Al-Tawal, D R; Arafah, M and Sweis, G J (2021) A model utilizing the artificial neural network in cost estimation of construction projects in Jordan. Engineering, Construction and Architectural Management, 28(9), pp. 2466-2488. ISSN 0969-9988
Abstract
Purpose: Cost estimation is one of the most significant steps in construction planning, which must be undertaken in the preliminary stages of any project; it is required for all projects to establish the project's budget. Confidence in these initial estimates is low, primarily due to the limited availability of suitable data, which leads the construction projects to frequently end up over budget. This paper investigated the efficacy of artificial neural networks (ANNs) methodologies in overcoming cost estimation problems in the early phases of the building design process. Design/methodology/approach: Cost and design data from 104 projects constructed over the past five years in Jordan were used to develop, train and test ANN models. At the detailed design stage, 53 design factors were utilized to develop the first ANN model; then the factors were reduced to 41 and were utilized to develop the second predictive model at the schematic design stage. Finally, 27 design factors available at the concept design stage were utilized for the third ANN model. Findings: The models achieved average cost estimation accuracy of 98, 98 and 97% in the detailed, schematic and concept design stages, respectively. Research limitations/implications: This paper formulated the aims and objectives to be applicable only in Jordan using historical data of building projects. Originality/value: The ANN approach introduced as a management tool is expected to provide the stakeholders in the engineering business with an indispensable tool for predicting the cost with limited data at the early stages of construction projects.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction; design; engineering; estimating; project management; technology |
| Index terms: | cost estimating, accuracy, artificial neural network, design stage, methodology, construction project, estimating, design factor, building design, concept design, construction planning, project management, estimate, Jordan |
| Subjects: | modelling and simulation, financial and cost management, construction planning, architectural design, professional practice, research methods, design practice, professional development, project management theory and practice, design process, production management, Geography |
| Topics: | Cost Management, Research Practice, Project Management, Geographical Context, Information Management, Design Practice, Site Management |
| Descriptive scope: | 3 PCT |
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