.01 g. Prior to starting their measurement, concrete specimens were dried entirely to constant mass. The following formula (Equation (1)) was used to calculate the mass change in concrete specimens :(1)W=Mt−The microstructure characteristics of concrete are the main parameters governing the performance of different types of concrete under such aggressive conditions. Therefore, in this study, microstructural characterization has been performed using SEM analysis. It has been used to analyze transition zone characteristics, the phase’s formation and deterioration, and crack propagation. In this investigation, SEM measurements were performed in the laboratory of Mansoura University, Egypt using a device named “JEOL JSM-651OLV, OXFORD” at magnifications of 5000 and 6500. The measurement focused on studying the influence of sulfate attacks on concrete characteristics. The test has been conducted at 180 days to study the characteristics of concrete microstructure before and after the sulfate attack.2, at the same w/b ratio and HRWA content, mixes prepared with BFC cement exhibited the highest slump values compared to other types of cement. The same trend can be detected for mixes with strength classes of 350 kg/cm2 and 450 kg/cm2. It is obvious from the results that the role of cement type and characteristics on fresh concrete workability is marginal while the w/b ratio and HRWA content are the main parameters with strong impact on concrete slump.Compressive strength results of the mixes at 28 and 180 days in water curing and 180 days in sulfate solutions for low strength concrete, medium strength concrete, and high strength concrete are given in In this investigation, the loss on compressive strength of concrete after exposure in a standardized sulfate solution was used to evaluate the performance of concrete with different types of cement under severe sulfate attack. It is clear from 3A that occurs due to a rise in the slag fraction of cement concrete is the fundamental cause of the improvement in sulfate and chloride resistance observed in cement concrete that contains a suitable amount of slag. As an added benefit, the addition of slag to concrete helps to reduce the quantities of Ca(OH)2 produced as a result of the reaction with slag, which leads to the creation of increased quantities of C–S–H gel that helps to increase the compressive strength [For medium strength concrete, the trend of strength loss was different from that of low strength concrete. The cement type as well as the w/b ratio appeared to be the governing parameters. For the same w/b ratio, the strength loss of CEM I was about twofold that of concrete made with SRP or BFC, as reflected by the results of mixes P350-0.45, SRP 350-0.45, and BFC350-0.45. When increasing the water content (w/b of 0.8), the strength deterioration of all mixes increased. For mix prepared with Portland cement, the strength deterioration reached 12.33% after sulfate exposure for 180 days. However, the resistance of sulfate attacked was better for mixes prepared with SRP and BFC since the strength loss were 7.84% and 7.16%, respectively. In the case of Portland cement, the hydration products, C-S-H, CH, and the pore structure had low tendency to resist the sulfate attack. While for concrete mixes that incorporated sulfate resistance cement or blast furnace slag cement, the hydration products included more C-S-H and less CH contents which improved the resistivity to sulfate attack. Additionally, the microstructure of SRP and BFC cements was denser and the pores were smaller, which hindered the penetration of sulfate solution significantly. Previous research asserted that large slag content in concrete enhances resistance to sulfate because the permeability of various types of water ions is decreased as a result of slag concentration . The decstrength . AccordiThe absolute compressive strength values for different mixes are presented in When exposed to harsh sulfate attack, concrete characteristics suffer severe deterioration with time. Mass change is considered as one of the main characteristics of concrete that can be significantly affected by exposure to sulfate solution for a long time. In this investigation, mass change with time 28, 56, 90, and 180 days were measured as can be seen in The SEM investigation unveiled diverse microstructures and morphologies of concrete specimens. This mode involves exceptional specimen preparation, such as ultra-thin sheets with extremely cleanly furbished surfaces. Nevertheless, in the instance of concrete, it was vital to observe the morphology of the surface as is, without any harm produced by specimen preparation. From the controlled mixes, mix BFC450-0.4 was selected as the testing mix. It obtained the highest compressive strength and sulfate resistance at 180 days. For mix BFC450-0.6 that was prepared with high w/b ratio, it can be seen that the micro-cracks were increased and spread, and the voids are shown obviously in For low strength concrete, cement types showed almost similar loss in compressive strength. Therefore, any type of cement applied in this study can be used in low strength concrete to resist sulfates.For medium strength concrete, mixes BFC350-0.45 and SRP350-0.45, which contained BFC and CEM I-SR5, respectively, achieved the least loss in compressive strength than that of concrete manufactured using OPC.For high strength concrete, all mixes showed almost similar loss in compressive strength. Therefore, it is possible to recommend any type of cement used to resist sulfates.The results confirmed that the quality of concrete, specifically with its low permeability (high cement content), is the best protection against sulfate attack.The mass change due to sulfate exposure depended mainly on w/b ratio and marginally on the type of cement.The SEM analysis revealed that more gel hydrates were detected in mix BFC450-0.4 that contained BFC and SF with HRWR. For high w/b mix, it could be seen that the micro-cracks were increased and spread, and the voids were shown obviously in a mix that contained BFC and SF without HRWR (mix BFC450-0.6).In this research, the effect of different types of cement on concrete sulfate resistance was investigated. Low strength concrete, medium strength concrete, and high strength concrete were designed, prepared, and tested. Blast furnace cement (BFC), sulfate resisting Portland cement (CEM I-SR5), and ordinary Portland cement (OPC) were used in this research. Prepared mixes were cured in water for 28 days, and then they were subjected to very severe sodium sulfate solutions  for 180 days. The performance of the fresh concrete was tested by a slump test. Compressive strength of concrete mixes was determined at 28 days. Durability was assessed using compressive strength and mass change at 180 days. Phases related to deterioration were also studied using scanning electron microscopy (SEM). The key findings and recommendations of this study are given as follows:


