Neural network cost estimating model for utility rehabilitation projects

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.

Citation

Shehab, T. and Farooq, M. (2013), "Neural network cost estimating model for utility rehabilitation projects", Engineering, Construction and Architectural Management, Vol. 20 No. 2, pp. 118-126. https://doi.org/10.1108/09699981311302991