Predicting Subcontractor Performance Using Artificial Intelligence

Authors

  • Ida Pradhan Graduate Student, Department of Construction, Engineering and Infrastructure Management, Asian Institute of Technology, Pathum Thani, Thailand
  • Bonaventura H.W. Hadikusumo Professor, Department of Construction, Engineering and Infrastructure Management, Asian Institute of Technology, Pathum Thani, Thailand
  • Samrakshya Karki Graduate Student, Department of Construction, Engineering and Infrastructure Management, Asian Institute of Technology, Pathum Thani, Thailand

DOI:

https://doi.org/10.3126/injet.v1i1.60943

Keywords:

Subcontractors, Schedule Performance, Quality Performance, Building construction contractors, Prediction model, Waikato Environment for Knowledge Analysis, algorithms/classifiers

Abstract

Subcontractors contribute to almost 90% of the overall construction work hence, assessing the performance of their work from commencement till the completion stage is essential. This paper focuses on identifying different factors affecting the subcontracted work performance and developing a predictive model using classification - based algorithm to find the proficient subcontractors.

Data collected from the building construction projects was analyzed and utilized. Expert validation method was carried out to validate the factors that were obtained from literature review and a survey was conducted to assess the subcontractor’s performance level. Different classification algorithms such as Naïve Bayes, Logistic, Multilayer Perceptron, Sequential Minimal Optimization (SMO), KStar, J48 and Random Forest were applied to the collected data. Waikato Environment for Knowledge Analysis (WEKA), an opensource machine learning tool was used to compare the performance of several algorithms. Statics were generated and compared using k-folds cross validation (k=10) method.

Among the seven algorithms/classifiers, Random Forest had the highest accuracy in schedule performance model and Multilayer Perceptron in quality performance model

Downloads

Download data is not yet available.
Abstract
134
PDF
107

Downloads

Published

2023-12-21

How to Cite

Pradhan, I., Hadikusumo, B. H., & Karki, S. (2023). Predicting Subcontractor Performance Using Artificial Intelligence. International Journal on Engineering Technology, 1(1), 192–203. https://doi.org/10.3126/injet.v1i1.60943

Issue

Vol. 1 No. 1 (2023)

Section

Articles

License

This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.