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

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

Deadline for manuscript submissions: 20 October 2026 | Viewed by 14614

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Special Issue Editors

Prof. Dr. Oladimeji Olalusi

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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

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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

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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
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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:

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

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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Published Papers (10 papers)

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Research

23 pages, 6472 KB

Open AccessArticle

Seismic Response Prediction of One-Story Bidirectionally Eccentric Structures Based on the BP Neural Network

by Yi Zhang and Xiaobin Hu

Buildings 2026, 16(7), 1329; https://doi.org/10.3390/buildings16071329 - 27 Mar 2026

Viewed by 290

Abstract

Predicting a structure’s seismic responses is of great importance during seismic design, especially in the preliminary stage. Hence, in this paper, a model for rapid prediction of the seismic responses of one-story bidirectionally eccentric structures based on the BP neural network is proposed. First, the equations of motion of one-story two-way eccentric structures under unidirectional seismic excitation are derived and solved through the modal response spectrum method. Factors, including eccentricity ratios and frequency ratios, are analyzed in detail to investigate their effects on these structures’ seismic responses. The results show that the eccentricity ratio perpendicular to the seismic direction has a greater impact on translational responses along the seismic direction and rotational responses than the eccentricity ratio along the seismic direction. In addition, the torsional–translational frequency ratio exhibits a larger impact on translational responses along the seismic direction and rotational responses than the translational frequency ratio. Following this, the back propagation (BP) neural network model is developed, fully considering the effects of various factors. A SHAP analysis is also conducted to compare the relative importance of different factors. The outcomes show that the seismic prediction model developed in this study achieves high accuracy while adequately considering the factors influencing the seismic responses of one-story two-way eccentric structures. Finally, an example is presented to demonstrate the applicability of the proposed prediction model. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)

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26 pages, 3654 KB

Open AccessArticle

From Experiment to Prediction: Machine Learning Solutions for Concrete Strength Assessment with Steel Clamps

by Panumas Saingam, Burachat Chatveera, Gritsada Sua-Iam, Preeda Chaimahawan, Chisanuphong Suthumma, Panuwat Joyklad, Qudeer Hussain and Afaq Ahmad

Buildings 2026, 16(4), 851; https://doi.org/10.3390/buildings16040851 - 20 Feb 2026

Viewed by 353

Abstract

This study examines the confined compressive strength (Fcc) of circular, square, and rectangular column geometries under varying confinement conditions. Results indicate that circular columns have the highest Fcc values, exceeding those of square and rectangular shapes. Increased confinement through clamps significantly enhances compressive strength. Five machine learning models, Linear Regression, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were used to predict Fcc based on geometric and confinement parameters. Linear Regression and Decision Tree models achieved moderate predictive performance, with R² values of 0.84 and 0.83, respectively, and relatively higher error measures (RMSE, MAE, and MAPE), indicating limited ability to capture complex nonlinear relationships in the data. In contrast, ensemble-based methods demonstrated superior performance. The Random Forest model improved the coefficient of determination to 0.90 while substantially reducing all error metrics, reflecting enhanced generalization through bagging. The boosting-based approaches yielded the best results, with AdaBoost achieving the highest R² value of 0.99 and the lowest RMSE, MAE, and MAPE among all models, followed closely by Gradient Boosting with an R² of 0.98. These results confirm that ensemble learning techniques, particularly boosting algorithms, yield more accurate and robust predictions than single learners for the problem studied. Data visualization techniques, including Regression Error Characteristic curves (REC) and SHapley Additive exPlanations (SHAP) value analysis, highlighted model performance and feature importance, emphasizing the roles of confinement and geometry in compressive strength. This research demonstrates the potential of machine learning to optimize structural engineering design and suggests further exploration of alternative shapes and confinement strategies to enhance structural integrity. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)

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24 pages, 6316 KB

Open AccessArticle

A Framework for Structural-Collapse-Sensitive Ground-Motion Identification Based on Unsupervised Clustering and Explainable Ensemble Learning

by Xi Zhao, Wen Pan and Liaoyuan Ye

Buildings 2026, 16(4), 820; https://doi.org/10.3390/buildings16040820 - 17 Feb 2026

Viewed by 483

Abstract

To address the small ATC-63 record set for collapse-oriented motion selection and the limited interpretability of data-driven approaches, this study proposes a framework for identifying structural-collapse-critical ground motions. Using 5074 records from the PEER NGA-West2 database, we applied STA/LTA event detection and extracted multi-source features. A Gaussian mixture model (GMM) was then used to perform unsupervised clustering and identify four physically interpretable groups. LightGBM, XGBoost, and Random Forest were employed to test the separability of the cluster labels, with all three models achieving F1 scores above 0.89 and LightGBM reaching an accuracy of about 93%. SHAP-based feature-importance analysis was used at the model level to clarify feature contributions and improve interpretability. Cluster 2 exhibits markedly higher relative seismic energy, stronger time-domain variability, and more dominant frequencies, forming a typical strong-motion hazard signature. For external engineering verification, 22 ATC-63 far-field records were mapped onto the full dataset to examine cluster-level enrichment and coverage. Cluster 2 shows significant enrichment in engineering markers and high coverage and is therefore identified as the collapse-sensitive phenotype cluster (COP). Overall, the framework provides a technical basis for ground-motion selection in collapse assessment, fragility analysis, and design evaluation. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)

