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Challenges in devel...
Challenges in developing and deploying AI in the engineering, procurement and construction industry
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- Dzhusupova, Rimman (författare)
- McDermott, Dept Elect & Instrumentat Control & Safety Syst, The Hague, Netherlands.
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- Bosch, Jan, 1967 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden.
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- Olsson, Helena Holmström (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Malmö university
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McDermott, Dept Elect & Instrumentat Control & Safety Syst, The Hague, Netherlands Chalmers tekniska högskola (creator_code:org_t)
- IEEE, 2022
- 2022
- Engelska.
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Ingår i: Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022. - : IEEE. ; , s. 1070-1075
- Relaterad länk:
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https://doi.org/10.1...
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- AI in the Engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-depth studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.
Ämnesord
- SAMHÄLLSVETENSKAP -- Annan samhällsvetenskap -- Tvärvetenskapliga studier inom samhällsvetenskap (hsv//swe)
- SOCIAL SCIENCES -- Other Social Sciences -- Social Sciences Interdisciplinary (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Annan teknik -- Övrig annan teknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Other Engineering and Technologies -- Other Engineering and Technologies not elsewhere specified (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Deep Learning
- innovation
- Machine Learning
- engineering
- procurement and construction (EPC) industry
- AI in the EPC industry
- Artificial Intelligence
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)