Shehab, T and Farooq, M (2013) Neural network cost estimating model for utility rehabilitation projects. Engineering, Construction and Architectural Management, 20(2), pp. 118-126. ISSN 0969-9988
Abstract
Purpose – The purpose of this paper is to present an artificial intelligent (AI) system for estimating the construction cost of water and sewer rehabilitation projects. Design/methodology/approach – To develop the proposed system, data pertaining to 54 sewer and water rehabilitation projects was collected. The collected data were analyzed using Pareto analysis technique to identify the most important factors that contribute positively to the cost estimation process. These factors were then utilized to develop a neural network (NN) model that estimates the construction cost of this class of projects. Findings – The study reveals a set of 23 factors that highly impact the construction cost of water and sewer network rehabilitation projects and presents a NN model that predicts the cost of these projects with high accuracy. Research limitations/implications – The proposed system was developed using information obtained from the city of San Diego, California, USA. The cost of these projects ranged from $800,000 to $7 million. The diameter of pipes installed in these projects ranged from 1 in. to 36 in. and their length was up to about 2.7 miles. Originality/value – The developed system saves time, improves the accuracy of the estimates and prevents problems that are usually associated with inaccurate estimates. The system will not only help funding authorities to ensure maximum utilization of resources, but will also help cities to manage their expenditures in a manner that assures satisfactory performance of their buried assets. Furthermore, the developed system is also believed to assist cities in comparing alternatives and the go/no-go decision making process.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural network; construction engineering works; cost estimate; costs; pipework systems; rehabilitation projects and water and sewer pipes; United States of America; utility programmes; utility projects; water supply and waste systems |
| Index terms: | estimate, cost estimate, construction engineering, funding, construction cost, decision-making process, pareto analysis, United States of America, rehabilitation project, estimating, methodology, neural network, programme, artificial neural network, accuracy, sewer, cost estimating, water supply |
| Subjects: | financial and cost management, economic analysis, artificial intelligence, modelling and simulation, quality control, infrastructure and transport systems, health conditions and diseases, decision analysis, project controls, professional development, Geography, engineering methods, research methods |
| Topics: | Quality Management, Digital Applications, Time Control, Geographical Context, Research Practice, Information Management, Engineering Principles, Cost Management, Business Strategy, Health and Safety, Risk Management |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here