A critical review of digital value engineering in building design towards automated construction.
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| Title: | A critical review of digital value engineering in building design towards automated construction. |
|---|---|
| Authors: | Khan, Abdul Mateen1 (AUTHOR), Alaloul, Wesam Salah1 (AUTHOR) wesam.alaloul@utp.edu.my, Musarat, Muhammad Ali1,2 (AUTHOR) |
| Source: | Environment, Development & Sustainability. Jul2026, Vol. 28 Issue 7, p17587-17632. 46p. |
| Subject Terms: | *Sustainability, Value engineering, Building information modeling, Industrialized building, Data integration, Building design & construction, Algorithms, Machine learning |
| Abstract: | Value engineering (VE) has the potential to increase sustainability in buildings by optimizing function-to-cost ratios. However, outdated manual procedures limit integration and consistency in construction projects, necessitating the evaluation of VE automation. This review examines VE automation in building design and construction. A systematic analysis of 664 publications from major databases was conducted, yielding 136 relevant articles. Bibliometric analysis using descriptive statistics and keyword mapping identified key VE methodologies and building types. Key technologies in VE automation include BIM, advanced algorithms, and data integration. These tools enable automated layout generation, material selection, and technical configurations, enhancing value optimization. BIM serves as a central data platform, improving stakeholder collaboration. Algorithms rapidly generate design alternatives, optimizing decision-making. Data integration ensures accuracy across project stages. Challenges include incomplete databases, lifecycle integration issues, and resistance to change. A continuous digital VE framework is proposed to address these barriers. This framework emphasizes the seamless integration of VE tools with BIM platforms, enhancing interoperability and user engagement. In the future, there will be further opportunities to advance VE automation through the creation of more advanced predictive analytics algorithms, more real-time data processing capabilities, and increased interoperability amongst various digital tools. The integration of machine learning and artificial intelligence into VE processes is also suggested to further enhance optimization and efficiency in construction projects. [ABSTRACT FROM AUTHOR] |
| Copyright of Environment, Development & Sustainability is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 194936962 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A critical review of digital value engineering in building design towards automated construction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khan%2C+Abdul+Mateen%22">Khan, Abdul Mateen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alaloul%2C+Wesam+Salah%22">Alaloul, Wesam Salah</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wesam.alaloul@utp.edu.my</i><br /><searchLink fieldCode="AR" term="%22Musarat%2C+Muhammad+Ali%22">Musarat, Muhammad Ali</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jul2026, Vol. 28 Issue 7, p17587-17632. 46p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Value+engineering%22">Value engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Building+information+modeling%22">Building information modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Industrialized+building%22">Industrialized building</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink><br /><searchLink fieldCode="DE" term="%22Building+design+%26+construction%22">Building design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Value engineering (VE) has the potential to increase sustainability in buildings by optimizing function-to-cost ratios. However, outdated manual procedures limit integration and consistency in construction projects, necessitating the evaluation of VE automation. This review examines VE automation in building design and construction. A systematic analysis of 664 publications from major databases was conducted, yielding 136 relevant articles. Bibliometric analysis using descriptive statistics and keyword mapping identified key VE methodologies and building types. Key technologies in VE automation include BIM, advanced algorithms, and data integration. These tools enable automated layout generation, material selection, and technical configurations, enhancing value optimization. BIM serves as a central data platform, improving stakeholder collaboration. Algorithms rapidly generate design alternatives, optimizing decision-making. Data integration ensures accuracy across project stages. Challenges include incomplete databases, lifecycle integration issues, and resistance to change. A continuous digital VE framework is proposed to address these barriers. This framework emphasizes the seamless integration of VE tools with BIM platforms, enhancing interoperability and user engagement. In the future, there will be further opportunities to advance VE automation through the creation of more advanced predictive analytics algorithms, more real-time data processing capabilities, and increased interoperability amongst various digital tools. The integration of machine learning and artificial intelligence into VE processes is also suggested to further enhance optimization and efficiency in construction projects. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environment, Development & Sustainability is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10668-024-05595-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 46 StartPage: 17587 Subjects: – SubjectFull: Sustainability Type: general – SubjectFull: Value engineering Type: general – SubjectFull: Building information modeling Type: general – SubjectFull: Industrialized building Type: general – SubjectFull: Data integration Type: general – SubjectFull: Building design & construction Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: A critical review of digital value engineering in building design towards automated construction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khan, Abdul Mateen – PersonEntity: Name: NameFull: Alaloul, Wesam Salah – PersonEntity: Name: NameFull: Musarat, Muhammad Ali IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 28 – Type: issue Value: 7 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
| ResultId | 1 |
