Error Prediction in Industrialized Construction: A Framework for AI-Powered Error Prediction in the On-Site phase of IHB
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Design and construction project management (MPDCM), MSc
Publicerad
2024
Författare
Burqan, Ahmad
Selwaiea, Khaled
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
ABSTRACT
The construction sector faces notable productivity challenges, as it is known for having
one of the lowest productivity levels compared to other industries. Industrialized House
Building (IHB) has emerged as a solution for low productivity. However, the sector still
faces productivity challenges as delays and cost overruns still exist, mainly due to errors
and variations in IHB projects, especially in the on-site stage. To address the challenge
of errors emerging in on-site IHB projects, this study aims to investigate the possibility
of implementing AI tools for error prediction to mitigate errors in the on-site stage of
IHB projects. The study employs an abductive approach through qualitative data
collected from a literature review, site visits, interviews, and inspection report analysis
in a thematic approach. The study identifies common errors and their impacts,
emphasizing the importance of early intervention and predictive technologies in
mitigating errors. Key findings reveal that AI can be employed for error prediction,
enhancing resource allocation and planning, and minimizing projects’ rework. The
study proposes a conceptual framework for an AI error prediction tool in the on-site
stage of IHB projects to assist project managers in the decision-making process. This
study contributes to IHB’s error management practices, bridging the gap between the
on-site stage of IHB and AI, and serving as a roadmap for IHB companies to implement
AI tools effectively. Finally, the research highlights the necessity of robust data
management practices and continuous improvement to leverage AI’s potential in the
IHB project.
Beskrivning
Ämne/nyckelord
aretificial intelligence , machine learning , error predication , defects , Industrialized House Building , on-site construction , resource optimization , continuous improvement , construction managment , prefabrication