Achieving on-site trustworthy AI implementation in the construction industry: a framework across the AI lifecycle

Simple item page
dc.contributor.authorYang, Lichao
dc.contributor.authorAllen, Gavin
dc.contributor.authorZhang, Zichao
dc.contributor.authorZhao, Yifan
dc.date.accessioned2025-01-09T11:58:18Z
dc.date.available2025-01-09T11:58:18Z
dc.date.freetoread2025-01-09
dc.date.issued2024-12-25
dc.date.pubOnline2024-12-25
dc.description.abstractIn 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.
dc.description.journalNameBuildings
dc.description.sponsorshipThis work was supported by “TRAMS-Enterprise—Trustworthy, Responsible AI and ML for construction, Secure and Enterprise Ready” funded by Innovate UK (Ref: 10093011).
dc.identifier.citationYang L, Allen G, Zhang Z, Zhao Y. (2024) Achieving on-site trustworthy AI implementation in the construction industry: a framework across the AI lifecycle. Buildings, Volume 15, Issue 1, January 2025, Article number 21
dc.identifier.eissn2075-5309
dc.identifier.elementsID561637
dc.identifier.issueNo1
dc.identifier.paperNo21
dc.identifier.urihttps://doi.org/10.3390/buildings15010021
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23327
dc.identifier.volumeNo15
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI AG
dc.publisher.urihttps://www.mdpi.com/2075-5309/15/1/21
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject3301 Architecture
dc.subject3302 Building
dc.subject4005 Civil engineering
dc.titleAchieving on-site trustworthy AI implementation in the construction industry: a framework across the AI lifecycle
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Achieving_on-site_trustworthy_AI-2024.pdf
Size:
984.31 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SATM)