Ottaviani, F M; De Marco, M; Audisio, A; Wong, J and Belack, C (2024) A brief review of artificial intelligence techniques for conceptual cost estimation in construction projects. In: Thomson, C (ed.) Proceedings of 40th Annual ARCOM Conference, 2-4 September 2024, London South Bank University, UK.
Abstract
Conceptual cost estimates, evaluated during a construction project's initiation phase, are fundamental for determining whether to invest in the project, validating its budget, or screening alternatives. Compared to traditional estimation techniques, artificial intelligence (AI) methods proved effective in assessing the nonlinear relationship between project variables and actual cost at completion. Due to the number and variability of available studies, it is not clear which AI techniques are most effective. This study systematically reviews previous works employing AI for conceptual cost estimation, focusing on the techniques adopted and the scorers utilised. The results show a rising trend in AI adoption, including supervised machine learning, knowledge-based, and evolutionary techniques. Performance-wise, the results hint at gradient boosting, random forest, and neural networks proving superior to both genetic algorithms and case-based reasoning techniques, which in turn prove superior to linear models. This review provides a brief overview of possible AI techniques and performance scorers to utilise for conceptual cost estimation in construction projects.
| Item Type: | Conference Paper (Paper) |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; conceptual cost estimate; project management; systematic literature review |
| Index terms: | project management, screening, machine learning, construction project, forest, artificial intelligence, actual cost, cost estimate, neural network, variability, case-based reasoning, genetic algorithm, estimation, systematic literature review, cost estimating |
| Subjects: | artificial intelligence, management, research evaluation and metrics, statistical analysis, environmental science, project management theory and practice, algorithms, production management, financial and cost management, cognitive psychology |
| Topics: | Sustainability, Project Management, Research Practice, Human Resources, Digital Applications, Cost Management |
| Descriptive scope: | 4 PCTE |
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