Use of evolutionary computing for modelling some complex problems in geotechnical engineering

In this paper, the feasibility of using evolutionary computing for solving some complex problems in geotechnical engineering is investigated. The paper presents a relatively new technique, i.e. evolutionary polynomial regression (EPR), for modelling three practical applications in geotechnical engineering including the settlement of shallow foundations on cohesionless soils, pullout capacity of small ground anchors and ultimate bearing capacity of pile foundations. The prediction results from the proposed EPR models are compared with those obtained from artificial neural network (ANN) models previously developed by the author, as well as some of the most commonly available methods. The results indicate that the proposed EPR models agree well with (or better than) the ANN models and significantly outperform the other existing methods. The advantage of EPR technique over ANNs is that EPR generates transparent and well-structured models in the form of simple and easy-to-use hand calculation formulae that can be readily used by practising engineers.

[1]  Jv Simmons,et al.  Interpretation of Field Tests on Small-scale Ground Anchors , 1986 .

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

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

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

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

[6]  Abolfazl Eslami,et al.  Bearing capacity of piles from cone penetration test data. , 1997 .

[7]  I. Jefferson,et al.  Soil mechanics in engineering practice , 1997 .

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

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

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

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

[12]  John Burland,et al.  Settlement of foundations on sand and gravel , 1985 .

[13]  J. Schmertmann,et al.  Improved Strain Influence Factor Diagrams , 1978 .

[14]  Luigi Berardi,et al.  Development of pipe deterioration models for water distribution systems using EPR , 2008 .

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

[16]  L C Reese,et al.  DRILLED SHAFTS: CONSTRUCTION PROCEDURES AND DESIGN METHODS , 1999 .

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

[18]  R. L. Kondner.  Hyperbolic Stress-Strain Response: Cohesive Soils , 1963 .

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

[20]  Orazio Giustolisi,et al.  A multi-model approach to analysis of environmental phenomena , 2007, Environ. Model. Softw..

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

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

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

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

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

[26]  Julio J. Valdés,et al.  Computational intelligence in earth sciences and environmental applications: Issues and challenges , 2006, Neural Networks.

[27]  Doreen Meier,et al.  Fundamentals Of Neural Networks Architectures Algorithms And Applications , 2016 .

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

[29]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

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

[31]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[32]  Richard J. Fragaszy,et al.  Uplift Testing of Model Anchors , 1988 .

[33]  Sarat Kumar Das,et al.  Discussion of “Intelligent computing for modeling axial capacity of pile foundations”Appears in Canadian Geotechnical Journal, 47(2): 230–243. , 2010 .

[34]  John H. Schmertmann,et al.  Static Cone to Compute Static Settlement over Sand , 1970 .

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

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

[37]  Holger R. Maier,et al.  Artificial Neural Network based Settlement Prediction Formula for Shallow Foundations on Granular Soils , 2002 .

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

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

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