Ant Colony Algorithm and Application in Inspection of Concrete Structure Defects

Wei Hua Zheng; Zong Hua Wang

doi:10.4028/www.scientific.net/AMM.204-208.3201

Paper Titles

Experimental Analyses of Dynamic Damage of the Reinforced Concrete Pile
p.3182

An Economic Analysis on Regeneration Concrete Hollow Block
p.3186

Evaluation of the Fire Resistance of H-Section Steel Columns and Beams
p.3190

Influence of Geometry Parameters on Durability of Concrete Structures Exposed to Chloride Environment
p.3194

Ant Colony Algorithm and Application in Inspection of Concrete Structure Defects
p.3201

Experimental Research on Shear Property of Steel Bar/Wire Mesh Mortar Strengthening Concrete Beams
p.3206

Classification of Durability of Concrete Structures Based on Environmental Action
p.3213

Study on Damage Patterns of Corroded High Performance Concrete Column Under Large Eccentric Loading
p.3218

Study of Post-Fire Bearing Capacity of Self-Stress Ceramsite Concrete Filled Steel Tubes
p.3222

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 204-208Ant Colony Algorithm and Application in Inspection...

Ant Colony Algorithm and Application in Inspection of Concrete Structure Defects

Abstract:

BP neural network detecting concrete defect, convergence is slower and accuracy is not high. In order to overcome the defect of BP algorithm, using a combination of Ant Colony optimization algorithm and BP neural network method, a mathematical model of Ant Colony neural network was established, enables Ant Colony neural network training, and verify the validity of the method. And concluded: using ant Colony neural network identification of concrete defects, the identification of the location more effective than on size.