Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry

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.