Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Application of machine learning to limited datasets: prediction of project success ; APPLICATION OF MACHINE LEARNING TO LIMITED DATASETS: PREDICTION OF PROJECT SUCCESS
Much research is conducted on the importance of success factors. This study contributes to the body of knowledge by using artificial intelligence (AI), specifically machine learning (ML), to analyse success factors through data from construction projects. Previously conducted studies have explored the use of AI to predict project success and identify important success factors in projects; however, to the extent of the authors’ knowledge, no studies have implemented the same method as this study. This study conducts quantitative analysis on a sample of 160 Norwegian construction projects, with data obtained from a detailed questionnaire delivered to relevant project team members. The method utilises ML through a Random Forest Classifier (RFC). The findings obtained from the analysis show that it is possible to use AI and ML on a limited dataset. Furthermore, the findings show that it is possible to identify the most important success factors for the projects in question with the developed model. The findings suggest that a group of selected processes is more important than others to achieve success. The identified success factors support the theoretically acknowledged importance of thorough and early planning and analysis, complexity throughout the project, leadership involvement, and processes supporting project success. ; publishedVersion
Application of machine learning to limited datasets: prediction of project success ; APPLICATION OF MACHINE LEARNING TO LIMITED DATASETS: PREDICTION OF PROJECT SUCCESS
Much research is conducted on the importance of success factors. This study contributes to the body of knowledge by using artificial intelligence (AI), specifically machine learning (ML), to analyse success factors through data from construction projects. Previously conducted studies have explored the use of AI to predict project success and identify important success factors in projects; however, to the extent of the authors’ knowledge, no studies have implemented the same method as this study. This study conducts quantitative analysis on a sample of 160 Norwegian construction projects, with data obtained from a detailed questionnaire delivered to relevant project team members. The method utilises ML through a Random Forest Classifier (RFC). The findings obtained from the analysis show that it is possible to use AI and ML on a limited dataset. Furthermore, the findings show that it is possible to identify the most important success factors for the projects in question with the developed model. The findings suggest that a group of selected processes is more important than others to achieve success. The identified success factors support the theoretically acknowledged importance of thorough and early planning and analysis, complexity throughout the project, leadership involvement, and processes supporting project success. ; publishedVersion
Application of machine learning to limited datasets: prediction of project success ; APPLICATION OF MACHINE LEARNING TO LIMITED DATASETS: PREDICTION OF PROJECT SUCCESS
Bang, Sofie (Autor:in) / Aarvold, Magnus (Autor:in) / Hartvig, Wilhelm (Autor:in) / Olsson, Nils (Autor:in) / Rauzy, Antoine (Autor:in)
01.01.2022
cristin:2048715
732-? ; 72 ; Journal of Information Technology in Construction (ITcon)
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
Project success prediction using an evolutionary support vector machine inference model
| Online Contents | 2010
Project success prediction using an evolutionary support vector machine inference model
| British Library Online Contents | 2010
Project success prediction using an evolutionary support vector machine inference model
| British Library Conference Proceedings | 2010