Attalla, M and Hegazy, T (2003) Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression. Journal of Construction Engineering and Management, 129(4), pp. 405-411. ISSN 0733-9364
Abstract
This paper investigates the challenging environment of reconstruction projects and describes the development of a predictive model of cost deviation in such high-risk projects. Based on a survey of construction professionals, information was obtained on the reasons behind cost overruns and poor quality from 50 reconstruction projects. For each project, the specific techniques used for project control were reported along with the actual cost deviation from planned values. Two indicators of cost deviation are used in this study: cost overrun to the owner, and the cost of rework to the contractor. Based on the information obtained, 36 factors were identified as having direct impact on the cost performance of reconstruction projects. Two techniques were then used to develop models for predicting cost deviation: statistical analysis, and artificial neural networks (ANNs). While both models had similar accuracy, the ANN model is more sensitive to a larger number of variables. Overall, this study contributes to a better understanding of the reasons for cost deviation in reconstruction projects and provides a decision support tool to quantify that deviation.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction management; cost control; project management; reconstruction; retrofitting |
| Index terms: | deviation, cost overrun, artificial neural network, decision support, planned value, reconstruction, cost control, construction professional, cost performance, owner, actual cost, statistical analysis, survey, rework, retrofitting, accuracy, project management, cost deviation, project control |
| Subjects: | financial and cost management, operations management, professional development, control systems, modelling and simulation, renovation and retrofit, economics, data collection methods, project management theory and practice, decision analysis, building construction, sociology, data science |
| Topics: | Stakeholder Management, Risk Management, Research Practice, Information Management, Engineering Principles, Project Management, Cost Management |
| Descriptive scope: | 4 PCEA |
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