Boyacioglu, S, Greenwood, David and Rogage, Kay (2022) Incorporating Emerging Technologies in the Forensic Analysis of Construction Project Delays. IOP Conference Series: Earth and Environmental Science, 1101 (5). 052029. ISSN 1755-1307
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Abstract
Considering the significant role of the construction industry in the global economy, its continuous adoption of new technological advances is both desirable and inevitable. These advances include Building Information Modelling (BIM) and Artificial Intelligence (AI)/Machine Learning (ML). However, not all sections of the industry currently embrace these developments. Forensic Delay Analysis (FDA) is an activity of specialists in extracting and presenting evidence contractual claims disputes that relate to project delays. Such delays are frequent and expensive, but the FDA process has benefitted little from these new technologies. The paper reports the initial work of a collaborative PhD project funded under the Intensive Industrial Innovation Programme of the European Regional Development Fund. The project explores the integration of BIM and AI/ML technologies within the FDA process. The potential of emerging technologies in different parts of the FDA process is first considered, followed by a systematic literature review (SLR) of published work that might support, refute, or exemplify such contributions. The findings show that BIM and AI/ML offer promising solutions to the current challenges of FDA and opportunities for enhancing the effectiveness of dispute resolution, but further work is needed to test the proposed improvements on real-world project workflows and to collect expert feedback to assess their effectiveness.
Item Type: | Article |
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Subjects: | G900 Others in Mathematical and Computing Sciences H200 Civil Engineering H300 Mechanical Engineering |
Department: | Faculties > Engineering and Environment > Mechanical and Construction Engineering Faculties > Engineering and Environment > Computer and Information Sciences |
Depositing User: | Rachel Branson |
Date Deposited: | 20 Dec 2022 10:02 |
Last Modified: | 20 Dec 2022 10:15 |
URI: | https://nrl.northumbria.ac.uk/id/eprint/50947 |
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