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Feasibility of machine learning application in pavement life cycle assessment: A review.

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Title: Feasibility of machine learning application in pavement life cycle assessment: A review.
Authors: Das, Bhaskar Pratim1 (AUTHOR) bhaskar.das@aalto.fi, Deka, Shankar1 (AUTHOR) shankar.deka@aalto.fi, Dettenborn, Taavi2 (AUTHOR) taavi.dettenborn@ramboll.fi, Bordoloi, Sanandam1 (AUTHOR) sanandam.bordoloi@aalto.fi
Source: Renewable & Sustainable Energy Reviews. May2026, Vol. 231, pN.PAG-N.PAG. 1p.
Subjects: Machine learning, Product life cycle assessment, Sustainability, Uncertainty (Information theory), Decision support systems, Inventory accounting, Environmental impact analysis
Abstract: The need to mitigate the environmental impacts of pavement systems has increased interest in life cycle assessment (LCA), but its implementation often faces challenges, such as data uncertainties, inconsistent impact methods, and limited decision-support capabilities. This review explores the utilization of machine learning (ML) to address these challenges and enhance LCA workflows. This review addresses the current practices and challenges in pavement LCA by structuring it around its four phases, i.e., goal and scope definition, inventory analysis, impact assessment, and interpretation. Diverse applications of data-driven ML techniques in pavement systems and LCA are highlighted. Review indicates that ML can enhance pavement LCA by predicting context-specific inventory data, clustering diverse datasets to detect inconsistencies, and simulating different allocation scenarios. Moreover, multiple impact categories forecasting seems possible with ML-based inventory analysis. ML-based visualisations, such as decision trees, can clarify variables' contributions to environmental outcomes. ML can also support sensitivity and uncertainty analyses to strengthen decision-making. • Reviews challenges in applying LCA for sustainable pavement systems. • Explores ML integration to enhance LCA accuracy and decision support. • Identifies ML tasks like regression, classification and algorithms like ANN, SVM, KNN. • Emphasizes ML role in inventory prediction and impact assessment. • Recommends ML for uncertainty analysis and visualisation in LCA. [ABSTRACT FROM AUTHOR]
Copyright of Renewable & Sustainable Energy Reviews is the property of Pergamon Press - An Imprint of Elsevier Science 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 191582479
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Feasibility of machine learning application in pavement life cycle assessment: A review.
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  Data: <searchLink fieldCode="AR" term="%22Das%2C+Bhaskar+Pratim%22">Das, Bhaskar Pratim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bhaskar.das@aalto.fi</i><br /><searchLink fieldCode="AR" term="%22Deka%2C+Shankar%22">Deka, Shankar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shankar.deka@aalto.fi</i><br /><searchLink fieldCode="AR" term="%22Dettenborn%2C+Taavi%22">Dettenborn, Taavi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> taavi.dettenborn@ramboll.fi</i><br /><searchLink fieldCode="AR" term="%22Bordoloi%2C+Sanandam%22">Bordoloi, Sanandam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sanandam.bordoloi@aalto.fi</i>
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  Data: <searchLink fieldCode="JN" term="%22Renewable+%26+Sustainable+Energy+Reviews%22">Renewable & Sustainable Energy Reviews</searchLink>. May2026, Vol. 231, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Product+life+cycle+assessment%22">Product life cycle assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Inventory+accounting%22">Inventory accounting</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+impact+analysis%22">Environmental impact analysis</searchLink>
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  Data: The need to mitigate the environmental impacts of pavement systems has increased interest in life cycle assessment (LCA), but its implementation often faces challenges, such as data uncertainties, inconsistent impact methods, and limited decision-support capabilities. This review explores the utilization of machine learning (ML) to address these challenges and enhance LCA workflows. This review addresses the current practices and challenges in pavement LCA by structuring it around its four phases, i.e., goal and scope definition, inventory analysis, impact assessment, and interpretation. Diverse applications of data-driven ML techniques in pavement systems and LCA are highlighted. Review indicates that ML can enhance pavement LCA by predicting context-specific inventory data, clustering diverse datasets to detect inconsistencies, and simulating different allocation scenarios. Moreover, multiple impact categories forecasting seems possible with ML-based inventory analysis. ML-based visualisations, such as decision trees, can clarify variables' contributions to environmental outcomes. ML can also support sensitivity and uncertainty analyses to strengthen decision-making. • Reviews challenges in applying LCA for sustainable pavement systems. • Explores ML integration to enhance LCA accuracy and decision support. • Identifies ML tasks like regression, classification and algorithms like ANN, SVM, KNN. • Emphasizes ML role in inventory prediction and impact assessment. • Recommends ML for uncertainty analysis and visualisation in LCA. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Renewable & Sustainable Energy Reviews is the property of Pergamon Press - An Imprint of Elsevier Science 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: 10.1016/j.rser.2026.116757
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Product life cycle assessment
        Type: general
      – SubjectFull: Sustainability
        Type: general
      – SubjectFull: Uncertainty (Information theory)
        Type: general
      – SubjectFull: Decision support systems
        Type: general
      – SubjectFull: Inventory accounting
        Type: general
      – SubjectFull: Environmental impact analysis
        Type: general
    Titles:
      – TitleFull: Feasibility of machine learning application in pavement life cycle assessment: A review.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Das, Bhaskar Pratim
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            NameFull: Deka, Shankar
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            NameFull: Dettenborn, Taavi
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            NameFull: Bordoloi, Sanandam
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 231
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            – TitleFull: Renewable & Sustainable Energy Reviews
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