Lateral load bearing capacity modelling of piles in cohesive soils in undrained conditions: An intelligent evolutionary approach

Abstract The complex behaviour of fine-grained materials in relation with structural elements has received noticeable attention from geotechnical engineers and designers in recent decades. In this research work an evolutionary approach is presented to create a structured polynomial model for predicting the undrained lateral load bearing capacity of piles. The proposed evolutionary polynomial regression (EPR) technique is an evolutionary data mining methodology that generates a transparent and structured representation of the behaviour of a system directly from raw data. It can operate on large quantities of data in order to capture nonlinear and complex relationships between contributing variables. The developed model allows the user to gain a clear insight into the behaviour of the system. Field measurement data from literature was used to develop the proposed EPR model. Comparison of the proposed model predictions with the results from two empirical models currently being implemented in design works, a neural network-based model from literature and also the field data shows that the EPR model is capable of capturing, predicting and generalizing predictions to unseen data cases, for lateral load bearing capacity of piles with very high accuracy. A sensitivity analysis was conducted to evaluate the effect of individual contributing parameters and their contribution to the predictions made by the proposed model. The merits and advantages of the proposed methodology are also discussed.

[1]  Anthony T. C. Goh,et al.  Empirical design in geotechnics using neural networks , 1995 .

[2]  Lymon C. Reese,et al.  Generalized Solutions for Laterally Loaded Piles , 1960 .

[3]  Jessica Daecher,et al.  Pile Foundation Analysis And Design , 2016 .

[4]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[5]  Orazio Giustolisi,et al.  Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression , 2011 .

[6]  Akbar A. Javadi,et al.  Applications of artificial intelligence and data mining techniques in soil modeling , 2009 .

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

[8]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[9]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[10]  J Biarez,et al.  BEARING CAPACITY AND SETTLEMENT OF PILE FOUNDATIONS , 1977 .

[11]  Anthony T. C. Goh,et al.  Pile Driving Records Reanalyzed Using Neural Networks , 1996 .

[12]  Anthony T. C. Goh.  Modeling soil correlations using neural networks , 1995 .

[13]  Akbar A. Javadi,et al.  An evolutionary approach to modelling the soil-water characteristic curve in unsaturated soils , 2012, Comput. Geosci..

[14]  Luigi Berardi,et al.  Predicting torsional strength of RC beams by using Evolutionary Polynomial Regression , 2012, Adv. Eng. Softw..

[15]  Uygar Özesmi,et al.  An artificial neural network approach to spatial habitat modelling with interspecific interaction , 1999 .

[16]  Akbar A. Javadi,et al.  A new approach for prediction of the stability of soil and rock slopes , 2010 .

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

[18]  D. Savić,et al.  A symbolic data-driven technique based on evolutionary polynomial regression , 2006 .

[19]  Mojtaba Mahmoodian,et al.  Prediction of sulphide build-up in filled sewer pipes , 2014, Environmental technology.

[20]  R. Abrahart,et al.  Detection of conceptual model rainfall—runoff processes inside an artificial neural network , 2003 .

[21]  Fred Moses,et al.  Soil Resistance Predictions From Pile Dynamics , 2004 .

[22]  C. G. Chua,et al.  Bayesian Neural Network Analysis of Undrained Side Resistance of Drilled Shafts , 2005 .

[23]  Murad Y. Abu-Farsakh,et al.  Assessment of Direct Cone Penetration Test Methods for Predicting the Ultimate Capacity of Friction Driven Piles , 2004 .

[24]  Holger R. Maier,et al.  PREDICTING SETTLEMENT OF SHALLOW FOUNDATIONS USING NEURAL NETWORKS , 2002 .

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

[26]  Akbar A. Javadi,et al.  Modelling stress-strain and volume change behaviour of unsaturated soils using an evolutionary based data mining technique, an incremental approach , 2012, Eng. Appl. Artif. Intell..

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

[28]  Jamshid Ghaboussi,et al.  Autoprogressive training of neural network constitutive models , 1998 .

[29]  Akbar A. Javadi,et al.  Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression , 2011, Comput. Geosci..

[30]  In Mo Lee,et al.  Prediction of pile bearing capacity using artificial neural networks , 1996 .

[31]  G G Mayerhof,et al.  Bearing Capacity and Settlement of Pile Foundations , 1976 .

[32]  Jean-Louis Briaud,et al.  MEASURED AND PREDICTED AXIAL RESPONSE OF 98 PILES , 1988 .

[33]  Akbar A. Javadi,et al.  An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement , 2011, Eng. Appl. Artif. Intell..