Phase change materials (PCMs) have the ability to absorb and release a large amount of energy during the process of transforming physical properties . PCMs are suitable for thermal energy storage and reducing energy consumption in buildings. The aim of the study is to assess the basic material properties and thermal behavior of fly ash foam concrete mixed with two different types of microencapsulated PCMs (PCM6D and PCM18D). We made five different varieties of fly ash foam concrete by replacing the equivalent unit weight of cement with PCM 0%, PCM 10% and PCM 30%. The results show that using a new type of mixer, the microencapsulated PCMs kept their spherical shapes without any cracks or damage in the foam concrete matrix. Differential scanning calorimetry analysis showed that PCM18D-30% had a latent heat capacity of 19.2 °C and 44.7 J/g, in liquid and solid phase with melting and freezing temperatures of 9.46 °C and 41.7 J/g respectively. Additionally, thermocycle analysis showed that it had maintained the temperature for 8 h within the phase change range. In conclusion, PCMs can reduce indoor temperature fluctuations and exhibit the potential for enhancing energy savings and thermal comfort of buildings. In many countries of the world, energy consumption has increased exponentially. Energy use in construction accounts for almost a third of the total consumption in most countries . That isAn effective solution to this increasing problem is the use of renewable energy in the building sector ,4,5. TakIn the building sector, it is suggested that this phase changing feature could offer various applications. For instance, this material can be used for walls, roofs and floors. In order to integrate PCMs into the building materials, two methods are commonly used: direct impregnation and microencapsulation of PCMs. In the first method, PCMs are absorbed into porous aggregates to form stable composite, which allows for use in construction materials ,9,10,11.In the second method, PCMs are microencapsulated within a protective shell, with a particle size between 15 and 30 μm . This prIn order to make PCM more widely practical as a building material, the above barriers must be removed. This issue can be addressed by using a suitable mixer, adjusting the speed and mixing with lightweight materials such as foam concrete. The use of foam is expected to prevent cracking of the PCM shells during the mixing process. Foamed concrete generally refers to lightweight concrete with a significantly reduced density, high porosity and low thermal conductivity ,22,23,24Therefore, it is important to test the possibility of incorporating microencapsulated PCM without any damage into fly ash foam concrete, which can melt and absorb heat during the daytime when the outside temperature increases. Then, the absorbed energy is kept at constant temperature. Later, when the temperature decreases, the phase changes from a liquid to a solid state, releasing all of the energy absorbed back into the environment , as showTwo types of microencapsulated PCM were studied: PCM6D (melting temperature: 4 to 8 °C) and PCM18D (melting temperature: 16 to 20 °C). The properties of the PCMs, provided by the manufacturer, are presented in The test samples were 40 mm × 40 mm × 160 mm in size, and PCM was replaced in ratios of 0%, 10% and 30% (cement ratio) by both PCMs (PCM6D and PCM18D) in 3 samples, respectively, and a total of 5 samples were prepared . PreviouThe compressive strength and density tests were performed at 28 days of age according to  standardIn order to evaluate the energy storage capacity, Differential scanning calorimetry (DSC) was used. The DSC was taken with a TA device with a temperature accuracy of ± 0.1 °C. It measures the difference in the heat flow rate between samples and inserts reference as a function of time and temperature . The mel−1, 1 °C min−1, 2 °C min−1, 5 °C min−1 and 10 °C min−1 were considered. The program of the test procedure is as follows: Starting with an initial isothermal period at 20 °C for 1 min prior to dynamic heating, up to +40 °C according to the proposed rates .In order to optimize the experimental parameter governing the DSC test, many investigations were analyzed using various dynamic rates and samples. In this way, different heating and cooling rates of 0.5 °C minFour 200 mm × 200 mm × 10 mm samples for each type were used to characterize the thermal conductivity according to  standardThe experimental program of the heat cycling test is shown in The thermal cycle test was carried out by placing the sample in a chamber with a programmable humidity and temperature (model ETAC dc-450) In order to assess the effect of PCM foam concrete used as a wall for buildings, two closed prototypes were built with laboratory-scale dimensions. The first prototype was measured from inside to outside: a 40 mm thick layer of PCM foam concrete around 200 mm × 200 mm × 20 mm specimen, and a 50 mm thick extruded polystyrene foam around 300 mm × 300 mm × 300 mm specimen. The schematic diagram of the physical models and cross-section of the model are shown in The reduction of compressive strength can be explained as follows: From the results, it can be observed that as the amount PCM increased, the foam volume also increased. The high amount of foam causes decreasing strength of PCM foam concrete. The additive PCMs do not seem to contribute to the compressive strength. They are more like voids than aggregates. The results are in line with the conclusion of , which rFor the foam concrete, consistency and stability are the main characteristics of the density test. It can be seen in Our results show that all the PCM mix compositions had similar consistency and stability that were close to the target densities. Additionally, the foam was able to mix with foam concrete thoroughly without collapsing when the OM mixer was used.3. It is clear that the value of PCM6D and PCM18D foam concrete density ranges between 1086–730 kg/m3. The highest density was observed at PCM18D-10% (1086 kg/m3) on the third day while the lowest density was for PCM6D-30% (730 kg/m3) on day 28. There is a density drop from day 3 to day 28, and the percentages were calculated as different as follows: for concrete PCM6-10%: 8.7%; PCM6D-30%: 21.3%; PCM18D-10%: 7.9% and PCM18D-30%: 13.6%, respectively. The results revealed that as the amount of PCM increased, the density of foam concrete decreased. It is because of the relatively low specific gravity of PCM when compared to other components in the foam concrete [The DSC test on PCM6D and PCM18D foam concretes was conducted by means of five different heating/cooling rates of 10 °C/min, 5 °C/min, 2 °C/min, 1 °C/min and 0.5 °C/min. The results of heating and cooling cycles on foam concrete PCM6D (10% and 30%) samples are shown in When scrutinizing all scanning rates of PCM6D foam concretes, the lowest latent heat (Δh) was found at PCM6D-10%, offering the heating/cooling rates of 0.5 °C/min. According to the graphs in The results of heating and cooling cycles on foam concrete PCM18D-10% and PCM18D-30% samples are shown in The The effect of the replacement of PCM into foam concrete specimens in thermal conductivity was measured on day 28. From PCM6D-10% to PCM18D-30%, each designated mix used to measure the thermal conductivity was plotted in Moving to PCM18D, substitution levels of 10% and 30% were found at 21.4% and 55.3%, respectively. The reduced trend of thermal conductivity with the increasing substitution level of PCM composite is attributed to the low thermal conductivity from the PCM6D to PCM18D composites (in the range of 0.1–0.3 W/m·K).The reason for this can be the impact of PCM