Potential of Different Machine Learning Methods in Cost Estimation of High-Rise Construction in Croatia

Abstract

The fundamental goal of a construction project is to complete the construction phase within budget, but in practice, planned cost estimates are often exceeded. The causes of overruns can be due to insufficient preparation and planning of the project, changes during construction, activation of risky events, etc. Also, construction costs are often calculated based on experience rather than scientifically based approaches. Due to the challenges, this paper investigates the potential of several different machine learning methods (linear regression, decision tree forest, support vector machine and general regression neural network) for estimating construction costs. The methods were implemented on a database of recent high-rise construction projects in the Republic of Croatia. Results confirmed the potential of the selected assessment methods; in particular, the support vector machine stands out in terms of accuracy metrics. Established machine learning models contribute to a deeper understanding of real construction costs, their optimization, and more effective cost management during the construction phase.