Artificial Neural Networks and Knowledge-Based Systems: Complementary AI Techniques for Civil Engineering

by Nabil Kartam, Univ of Maryland, College Park, United States,
Ian Flood, Univ of Maryland, College Park, United States,
Tanit Tongthong, Univ of Maryland, College Park, United States,



Document Type: Proceeding Paper

Part of: Computing in Civil and Building Engineering

Abstract: This paper considers the feasibility of integrating Artificial Neural Networks (ANNs) and Knowledge-Based Systems (KBSs) to solve civil engineering problems. KBSs are superior at representing human judgments, and in solving problems by reasoning with heuristic knowledge. ANNs offer different problem-solving characteristics including learning from example, recognizing patterns, and processing data in parallel. The integration of both technologies provides a hybrid system with the merits of both techniques, and with a broader scope of application within civil engineering. Consideration is given to the advantages of ANN and KBS hybrids, alternative methods of integration, and their potential applications.

Subject Headings: Systems engineering | Neural networks | Knowledge-based systems | Data processing | Algorithms | Hybrid methods | Feasibility studies | Human factors

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