substitution, and the fact that independent cell foam does not allow heat loss from the pores, which may affect lower conductivity values ,40.PCM6D mainly has two melting ranges, the first one was 4–8 °C and the second one 18–24 °C. The temperature behavior ranges from 10–50 °C. The cycling conditions were a total of 24 h per cycle through heating and cooling for 8 h with a heating rate of 2.5 °C/min. From heat cycle tests of PCM6D-10% and PCM6D-30% foam concretes, the lowest latent heat was found for PCM6D-10%. There was almost no difference compared to No-PCM foam concrete. The highest latent heat was found in PCM6D-30% . ConsideContrarily, when the temperature decreased from 20 °C to 10 °C, PCM foam concrete started freezing. The temperature was delayed for around 3 h. The highest latent heat was found in PCM18D-30%. Considering the PCM18D-30%, the temperatures increased from 10 °C to 20 °C, PCM foam concrete started melting and delayed the temperature for around 3 h. Contrarily, when the temperature decreased from 20 °C to 10 °C, PCM foam concrete started freezing. The temperature was delayed for around 8 h.The observation of the microstructure of the PCM foam concrete mix by SEM produced the images of the PCM foam concrete samples. These contain (A) No-PCM, (B) PCM-10% and (C) PCM-30%, which are shown in As a result of mechanical tests, the 28 day compressive strength of both PCM6D-30% and PCM-18D-30% were 3.5 MPa and 4.5 MPa, respectively. Both types of fly ash foam concrete have sufficient mechanical properties to be used in applications such as the ALC panel with compressive strength of 3 MPa.The results of the DSC test showed that it indicates that all types of PCM foam concrete performed as predicted, melting and freezing within the exact temperature rates and the energy storage capacity increased with amount of PCM in the foam concrete. PCM18D-30% had a latent heat capacity of 19.2 °C and 44.7 J/g in liquid and solid phase with melting and freezing temperatures of 9.4 °C and 41.7 J/g, which was highest of all other types.The results of thermal conductivity showed that the increasing substitution level of PCM composite attributes to the low thermal conductivity from the PCM6D and PCM18D composites (in the range of 0.1–0.3 W/m.K).The results of the thermocycle analysis found that PCM foam concrete types performed well, maintaining the temperature within the range for a certain period of time. The highest maintained temperature was found for PCM18D, for which the temperature delay was the longest, around 8 h.As a result of SEM analysis, the microencapsulate PCM kept their spherical shapes without virtually any cracks or damage. The OM mixer was shown to be highly effective for mixing PCMs in the foam concrete, which is one of the fundamental findings of this research.This research presents an overview of the influences of the basic material properties and thermal behavior of foam concrete mixed with PCM to identify the contribution of PCMs to the mixture. From the results, the following conclusion can be made:To conclude, PCM foam concrete had some useful characteristics such as better thermal performance and latent heat storage. The results revealed that PCMs can reduce indoor temperature fluctuations as they exhibit the potential for enhancing energy savings and thermal comfort of buildings. Moreover, future studies can use the energy simulation program to provide comparative results for PCM foam concrete, and, based on the experimental and simulation data, it can be further investigated at the real scale dimension of buildings.


Due to its availability and affordable processing, date palm fiber (DPF) is among the natural and sustainable fibers used in cementitious composites. Furthermore, DPF is an agricultural, organic, and fibrous material that when subjected to higher temperature can easily degrade and cause reduction in strength. Therefore, the influence of elevated temperatures on the unit weight and strengths of DPF-reinforced concrete needs to be examined. Under this investigation, DPF is used in proportions of 0–3% weight of binder to produce a DPF-reinforced concrete. Silica fume was utilized as a supplemental cementitious material (SCM) in various amounts of 0%, 5%, 10%, and 15% by weight to enhance the heat resistance of the DPF-reinforced concrete. The concrete was then heated to various elevated temperatures for an hour at 200 °C, 400 °C, 600 °C, and 800 °C. After being exposed to high temperatures, the weight loss and the compressive and relative strengths were examined. The weight loss of DPF-reinforced concrete escalated with increments in temperature and DPF content. The compressive and relative strengths of the concrete improved when heated up to 400 °C, irrespective of the DPF and silica fume contents. The heat resistance of the concrete was enhanced with the replacement of up to 10% cement with silica fume when heated to a temperature up to 400 °C, where there were enhancements in compressive and relative strengths. However, at 800 °C, silica fume caused a significant decline in strength. The developed models for predicting the weight loss and the compressive and relative strengths of the DPF-reinforced concrete under high temperature using RSM have a very high degree of correlation and predictability. The models were said to have an average error of less than 6% when validated experimentally. The optimum DPF-reinforced concrete mix under high temperature was achieved by adding 1% DPF by weight of binder materials, replacing 12.14% of the cement using silica fume, and subjecting the concrete to a temperature of 317 °C. The optimization result has a very high desirability of 91.3%. At 300 °C, dehydration and thermal expansion of the aggregates cause internal stresses that lead to microcracks in the microstructure of concrete [2 and C-S-H decomposition takes place at around 400 °C and above 600 °C, respectively. The decomposition of these two hydration products accelerates at temperature around 800 °C, which results in a significant decrease in the concrete’s strength. Furthermore, the conversion of Ca(OH)2 to quicklime (CaO) takes place during its decomposition. The reaction is reversible, as the Ca(OH)2 can be reformed during the cooling process when the CaO comes into contact with moisture. Series of this repeated reaction cause expansion and contraction and consequently cracking in the concrete’s microstructure [2 into lime in concrete under high temperature is by the use of pozzolanic materials, which include silica fume, fly ash and slag, etc., because of their high silica (SiO2) content. The SiO2 will react with and consume the Ca(OH)2 to produce excess products of hydration like calcium aluminate silicate hydrates and C-S-H, thereby reducing the amount of Ca(OH)2 in the cement paste [In recent years, concrete usage has increased in areas of high temperature applications. When concrete is subjected to elevated temperatures, serious damage might occur because of the induced chemical and physical changes that can cause a significant decline in its mechanical properties and its durability performance. This can further lead to a reduction in the structural integrity of the concrete member, resulting in spalling and, consequently, failure of the concrete structural element ,2. The pconcrete ,4,5. The2 