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

Evaluation of end bearing capacity of drilled shafts in sand by numerical and SPT-based methods

Drilled shafts are a common type of pile foundations which are often used as foundations for buildings, bridges and other structures. The end bearing capacity of drilled shafts, which plays an important role in their design particularly in sandy soils, has traditionally been estimated using empirical or semi-empirical methods. With advances in computing power, it is now possible to conduct more realistic analyses. In this paper, at first, the end bearing capacity of drilled shafts in sandy soils is analyzed numerically and validated with the results of pile load test. Then, the numerical results are compared with the results of Standard Penetration Test (SPT)-based methods. The comparison indicated that there is a satisfactory agreement between the results of numerical method proposed in this paper and the results achieved by SPT-based methods

Collapsibility Prediction of Stabilized Soil with Styrene-Butadiene Rubber Polymer Using ANFIS

Collapsible soils are among the problematic soils in nature that, due to moisture content increasing and under the same stress, show a high rate of decrease in volume. This volume reduction leads to loss of soil structure and ultimately to significant subsidence. Such soils in many parts of the world, including the Kerman province of Iran, necessitate researches regarding the collapsible soils\u27 behavior and characteristics. This study aims to investigate the effect of butadiene rubber on the stabilization of collapsible soils. The tested fine-grained soils that have been sampled from two different sites were stabilized through injecting different percentages of butadiene (the number of experiments was 84). The ASTM D5333 Double-Consolidation Method was applied to examine the stabilized soils on intact soil samples. The results show that the penetrations of butadiene rubber and the formation of butadiene rubber columns have led to a reduction in soil collapse. Considering the development of intelligent systems using the prediction behavior of stabilized collapsible soils, the adaptive neural-fuzzy inference system (ANFIS) model was used to predict the degree of collapsibility of soil samples stabilized by injection of Styrene Butadiene Rubber

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frame

Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

This paper presents an application of two advanced approaches, Artificial Neural Networks (ANN) and Princi­pal Component Analysis (PCA) in predicting the axial pile capacity. The combination of these two approaches allowed the development of an ANN model that provides more accurate axial capacity predictions. The model makes use of Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian Regularization (BR), and it is established through the incorporation of approximately 415 data sets obtained from data published in the literature for a wide range of un-cemented soils and pile configurations. The compiled database includes, respectively 247 and 168 loading tests on large-and low-displacement driven piles. The contributions of the soil above and below pile toe to the pile base resistance are pre-evaluated using separate finite element (FE) analyses. The assessment of the predictive performance of the new method against a number of traditional SPT-based approaches indicates that the developed model has attractive capabili­ties and advantages that render it a promising tool. To facilitate its use, the developed model is translated into simple design equations based on statistical approaches

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

Modelling of geotechnical problems using soft computing

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks (ANN), support vector machine (SVM), multivariate adaptive regression splines (MARS) has been employed for developing the predictive models to estimate the needed parameters. In this report, four geotechnical problems like compaction parameters of sandy soil, compression index of clay, relative density of clean sand and side resistance of drilled shaft have been modeled. Various error criteria such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (R) have been considered for the comparison of different models. Finally different sensitivity analysis has been shown to identify the significance of different input parameters that affects the developed models. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing may provide new approaches and methodologies to minimize the potential inconsistency of correlations