A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> Abstract Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed. <s> BIB001 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect. Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository. <s> BIB002 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> Artificial neural networks are appearing as useful alternatives to traditional statistical modelling techniques in many scientific disciplines. This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences. <s> BIB003 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> Abstract Genetic algorithms (GA) has given rise to two new fields of research where (global) optimisation is of crucial importance: ‘ genetic based machine learning ’ (GBML) and ‘ genetic programming ’ (GP). An introduction by the authors to GA and GBML was given in two previous papers (Eng. Appl. Artif. Intell. 9(6) (1996) 681; Eng. Appl. Artif. Intell. 13(4) (2000) 381). In this paper, the last domain (GP) will be introduced, thereby making up a trilogy which gives a general overview of the whole field. In this third part, an overview will be given of the basic concepts of GP as defined by Koza. A first (educational) example of GP is given by solving a simple symbolic regression of a sinus function. Finally, a more complex application is presented in which GP is used to construct the mathematical equations for an industrial process. To this end, the case study ‘fibre-to-yarn production process’ is introduced. The goal of this study is the automatic development of mathematical equations for the prediction of spinnability and (possible) resulting yarn strength. It is shown that (relatively) simple equations can be obtained which describe accurately 90% of the fibre-to-yarn database. <s> BIB004 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> Abstract In this paper, artificial neural networks (ANNs) are applied to model the stress–strain behavior of in situ sandy soils containing nonplastic fines. A main drawback of these types of models is discussed, i.e. an ANN based model gives no information how the model inputs affect the output. A systematic approach is therefore presented to acquire and verify the stored knowledge of a general ANN based constitutive soil model. Sensitivities of the output to corresponding inputs are defined mathematically. A sensitivity analysis is then performed to extract the dominant rules of the proposed model, which compare favorably with experimental observations. <s> BIB005 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classicalmathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty. <s> BIB006 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> The drilling of a number of boreholes to determine the soil profile of a given area is time consuming and costly. This paper describes estimated soil profiles obtained using a model based on artificial neural networks (ANN). ANN is a powerful data-modelling tool capable of capturing and representing complex relationships between input and output. It deals with many multi-variate problems for which an exact analytical model does not exist or is very difficult and time consuming to develop. The main settlement in the Adapazari region was selected to demonstrate the capability of such model. The results obtained using ANN are promising when compared with the soil profile obtained from boreholes. <s> BIB007 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> This paper presents a fuzzy-logic-based system for selecting contractors. This tool, which was based on the fuzzy control technique, was created for the private sector client in traditional design–bid–build projects with one-step selection processes, but its philosophy can also serve other types of clients, industries, contracts, and selection processes. The system develops an assessment of different qualitative and quantitative issues that influence a contractor’s suitability for constructing a specific design in a specific environment (client’s needs and objectives, objectives prioritization, etc.), taking into account the risk of not achieving the client’s objectives. A computer application was developed and validated, including a Delphi analysis with professionals who are experts in contractor selection. The application covers the possibility of using different selection policies, when the essential project objective is cost, time or quality. As part of the validation process, a neural network was dev... <s> BIB008 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> ABSTRACT Mafi, S., Yeganeh-Bakhtiary, A., Kazeminezhad, M.H., 2013. Prediction formula for longshore sediment transport rate with M5' algorithm One of the most vital tasks for coastal engineers is to calculate the gross longshore sediment transport rate (LSTR) to control the shoreline erosion and beach evolution. In the past decades, several empirical formulas or parametric models have been proposed for predicting the gross LSTR as a function of the breaking wave characteristics, bed materials and beach slope. The main downside with these formulas is that they give wide range of different rate of predictions; consequently their reliability under the changing hydrodynamic conditions is very uncertain. In the present study, an alternative approach based on the Regression Trees (M5') was applied to present a new formula for prediction of LSTR in terms of the longshore sediment transport coefficient (e). Several high-quality data sets were employed, which comprise of the wave parameters, sediment characterist... <s> BIB009 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> Abstract In this paper, we describe the designed and tested system of automated double ring infiltrometer (DRI) that we have developed using an Arduino microcontroller, a Hall effect sensor, a peristaltic pump, a water level sensor, and a constant-level float valve. The system can be used for infiltration measurements in both single ring falling head and double ring constant head methods. The precise measurements of the current method compared to previous designed systems are not affected by sunlight, and due to the method of flow measurement, remain accurate even for low infiltration values. The set-up has an easy real-time data storage on a micro-SD card without a need of a portable computer in the field. It only requires a single reservoir for both inner and outer rings to which, water can be added anytime needed without affecting the measurements. The system automatically detects when the steady state infiltration rate is reached and concludes the testing and stops measurements. The system is mounted in a portable and weather resistant box and is applied to run DRI testing in the field to check the applicability and accuracy of the portable set-up in field measurements. Manual testing was also performed in the field for comparison with the automated system measurements. Overall system architecture, and the design of hardware and software components are presented in details. The system configuration is illustrated for better understanding of the set-up. <s> BIB010 </s> A review of Genetic Programming and Artificial Neural Network applications in pile foundations <s> Overview of artificial intelligence <s> Abstract The main objective of this paper was to use different soft computing methods to forecast hurricane wave height over Gulf of Mexico. For this purpose, three well-known soft computing methods; Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) were used. The friction velocity was also used instead of wind speed at 10 m height. For this case, three different assumptions were considered for computing friction velocity. For the analysis, one month (from August 20th, 2008 to September 20th, 2008) data including passage of hurricanes Gustav and Ike over Gulf of Mexico were collected from six different buoys. Next, seven different parameters were used to train the models based on the data observed from five buoys. Later the trained models were used to forecast observed wave height in the sixth buoy in different lead times. The results showed that using more number of input parameters leads to better performance of the methods, especially in longer lead times. Moreover, in longer lead times, the effect of other parameters such as air pressure were increased and assisted the models to outperform previous models. In addition; different assumptions for friction velocity eventuated comparable results. <s> BIB011