consumption and the formation of a dense microstructure, was the main cause of the increase in strength with the silica fume addition. Poon and Azhar [Behnood and Ziari  varied tnd Azhar  investignd Azhar  reportedFibers have been used as additives in concrete to enhance its performance, especially under tensile load. Another benefit of fiber in concrete is it improves its heat resistance by delaying and preventing the thermal crack propagation and spalling of the concrete whenever it is exposed to an elevated temperature ,10. SomeDPF is among the most widely available natural fibers, particularly in North Africa and the Middle East countries, obtained mostly as a waste material from palm trees. DPF has been utilized as a composite of cements such as mortar, concrete, and bricks owing to its merits, such as its eco-friendliness, availability, environmental sustainability, low processing cost, and higher strength-to-cost ratio ,14,15. TDPF is a vegetable fiber and an organic material containing high amounts of lignin, hemicellulose, and cellulose, with other organic and inorganic constituents ,15,23. T2/kg, and 630 kg/m3, respectively. 3, a fineness modulus equal to 2.3, and a water absorption rate equal to 1.9%. It was in a saturated, surface-dried condition. The fine aggregate’s sieve examination revealed that it was a well-graded sand with all of its particles falling inside the permitted range specified by ASTM C33/C33M [3. The amount of mixing water was decreased by 10%, while a superplasticizer dosage of 1% by weight of binder material was added. The superplasticizer had a density of 1060 kg/m3 and was based on polycarboxylate.The main binder employed in this study was type I ordinary Portland cement, which met the requirements specified in ASTM C150/C150M  and has C33/C33M . Figure The DPF was obtained in its raw state, in a rectangular-form woven mesh of about 300–500 mm in length and 200–300 mm in width. The raw fiber was first separated into single fibers by hand, and then immersed in water and further separated and cut into single-fiber diameters of 0.2–1.0 mm and lengths ranging between 20–30 mm. A 3% NaOH alkaline solution was then prepared, and the single DPF was further immersed in the solution for 3 h to remove dirt, dust, and any other impurities. After that, the fiber was taken out of the alkaline medium and properly cleaned using water. After 48 h of air drying, the fiber was applied to the concrete. The mix design and proportioning of the control concrete was done based on the standard specifications outlined in ACI 211.1R , adoptinThe concrete was sampled, mixed, and cured in accordance to the methods outlined in ASTM C192/C192M . The driLW and cR are the weight loss and relative compressive strength, respectively, in %, iW is the initial weight of sample before subjecting to elevated temperature in g, tW is the final weight of the sample after heating and cooling in g. nF is the compressive strength of the sample at normal temperature in MPa, and tF is the compressive strength of the sample after temperature t under consideration in MPa.Following the 28-day curing period, the concrete cubes (100 mm in size) were removed and dried. Upon drying, the samples were then weighed and then placed in the furnace and subjected to different temperatures of 200 °C, 400 °C, 600 °C, and 800 °C. The samples were heated at the rate of 10 °C/min until they reached the desired temperature. The samples were then heated at the constant desired temperature for 2 h. The heating process of the samples in the furnace is shown in Weight loss is one of the properties that is normally calculated to measure the overall physical changes that occur in concrete and mortar when subjected to factors that cause deterioration, such as elevated temperature . The fin2 from silica fume and the Ca(OH)2 from cement. This reduced the evaporation of water and the deterioration of the concrete. Another reason can be ascribed to the higher thermal resistance of silica fume when related to that of cement, as reported by some studies [2). This leads to increased porosity, microcracks, and deterioration and, hence, causes lower unit weight. Additionally, at higher temperatures above 350 °C, the Ca(OH)2 easily decomposes and converts to lime (CaO) by losing water. During cooling, the expansion of lime takes place, which easily causes an expansion in volume and damage resulting in increased weight loss [Silica fume as SCM reduced the weight loss of the DPF concrete when exposed to elevated temperatures of up to 400 °C. At 200 °C, in comparison to the control, the weight loss of DPF concretes with 5% silica fume, namely D1-5 and D2-5, was less by 13.56% and 6.78%, respectively. Similarly, for DPF concretes with 10% silica fume, namely D1-10, D2-10, and D3-10 the weight loss was less by 45.76%, 27.12%, and 1.69%, respectively. For mixes containing 15% silica fume, only mix D1-15 had a lower weight reduction by 18.64% compared to the control. At 400 °C, mixes D1-5, D2-5, D1-10, D2-10, and D15-1 had lower weight losses of 9.57%, 3.19%, 10.64%, 13.83%, and 20.21%, respectively, compared to the control mix. Additionally, comparing the weight loss of mixes without any silica fume, i.e., mixes D1-0, D2-0, and D3-0 with their corresponding mixes containing 5%, 10%, and 15% silica fume, it can be observed that the latter mixes with silica fume have lower weight loss than their corresponding mixes without silica fume at 200 °C and 400 °C. This is to say that the addition of silica fume partially alleviated the undesirable effect of DPF addition on the weight loss and deterioration of the concrete when exposed to temperatures of up to 400 °C. The decline in weight loss caused by silica fume can be ascribed to the densification of the microstructure that is due to the pozzolanic reaction between the SiO studies ,35; this studies . The incght loss . Aydın aght loss  have als2) from either the cement or the silica fume [One of the major effects of fire or high temperature on concrete is the reduction in its structural performance. The compressive strength is the main structural performance property of the concrete. The compressive strengths and relative strengths of the DPF-reinforced concrete after subjection to normal temperature (24 °C), 200 °C, 400 °C, 600 °C, and 800 °C are presented in ica fume . Anotherica fume ,39.The compressive strengths and relative strengths of the DPF-reinforced concrete reduced significantly when heated to temperatures of 600 °C and 800 °C irrespective of the DPF and silica fume contents. However, compared to the control mix, mixes with DPF only or with DPF and silica fume exhibited more significant reductions in strength at higher temperatures above 400 °C. For the control mix, there was a reduction in strength by 28.41% and 46.75% when heated to 600 °C and 800 °C, respectively, compared to its strength at normal temperature. For mixes with only DPF without silica fume, in comparison to their respective strengths at normal temperatures, when exposed to temperatures of 600 °C and 800 °C, mix D1-0 had reductions of 48.5% and 61.8%, respectively; for mix D2-0 there were reductions of 40.21% and 57%, respectively, and for D3-0 there were reductions of 57.8% and 68.7%, respectively. Tanyildizi  also obs2) from cement hydration reacts when