Abstract
The main reason of this article is to assist members of the construction project team in understanding the elements that must be regularly checked in order to finish the work on time and on budget. As a result, the study's goal was to create a neural network model (ANN) model that could predict the performance of construction works based on the different speculation specified. Despite the frequency of delay studies, productive research to advance tools and capacity to address the fundamental problem is lacking. As this give idea of outlines the improvement of a machine learning algorithms for finding the risk of delay in high rise building. 36 delay risk variables were initially discovered in existing literature and then converted into questionnaires to examine the likelihood and consequences of the risk factors. A data collection for machine learning applications was created using 48 usable replies gathered from subject matter experts. The approaches of K-Nearest Neighbors (KNN), Neural Networks (ANN), Support Vector Machines (SVM), and Ensemble were investigated. The most important independent factors, according to feature subset selection, were "slowness in decision making," "delay in sub-contractors' work," "architects'/structural engineers' late issuing of instruction," and "waiting for approval of drawings and material supply." The model for finding the risk of delay was find out by ANN, and it was then finished with a classification accuracy of 93.75 percent. After the final model created in this study might help construction companies manage project risk on high rise building.
Keywords
Factors affecting project performance, Artificial neural networks, risk assessment, KNN, ANN, SVM, ensemble methods.
Citations
IRE Journals:
Aditya Joseph , Dr. K J George
"Artificial Intelligence in Project Management" Iconic Research And Engineering Journals Volume 5 Issue 10 2022 Page 112-120
IEEE:
Aditya Joseph , Dr. K J George
"Artificial Intelligence in Project Management" Iconic Research And Engineering Journals, 5(10)
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