Integration of Fuzzy Logic, Particle Swarm Optimization and Neural Networks in Quality Assessment of Construction Project

Abstract

The current paper presents an approach that integrates soft-computing techniques in order to facilitate the computer-aided quality assessment of construction project. We confirmed the weight of each index quantitatively by mean s of Group-decision AHP according to an established index system. Then, we defined the elements of an assessment matrix using fuzzy and a quality assessment model for construction project is set up. The adoption of a particle swarm optimization (PSO) model to train perceptions in assessment and predicting the quality of construction projects in China. The Particle Swarm Optimization (PSO) technique is used to train the multi-layered feed forward neural networks to discriminate the different operating conditions. Comparing with back-propagation Artificial Neural Network (ANN) and ANN based on genetic algorithms, the simulated results of quality assessment of construction projects show that training the neural network by PSO technique gives more accurate results (in terms of sum square error) and also faster (in terms of number of iterations and simulation time) than BPN and GA-based ANN.