heated to higher temperatures, decomposing and converting to lime (CaO) and water vapor at about 350 °C. In the process of cooling, expansion of the CaO takes place and causes serious damage to the cement matrix, causing internal stresses and microcracks, consequently reducing the strength. Silica fume, because of its high SiO2, consumes the excess Ca(OH)2 and produces tobermorite and C-S-H, which can improve strength. This pozzolanic reaction hinders the conversion of Ca(OH)2 into lime and prevents the expansion effects [From  effects . However effects  and Tany effects . This si effects ,40.RSM is a special technique, one of the most suitable mathematical and statistical techniques for developing model equations to predict one or more responses (properties) using a single variable or a combination of several input variables. Furthermore, RSM has been found to be one of the most appropriate methods for performing optimization through the multi-objective optimization by setting up the desired goals for both the variables and responses ,43. The Y denotes the output (response), r0 denotes the intercept where X1 = X2 = 0, r1 and r2 denote the coefficients of the first and second variables, respectively, X1 and X2 denote the first and second input (variables), respectively, i and j denote the linear and quadratic coded values, respectively, for the input, n denotes the number of inputs, and Є denotes the error.The relationship between the input variables and responses developed by the RSM can be represented by a generalized equation in the form of a linear relationship, as shown in Equation (3), when curvature does not exist. However, in actuality, curvature mostly exists, thus making the linear relationship not appropriate. Hence, polynomials with second or higher degrees, as given by Equation (4), are best suited to represent the RSM generalized equation ,45.(3)YRSM is one of the most suitable mathematical and statistical techniques for the mix proportioning of concrete, especially when many additives (variables) are used in the concrete. It is used to investigate the effects of one or more variables on the performance of the concrete. Additionally, RSM is best used for developing model equations for predicting one or more properties of the concrete, meaning the said property of the concrete can be calculated using the model equation without the need for carrying out experimental work or other field studies. RSM can also be used to optimize and achieve the best properties of the concrete using multi-objective optimization. This process will give the mix combinations that can be used to achieve the desired or highest properties of the concrete. Several studies have utilized RSM for mix proportioning, modeling, and optimization of different concrete properties using RSM ,46,47,48For the RSM design, analysis, and optimization, Design Expert Version 11 software was utilized in this research. The Box–Behnken design was selected and adopted for the design because of its suitability. The variables used were DPF, silica fume, and temperature. All the variables were coded with low as −1, middle as 0, and high as +1. The levels and ranges for all the variables are presented in p-value. For a model or its term to be statistically significant, the probability of its Fisher statistical value  must be less than 0.05. In other words, if p < 0.05, then the model or its term is significant, meaning that the null hypothesis that there is no relationship between the variables and the models is false and, therefore, rejected. As shown in p-values are less than 0.05 . With regards to the models’ terms, for weight loss, the D, T, D × T, and T2 were statistically significant in the model as they have p < 0.05, while the terms S, D × S, D2, and S2 were not significant statistically as their p-values were more than 0.05. Similarly, for the compressive strength model, the significant terms with p < 0.05 were D, S, T, S2, and T2, while the terms D × S, D × T, S × T, and D2 were not significant. Lastly, for the relative strength model, only the terms T and T2 have p-values less than 0.05 and are said to be significant, while all other model terms were not significant with P-values less than 0.05. The significance of each model relative to its lack of fit needs to be checked. Lack of fit is the measure of the predicted model that misses the observations. A nonsignificant lack of fit denotes a good model, i.e., the p-value of its lack of fit should be larger than 0.05. The weight loss, compressive strength, and relative strength models all have a nonsignificant lack of fit with respect to their pure errors, as the p-values of their lack of fits are greater than 0.05. Hence, the models have a good fit with their corresponding experimental data. The F-values of 3.58, 3.73, and 4.23 for weight loss, compressive strength, and relative strength models, respectively, indicated that there are 12.51%, 11.79%, and 9.85% probabilities, respectively, that a lack of fit of those values could occur because of distorted data (noise) [LW stands for weight loss in %, cF stands for compressive strength in MPa, cR stands for residual strengths in %, D and S represent DPF and silica, respectively, in %, and T represents elevated temperature in °C.RSM was used to create models for forecasting the weight loss and strengths of DPF-reinforced concrete under severe conditions (subjected to elevated temperatures). The analysis of variance (ANOVA) was used to test the statistical significance and adequacy of the models. In all the models, D, S, and T represent DPF, silica fume, and temperatures, respectively. All the models and each of their terms were tested for statistical significance using a probability test, i.e.,  (noise) ,49. Ther2) was calculated using PRESS and the results are summarized in 2 values range between zero (0) and one (1), with models having higher R2 values close to one said to be highly correlated with the experimental data and have a very good predicting ability. On the other hand, models with lower R2 values are said to have a poor fitness accuracy with the experimental data and low predicting ability. The models developed to forecast the weight loss, compressive strength, and residual strength all have a very high R2 values larger than 0.94. The R2 values of 0.998, 0.945, and 0.946 for weight loss, compressive strength, and residual strength models, respectively, denote that the models have very high fitness. Only about 2%, 5.5%, and 5.6% of the experimental results for weight loss, compressive strength, and residual strength models, respectively, cannot be fully correlated into the models. The variability of predicted and adjusted R2 numbers for each of the models was further used to validate its fitness and adequacy. For a well-fitted model, the variation of its predicted and adjusted R2 values should be lower than 0.2. If not, the models and/or the experimental data are said to have problems because of large block effects. If this problem occurs for the models to be used, necessary corrections are required by carrying out any of the following: model reduction, looking for outliers, transformation, or using higher-order polynomials for the model [2 values for the weight loss model is less than 0.2; therefore, the model and experimental data said to have