Artificial Intelligence Techniques for Bridge Reliability Assessment

Bridge safety is receiving more attention when some
bridges lost their function in recent years. Are the
bridges in service today still safe as they approach
their designed service life? Will a bridge built
today be able to safely carry traffic loads for its
intended 75-year service life? This book answers
such questions by developing Artificial intelligence
techniques for bridge reliability assessment.

The techniques presented include new analysis models
of highway bridges, a new Shredding Genetic
algorithm (SGA) that is efficient to identify
dominant failure, and a new solver to improve the
efficiency of nonlinear analysis. These techniques
can be used to analyze the reliability of a
structure that could not be analyzed by traditional
methods. Plenty of on-site and full-scale bridge
experiments are introduced to form a solid benchmark
for bridge nonlinear analysis. Computer simulations
of these experiments demonstrate the efficiency and
applicability of the proposed algorithms. This book
should be helpful in bridge design and research for
large and complex systems, and can be used in the
safety evaluation of the bridges in service.

Autorentext

Linzhong Deng is a senior architect at ProPlus Solutionsand a PhD candidate in Computational Mathematics & Engineeringat Stanford University. He co-founded Revosys, which merged with ProPlus in 2008, to analyze SOC yield. He received a PhD in CE at CUNY in 2000. His research includes reliability analysis and sparse matrix computation.

Klappentext

Bridge safety is receiving more attention when some bridges lost their function in recent years. Are the bridges in service today still safe as they approach their designed service life? Will a bridge built today be able to safely carry traffic loads for its intended 75-year service life? This book answers such questions by developing Artificial intelligence techniques for bridge reliability assessment. The techniques presented include new analysis models of highway bridges, a new Shredding Genetic algorithm (SGA) that is efficient to identify dominant failure, and a new solver to improve the efficiency of nonlinear analysis. These techniques can be used to analyze the reliability of a structure that could not be analyzed by traditional methods. Plenty of on-site and full-scale bridge experiments are introduced to form a solid benchmark for bridge nonlinear analysis. Computer simulations of these experiments demonstrate the efficiency and applicability of the proposed algorithms. This book should be helpful in bridge design and research for large and complex systems, and can be used in the safety evaluation of the bridges in service.