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Making the Black Box Transparent: State of the Art in Explainable Machine Learning for Structural Design and Assessment.

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Title: Making the Black Box Transparent: State of the Art in Explainable Machine Learning for Structural Design and Assessment.
Authors: Zaker Esteghamati, Mohsen, Wang, Jingcheng, Wang, Xiaowei, Paal, Stephanie G., Murphy, Jacob, Namin, Ali, Salman, Abdullahi, Sabari, Adam Ado, Ibrar, Muhammad Ahsan, Mazumder, Ram, Li, Yue, Shafieezadeh, Abdollah
Source: Journal of Structural Engineering; Jun2026, Vol. 152 Issue 6, p1-22, 22p
Subject Terms: Structural engineering, Machine learning, Structural design, Structural analysis (Engineering)
Abstract: Machine learning (ML)–based solutions have gained traction in various structural engineering applications, from structural design to assessment and monitoring. Nevertheless, the black-box nature of advanced ML models and the resultant limited interpretation and transparency are among the primary barriers to their broader adoption and implementation in the field. eXplainable ML (XML) is an interdisciplinary field that improves understanding of ML model performance. Despite the potential of XML to increase ML accessibility, the scattered available literature and the lack of a domain-specific holistic review have created a significant gap in knowledge about its application in structural engineering. Therefore, this paper presents a targeted review of XML—its definition, nomenclature and taxonomy, frequently used algorithms, and domain-specific literature. Additionally, three case studies are presented to illustrate different classes of XML algorithms and their implementation in diverse structural engineering problems at the component, structure, and inventory levels, providing insights into how these techniques can provide engineering-oriented interpretations that enhance understanding of studied problems. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Structural Engineering is the property of American Society of Civil Engineers 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: Complementary Index
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  Data: Journal of Structural Engineering; Jun2026, Vol. 152 Issue 6, p1-22, 22p
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  Data: <searchLink fieldCode="DE" term="%22Structural+engineering%22">Structural engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+design%22">Structural design</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+analysis+%28Engineering%29%22">Structural analysis (Engineering)</searchLink>
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  Data: Machine learning (ML)–based solutions have gained traction in various structural engineering applications, from structural design to assessment and monitoring. Nevertheless, the black-box nature of advanced ML models and the resultant limited interpretation and transparency are among the primary barriers to their broader adoption and implementation in the field. eXplainable ML (XML) is an interdisciplinary field that improves understanding of ML model performance. Despite the potential of XML to increase ML accessibility, the scattered available literature and the lack of a domain-specific holistic review have created a significant gap in knowledge about its application in structural engineering. Therefore, this paper presents a targeted review of XML—its definition, nomenclature and taxonomy, frequently used algorithms, and domain-specific literature. Additionally, three case studies are presented to illustrate different classes of XML algorithms and their implementation in diverse structural engineering problems at the component, structure, and inventory levels, providing insights into how these techniques can provide engineering-oriented interpretations that enhance understanding of studied problems. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Structural Engineering is the property of American Society of Civil Engineers 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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              Text: Jun2026
              Type: published
              Y: 2026
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