A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Predicting the construction projects time and cost overruns using K-nearest neighbor and artificial neural network: a case study from Jordan
Cost and time overruns are currently posing a worldwide challenge to completing construction projects. Previous research looked at the factors that contributed to schedule and expense overruns to find a solution. Machine learning (ML) strategies have been successfully applied in a wide range of research fields to extract novel and important information from data. These strategies, however, have only recently been implemented in the construction industry. The goal of this research is to build a model capable of predicting project cost and time overruns using an appropriate data analysis approach and cost overrun elements as predictors. The specific goal of this research is to: After reviewing the relevant research, a number of risk indicators that are easily measurable and analysable in building projects were discovered. Delays or cost overruns, as well as the identification of the causes of these problems, as well as solutions that address the difficulty of predicting the values A case study was conducted to validate the model using an actual data set consisting of completed projects. The final model is simple, easy to understand, and quite accurate (83.76% in the KNN model and 99.28% in the ANN model), and it employs three data mining processes: clustering, feature selection, and prediction. These stages result in improved model performance.
Predicting the construction projects time and cost overruns using K-nearest neighbor and artificial neural network: a case study from Jordan
Cost and time overruns are currently posing a worldwide challenge to completing construction projects. Previous research looked at the factors that contributed to schedule and expense overruns to find a solution. Machine learning (ML) strategies have been successfully applied in a wide range of research fields to extract novel and important information from data. These strategies, however, have only recently been implemented in the construction industry. The goal of this research is to build a model capable of predicting project cost and time overruns using an appropriate data analysis approach and cost overrun elements as predictors. The specific goal of this research is to: After reviewing the relevant research, a number of risk indicators that are easily measurable and analysable in building projects were discovered. Delays or cost overruns, as well as the identification of the causes of these problems, as well as solutions that address the difficulty of predicting the values A case study was conducted to validate the model using an actual data set consisting of completed projects. The final model is simple, easy to understand, and quite accurate (83.76% in the KNN model and 99.28% in the ANN model), and it employs three data mining processes: clustering, feature selection, and prediction. These stages result in improved model performance.
Predicting the construction projects time and cost overruns using K-nearest neighbor and artificial neural network: a case study from Jordan
Asian J Civ Eng
Arabiat, Ahmad (author) / Al-Bdour, Hamza (author) / Bisharah, Majdi (author)
Asian Journal of Civil Engineering ; 24 ; 2405-2414
2023-11-01
10 pages
Article (Journal)
Electronic Resource
English
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