no problem and the developed model equation can be used even without removing the nonsignificant terms. However, for the compressive and relative strength models, their adjusted R2 minus predicted R2 numbers are higher than 0.2; therefore, something is wrong with the model and/or the experimental data. To solve this problem, model reduction was carried out using a backward elimination method by which the nonsignificant model terms were removed/reduced. Following backward elimination, the models’ predicted and adjusted R2 values became reasonable in compliance as their variation was reduced to less than 0.2. Therefore, for compressive strength and relative strength models, Equations (9) and (10) are appropriate for predicting their values. Moreover, the consistencies of the models were checked by comparing their respective standard deviations to their mean values. All the models have a reasonably lower standard deviation when compared to their means, and, hence, this justifies the less variability between the models and their experimental data. The models’ high adequate precision values greater than 4 also justified the models being appropriate for navigating the design space [The predicted residual error sum of squares (PRESS) was used to further check the correlation and efficiency of the models statistically. The coefficient of determination , Equation (9), and Equation (10) for weight loss, compressive strength, and relative strength, respectively.2 value among the models and, hence, the best predicting ability. With regards to the predicted versus actual plots, for all the models the experimental data were closely corrected with the predicted models as the data points fall along the reference straight trend line. Here also, the model for weight loss has the best fitness, which justified its high degree of correlation and predictability.The normal plot against studentized residuals and predicted versus actual plots for all the responses are presented in The perturbation plots were used to investigate and compare the effects of all the variables at a particular reference point on a response. This is normally measured by how each of the variables moves away from the reference point (taken as the zero point on the plot), while all other variables are held constant at that point ,50. In tThe 2D contour and 3D response graphs were plotted to assess the interactive relationship between the variables and responses under consideration. One of the main advantages of using RSM is the ability to carry out mix optimization by setting out some goals to achieve the desired output. In this study, optimization was carried out to achieve the optimum performance of the DPF-reinforced concrete in terms of strength and weight retention after subjection to severe environmental conditions (elevated temperatures). The summary of the optimization goals and criteria is presented in M and N are the experimental and predicted (model) responses, respectively.The developed models for predicting the weight loss, compressive strength, and residual strength of the DPF-reinforced concrete under high temperature were validated experimentally by carrying out a series of additional experiments and comparing the results with those calculated using the developed model equations. This model was carried out to check the practical ability of the proposed models. The variables from the optimization solutions and some randomly selected proportions of the variables were used as the mix proportions for preparing the samples for the experimental work, while other constituent materials were kept constant. The responses were also estimated from the models using the same proportions of variables as those used for the experiments. The percentage errors between the experimental and predicted models were then calculated using Equation (11):(1)The weight loss of the DPF-reinforced concrete worsened with an increase in temperature and DPF addition. The weight loss escalated significantly when subjected to temperatures of 600 °C and above.(2)The compressive and relative strengths of the DPF-reinforced concrete was enhanced when exposed to temperatures of up to 400 °C, irrespective of the DPF and silica fume contents. However, at higher temperatures of above 400 °C, there was significant loss in strength in the concrete.(3)The compressive strength and relative strength of the DPF-reinforced concrete decreased with increments in DPF content, irrespective of the temperatures it was subjected to.(4)Silica fume as a partial substitute for cement in DPF-reinforced concrete improves its heat resistance when subjected to a temperature up to 400 °C, where the weight loss declined and the strengths improved with the replacement of up to 10% cement with silica fume, irrespective of the DPF contents in the concrete. However, at temperatures of 600 °C and above, the silica fume addition significantly decreased the strength of the DPF-reinforced concrete.(5)Models were developed to predict the weight loss and compressive strengths of DPF-reinforced concrete in severe conditions (elevated temperatures) using RSM. The developed models have a very high degree of correlation and predictability. The models were said to have an average error of less than 6% when validated experimentally.(6)Based on the multi-objective optimization, the highest performance of the DPF-reinforced concrete under elevated temperature was achieved by adding 1% DPF by weight of binder materials, replacing 12.14% of the cement using silica fume, and subjecting the concrete to a temperature of 317 °C. The optimization result has a very high desirability of 91.3%.(7)Therefore, for DPF-reinforced concrete to be used in areas subjected to high temperature, it is recommended to use silica fume as a partial replacement for cement in the mix to improve its heat resistance.In this study, the effect of elevated temperature on the strengths of DPF-reinforced concrete containing silica fume as a partial substitute for cement was investigated. Based on the experimental and RSM analyses, the following conclusions are summarized:


It was observed that the connector with a high damage degree exhibited low capacity and a failure closer to pull-out failure than concrete cone breakout failure. Based on the numerical results, reduction factors for quantitatively assessing the influence of head-sectional damage degree on the loading capacity and stiffness of connectors were proposed. With reference to the Concrete Capacity method, the reduction in tensile capacity of connectors with head-sectional damage was found to be caused by the decrease in the projected area of the concrete cone due to the reduction in head diameter, concrete cone angle, and embedment depth. Meanwhile, numerical results showed that the stiffness of a connector at a high embedment depth or in high strength concrete was more sensitive to head-sectional damage. It was also found that the elastic modulus of the weak region significantly affected the stiffness of connectors, while the influence of its thickness on the capacity and stiffness was insignificant.An extensive numerical study was carried out due to the concern that head-sectional damage caused by corrosion poses a threat to the tensile performance of headed stud connectors. Three-dimensional finite element models