A neuro-fuzzy computational approach to constructability knowledge acquisition for construction technology evaluation

This paper describes a methodology for constructability knowledge acquisition of construction technologies. The methodology combines a neuro-fuzzy network-based approach with genetic algorithms. The combination of fuzzy logic with learning abilities of neural networks and genetic algorithms may allow for automatic acquisition of constructability knowledge from training examples and for providing understandable explanations for the reasoning process. The proposed methodology can provide a mechanism to trace back factors causing unsatisfactory construction performance and the necessary feedback to construction engineers for technology innovation. An application example is provided to demonstrate the capabilities of the proposed methodology.