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.

[1]  Akbar A. Javadi,et al.  A new genetic programming model for predicting settlement of shallow foundations , 2007 .

[2]  Weng Tat Chan,et al.  NEURAL NETWORK: AN ALTERNATIVE TO PILE DRIVING FORMULAS , 1995 .

[3]  Yoon-Seok Hong,et al.  Evolutionary self-organising modelling of a municipal wastewater treatment plant. , 2003, Water research.

[4]  M. A. A. Kiefa.  GENERAL REGRESSION NEURAL NETWORKS FOR DRIVEN PILES IN COHESIONLESS SOILS , 1998 .

[5]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[6]  Anthony T. C. Goh,et al.  PREDICTION OF PILE CAPACITY USING NEURAL NETWORKS , 1997 .

[7]  Candida Ferreira.  Gene expression programming , 2006 .

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  Nader Nariman-zadeh,et al.  Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms , 2009 .

[11]  James H. Long,et al.  Accuracy of Methods for Predicting Axial Capacity of Deep Foundations , 1999 .

[12]  Lale Özbakir,et al.  Prediction of compressive and tensile strength of limestone via genetic programming , 2008, Expert Syst. Appl..

[13]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[14]  Abolfazl Eslami,et al.  Pile capacity by direct CPT and CPTu methods applied to 102 case histories , 1997 .

[15]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[16]  Prabir Kumar Basudhar,et al.  Undrained lateral load capacity of piles in clay using artificial neural network , 2006 .

[17]  Fred H. Kulhawy,et al.  Manual on estimating soil properties for foundation design , 1990 .

[18]  Omar Mohammed Alsamman.  The use of CPT for calculating axial capacity of drilled shafts , 1995 .

[19]  Mohamed A. Shahin,et al.  Intelligent computing for modeling axial capacity of pile foundations , 2010 .

[20]  Ashraf F. Ashour,et al.  Empirical modelling of shear strength of RC deep beams by genetic programming , 2003 .

[21]  Holger R. Maier,et al.  DATA DIVISION FOR DEVELOPING NEURAL NETWORKS APPLIED TO GEOTECHNICAL ENGINEERING , 2004 .

[22]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[23]  Ali Firat Cabalar,et al.  Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming , 2009, Expert Syst. Appl..

[24]  F. L. Beringen,et al.  Pile foundations for large North Sea structures , 1979 .

[25]  Akbar A. Javadi,et al.  Evaluation of liquefaction induced lateral displacements using genetic programming , 2006 .