¹Hunan University, Changsha, Hunan, China, 410079
²Ministry of Finance Fiscal Science Research Institute, Beijing, China, 100142

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Abstract—Aimed at coping with the complexity of construction engineering cost evaluation, the advantages of rough set theory, particle swarm algorithm and BP neural network are integrated to put forward a new model of construction engineering cost evaluation, namely, the model of construction engineering cost evaluation of optimized particle swarm and BP neural network on the basis of rough set theory. First, rough set theory was used to reduce the factors affecting construction engineering cost and optimize input variables of BP neural network. Then, the improved particle swarm algorithm with constriction factors is adopted to optimize the initial weights and thresholds. Through this method, BP neural network can be used in a better way to solve nonlinear problems and to improve the rate of convergence and the ability to search global optimum. An engineering project in a city of Hunan is selected to make empirical analysis. It shows that based on the features of engineering, this new model enjoys a high practical value as it can be applied to make scientific evaluation of costs of construction engineering.

Index Terms—cost evaluation, Rough Sets, Particle Swarm Optimization, Artificial Neural Networks

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Cite: Yuan Hong, Haibo Liao, Yazhi Jiang, "Construction Engineering Cost Evaluation Model and Application Based on RS-IPSO-BP Neural Network," Journal of Computers vol. 9, no. 4, pp. 1020-1025, 2014.