Neural Network-Based Prediction Model for Sites' Overhead in Commercial Projects

Neural Network-Based Prediction Model for Sites' Overhead in Commercial Projects

Ali Hassan Zeinhom Hassan, Amira M. Idrees, Ahmed I. B. Elseddawy
Copyright: © 2023 |Pages: 24
DOI: 10.4018/IJeC.318143
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Abstract

Construction companies need to improve the accuracy of their projects' budgeting to achieve the targeted profit. Site overheads are the expenses related to a project but are not allocated to a specific work package. The main objective of this research is to develop a neural network model for commercial projects to predict and estimate project site overhead costs as a percentage of the direct cost. The focal point of the research is focused on the main factors affecting site overhead costs for commercial projects in Egypt. These factors and their weights were identified by experts through the collected structured data. Cost data for 55 projects in the past seven years was collected with various conditions of company rank, direct cost, project duration, project location, contract type, and type of company ownership. The results have shown that the best model developed consists of six input neurons; two hidden layers with six and five neurons respectively, and one output layer representing the percentage of project site overhead. The model was tested on six projects with accuracy of 84%.
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1. Introduction

The construction industry is one of the major sectors in the Egyptian economy, especially real estate & Commercial Buildings (Idrees, ElSeddawy, & Zeidan, 2019). The construction sector has a great impact on the Gross Domestic Product (GDP), it represents about 16.5% of the total Egyptian GDP. However, after the Egyptian revolution in 2011 and the floating exchange rate for the Egyptian pound in November 2016 followed by a high inflation rate, many sectors suffered due to the unstable economic situation accompanied by the political risks in Egypt (Idrees, El Seddawy, & EL Moaaz, 2019) (Khedr, Idrees, & El Seddawy, 2016). With all these factors into consideration, an accurate estimate for the project cost is needed by building construction companies in Egypt, and controlling project costs has become more important and with greater impact than before (Idrees, Alsheref, & ElSeddawy, 2019). Cost is considered one of the main three challenges that face building construction companies, where the success of any project is measured by its completion within the allocated budget, through the planned baseline, and with the desired quality. So an inaccurate estimation can easily lead to a cost overrun of the project, which is reflected on the company's profit. Accordingly, an accurate cost estimate in the early stages is considered a critical stage in producing a project cost, which allows contractors to evaluate the feasibility of the project (Hastak, 2015).

The absence of structured and accurate methods that can assess site overhead costs for commercial projects in Egypt put construction companies at risk of an inaccurate estimate of bid package that may affect the profit margin of the company (Khedr, El Seddawy, & Idrees, 2014). Most of the building construction companies find no difficulty in estimating the direct cost of a project, the inaccuracy appears in the estimating of the overhead costs, producing a cost variance between estimated budget and actual cost either cost over-run or a cost-saving (Hassouna, Khedr, Idrees, & ElSeddawy, 2020).

The objective of this research is to develop an artificial intelligence model based on Artificial Neural Network (ANN) that can enhance the contractor's ability to estimate site overhead cost as a percentage of the project direct cost in the commercial construction market in Egypt. This would improve the companies' performance in predicting overheads for the upcoming projects and increase the company competitive advantage by improving the bid accuracy and also will lead to: Help to control factors affecting site expenditures, create an information system and historical data for projects to improve the predictability of site overheads in future projects and Decrease time and effort spent during the process of site overheads estimation.

The research methodology followed main steps which are more clarified and discussed in details in the following sections, these steps are: discussing the literature review on construction cost categories and identify the main factors influencing site overhead cost in the construction industry, Conducting a survey with industry experts to identify the factors influencing site overhead in the commercial construction industry in Egypt, apply data collection of actual project data according to the concluded factors, perform sensitivity analysis of the collected project data to study the bounding relationship between each factor and site overhead percentage, design and develop an artificial neural network model, test and validate the model, and finally develop a graphical user interface for the developed model to be easily used. The remaining of the research provide more elaboration for each step in the research methodology. The study outline could be stated as follows:

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