Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI Lifecycle
In recent years, the application of artificial intelligence (AI) technology in the construction industry has rapidly emerged, particularly in areas such as site monitoring and project management. This technology has demonstrated its great potential in enhancing safety and productivity in constructio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/1/21 |
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| author | Lichao Yang Gavin Allen Zichao Zhang Yifan Zhao |
| author_facet | Lichao Yang Gavin Allen Zichao Zhang Yifan Zhao |
| author_sort | Lichao Yang |
| collection | DOAJ |
| description | In recent years, the application of artificial intelligence (AI) technology in the construction industry has rapidly emerged, particularly in areas such as site monitoring and project management. This technology has demonstrated its great potential in enhancing safety and productivity in construction. However, concerns regarding the technical maturity and reliability, safety, and privacy implications have led to a lack of trust in AI among stakeholders and end users in the construction industry, which slows the intelligent transformation of the industry, particularly for on-site AI implementation. This paper reviews frameworks for AI system design across various sectors and government regulations and requirements for achieving trustworthy and responsible AI. The principles for the AI system design are then determined. Furthermore, a lifecycle design framework specifically tailored for AI systems deployed in the construction industry is proposed. This framework addresses six key phases, including planning, data collection, algorithm development, deployment, maintenance, and archiving, and clarifies the design principles and development priorities needed for each phase to enhance AI system trustworthiness and acceptance. This framework provides design guidance for the implementation of AI in the construction industry, particularly for on-site applications, aiming to facilitate the intelligent transformation of the construction industry. |
| format | Article |
| id | doaj-art-ba6a95d1e7eb4eeba3f6cc4f663a397b |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-ba6a95d1e7eb4eeba3f6cc4f663a397b2025-01-10T13:15:48ZengMDPI AGBuildings2075-53092024-12-011512110.3390/buildings15010021Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI LifecycleLichao Yang0Gavin Allen1Zichao Zhang2Yifan Zhao3Faculty of Engineering and Applied Science, Cranfield University, Cranfield MK43 0AL, UKGlideology, Malvern WR14 4PA, UKFaculty of Engineering and Applied Science, Cranfield University, Cranfield MK43 0AL, UKFaculty of Engineering and Applied Science, Cranfield University, Cranfield MK43 0AL, UKIn recent years, the application of artificial intelligence (AI) technology in the construction industry has rapidly emerged, particularly in areas such as site monitoring and project management. This technology has demonstrated its great potential in enhancing safety and productivity in construction. However, concerns regarding the technical maturity and reliability, safety, and privacy implications have led to a lack of trust in AI among stakeholders and end users in the construction industry, which slows the intelligent transformation of the industry, particularly for on-site AI implementation. This paper reviews frameworks for AI system design across various sectors and government regulations and requirements for achieving trustworthy and responsible AI. The principles for the AI system design are then determined. Furthermore, a lifecycle design framework specifically tailored for AI systems deployed in the construction industry is proposed. This framework addresses six key phases, including planning, data collection, algorithm development, deployment, maintenance, and archiving, and clarifies the design principles and development priorities needed for each phase to enhance AI system trustworthiness and acceptance. This framework provides design guidance for the implementation of AI in the construction industry, particularly for on-site applications, aiming to facilitate the intelligent transformation of the construction industry.https://www.mdpi.com/2075-5309/15/1/21artificial intelligencetrustworthy and responsible AIdigital construction |
| spellingShingle | Lichao Yang Gavin Allen Zichao Zhang Yifan Zhao Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI Lifecycle Buildings artificial intelligence trustworthy and responsible AI digital construction |
| title | Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI Lifecycle |
| title_full | Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI Lifecycle |
| title_fullStr | Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI Lifecycle |
| title_full_unstemmed | Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI Lifecycle |
| title_short | Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI Lifecycle |
| title_sort | achieving on site trustworthy ai implementation in the construction industry a framework across the ai lifecycle |
| topic | artificial intelligence trustworthy and responsible AI digital construction |
| url | https://www.mdpi.com/2075-5309/15/1/21 |
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