Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry
Publish Year: 1395
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JOIE-9-19_009
تاریخ نمایه سازی: 22 آبان 1397
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
Keywords:
Construction projects , Time prediction , Artificial neural networks , locally linear neuro-fuzzy model
Authors
Behnam Vahdani
Assistant Professor, Industrial Engineering Research Center, Islamic Azad University, Qazvin Branch, Qazvin, Iran
Seyed Meysam Mousavi
Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
Morteza Mousakhani
Associate Professor, Department of Business Management, Faculty of Management and Economics, Science and Research Branch, Islamic AzadUniversity, Tehran, Iran
Hassan Hashemi
Ph.D. Student, Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran