A Web-Based Integrated System for Construction Project Cost Prediction
Construction cost estimation and prediction, the basis of cost budgeting and cost management, is crucial for construction firms to survive and grow in the industry. The objective of this paper is to presented a novel method integrating fuzzy logic(FL), rough sets (RS) theory and artificial neural network (ANN) which inherent in. The particle swarm optimization (PSO) technique is used to train the multi-layered feed forward neural networks With this model integrating WWW and historical construction data to estimate conceptual construction cost more precisely during the early stage of project. Becouse there are many factors affecting the cost of building and some of the factors are related and redundant, rough sets theory is applied to find relevant factors to the cost, which are used as inputs of an articial neural-network to predict the cost of construction project. Therefore, the main characteristic attributes were withdraw, the complexity of neural network system and the computing time was reduced, as well. A case study was carried out on the cost estimate of a sample project using the model. The results show that the integrating rough sets theory and articial neural network can help understand the key factors in construction cost forecast, and it provided a way for projecting more reliable construction costs.
Keywordsfuzzy logic rough sets artificial neural network particle swarm optimization construction cost estimation
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