A novel time-depended evolutionary fuzzy SVM inference model for estimating construction project at completion

Authors:
Min-Yuan Cheng;Nhat-Duc Hoang;Andreas F. V. Roy;Yu-Wei Wu
Affiliations:
Dept. of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan, ROC;Dept. of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan, ROC;Dept. of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan, ROC and Department of Civil Engineering, Parahyangan Catholic ...;Dept. of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan, ROC
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

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Abstract

Construction projects frequently face cost overruns during the construction phase. Thus, a proactive approach is essential for monitoring project costs and detection of potential problems. In construction management, Estimate at Completion (EAC) is an indicator for assisting project managers in identifying potential problems and developing appropriate responses. This study utilizes weighted Support Vector Machine (wSVM), fuzzy logic, and fast messy Genetic Algorithm (fmGA) to handle distinct characteristics in EAC prediction. The wSVM is employed as a supervised learning technique that can address the features of time series data. The fuzzy logic is aimed to enhance the model capability of approximate reasoning and to deal with uncertainty in EAC prediction. Moreover, fmGA is utilized to optimize model's tuning parameters. Simulation results show that the new developed model has achieved a significant improvement in EAC forecasting.