Publication result detail

Wide adoption of AI in Construction: A Case study on machine learning for delay prediction and schedule enhancement with external factors

VENKRBEC, V.

Original Title

Wide adoption of AI in Construction: A Case study on machine learning for delay prediction and schedule enhancement with external factors

English Title

Wide adoption of AI in Construction: A Case study on machine learning for delay prediction and schedule enhancement with external factors

Type

Peer-reviewed article not indexed in WoS or Scopus

Original Abstract

Construction industry frequently faces delays due to both internal and external factors, impacting project schedules and costs. This study explores the use of machine learning (ML) algorithms to predict delays and optimize construction schedules. By integrating various factors, including workforce availability, material delivery timelines, weather conditions, and geopolitical events, the algorithm estimates potential delays in construction projects. Data is extracted from Building Information Modeling (BIM) systems using Industry Foundation Classes (IFC), ensuring a comprehensive and up-to-date dataset. The model leverages regression techniques like XGBoost to train on historical data, enabling accurate predictions of project delays. The results show that machine learning can significantly improve delay prediction accuracy compared to traditional methods, offering a more proactive approach to project management. The integration of external factors such as economic instability and geopolitical tensions further enhances the model’s precision, making it a valuable tool for risk management in modern construction projects.

English abstract

Construction industry frequently faces delays due to both internal and external factors, impacting project schedules and costs. This study explores the use of machine learning (ML) algorithms to predict delays and optimize construction schedules. By integrating various factors, including workforce availability, material delivery timelines, weather conditions, and geopolitical events, the algorithm estimates potential delays in construction projects. Data is extracted from Building Information Modeling (BIM) systems using Industry Foundation Classes (IFC), ensuring a comprehensive and up-to-date dataset. The model leverages regression techniques like XGBoost to train on historical data, enabling accurate predictions of project delays. The results show that machine learning can significantly improve delay prediction accuracy compared to traditional methods, offering a more proactive approach to project management. The integration of external factors such as economic instability and geopolitical tensions further enhances the model’s precision, making it a valuable tool for risk management in modern construction projects.

Keywords

Machine Learning; Construction Delays; BIM; IFC; Schedule Optimization; External Factors.

Key words in English

Machine Learning; Construction Delays; BIM; IFC; Schedule Optimization; External Factors.

Authors

VENKRBEC, V.

RIV year

2025

Released

31.12.2024

Publisher

Czech Journal of Civil Engineering

Location

Brno

ISBN

2336-7148

Periodical

Czech Journal of Civil Engineering

Volume

2024

Number

01

State

Czech Republic

Pages from

91

Pages to

103

Pages count

13

URL

https://cjce.cz/journal/article/view/231

BibTex

@article{BUT196504,
  author="Václav {Venkrbec}",
  title="Wide adoption of AI in Construction: A Case study on machine learning for delay prediction and schedule enhancement with external factors",
  journal="Czech Journal of Civil Engineering",
  year="2024",
  volume="2024",
  number="01",
  pages="91--103",
  doi="10.51704/cjce.2024.vol10.iss1.pp91-103",
  url="https://cjce.cz/journal/article/view/231"
}