Predicting the Cost of Rework in High-Rise Buildings Using Graph Convolutional Networks

Fatemeh Mostofi*, Onur Behzat Tokdemir, Vedat Toǧan, David Arditi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

To reduce the risk of unexpected cost of rework (COR), a variety of predictive models have been developed in the construction management literature. However, they primarily focus on prediction accuracy, and rather less attention has been paid to the trustworthiness of prediction models. This increases operational risk and hinders its integration in related decision-making. Aiming to reduce the utilization risk and increase the reliability of COR prediction models, this study exploits the graph convolutional network (GCN) model, which enhances representativeness by accommodating interrelationships among the root causes of nonconformances. The GCN can process a more representative input network that provides COR records while factoring in the shared root causes of nonconformance in the resulting COR. The proposed approach achieved a COR prediction accuracy as high as 85%, which is significantly higher than that of any existing cost prediction model. The demonstrated accuracy and lower risk of the proposed GCN model thus enhance the reliability of the prediction and trust in its outcome, facilitating its integration into developing rework prevention strategies and relevant resource allocation for construction professionals. The study contributes to construction project management by proposing a novel COR prediction model that embodies accuracy, representativeness, and interpretability. Whereas we tailored the GCN model to predict COR with a focus on nonconformance root causes, it is noted that rework costs can also be influenced by other project factors, such as site safety.

Original languageEnglish
Article number04024085
JournalJournal of Construction Engineering and Management - ASCE
Volume150
Issue number8
DOIs
Publication statusPublished - 1 Aug 2024

Bibliographical note

Publisher Copyright:
© 2024 American Society of Civil Engineers.

Keywords

  • Construction cost of rework (COR) prediction
  • Graph convolutional network (GCN)
  • High-rise building construction
  • Nonconformance report
  • Representative machine learning

Fingerprint

Dive into the research topics of 'Predicting the Cost of Rework in High-Rise Buildings Using Graph Convolutional Networks'. Together they form a unique fingerprint.

Cite this