State-of-the-art review of some artificial intelligence applications in pile foundations

Abstract Geotechnical engineering deals with materials (e.g. soil and rock) that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically-based engineering methods. Artificial intelligence (AI) is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering applications because it has demonstrated superior predictive ability compared to traditional methods. This paper provides state-of-the-art review of some selected AI techniques and their applications in pile foundations, and presents the salient features associated with the modeling development of these AI techniques. The paper also discusses the strength and limitations of the selected AI techniques compared to other available modeling approaches.

[1]  Mark B. Jaksa,et al.  Neural network prediction of pullout capacity of marquee ground anchors , 2005 .

[2]  Mohamed A. Shahin,et al.  Use of evolutionary computing for modelling some complex problems in geotechnical engineering , 2015 .

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

[4]  Mohamed A. Shahin,et al.  Load–settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks , 2014 .

[5]  Mohamed A. Shahin,et al.  Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks , 2014 .

[6]  Dong-Sheng Jeng,et al.  Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model) , 2011, Eng. Appl. Artif. Intell..

[7]  Joseph E. Bowles,et al.  Foundation analysis and design , 1968 .

[8]  F. H. Kulhawy,et al.  Case history evaluation of the behavior of drilled shafts under axial and lateral loading. Final report , 1994 .

[9]  Wei Wang,et al.  Artificial Neural Network Model for Time-Dependent Vertical Bearing Capacity of Preformed Concrete Pile , 2010 .

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

[11]  Dimitri Solomatine,et al.  Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology , 2009 .

[12]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[13]  Ian Flood,et al.  Towards the next generation of artificial neural networks for civil engineering , 2008, Adv. Eng. Informatics.

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

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

[16]  Hamid Nikraz,et al.  Predicting axial capacity of driven piles in cohesive soils using intelligent computing , 2012, Eng. Appl. Artif. Intell..

[17]  L. Reese,et al.  Analysis and Design of Shallow and Deep Foundations , 2005 .

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

[19]  John Burland,et al.  SHAFT FRICTION OF PILES IN CLAY--A SIMPLE FUNDAMENTAL APPROACH , 1973 .

[20]  Hamid Nikraz,et al.  Predicting pile dynamic capacity via application of an evolutionary algorithm , 2014 .

[21]  Hamid Nikraz,et al.  Simulating pile load-settlement behavior from CPT data using intelligent computing , 2011 .

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

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

[24]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

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

[26]  Dong-Sheng Jeng,et al.  An optimised product-unit neural network with a novel PSO-BP hybrid training algorithm: Applications to load-deformation analysis of axially loaded piles , 2013, Eng. Appl. Artif. Intell..

[27]  Holger R. Maier,et al.  Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications , 2009, Adv. Artif. Neural Syst..

[28]  Holger R. Maier,et al.  Application of Artificial Neural Networks to Forecasting of Surface Water Quality Variables: Issues, Applications and Challenges , 2000 .

[29]  Hyun-ki Park,et al.  Neural Network Model for Predicting the Resistance of Driven Piles , 2010 .

[30]  Luigi Berardi,et al.  Modelling sewer failure by evolutionary computing , 2006 .

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

[32]  Mohamed A. Shahin,et al.  Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions , 2012 .

[33]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[34]  Holger R. Maier,et al.  ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING , 2001 .

[35]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

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

[37]  A S Vesic,et al.  DESIGN OF PILE FOUNDATIONS , 1977 .

[38]  Harry G. Poulos,et al.  Pile foundation analysis and design , 1980 .

[39]  Robert M. Semple,et al.  Shaft Capacity of Driven Pipe Piles in Clay , 1984 .

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

[41]  John H. Schmertmann,et al.  GUIDELINES FOR CONE PENETRATION TEST. (PERFORMANCE AND DESIGN) , 1978 .

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

[43]  Avi Ostfeld,et al.  Data-driven modelling: some past experiences and new approaches , 2008 .

[44]  Mark B. Jaksa,et al.  Prediction of pile settlement using artificial neural networks based on standard penetration test data , 2009 .

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

[46]  Mahesh Pal,et al.  Modelling pile capacity using Gaussian process regression , 2010 .

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

[48]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[49]  Martin Brown,et al.  A perspective and critique of adaptive neurofuzzy systems used for modelling and control applications , 1995, Int. J. Neural Syst..

[50]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[51]  Harry M. Coyle,et al.  NEW DESIGN CORRELATIONS FOR PILES IN SAND , 1981 .

[52]  Anthony T. C. Goh.  Nonlinear modelling in geotechnical engineering using neural networks , 1994 .

[53]  M. Jaksa,et al.  Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks , 2006 .

[54]  A. T. C. Goh,et al.  Back-propagation neural networks for modeling complex systems , 1995, Artif. Intell. Eng..

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

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

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

[58]  M. Randolph,et al.  Analysis of Deformation of Vertically Loaded Piles , 1978 .

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

[60]  Rana Imam,et al.  Regression versus artificial neural networks: Predicting pile setup from empirical data , 2014 .

[61]  Bashar Tarawneh,et al.  Pipe pile setup: Database and prediction model using artificial neural network , 2013 .

[62]  Hamid Nikraz,et al.  Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming , 2011 .

[63]  Amir Hossein Gandomi,et al.  A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems , 2011, Neural Computing and Applications.

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

[65]  Braja M. Das,et al.  Principles of Foundation Engineering , 1984 .

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

[67]  John B. Dunckee.  The Iron Wharf At Fort Monroe, Va. , 1892 .

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

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