Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint

  • Mazen A. Al-Sinan*
  • , Abdulaziz A. Bubshait
  • , Zainab Aljaroudi
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity.

Original languageEnglish
Article number934
JournalBuildings
Volume14
Issue number4
DOIs
StatePublished - Apr 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • BIM
  • ChatGPT
  • GPT
  • LLMs
  • autonomous project management system
  • autonomous systems
  • construction
  • machine learning
  • project scheduling

ASJC Scopus subject areas

  • Architecture
  • Civil and Structural Engineering
  • Building and Construction

Fingerprint

Dive into the research topics of 'Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint'. Together they form a unique fingerprint.

Cite this