Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature Review
The transition from traditional knowledge retrieval to artificial intelligence-powered knowledge retrieval signifies a fundamental change in data processing, analysis, and use in infrastructure projects. This systematic review presents a thorough literature analysis, examining the transition of trad...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/2/35 |
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| author | Fredrick Ahenkora Boamah Xiaohua Jin Sepani Senaratne Srinath Perera |
| author_facet | Fredrick Ahenkora Boamah Xiaohua Jin Sepani Senaratne Srinath Perera |
| author_sort | Fredrick Ahenkora Boamah |
| collection | DOAJ |
| description | The transition from traditional knowledge retrieval to artificial intelligence-powered knowledge retrieval signifies a fundamental change in data processing, analysis, and use in infrastructure projects. This systematic review presents a thorough literature analysis, examining the transition of traditional knowledge retrieval strategies from manual-based and statistical models to modern AI methodologies. This study systematically retrieved data from 2015–2024 through Scopus, Google Scholar, Web of Science, and PubMed. This study underscores the constraints of traditional approaches, particularly their reliance on manually generated rules and domain-specific attributes, in comparison to the flexibility and scalability of AI-powered solutions. This review highlights limitations, including data bias, computing requirements, and interpretability in the AI-powered knowledge retrieval systems, while exploring possible mitigating measures. This paper integrates current research to clarify the advancements in knowledge retrieval and discusses prospective avenues for integrating AI technology to tackle developing data-driven concerns in knowledge retrieval for infrastructure projects. |
| format | Article |
| id | doaj-art-7d39d12719ec4e0395cc6f358377a6e4 |
| institution | DOAJ |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-7d39d12719ec4e0395cc6f358377a6e42025-08-20T03:12:15ZengMDPI AGInfrastructures2412-38112025-02-011023510.3390/infrastructures10020035Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature ReviewFredrick Ahenkora Boamah0Xiaohua Jin1Sepani Senaratne2Srinath Perera3Centre for Smart Modern Construction, School of Engineering Design & Built Environment, Western Sydney University, Sydney, NSW 2747, AustraliaCentre for Smart Modern Construction, School of Engineering Design & Built Environment, Western Sydney University, Sydney, NSW 2747, AustraliaCentre for Smart Modern Construction, School of Engineering Design & Built Environment, Western Sydney University, Sydney, NSW 2747, AustraliaCentre for Smart Modern Construction, School of Engineering Design & Built Environment, Western Sydney University, Sydney, NSW 2747, AustraliaThe transition from traditional knowledge retrieval to artificial intelligence-powered knowledge retrieval signifies a fundamental change in data processing, analysis, and use in infrastructure projects. This systematic review presents a thorough literature analysis, examining the transition of traditional knowledge retrieval strategies from manual-based and statistical models to modern AI methodologies. This study systematically retrieved data from 2015–2024 through Scopus, Google Scholar, Web of Science, and PubMed. This study underscores the constraints of traditional approaches, particularly their reliance on manually generated rules and domain-specific attributes, in comparison to the flexibility and scalability of AI-powered solutions. This review highlights limitations, including data bias, computing requirements, and interpretability in the AI-powered knowledge retrieval systems, while exploring possible mitigating measures. This paper integrates current research to clarify the advancements in knowledge retrieval and discusses prospective avenues for integrating AI technology to tackle developing data-driven concerns in knowledge retrieval for infrastructure projects.https://www.mdpi.com/2412-3811/10/2/35knowledge retrievalAIinfrastructure projectsknowledge managementinformation extractiontraditional knowledge retrieval |
| spellingShingle | Fredrick Ahenkora Boamah Xiaohua Jin Sepani Senaratne Srinath Perera Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature Review Infrastructures knowledge retrieval AI infrastructure projects knowledge management information extraction traditional knowledge retrieval |
| title | Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature Review |
| title_full | Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature Review |
| title_fullStr | Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature Review |
| title_full_unstemmed | Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature Review |
| title_short | Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature Review |
| title_sort | transition from traditional knowledge retrieval into ai powered knowledge retrieval in infrastructure projects a literature review |
| topic | knowledge retrieval AI infrastructure projects knowledge management information extraction traditional knowledge retrieval |
| url | https://www.mdpi.com/2412-3811/10/2/35 |
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