| Abstrakt: |
Sustainable development is supported by infrastructure projects that have a long-term impact on economic development, societies, and the environment. In this paper, the aim is to estimate the cost performance, investigate the best-fit function for modeling the correlations between cost overruns and three variables, and identify the root causes of overruns in South Asian infrastructure projects. In the past, linear regression analysis has been utilized to model the correlations between cost overruns and project size, implementation period, and time overruns. Modeling these correlations requires the study and application of other regression functions. A database of 138 infrastructure projects from the South Asian region is established from the collected data. A methodology based on mixed methods for qualitative and quantitative data analysis is developed to achieve the aims of the paper. A mixed method encompasses a probabilistic and statistical approach alongside machine learning as quantitative methods and employs content analysis facilitated by NVivo v. 11 software as a qualitative method. Based on the results, the average cost overrun in infrastructure projects in South Asia is 3.3%. The random forest regression function, a machine learning technique, is tested as the most suitable function for modeling the impact between cost overruns and other variables compared to the linear and quadratic regression functions. The practical application is to support project stakeholders in the process of cost estimation during the decision-making phase of the project, to predict overruns in future infrastructure projects using machine learning techniques such as random forest regression, and to contribute to overall sustainable development. [ABSTRACT FROM AUTHOR] |
