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Symmetry 2019, 11(2), 190; https://doi.org/10.3390/sym11020190

Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects

1
Electricity Distribution Branch in Najaf, General Company of Middle Electricity Distribution, Ministry of Electricity, Najaf 54001, Iraq
2
Department of Civil Engineering, Yildiz Technical University, 34220 Esenler, Istanbul, Turkey
*
Author to whom correspondence should be addressed.
Received: 9 January 2019 / Revised: 25 January 2019 / Accepted: 28 January 2019 / Published: 8 February 2019
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Abstract

“Estimation at completion” (EAC) is a manager’s projection of a project’s total cost at its completion. It is an important tool for monitoring a project’s performance and risk. Executives usually make high-level decisions on a project, but they may have gaps in the technical knowledge which may cause errors in their decisions. In this current study, the authors implemented new coupled intelligence models, namely global harmony search (GHS) and brute force (BF) integrated with extreme learning machine (ELM) for modeling the project construction estimation at completion. GHS and BF were used to abstract the substantial influential attributes toward the EAC dependent variable, whereas the effectiveness of ELM as a novel predictive model for the investigated application was demonstrated. As a benchmark model, a classical artificial neural network (ANN) was developed to validate the new ELM model in terms of the prediction accuracy. The predictive models were applied using historical information related to construction projects gathered from the United Arab Emirates (UAE). The study investigated the application of the proposed coupled model in determining the EAC and calculated the tendency of a change in the forecast model monitor. The main goal of the investigated model was to produce a reliable trend of EAC estimates which can aid project managers in improving the effectiveness of project costs control. The results demonstrated a noticeable implementation of the GHS-ELM and BF-ELM over the classical and hybridized ANN models. View Full-Text
Keywords: construction project monitoring; coupled intelligent model; substantial input section; extreme learning machine construction project monitoring; coupled intelligent model; substantial input section; extreme learning machine
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AlHares, E.F.T.; Budayan, C. Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects. Symmetry 2019, 11, 190.

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