of pull-out tests were established, with both material and geometric nonlinearities being considered. In particular, the concrete weak region due to bleeding was simulated. The simulation method was verified by the results of pull-out tests on two connectors with different damage degrees. Tensile performance of headed stud shear connectors of various shaft diameters ( In steel and concrete composite structures, shear connectors are an essential component for ensuring the composite action. Once the connection between steel and concrete loses its integrity, the integrity of the entire structure comes into jeopardy . TherefoHeaded stud shear connectors are the most common type of shear connector , and manThe above-mentioned studies are all about the shear performance of damaged headed stud shear connectors, and as far as the authors know, there is no research focusing on the adverse effect of damage on the tensile performance of the headed stud shear connector. It has been reported by authors  that theIn the present study, an extensive numerical study was carried out to quantitatively assess the influence of damage degree on the loading capacity and stiffness of connectors with different sizes. Pull-out tests on single-headed stud shear connectors with head-sectional damage were conducted to verify the simulation method. Based on the numerical results, capacity and stiffness reduction factors were proposed.ds) were used. The nominal diameter of the stud head (dh) was 32 mm. The damage degree of the section at the stud head (ρh) was the variable. ρh was calculated by , where Ah,0 was the nominal cross-sectional area of the stud head and Ah,d was the measured real value. NC19-2 specimens adopted the corroded headed studs after the accelerated corrosion tests conducted by authors [ρh was obtained by mechanically grinding the stud head.Two-headed stud shear connectors were tested under tension loading, as shown in  authors . For NC1The test setup is shown in The standard headed stud  was madeNu) and the displacement at the ultimate load (δu) are summarized in The load–displacement curves of specimens are shown in The failure modes of specimens after the studs were pulled out are shown in To study more thoroughly the performance of head-damaged stud shear connectors under tensile load, finite element models of pull-out tests were established with a commercial finite element package ABAQUS . In viewDue to the symmetry of the specimen, only a quarter of the specimen was modeled. • ConcreteThe failure process of the connector under tension loading is greatly affected by the concrete properties. The tensile stiffness degradation of the connector is directly related to the concrete plastic deformation or cracking. When the connector failed in concrete cone breakout, concrete tensile properties are the main factor affecting the loading capacity, while the properties of concrete under local loading are the main factor when the headed stud failed in pulled-out failure mode. Therefore, it is necessary to carefully determine the constitutive model for concrete material.The plasticity-based constitutive model for the concrete  in ABAQUS software was employed to simulates concrete plastic deformation, cracking, and crushing in compression. The yield function, flow rule, hardening rule, and uniaxial stress–strain curves need to be defined for the model.fc. Beyond this point, concrete plastic deformation occurs. The second part of curve is nonlinear and can be determined by Equation (1). The third part of the curve is a nonlinear descending part, and the compressive stress is represented as a function of strain according to Equation (2), as suggested by [For concrete in compression, a stress–strain curve which consists of three parts was adopted, as shown in ested by .(1)σc=(o be 1.7 .wmax) was taken as 0.18 mm [For concrete in tension, the tensile stress is assumed to be linearly with respect to strain up to the concrete tensile strength. Beyond this point, concrete crack initiates. Then, a bi-linear tensile stress–crack width diagram is assumed, as shown in  0.18 mm .Besides uniaxial compressive and tensile constitutive model, there are several key parameters in CDP model determining the yield function, flow rule, and hardening rule . By refe• Headed stud, structural steel, and reinforcing steelEs), yield strength (fy), and ultimate strength (fu) were the same as the test values given above, and the ultimate strain (εu) was taken as 0.6%, respectively. The stress–strain relationship for steel is presented in A tri-linear model was used to model the steel material of headed stud. The values of elastic modulus  were used in order to achieve an accurate result from these analyses with large amounts of plastic deformation. Moreover, C3D10M was used for modeling anchor bolts in order that they shared nodes with the concrete slabs in the interface, because the slip between anchor bolts and concrete slab were not paid attention to. For the headed stud and steel plate, three dimensional eight-node elements (C3D8R) with a linear approximation of displacements, reduced integration with hourglass control eight nodes, and three translational degrees of freedom were adopted to improve the rate of convergence. For rebars, two-node linear three-dimensional truss elements (T3D2) with linear approximation of displacement, two nodes, and three translational degrees of freedom were used.The predicted numerical responses of specimens are compared with test results in One of the important differences between the test specimens is the failure mode. For further validation, attention was paid to the failure mode predicted by the numerical simulations. As shown in The above-described validation procedure demonstrates the reliability of the employed modelling procedure and corresponding key parameters selected for the concrete damage plasticity model in this study.The number of specimens in the experimental study was limited. Therefore, in order to further investigate the influence of head-sectional damage on the tensile performance of the connector, a number of finite element models of connectors with different shaft diameters and head diameters  were established. The remaining main parameters of these models were consistent with the models for tested specimens mentioned above.The size of the stud head affects the stress and strain state of the model. As a result, concrete cracking process and the formation and development of concrete cone is affected, and ultimately the failure mode of the connector could be changed.The concrete near the stud head cracked at the beginning of loading, and the smaller the head diameter, the cracking was earlier and faster. Calculated tensile capacities of models with different stud head sizes are presented in • Proposed capacity reduction factorNc,m is the standard concrete cone breakout capacity; Nc is the modified concrete cone breakout capacity; Ab is the anchor bearing area; Abcode is the equivalent bearing area corresponding to a bearing stress of σb = 15fc under the anchor head at peak load, which can be determined from Equation (5) [fc is the concrete compressive strength.The direct cause of the reduction in the tensile capacity is the decrease of the bearing area. Thus, the capacity reduction factor can be written as a function of the bearing area. Nilforoush et al.  have protion (5) .