Expert System with Learning Ability for Retrofitting Steel Bridges

Abstract

An expert system for selecting the methods for retrofitting fatigue cracking in steel bridges is developed. The expert system involves a knowledge base with new knowledge representations and a new inference engine. The knowledge is based on 90 cases of fatigue cracking in existing steel bridges, and a knowledge base is constructed with knowledge being represented as production relations. All the relations in the knowledge base are assumed to have a certainty factor. The new inference engine was developed in the C language so that it has the ability to learn by using the knowledge‐based network model. The inference engine is able to modify relations with a certainty factor. Therefore, the knowledge‐based network model can grow into an optimum knowledge‐based network. The present system is evaluated for the actual fatigue cracking in three steel bridges. It is found that the system is able to infer reasonable methods for retrofitting cracked members.