A taxonomy of machine learning techniques for construction cost estimation

Construction projects require significant funding and are exposed to several risks. Public construction projects require a major proportion of the annual government budget. Their actual cost estimation concerns a known and existing problem for the construction sector, while several project failures in terms of budget extension can be documented around the world. Accurate construction cost predictions are essential in mitigating time-related risks and play a crucial role in the decision-making process for managers. Inaccurate cost estimations can result in investment project disruptions. Research about machine learning (ML) techniques regarding construction cost estimation is intensifying, which aims to develop new ML techniques or update existing ones. This article contains a systematic literature review of ML techniques for construction project cost estimation. This review included an in-depth analysis of 219 studies, which contain the most prominent machine learning techniques. This article attempts to define a classification of the identified ML techniques, with the following criteria: intelligent technique that was followed and the application domain. The taxonomy that was generated contains ML techniques about construction cost estimation and their application, which offers useful guidance for both researchers and practitioners.