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Machine Learning for Civil Engineering: Recent Advances and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 1198

Special Issue Editors


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Guest Editor
Software Engineering Laboratory, Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain
Interests: image processing; machine learning; software engineering

E-Mail Website
Guest Editor
Software Engineering Laboratory, Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain
Interests: application of machine learning to civil engineering; application of IoT to civil engineering; machine learning and the environment
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Aerospace Technology and Science, Centro Universitario de la Defensa, Universidad Politécnica de Cartagena, C/Coronel López Peña S/N, Base Aérea de San Javier, 30720 Murcia, Spain
Interests: embedded electronics; machine learning; image processing; security and defense; indirect monitoring

Special Issue Information

Dear Colleagues,

The civil engineering sector is undergoing a transformative shift through the integration of machine learning techniques into design, analysis, monitoring and decision-making processes. This Special Issue aims to explore the latest advances and practical applications of machine learning across all branches of civil engineering, including structural health monitoring, geotechnical modeling, hydraulic forecasting, traffic and transport systems, smart infrastructure and construction optimization.

We invite original research articles, review papers and case studies that highlight innovative uses of supervised, unsupervised and reinforced learning techniques in civil engineering contexts. Contributions should emphasize real-world implementations, data challenges, model deployment, interpretability and performance evaluation in operational environments. Studies that critically assess limitations, scalability issues and domain-specific barriers are particularly encouraged.

This Special Issue seeks to bridge the gap between data-driven methods and civil engineering practice, providing a comprehensive reference for researchers, practitioners and policy-makers interested in the integration of AI into infrastructure and the built environment.

Dr. Virginia Mato-Abad
Dr. Alberto José Alvarellos González
Dr. Daniel Carreres Prieto
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • civil engineering
  • data-driven modeling
  • infrastructure systems
  • structural engineering
  • environmental engineering
  • smart cities
  • monitoring and prediction
  • engineering applications
  • artificial intelligence in engineering

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Published Papers (1 paper)

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Research

19 pages, 3959 KB  
Article
Machine Learning Surrogate for Seismic Response of a Wooden House: A Comparison of SHAP, Sobol, and Morris Sensitivity Analyses
by Tokikatsu Namba
Appl. Sci. 2026, 16(7), 3201; https://doi.org/10.3390/app16073201 - 26 Mar 2026
Cited by 1 | Viewed by 662
Abstract
Understanding the influence of structural parameters on the seismic response of wooden houses is essential for improving structural performance and model reliability. However, conducting extensive parametric studies using nonlinear time-history analysis is computationally expensive. To address this issue, this study proposes a machine [...] Read more.
Understanding the influence of structural parameters on the seismic response of wooden houses is essential for improving structural performance and model reliability. However, conducting extensive parametric studies using nonlinear time-history analysis is computationally expensive. To address this issue, this study proposes a machine learning (ML) surrogate framework for efficiently evaluating the seismic response of a wooden house and interpreting the importance of structural parameters. A dataset consisting of 289 nonlinear structural simulations was used to train the surrogate model, enabling efficient evaluation of parameter importance through multiple sensitivity analysis methods. A Gradient Boosting regression model was developed to approximate the results of nonlinear structural analyses. The surrogate model predicted the maximum inter-story drift with high accuracy, achieving a coefficient of determination of R2 = 0.90. Using the trained surrogate model, six sensitivity analysis methods were applied: SHAP, Structural Perturbation, Drop-column Importance, Permutation Importance, Sobol sensitivity analysis, and the Morris method. The results showed that most sensitivity analysis methods consistently identified wall-related parameters, particularly W1, W3, and W4, as the dominant factors influencing structural response. This tendency was observed in both elastic and nonlinear response ranges, although the influence of these parameters became more pronounced under nonlinear conditions. While the Morris method produced slightly different sensitivity magnitudes due to its screening-based formulation, it still identified the same dominant parameters as the other approaches. The results demonstrate that the proposed ML surrogate framework, combined with explainable AI techniques, can effectively identify key structural parameters governing the seismic response of wooden structures. This approach provides a computationally efficient tool for structural sensitivity analysis and may support improved structural modeling and seismic performance evaluation. Full article
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