A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Reliability Assessment of Geotechnical Serviceability State Using Neural Networks
One important aspect in the design of many geotechnical structures such as tunnels, retaining walls, and piles is the serviceability failure performance of the structure, i.e., when the actual displacement of the structure exceeds the maximum allowable displacement. A risk-based approach to serviceability performance "failure" is necessary to incorporate systematically the uncertainties associated with various design parameters. This paper demonstrates the use of an integrated neural network-reliability method to assess the risk of serviceability failure through calculation of the reliability index. An illustrative example for a braced retaining wall is presented using this approach.
Reliability Assessment of Geotechnical Serviceability State Using Neural Networks
One important aspect in the design of many geotechnical structures such as tunnels, retaining walls, and piles is the serviceability failure performance of the structure, i.e., when the actual displacement of the structure exceeds the maximum allowable displacement. A risk-based approach to serviceability performance "failure" is necessary to incorporate systematically the uncertainties associated with various design parameters. This paper demonstrates the use of an integrated neural network-reliability method to assess the risk of serviceability failure through calculation of the reliability index. An illustrative example for a braced retaining wall is presented using this approach.
Reliability Assessment of Geotechnical Serviceability State Using Neural Networks
Goh, Anthony T. C. (author) / Kulhawy, Fred H. (author)
GeoCongress 2006 ; 2006 ; Atlanta, Georgia, United States
GeoCongress 2006 ; 1-4
2006-02-21
Conference paper
Electronic Resource
English
Reliability Assessment of Geotechnical Serviceability State Using Neural Networks
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