The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Safety Assessment on Construction Sites

摘要

This paper innovatively proposes a hybrid intelligent system combining rough set approach and artificial neural network (ANN) that predicts the safety performance of construction site for breaking through the limitations of conventional method. Redundant attribute is removed with no information loss through rough set approach, by which the reduced information table is obtained. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. The rules developed by rough set analysis show the best prediction accuracy if an empirical does match any of the rules. The effectiveness of our methodology was verified with an empirical study that compared neural network approach with the hybrid approach. And the results show that this method can be an effective tool to predict the safety performance of construction project sites, which is useful to provide a scientific basis for the management and decisions of accident prevention.