Shiha, A; Dorra, E M and Nassar, K (2020) Neural networks model for prediction of construction material prices in Egypt using macroeconomic indicators. Journal of Construction Engineering and Management, 146(3): 04020010, ISSN 0733-9364
Abstract
Adequate cost estimation at the planning phase is an integral part of a construction project's success. Many uncertainties disturb the planners' initial estimations and lead to cost overruns. Although many researchers highlighted the correlation between economic conditions and construction costs, accurate quantification of the impact of this correlation has not yet been reached. This paper proposes three models that utilize artificial neural networks (ANNs) to predict the future prices of major construction materials, namely steel reinforcement bars and portland cement, in the context of the Egyptian construction industry 6 months ahead. A Microsoft Excel spreadsheet that also utilizes genetic algorithm (GA), NeuralTools software, and Python programing language in Spyder software was used to develop the three models. Historical data of steel and cement prices as well as macroeconomic indicators in Egypt from May 2008 to June 2018 were used for training, testing, and validation of the proposed models. The inputs to the proposed ANN models are the identified leading economic indicators such as gross domestic product, unemployment rate, and Consumer Price Index (CPI). The developed ANN models show promising results in prediction of month-to-month variations in material prices while having mean-absolute-percentage error that ranges from 4.0% to 11% for the different models. The proposed models can potentially be useful tools for construction contractors as well as owners for predicting and quantifying the fluctuations of major construction materials prices to prepare mitigation measures that will reduce the extra costs incurred.
| Item Type: | Article |
|---|---|
| Index terms: | Egypt, artificial neural network, genetic algorithm, cost estimating, neural network, variation, mitigation, validation, unemployment, construction material, planner, testing, cost overrun, economic condition, quantification, construction cost, estimation, construction industry, economic indicator, spreadsheet, owner, construction contractor, gross domestic product, construction project, reinforcement |
| Subjects: | industry analysis, data science, financial and cost management, economic analysis, building materials, Geography, algorithms, production management, measurement and scaling, profession, professional practice, sociology, data analysis and analytics, contractual condition, financial risk, modelling and simulation, professional development, artificial intelligence, practitioner |
| Topics: | Project Management, Research Practice, Digital Applications, Cost Management, Contract Administration, Roles and Professions, Construction Materials, Stakeholder Management, Engineering Principles, Geographical Context, Information Management, Business Strategy |
| 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