Artificial intelligence is a scientific discipline focused on the design and development of algorithms used to evolve behaviors based on empirical data. AI techniques can be used in solving engineering problems BIB010   BIB011 BIB009    even if the underlying relationships are unknown or the physical meaning is difficult to explain. This is one of the main advantages of these techniques when compared to most physically-based empirical and statistical methods. AI has the capability of learning by examples of data inputs and outputs presented to them so that the subtle functional relationships among the data are captured. Thus, AI models do not require numerous assumptions about the physical behavior of the system and mainly rely on the data to determine the structure and parameters that govern a system. This is in contrast to most physically-based models that use physical laws to derive the underlying relationships of the system and require prior knowledge about the nature of the relationships among the data. Therefore, AI-based solutions can often provide valuable alternatives for efficiently solving problems in the geotechnical engineering. AI uses available data to map between the system inputs and the corresponding outputs using machine learning. Mapping process is done by repeatedly presenting examples of the inputs and model outputs in order to find the function that minimizes the error between the actual outputs and the predictions of the AI model. Statistical regression analysis of data with non-linear relationship can be applied successfully only if prior knowledge of the nature of the non-linearity exists. However, for AI models, this prior knowledge of the nature of the non-linearity is not required. In the broad area of engineering problems, it is likely to encounter complex and highly non-linear conditions where traditional regression analyses are inadequate BIB003 . There are several AI algorithms; amongst them ANN and GP are more applicable for prediction of non-linear phenomena in engineering problems. A brief overview of these techniques is presented below. Artificial Neural Networks were first introduced by McCulloch and Pitts BIB001 . ANN as described by Bendana et al. BIB008 is a massively parallel distributed processor which can store information taken from a data set that is supplied out of the network. Artificial Neural Networks are computational models based on the information processing system of the human brain and nervous system BIB005 . They can be considered as a group of simple, highly interconnected elements that process the information by their dynamic state response to external inputs. ANNs learn from data examples presented to them. Because of this, they can be used even if the underlying relationships among the data are unknown or the physical meaning is difficult to explain. Comparing this capability with other traditional empirical and statistical methods which require prior knowledge about the nature of the relationships reveals the applicability of this method in modeling the complex behaviors between inputs and outputs BIB007 . Since the early 1990s, ANNs have been applied successfully to almost every problem in engineering. A typical structure of ANNs is composed of a number of interconnected processing elements, commonly referred to as neurons. The neurons are logically arranged in layers that interact with each other via weighted connections. The main three set of layers include input layer, hidden layers, and output layer. Each neuron is connected to all the neurons in the next layer. Patterns are presented to the network via the input layer. This layer communicates to one or more hidden layers where the actual processing is done via a system of weighted connections. The hidden layers enable these networks to represent and compute complicated associations between inputs and outputs. The hidden layer subsequently links to an output layer which holds the response of the network to the input. In addition, there is also a bias with modifiable weighted connections, which is only connected to neurons in the hidden and output layers. ANNs can be autonomous and learn by input from outside "teachers" or even self-teaching from written in rules  . Typical structure and operation of ANNs is shown in Fig. 1 . The overall performance of the ANN model can be assessed by several criteria including coefficient of determination (R 2 ), mean squared error, mean absolute error, minimal absolute error, and maximum absolute error. A well-trained model should result in R 2 value close to 1 and small values of error terms BIB005 . Genetic Programming is an example of AI inspired by biological evolution extending from genetic algorithms. It can be considered as an evolutionary algorithm-based methodology used to find computer programs that perform a given computational task  . The technique was introduced by Koza BIB002 as a domain-independent problem-solving approach in which computer programs composed of functions and terminals are evolved to solve, or approximately solve, problems by generating a structured representation of the data. The structural representation imitates the biological evolution of living organisms, and emulates naturally occurring genetic operations. The ability to provide the relationship between a set of inputs and the corresponding outputs in a simple mathematical form accessible to the users is the main advantage of the GP over the ANNs. The first step of GP modelling is the creation of initial population of computer models (also called chromosomes). The initial population includes a randomly selected set of functions and terminals defined by the user to suit a certain problem. The functions and terminals represent the building blocks of the GP models and are arranged in a treelike structure to form a computer model that contains a root node, branches of functional nodes, and terminals (Fig. 2) . Examples of functions and terminals used in GP are standard arithmetic operations, Boolean logic functions, trigonometric functions, numerical constants, logical constants, variables, and user-defined operators BIB004 . Analysis in GP starts with determining a set of functions that represent the nature of the problem or data. Each individual in the population receives a measure of its fitness in the current environment. The fitness criteria are calculated by the objective function i.e., how good the individual is at competing with the rest of the population. BIB006 New population is created by applying reproduction, cross-over, and mutation to certain proportions of the computer models. Reproduction is the copying of a computer model from an existing population into the new population without any change; crossover, as shown in Fig. 3 , is the genetic recombining of randomly chosen parts of two computer models; and mutation is the replacement of a randomly selected functional or terminal node with others from the same function or terminal set. The existing population will then be replaced with the new population. This evolutionary process is continued until a termination criterion is met, which can be either an acceptable error or a maximum number of generations. Finally, the best computer model is generated by GP using the fitness function adopted.