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

Enas Fathi Taher Al Hares,Cenk Budayan

doi:10.3390/sym11020190

Enas Fathi Taher Al Hares, Cenk Budayan

Open Access

https://doi.org/10.3390/sym11020190

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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.

Highlights

IntroductionPoor performances have often been recorded in project management due to its risky nature
Poor performances have often been recorded in project management due to its risky nature.The constant environmental changes and other external constraints have made risk management a serious issue in the construction industry [1,2]
This research explored a new hybrid data-intelligence predictive model called global harmony search integrated with extreme learning machine which can assist construction managers to reliably control project cost and make accurate estimate completion time (EAC) predictions

Summary

Introduction

Poor performances have often been recorded in project management due to its risky nature. The constant environmental changes and other external constraints have made risk management a serious issue in the construction industry [1,2]. Project monitoring must be given adequate attention,. The initial stage of most construction activities focusses on budget planning, effectively neglecting the impact of changes in the engineering cost and the updating of information during construction [3], and this has prevented an effective detection of the problems associated with project cost control. Owing to the dynamic nature of project conditions upon the commencement of a project, there is a need for a regular revision of the project budget for an effective project execution. One of the managerial and monitoring tools used by project managers is the Earned Value

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