Vahdani, B., Mousavi, S., Mousakhani, M., Hashemi, H. (2016). Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry. Journal of Optimization in Industrial Engineering, 9(19), 97-103. doi: 10.22094/joie.2016.231
Behnam Vahdani; Seyed Meysam Mousavi; Morteza Mousakhani; Hassan Hashemi. "Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry". Journal of Optimization in Industrial Engineering, 9, 19, 2016, 97-103. doi: 10.22094/joie.2016.231
Vahdani, B., Mousavi, S., Mousakhani, M., Hashemi, H. (2016). 'Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry', Journal of Optimization in Industrial Engineering, 9(19), pp. 97-103. doi: 10.22094/joie.2016.231
Vahdani, B., Mousavi, S., Mousakhani, M., Hashemi, H. Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry. Journal of Optimization in Industrial Engineering, 2016; 9(19): 97-103. doi: 10.22094/joie.2016.231
Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry
1Assistant Professor, Industrial Engineering Research Center, Islamic Azad University, Qazvin Branch, Qazvin, Iran
2PhD Student, Young Researches Club, South Tehran Branch, Islamic Azad University, Tehran, Iran
3Associate Professor, Department of Business Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad
University, Tehran, Iran
4MSc, Young Researches Club, South Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract
This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is intended for use by members of the project team in performing the time control of projects in the construction industry. The present paper addresses the effects of different factors on the project time and schedule by using both fuzzy sets theory (FST) and artificial neural networks (ANNs) in a construction project in Iran. The construction project is investigated to demonstrate the use and capabilities of the proposed model to see how it allows users and experts to actively interact and, consequently, make use of their own experience and knowledge in the estimation process. The proposed model is also compared to the well-known intelligent model (i.e., BPNN) to illustrate its performance in the construction industry.
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