Recent advances and future challenges for artificial neural systems in geotechnical engineering applications

Authors:
Mohamed A. Shahin;Mark B. Jaksa;Holger R. Maier
Affiliations:
Department of Civil Engineering, Curtin University of Technology, Perth, WA, Australia;School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia;School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia
Venue:
Advances in Artificial Neural Systems
Year:
2009

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Abstract

Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classicalmathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.