Review of Supervised Machine learning cost estimation techniques for building projects

Abstract

Cost is considered a vital parameter in determining the success of a construction project. Project costs control and monitoring prevent budget overruns and safeguard expected profits, regardless of the project's size, scope, or complexity. Traditional methods for estimating project costs are facing growing challenges as demand for more accurate, adaptable strategies that respond to evolving market dynamics and technological progress increases. This study offers insight into supervised ML-based cost estimation techniques, highlighting the models employed, the geographical area of the studies, sample sizes, input and output variables, and property types. The findings indicate that there has been some progress in applying supervised ML for cost estimation. Asia accounts for the most studies (65.96%), followed by Africa (10.64%) and Europe (14.89%). Oceania and North America each account for 4.26%, indicating a restricted research scope in these areas. Additionally, 62% of the studies employed multiple algorithms to enhance the reliability of the results. Moreover, most studies focused on construction costs rather than total project costs or total capital investment (project investment) and on residential and educational property types. The findings suggest that extensive testing and applications are necessary to gain a comprehensive understanding of global perspectives, particularly outside Asia, and in commercial properties such as retail and office buildings.