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42 pages, 10041 KB

Open AccessArticle

Probabilistic Prediction of Concrete Compressive Strength Using Copula Functions: A Novel Framework for Uncertainty Quantification

by Cheng Zhang, Senhao Cheng, Shanshan Tao, Shuai Du and Zhengjun Wang

Buildings 2026, 16(4), 754; https://doi.org/10.3390/buildings16040754 - 12 Feb 2026

Viewed by 430

Abstract

Traditional machine learning models for concrete compressive strength prediction provide only single-value estimates without quantifying the probability of meeting design requirements, leaving engineers unable to make risk-informed decisions. This study addresses this critical limitation by developing a novel probabilistic prediction framework that integrates explainable machine learning with Copula-based joint distribution modeling. Using a dataset of 1030 concrete samples with curing ages ranging from 1 to 365 days, we first established an XGBoost 2.1.4 prediction model achieving R² = 0.9211 (RMSE = 4.51 MPa) on the test set. SHAP 0.49.1 (SHapley Additive exPlanations) analysis identified curing age (33.3%) and water–cement ratio (28.8%) as the dominant features, together accounting for 62.1% of predictive importance. These two controllable engineering parameters were then selected as core variables for probabilistic modeling. The key innovation lies in integrating Copula-based dependence modeling with explainable machine learning (XGBoost–SHAP) to quantify the compliance probability of concrete strength under specific mix designs and curing conditions, thereby supporting risk-informed quality control decisions. Through systematic comparison of five Copula families (Gaussian, Student t, Clayton, Gumbel, and Frank), we identified optimal dependence structures: Gaussian Copula (ρ = −0.54) for the water–cement ratio–strength relationship and Clayton Copula for the age–strength relationship, revealing asymmetric tail dependence patterns invisible to conventional correlation analysis. The three-dimensional Copula model enables engineers to estimate compliance probability—the likelihood of concrete achieving target strength under specific mix designs and curing conditions. We propose an illustrative three-tier decision rule for construction quality management based on the compliance probability P: P ≥ 0.95 (high-confidence approval), 0.80 ≤ P < 0.95 (warning zone requiring enhanced monitoring), and P < 0.80 (high risk suggesting corrective actions such as mix adjustment or extended curing), noting that these thresholds can be recalibrated to project-specific risk tolerance and local specifications. This framework supports a paradigm shift from reactive “mix-then-test” quality control to proactive “predict-then-decide” construction management, providing quantitative risk assessment tools previously unavailable in deterministic prediction approaches. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)

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18 pages, 1957 KB

Open AccessArticle

Optimisation of Interlayer Bond Strength in 3D-Printed Concrete Using Response Surface Methodology and Artificial Neural Networks

by Lenganji Simwanda, Abayomi B. David, Gatheeshgar Perampalam, Oladimeji B. Olalusi and Miroslav Sykora

Buildings 2025, 15(20), 3794; https://doi.org/10.3390/buildings15203794 - 21 Oct 2025

Cited by 1 | Viewed by 1552

Abstract

Enhancing interlayer bond strength remains a critical challenge in the extrusion-based 3D printing of cementitious materials. This study investigates the optimisation of interlayer bond strength in extrusion-based 3D-printed cementitious materials through a combined application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). Using a concise yet comprehensive dataset, RSM provided interpretable main effects, curvature, and interactions, while the ANN captured non-linearities beyond quadratic forms. Comparative analysis revealed that the RSM model achieved higher predictive accuracy (

R^{2} = 0.95

) compared to the ANN model (

R^{2} = 0.87

). Desirability-based optimisation confirmed the critical importance of minimising casting delays to mitigate interlayer weaknesses, with RSM suggesting a water-to-cement (W/C) ratio of approximately 0.45 and a minimal time gap of less than 5 min, while ANN predicted slightly lower optimal W/C values but with reduced reliability due to the limited dataset. Sensitivity analysis using partial dependence plots (PDPs) further highlighted that ordinary Portland cement (OPC) content and W/C ratio are the dominant factors, contributing approximately 2.0 and 1.8 MPa respectively to the variation in predicted bond strength, followed by superplasticiser dosage and silica content. Variables such as water content, viscosity-modifying agent, and time gap exhibited moderate influence, while sand and fibre content had marginal effects within the tested ranges. These results demonstrate that RSM provides robust predictive performance and interpretable optimisation guidance, while ANN offers flexible non-linear modelling but requires larger datasets to achieve stable generalisation. Integrating both methods offers a complementary pathway to advance mix design and process control strategies in 3D concrete printing. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)

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24 pages, 1094 KB

Open AccessArticle

Machine Learning-Based Surrogate Ensemble for Frame Displacement Prediction Using Jackknife Averaging

by Zhihao Zhao, Jinjin Wang and Na Wu

Buildings 2025, 15(16), 2872; https://doi.org/10.3390/buildings15162872 - 14 Aug 2025

