An adaptive neuro-fuzzy inference system for bridge risk assessment

Authors:
Ying-Ming Wang;Taha M. S. Elhag
Affiliations:
Institute of Soft Science, Fuzhou University, Fuzhou 350002, PR China;School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, P.O. Box 88, Manchester M60 1QD, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

Bridge risks are often evaluated periodically so that the bridges with high risks can be maintained timely. This paper develops an adaptive neuro-fuzzy system (ANFIS) using 506 bridge maintenance projects for bridge risk assessment, which can help Highways Agency to determine the maintenance priority ranking of bridge structures more systematically, more efficiently and more economically in comparison with the existing bridge risk assessment methodologies which require a large number of subjective judgments from bridge experts to build the complicated nonlinear relationships between bridge risk score and risk ratings. The ANFIS proves to be very effective in modelling bridge risks and performs better than artificial neural networks (ANN) and multiple regression analysis (MRA).