Predicting the Impact of Construction Rework Cost Using an Ensemble Classifier

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Mostofi F., TOĞAN V., Ayozen Y. E., Tokdemir O. B.

SUSTAINABILITY, vol.14, no.22, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 22
  • Publication Date: 2022
  • Doi Number: 10.3390/su142214800
  • Journal Name: SUSTAINABILITY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Aerospace Database, CAB Abstracts, Communication Abstracts, Food Science & Technology Abstracts, Geobase, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: construction rework, cost estimation, nonconformance report, voting classifier, ensemble learning, machine learning, OF-QUALITY, DESIGN, SAFETY, METHODOLOGY, PATTERNS, PROJECTS, ERRORS, MODEL, SENSE
  • Karadeniz Technical University Affiliated: Yes


Predicting construction cost of rework (COR) allows for the advanced planning and prompt implementation of appropriate countermeasures. Studies have addressed the causation and different impacts of COR but have not yet developed the robust cost predictors required to detect rare construction rework items with a high-cost impact. In this study, two ensemble learning methods (soft and hard voting classifiers) are utilized for nonconformance construction reports (NCRs) and compared with the literature on nine machine learning (ML) approaches. The ensemble voting classifiers leverage the advantage of the ML approaches, creating a robust estimator that is responsive to underrepresented high-cost impact classes. The results demonstrate the improved performance of the adopted ensemble voting classifiers in terms of accuracy for different cost impact classes. The developed COR impact predictor increases the reliability and accuracy of the cost estimation, enabling dynamic cost variation analysis and thus improving cost-based decision making.