Abstract
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.
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References
Cheng, M.-Y., Tsai, H.-C., Hsieh, W.-S.: Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model. Automation in Construction (2008)
Yu, W.-d., Lai, C.-c., Lee, W.-l.: A WICE approach to real-time construction cost estimation
Moon, S.W., Kim, J.S., Kwon, K.N.: Effectiveness of OLAP-based cost data management in construction cost estimate (2008)
Shi, H., Li, W.: The Integrated Methodology of Rough Set Theory and Artificial Neural-Network for Construction Project Cost Prediction. In: Second International Symposium on Intelligent Information Technology Application, December 2008, pp. 60–64 (2008)
Pawlak, Z.: Rough Sets-Theoretical Aspects of Reasoning about Data. Klystron Academic Publisher (1994)
Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the 1997 International Conference on Evolutionary Computation, Indianapolis, pp. 303–308 (1997)
Ahn, B., Cho, S., Kim, C.: The integrated methodology of rough-set theory and articial neural-network for business failure prediction. Expert Syst. Appl. 18(2), 65–74 (2000)
Arditi, D., Suh, K.: Expert system for cost estimating software selection. Cost Engineering 33(6), 9–19 (1991)
Chau, K.W.: Application of a PSO-based neural network in analysis of outcomes of construction claims. Automation in Construction 16, 642–646 (2007)
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Shi, H., Li, W. (2010). A Web-Based Integrated System for Construction Project Cost Prediction. In: Luo, Q. (eds) Advancing Computing, Communication, Control and Management. Lecture Notes in Electrical Engineering, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05173-9_5
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DOI: https://doi.org/10.1007/978-3-642-05173-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05172-2
Online ISBN: 978-3-642-05173-9
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