Cited by 3 | Viewed by 1480

Abstract

High-fidelity finite element analysis (FEA) plays a key role in structural engineering by enabling accurate simulation of displacement, stress, and internal forces under static loads. However, its high computational cost limits applicability in real-time control, iterative design, and large-scale uncertainty quantification. Surrogate modeling provides a computationally efficient alternative by learning input–output mappings from precomputed simulations. Yet, the performance of individual surrogates is often sensitive to data distribution and model assumptions. To enhance both accuracy and robustness, we propose a model averaging framework based on Jackknife Model Averaging (JMA) that integrates six surrogate models: polynomial response surfaces (PRSs), support vector regression (SVR), radial basis function (RBF) interpolation, eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Three ensembles are formed: JMA1 (classical models), JMA2 (tree-based models), and JMA3 (all models). JMA assigns optimal convex weights using cross-validated out-of-fold errors without a meta-learner. We evaluate the framework on the Static Analysis Dataset with over 300,000 FEA simulations. Results show that JMA consistently outperforms individual models in root mean squared error, mean absolute error, and the coefficient of determination, while also producing tighter, better-calibrated conformal prediction intervals. These findings support JMA as an effective tool for surrogate-based structural analysis. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)

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29 pages, 4371 KB

Open AccessArticle

An Explainable Machine Learning-Based Prediction of Backbone Curves for Reduced Beam Section Connections Under Cyclic Loading

by Emrah Tasdemir, Mustafa Yavuz Cetinkaya, Furkan Uysal and Samer El-Zahab

Buildings 2025, 15(13), 2307; https://doi.org/10.3390/buildings15132307 - 30 Jun 2025

Cited by 1 | Viewed by 1783

Abstract

Reduced Beam Sections (RBS) are used in steel design to promote ductile behavior by shifting inelastic deformation away from critical joints, enhancing seismic performance through controlled energy dissipation. While current design guidelines assist in detailing RBS connections, moment–rotation curves—essential for understanding energy dissipation—require extensive testing and/or modeling. Machine learning (ML) offers a promising alternative for predicting these curves, yet few studies have explored ML-based approaches, and none, to the best of the authors’ knowledge, have applied Explainable Artificial Intelligence (XAI) to interpret model predictions. This study presents an ML framework using Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), and Ridge Regression (RR) trained on 500 numerical models to predict the moment–rotation backbone curve of RBS connections under cyclic loading. Among all the models applied, the ANN obtained the highest R² value of 99.964%, resulting in superior accuracy. Additionally, Shapley values from XAI are employed to evaluate the influence of input parameters on model predictions. The average SHAP values provide important insights into the performance of RBS connections, revealing that cross-sectional characteristics significantly influence moment capacity. In particular, flange thickness (t_f), flange width (b_f), and the parameter “c” are critical factors, as the flanges contribute the most substantially to resisting bending moments. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)

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16 pages, 20094 KB

Open AccessArticle

The Optimal Cost Design of Reinforced Concrete Beams Using an Artificial Neural Network—The Effectiveness of Cost-Optimized Training Data

by Jaemin So, Seungjae Lee, Jonghyeok Seong and Donwoo Lee

Buildings 2025, 15(9), 1577; https://doi.org/10.3390/buildings15091577 - 7 May 2025

Cited by 2 | Viewed by 2073

Abstract

This study presents a method for the automated design of reinforced concrete (RC) beam cross-sections using an artificial neural network (ANN) trained with cost-optimized data generated by the crow search algorithm (CSA). To effectively employ the CSA, recognized for its benefits in addressing engineering problems among metaheuristic algorithms, the design variables of the RC beam cross-section were mediated. The goal is to improve the design efficiency and prediction accuracy by using data with clear trends derived through metaheuristic optimization. The ANN model is trained with input variables, including the design bending moment and the beam height, and outputs design variables, such as the beam width, number of reinforcement bars, and bar diameters. The model trained with the CSA-optimized data is compared with one trained using randomly generated data. The results show that the CSA-trained model achieves a higher prediction accuracy across all the output variables, with a particularly strong linear relationship for beam width. Additionally, a design scenario demonstrates that the CSA-based model can propose a cross-section with an approximately 17.3% cost reduction compared to the random model. The design based on the CSA methodology demonstrates greater efficiency, as it employs a smaller value for the beam width and requires less reinforcement while still satisfying the flexural requirements. Conversely, the design utilizing the random dataset proves inefficient in terms of both the value for the beam width and the reinforcement layout. A SHAP analysis further confirms that the CSA-based model learns more meaningful structural relationships. The findings emphasize the critical role of high-quality training data in ANN-based structural design and suggest the potential for extending the framework to multi-objective design tasks. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications)

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33 pages, 7006 KB

Open AccessArticle

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 6 | Viewed by 1840

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) 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 KB

Open AccessArticle

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 5 | Viewed by 1577

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 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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