Evaluation of Pile Lateral Capacity in Clay Applying Evolutionary Approach

This paper presents the development of a new model to predict the lateral capacity of piles inserted into clayey soils and subjected to lateral loads. Gene Expression Programming (GEP) has been utilized for this purpose. The data used for development of the GEP model is collected from the literature and comprise 38 data points. The data are divided into two subsets: Training set for model calibration and independent validation set for model verification. Predictions from the GEP model are compared with the results of experimental data. The model has achieved a coefficient of correlation, r, of 0.95 for training and validation sets and average prediction ratio (APR) of 0.97 and 1.04 for training and validation sets respectively. The results indicate that the GEP model performs very well and able to predict the pile lateral capacity accurately

Effect of Sand Percentage on the Compaction Properties and Undrained Shear Strength of Low Plasticity Clay

This paper investigates the influence of sand content on the mechanical behavior of a low plasticity clay that collected from south of Iraq (Sumer town). Samples have been prepared with sand contents of 0%, 10%, 20%, 30%, and 40% of the clay weight. Standard Proctor and unconfined compression tests have been carried out and the optimum moisture content, maximum dry density, and undrained shear strength have been determined. The results show a gradual increasing trend of the maximum dry density with the increase of the sand content up to 30%. The highest dry density reaches 1.90 g/cm3 corresponding to an optimum moisture content of 12%. In addition, this paper shows that the undrained shear strength is inversely proportional to the increase of the percentage of sand. The results of this work provide a useful addition to the literature regarding the behaviour or low plasticity clay-sand mixture

Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

© 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. In this study, the least square support vector machine (LSSVM) algorithm was applied to predicting the bearing capacity of bored piles embedded in sand and mixed soils. Pile geometry and cone penetration test (CPT) results were used as input variables for prediction of pile bearing capacity. The data used were collected from the existing literature and consisted of 50 case records. The application of LSSVM was carried out by dividing the data into three sets: a training set for learning the problem and obtaining a relationship between input variables and pile bearing capacity, and testing and validation sets for evaluation of the predictive and generalization ability of the obtained relationship. The predictions of pile bearing capacity by LSSVM were evaluated by comparing with experimental data and with those by traditional CPT-based methods and the gene expression programming (GEP) model. It was found that the LSSVM performs well with coefficient of determination, mean, and standard deviation equivalent to 0.99, 1.03, and 0.08, respectively, for the testing set, and 1, 1.04, and 0.11, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the LSSVM was accurate in predicting the pile bearing capacity. The results of comparison also showed that the proposed algorithm predicted the pile bearing capacity more accurately than the traditional methods including the GEP model

Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks

The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other and design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load-settlement response of piles to be well predicted. However, it is well known that the actual load-settlement response of pile foundations can only be obtained by load tests carried out in-situ, which are expensive and time-consuming. In this technical note, the recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the load-settlement response of steel driven piles subjected to axial loading. The developed RNN model was calibrated and validated using several in-situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the developed RNN model has the ability to reliably predict the load-settlement response of axially loaded steel driven piles and can thus be used by geotechnical engineers for routine design practice

Soft computing for modeling punching shear of reinforced concrete flat slabs

This paper presents applying gene expression programming (GEP) approach for predicting the punching shear strength of normal and high strength reinforced concrete flat slabs. The GEP model was developed and verified using 58 case histories that involve measured punching shear strength. The modeling was carried out by dividing the data into two sets: a training set for model calibration, and a validation set for verifying the generalization capability of the model. It is shown that the model is able to learn with high accuracy the complex relationship between the punching shear and the factors affecting it and produces this knowledge in the form of a function. The results have demonstrated that the GEP model performs very well with coefficient of determination, mean, standard deviation and probability density at 50% equivalent to 0.98, 0.99, 0.10 and 0.99, respectively. Moreover, the GEP predicts punching shear strength more accurately than the traditional methods

Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming

Numerous methods have been proposed to assess the axial capacity of pile foundations. Most of the methods have limitations and therefore cannot provide consistent and accurate evaluation of pile capacity. However, in many situations, the methods that correlate cone penetration test (CPT) data and pile capacity have shown to provide better results, because the CPT results provide more reliable soil properties. In an attempt to obtain more accurate correlation of CPT data with axial pile capacity, gene expression programming (GEP) technique is used in this study. The GEP is a relatively new artificial intelligent computational technique that has been recently used with success in the field of engineering. Three GEP models have been developed, one for bored piles and two other models for driven piles (a model for each of concrete and steel piles). The data used for developing the GEP models are collected from the literature and comprise a total of 50 bored pile load tests and 58 driven pile load tests (28 concrete pile load tests and 30 steel pile load tests) as well as CPT data. For each GEP model, the data are divided into a training set for model calibration and an independent validation set for model verification. The performances of the GEP models are evaluated by comparing their results with experimental data and the robustness of each model is investigated via sensitivity analyses. The performances of the GEP models are evaluated further by comparing their results with the results of number of currently used CPT-based methods. Statistical analyses are used for the comparison. The results indicate that the GEP models are robust and perform well

Modelling pile capacity and load-settlement behaviour of piles embedded in sand & mixed soils using artificial intelligence

This thesis presents the development of numerical models which are intended to be used to predict the bearing capacity and the load-settlement behaviour of pile foundations embedded in sand and mixed soils. Two artificial intelligence techniques, the gene expression programming (GEP) and the artificial neural networks (ANNs), are used to develop the models. The GEP is a developed version of genetic programming (GP). Initially, the GEP is utilized to model the bearing capacity of the bored piles, concrete driven piles and steel driven piles. The use of the GEP is extended to model the load-settlement behaviour of the piles but achieved limited success. Alternatively, the ANNs have been employed to model the load-settlement behaviour of the piles.The GEP and the ANNs are numerical modelling techniques that depend on input data to determine the structure of the model and its unknown parameters. The GEP tries to mimic the natural evolution of organisms and the ANNs tries to imitate the functions of human brain and nerve system. The two techniques have been applied in the field of geotechnical engineering and found successful in solving many problems.The data used for developing the GEP and ANN models are collected from the literature and comprise a total of 50 bored pile load tests and 58 driven pile load tests (28 concrete pile load tests and 30 steel pile load tests) as well as CPT data. The bored piles have different sizes and round shapes, with diameters ranging from 320 to 1800 mm and lengths from 6 to 27 m. The driven piles also have different sizes and shapes (i.e. circular, square and hexagonal), with diameters ranging from 250 to 660 mm and lengths from 8 to 36 m. All the information of case records in the data source is reviewed to ensure the reliability of used data.The variables that are believed to have significant effect on the bearing capacity of pile foundations are considered. They include pile diameter, embedded length, weighted average cone point resistance within tip influence zone and weighted average cone point resistance and weighted average sleeve friction along shaft.The sleeve friction values are not available in the bored piles data, so the weighted average sleeve friction along shaft is excluded from bored piles models. The models output is the pile capacity (interpreted failure load).Additional input variables are included for modelling the load-settlement behaviour of piles. They include settlement, settlement increment and current state of loadsettlement. The output is the next state of load-settlement.The data are randomly divided into two statistically consistent sets, training set for model calibration and an independent validation set for model performance verification.The predictive ability of the developed GEP model is examined via comparing the performance of the model in training and validation sets. Two performance measures are used: the mean and the coefficient of correlation. The performance of the model was also verified through conducting sensitivity analysis which aimed to determine the response of the model to the variations in the values of each input variables providing the other input variables are constant. The accuracy of the GEP model was evaluated further by comparing its performance with number of currently adopted traditional CPT-based methods. For this purpose, several ranking criteria are used and whichever method scores best is given rank 1. The GEP models, for bored and driven piles, have shown good performance in training and validation sets with high coefficient of correlation between measured and predicted values and low mean values. The results of sensitivity analysis have revealed an incremental relationship between each of the input variables and the output, pile capacity. This agrees with what is available in the geotechnical knowledge and experimental data. The results of comparison with CPT-based methods have shown that the GEP models perform well.The GEP technique is also utilized to simulate the load-settlement behaviour of the piles. Several attempts have been carried out using different input settings. The results of the favoured attempt have shown that the GEP have achieved limited success in predicting the load-settlement behaviour of the piles. Alternatively, the ANN is considered and the sequential neural network is used for modelling the load-settlement behaviour of the piles.This type of network can account for the load-settlement interdependency and has the option to feedback internally the predicted output of the current state of loadsettlement to be used as input for the next state of load-settlement.Three ANN models are developed: a model for bored piles and two models for driven piles (a model for steel and a model for concrete piles). The predictive ability of the models is verified by comparing their predictions in training and validation sets with experimental data. Statistical measures including the coefficient of correlation and the mean are used to assess the performance of the ANN models in training and validation sets. The results have revealed that the predicted load-settlement curves by ANN models are in agreement with experimental data for both of training and validation sets. The results also indicate that the ANN models have achieved high coefficient of correlation and low mean values. This indicates that the ANN models can predict the load-settlement of the piles accurately.To examine the performance of the developed ANN models further, the prediction of the models in the validation set are compared with number of load-transfer methods. The comparison is carried out first visually by comparing the load-settlement curve obtained by the ANN models and the load transfer methods with experimental curves. Secondly, is numerically by calculating the coefficient of correlation and the mean absolute percentage error between the experimental data and the compared methods for each case record. The visual comparison has shown that the ANN models are in better agreement with the experimental data than the load transfer methods. The numerical comparison also has shown that the ANN models scored the highest coefficient of correlation and lowest mean absolute percentage error for all compared case records.The developed ANN models are coded into a simple and easily executable computer program.The output of this study is very useful for designers and also for researchers who wish to apply this methodology on other problems in Geotechnical Engineering. Moreover, the result of this study can be considered applicable worldwide because its input data is collected from different regions

