Cost estimation for electric light and power elements during building design

Abstract

Purpose – The study reported in this paper proposed the use of artificial neural networks (ANN) as viable alternative to regression for predicting the cost of building services elements at the early stage of design. The purpose of this paper is to develop, test and validate ANN models for predicting the costs of electrical services components. Design/Methodology/Approach – The research is based on data mining of over 200 building projects in the office of a medium size electrical contractor. Of the over 200 projects examined, 71 usable data were found and used for the ANN modeling. Regression models were also explored using IBM Statistical Package for Social Sciences Statistics Software 21, for the purpose of comparison with the ANN models. Findings – The findings show that the cost forecasting models based on ANN algorithm are more viable alternative to regression models for predicting the costs of light wiring, power wiring and cable pathways. The ANN prediction errors achieved are 6.4, 4.5 and 4.5 per cent for the three models developed whereas the regression models were insignificant. They did not fit any of the known regression distributions. Practical implications – The validated ANN models were converted to a desktop application (user interface) package – “Intelligent Estimator.” The application is important because it can be used by construction professionals to reliably and quickly forecast the costs of power wiring, light wiring and cable pathways using building variables that are readily available or measurable during design stage, i.e. fully enclosed covered area, unenclosed covered area, internal perimeter length and number of floors. Originality/value – Previous studies have concluded that the methods of estimating the budget for building structure and fabric work are inappropriate for use with mechanical and electrical services. Thus, this study is unique because it applied the ANN modeling technique, for the first time, to cost modeling of electrical services components for building using real world data. The analysis shows that ANN is a better alternative to regression models for predicting cost of services elements because the relationship between cost and the cost drivers are non-linear and distribution types are unknown.

Hybrid approach to reducing estimating overfitting and collinearity

“…ANN models produce reasonable predictions with nonlinearity in the data (Hassim et al , 2018). In applications to construction projects, Kim et al (2004), for example, develop an ANN for cost estimation using data from 530 projects in Korea, showing its accuracy to be slightly higher than that provided by regression; Cheung et al (2006) use ANN to predict project performance based on information available at the bidding stage from the Hong Kong Housing Authority; while Aibinu et al (2015) propose the use of ANN as a viable alternative to regression for predicting the costs of electrical services components during the building design stage. However, ANN is a “black box” method and suffers the potential drawback of having to retrain the model completely with all data whenever a new case is added.…”

Initial cost forecasting model of mid-rise green office buildings

“…The developed model was found to generate accurate and effective early-stage cost estimation in construction projects. Several other models based on NN have been developed in recent years, such as those reported by Aibinu et al (2015) and Juszczyk (2013).…”

Model for Estimating Construction Costs for Low-Rise Residential Buildings

“…The Artificial Neural Network (ANN)-based evolutionary fuzzy hybrid neural network (EFHNN) developed by Cheng et al [12] was claimed to be effective for precise cost estimation of construction projects during their initial stages. Few other recent NN-based models included those reported by Juszczyk [13], Bala et al [1] and Aibinu et al [14].…”