Damage Detection of Self-Anchored Suspension Bridge Based on Neural Network Model and Genetic-Simulated Annealing Algorithm

Qing Chen Zhang; Quan Sheng Sun

doi:10.4028/www.scientific.net/AMR.243-249.1963

Paper Titles

Mechanical Analysis of Deck Pavement of Curved Continuous Steel Box Girder
p.1941

Energy Dissipation of Super-Long-Span Cable-Stayed Bridge Using Metal Alloy Brace
p.1947

Study on Ultimate Load-Carrying Capacity of Long-Span Composite Girder Cable-Stayed Bridge with Three Pylons
p.1952

Applications of Modified Equivalent Load Method in the Possibility of the Transportation of Large Equipment
p.1957

Damage Detection of Self-Anchored Suspension Bridge Based on Neural Network Model and Genetic-Simulated Annealing Algorithm
p.1963

Vertical Load-Carrying Natural Frequency of Railway Double-Track Steel Truss Continuous Bridge
p.1968

Study on Disease Analysis and Reinforcement Technology of Truss Arch Bridge
p.1972

Research of the Forecast and Early Warning System for Bridges
p.1978

Applications of Artificial Neural Networks in Bridge Engineering
p.1984

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 243-249Damage Detection of Self-Anchored Suspension...

Damage Detection of Self-Anchored Suspension Bridge Based on Neural Network Model and Genetic-Simulated Annealing Algorithm

Abstract:

According to the characteristics of self-anchored suspension bridge, a new method to detect damage is introduced in this paper.It works in two stages.First, a BP neural network model is built to predict damaged position. Next, based on the characteristics of genetic algorithm and simulated annealing algorithm, a new approach, genetic-simulated annealing algorithm, is put forward to identify damage extent of detected positions. Compared with the traditional genetic algorithm, the global convergence effect of this algorithm is enhanced by using of the Metropolis acceptance rule of the simulated annealing algorithm in the searching process.