Predicting axial capacity of driven piles in cohesive soils using intelligent computing

An accurate prediction of pile capacity under axial loads is necessary for the design. This paper presents the development of a new model to predict axial capacity of pile foundations driven into cohesive soils. Gene expression programming technique (GEP) has been utilized for this purpose. The data used for development of the GEP model is collected from the literature and comprise a series of in-situ driven piles load tests as well as cone penetration test (CPT) results. The data are divided into two subsets: training set for model calibration and independent validation set for model verification. Predictions from the GEP model are compared with experimental data and with predictions of number of currently adopted CPT-based methods. The results have demonstrated that the GEP model performs well with coefficient of correlation, mean and probability density at 50% equivalent to 0.94, 0.96 and 1.01, respectively, indicating that the proposed model predicts pile capacity accurately. 2011 Elsevier Ltd. All rights reserved

Predicting pile dynamic capacity via application of an evolutionary algorithm

This study presents the development of a new model obtained from the correlation of dynamic input and SPT data with pile capacity. An evolutionary algorithm, gene expression programming (GEP), was used for modelling the correlation. The data used for model development comprised 24 cases obtained from existing literature. The modelling was carried out by dividing the data into two sets: a training set for model calibration and a validation set for verifying the generalization capability of the model. The performance of the model was evaluated by comparing its predictions of pile capacity with experimental data and with predictions of pile capacity by two commonly used traditional methods and the artificial neural networks (ANNs) model. It was found that the model performs well with a coefficient of determination, mean, standard deviation and probability density at 50% equivalent to 0.94, 1.08, 0.14, and 1.05, respectively, for the training set, and 0.96, 0.95, 0.13, and 0.93, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the model is accurate in predicting pile capacity. The results of comparison also showed that the model predicted pile capacity more accurately than traditional methods including the ANNs model. © 2014 The Japanese Geotechnical Society