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Emerging Trends in Machine Learning for Structural Engineering: Innovations and...
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Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 3264

Special Issue Editors

Prof. Dr. Oladimeji Olalusi

E-Mail Website
Guest Editor
Adjunct Professor, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban, South Africa
Interests: applied artificial intelligence; structural engineering; civil engineering; structural reliability; risk analysis
Dr. Lenganji Simwanda

E-Mail Website
Guest Editor
Postdoctoral Fellow, Department of Structural Reliability, Klokner Insitute, Czech Technical University in Prague, Czech Republic
Interests: machine learning; structural reliability; uncertainty quantification; finite element analysis; ultra-high-performance concrete; thin-walled steel; concrete; composite structures
Dr. Gatheeshgar Perampalam

E-Mail Website
Guest Editor
School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, UK
Interests: machine learning; structural engineering; optimization of structural members; steel structures; fire and thermal performance of buildings; composite structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are standing at the brink of a technological revolution in structural engineering, in which machine learning (ML) is set to emerge as a pivotal force in reshaping traditional methodologies. This Special Issue, entitled "Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications", aims to capture and disseminate cutting-edge research on topics where ML technologies intersect with structural engineering to enhance the design, analysis, and sustainability of infrastructure.

As we advance further into the 21st century, the drive for smarter, more resilient, and more sustainable construction grows ever stronger and machine learning offers unprecedented capabilities in this regard, from optimizing the design of complex structures to enabling real-time monitoring and predictive maintenance. This Special Issue seeks to explore these advancements comprehensively, highlighting both theoretical innovations and practical implementations.

We invite submissions of original research, both theoretical and experimental, detailed case studies, and comprehensive review papers. Submissions should demonstrate novel ML applications within the context of structural engineering and contribute significantly to the existing body of knowledge.

Topics of interest include, but are not limited to:

  1. Machine Learning: applications in predictive maintenance, automated design, and real-time structural health monitoring;
  2. Finite Element Analysis: the use of advanced computational models for ML applications, etc.;
  3. Cold-Formed Steel Structures: innovations and ML applications in the design and analysis;
  4. Modular Construction: ML-driven optimization of prefabrication and assembly processes;
  5. Innovative Concrete Materials and Structures: AI in the development and implementation of new concrete materials;
  6. Sustainable Structures and Materials: the integration of ML into the design and management of sustainable construction practices;
  7. Risk Assessment and Disaster Mitigation: machine learning models for assessing risks and enhancing the resilience of structures against natural disasters;
  8. Data Analytics in Construction Management: using ML to analyze project data for better decision-making and operational efficiency;
  9. Automated Compliance Checking: ML algorithms to ensure designs meet regulatory and safety standards;
  10. Smart Sensors and IoT: the integration of ML with IoT devices for enhanced monitoring and control systems at construction sites;
  11. 3D Printing and Digital Fabrication: ML applications in optimizing 3D printing techniques and processes for building components;
  12. Lifecycle Assessment: ML techniques to evaluate the environmental impact of building materials and methods throughout their lifecycle;
  13. Fire performance assessment of structures: ML applications in the fire performance assessment of structures.

Prof. Dr. Oladimeji Benedict Olalusi
Dr. Lenganji Simwanda
Dr. Gatheeshgar Perampalam
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Buildings 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 2600 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 in structural engineering
  • predictive maintenance
  • real-time structural health monitoring
  • finite element analysis
  • cold-formed steel structures
  • modular construction
  • advanced concrete materials
  • sustainable construction practices
  • disaster risk assessment
  • construction data analytics
  • automated compliance in engineering
  • smart sensors and iot in construction
  • 3D printing in construction
  • lifecycle assessment of building materials
  • fire performance modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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33 pages, 7006 KiB  
Article
Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups
by Abayomi B. David, Oladimeji B. Olalusi, Paul O. Awoyera and Lenganji Simwanda
Buildings 2024, 14(12), 3946; https://doi.org/10.3390/buildings14123946 - 11 Dec 2024
Cited by 1 | Viewed by 876
Abstract
Accurate shear capacity estimation for reinforced concrete (RC) beams without stirrups is essential for reliable structural design. Traditional code-based methods, primarily empirical, exhibit variability in predicting shear strength for these beams. This paper assesses the effectiveness of mechanics-based and optimized machine learning (ML) [...] Read more.
Accurate shear capacity estimation for reinforced concrete (RC) beams without stirrups is essential for reliable structural design. Traditional code-based methods, primarily empirical, exhibit variability in predicting shear strength for these beams. This paper assesses the effectiveness of mechanics-based and optimized machine learning (ML) models for predicting shear strength in stirrup-less, slender beams using a dataset of 784 tests. Seven ML models—artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), AdaBoost, gradient boosting (GBR), and extreme gradient boosting (XGB)—were compared against three mechanics-based models: the Tran’s NLT Model (2020), the Multi-Action Shear Model (MASM), and the Compression Chord Capacity Model (CCC). Among the ML models, XGB and GBR demonstrated the highest predictive accuracy, with coefficients of determination (R2) of 0.974 and 0.966, respectively, indicating strong correlation with experimental data. Performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE) showed that XGB and GBR consistently outperformed other models, yielding lower error margins. Statistical analysis revealed minimal bias and variability in the predictions of XGB and GBR, with a coefficient of variation (CoV) of 14%, ensuring high reliability. The NLT model, the most accurate of the mechanical-based models, achieved a mean of 1.02 and a CoV of 16% for its model error, demonstrating reasonable prediction reliability but falling behind XGB and GBR in accuracy. With Shapley additive explanations (SHAPs), the beam width and depth were identified as primary predictors of shear strength, providing critical insights for enhancing design and construction practises. Full article
(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)
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19 pages, 7610 KiB  
Article
Load Capacity Prediction of Corroded Steel Plates Reinforced with Adhesive and High-Strength Bolts Using a Particle Swarm Optimization Machine Learning Model
by Xianling Zhou, Ming Li, Qicai Li, Guohua Sun and Wenyuan Liu
Buildings 2024, 14(8), 2351; https://doi.org/10.3390/buildings14082351 - 30 Jul 2024
Cited by 1 | Viewed by 927
Abstract
A machine learning (ML) model, optimized by the Particle Swarm Optimization (PSO) algorithm, was developed in this study to predict the shear slip load of adhesive/bolt-reinforced corroded steel plates. An extensive database comprising 490 experimental or numerical specimens was initially employed to train [...] Read more.
A machine learning (ML) model, optimized by the Particle Swarm Optimization (PSO) algorithm, was developed in this study to predict the shear slip load of adhesive/bolt-reinforced corroded steel plates. An extensive database comprising 490 experimental or numerical specimens was initially employed to train the ML models. Eight ML algorithms (RF, AdaBoost, XGBoost, GBT, SVR, kNN, LightGBM, and CatBoost) were utilized for shear slip load prediction, with their hyperparameters set to default values. Subsequently, the PSO algorithm was employed to optimize the hyperparameters of the above ML algorithms. Finally, performance metrics, error analysis, and score analysis were employed to evaluate the prediction capabilities of the optimized ML models, identifying PSO-GBT as the optimal predictive model. A user-friendly graphical user interface (GUI) was also developed to facilitate engineers using the PSO-GBT model developed in this study to predict the shear slip load of adhesive/bolt-reinforced corroded steel plates. Full article
(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)
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