(5)AbcoAb,0 is the original bearing area; Ab,d is the bearing area after damage and is the function of the damage degree:Ah,0 and Ah,d are the cross sectional area of stud head before and after damage; As,0 is the cross sectional area of stud shaft. Thus, the capacity reduction factor is written as follows.Then, the ratio of tensile capacities of connectors after damage and before damage is obtained.Tensile capacities of damaged connectors were calculated according to Equation (8) and were drawn with a red line in The key dimensions such as the head diameter and the shaft diameter of the standard headed studs of different specifications have been specified in standards . In orde• Theoretical analysis on reasons for the capacity reductionAc, as shown in α is the semi vertical angle of the cone.According to the CC method, the concrete cone breakout capacity is proportional to the projected area of the concrete cone, as shown in Equation (10).ρh, the diameter of the stud head is hef,0 and hef,d are the effective embedded depths before and after damage, respectively; α0 and αd are the angles before and after damage, respectively.The head-sectional damage affects the shape of the concrete cone and reduces its projected area. When the damage degree is αd and α0 are 45°. When the damage degree is less than 20%, this curve is close to the curve calculated by the empirical formula, Equation (8), while the tensile capacity is greatly overestimated when the damage degree is greater than 20%. This indicates that there are other reasons for the reduction in tensile capacity.The reduction in head diameter is not the only reason for the reduction in tensile capacity. The blue dash-dot line in α of the concrete cone decreases slightly, that is, αd in Equation (12) is also a function of ρh. The red dashed line in αd and α0 are 44° and 45°, respectively. The curve is generally below all the curves calculated by Equation (8), thereby confirming the significant influence of α on the tensile capacity.Another important reason is that the concrete cone becomes steeper. The foregoing simulation results in this paper, as well as the experimental and simulation results reported in the literature ,14, showhef,d is less than hef,0 [hef,d and hef,0 takes 94 mm and 95 mm, respectively, and both αd and α0 are 45°. It can be seen that the reduction factor is further reduced.Smaller embedment depth at failure is another key reason for the reduction in tensile capacity. The displacement corresponding to the ultimate load of the damaged connector increases with the increase of the damage degree, as shown in an hef,0 . The purThe tensile stiffness of a connector is a key parameter to be used in the design of steel and concrete composite structures. The tensile stiffness of each connector in grouped connectors determines the tensile force distribution between the connectors. Meanwhile, it affects the overall rigidity of composite structures.The foregoing research has pointed out that the tensile stiffness decreases with the increase in the degree of head-sectional damage. However, the adverse effect of head-sectional damage on tensile stiffness has not been paid attention to by Yang et al.  who beliδCon, i.e., the displacement of the head caused by the compression deformation of the concrete) and the tensile deformation of the stud shaft (δSte).The measured displacement of the headed stud under tension loading is the sum of the displacement of the head relative to the concrete slab .N0.1 is the load when the calculated δCon is 0.1 mm.Here, corrosion of the stud shaft was not considered. Therefore, only The relationship between the tensile stiffness and the damage degree is basically linear. The black line with black rectangles symbols in In order to obtain a widely applicable formula for calculating the tensile stiffness of a damaged connector, the influence of the embedment depth, concrete strength, and the concrete weak region due to bleeding on the tensile stiffness was analyzed through 110 models.• Effect of embedment depthFor a connector with larger embedment depth, the influence of head-sectional damage on stiffness is larger. δCon,E) and plastic deformation  of concrete, as shown in Equation (15). The concrete plastic deformation would increase due to the increase in bearing stress when the damage degree increases. Since the proportion of concrete plastic deformation in the total concrete deformation of the connectors with smaller embedment depths is greater, under the same bearing stress increase, the increase of plastic deformation in these connectors is relatively slow. As a result, the influence of head-sectional damage on the stiffness is smaller for the connectors with smaller embedment depth. This suggests that the calculation results of the connectors with larger embedment depths can be used to safely evaluate the adverse effects of corrosion on tensile stiffness.This can be explained by analyzing the composition of concrete compression deformation. The strain state of concrete in low-load stage shown in hef. Furthermore, as shown in • Effect of concrete strengthFor a connector embedded in high strength concrete, the tensile stiffness is larger and the influence of head-sectional damage on stiffness is larger. • Effect of the concrete weak region due to bleeding• Proposed stiffness reduction factor(1)Concrete weak region due to bleeding should be considered in the finite element models, and it was found that its elastic modulus significantly affected the stiffness of connectors, while the influence of its thickness on the capacity and stiffness was minor. After modeling a weak region with an elastic modulus of 2 GPa and a thickness of 4mm, the simulation results demonstrated good agreement with the test results in terms of the initial stiffness and the ultimate load.(2)When the head-sectional damage degree was 38%, the failure mode changed from concrete cone breakout to pull-out failure, resulting in a decrease in loading capacity and ductility. The numerical results show that the load, when concrete, near the stud head cracked decreased with the damage degree, while the crack width increased with the damage degree. The cracking load was found to be less than 10% of the connector’s tensile capacity, which was much smaller than the 30% reported by Eligehausen and Sawade . Meanwhi(3)An equation of capacity reduction factor with the bearing area and damage degree as parameters (Equation (8)) was proposed and verified by numerical results. Furthermore, a concise formula, Equation (9), was suggested to calculate the reduction factor of different types of headed stud shear connectors easily and on the safe side. With reference to the Concrete Capacity method, the decrease in head diameter, concrete cone angle, and embedment depth was found to be the reason for the reduction in tensile capacity of connectors with head-sectional damage.(4)Tensile stiffness was observed to decrease with the decrease in the stud’s head size, which has not been considered by Yang et al. . A lineaAn extensive numerical study and two pull-out tests were conducted to evaluate the effect of head-sectional damage on the behavior of headed stud shear connectors under tension loading, and the following conclusions were obtained: