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Machine Learning-Driven Modeling and Optimization in Structural Engineering
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Machine Learning-Driven Modeling and Optimization in Structural Engineering

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2357

Special Issue Editors

Dr. Enming Li

E-Mail Website
Guest Editor
Department of Mining Engineering and Earth Sciences, Polytechnic University of Madrid, 28040 Madrid, Spain
Interests: machine learning and hybrid AI models in engineering; fiber-reinforced materials; optimization algorithms
Dr. Bin Xi

E-Mail Website
Guest Editor
Paul Scherrer Institute PSI, Forschungsstrasse 111, 5232 Villigen, Switzerland
Interests: concrete durability; recycled concrete; low-carbon materials
Special Issues, Collections and Topics in MDPI journals
Dr. Chengkai Fan

E-Mail Website
Guest Editor
Department of Mining, Metallurgical and Materials Engineering, Université Laval, Québec, QC G1V 0A6, Canada
Interests: machine learning in engineering applications; data-driven modeling and optimization; sensor-based monitoring and decision support

Special Issue Information

Dear Colleagues,

This Special Issue of Buildings, titled “Machine Learning-Driven Modeling and Optimization in Structural Engineering”, will focus on the in-depth integration of machine learning with structural engineering. It will center fundamental modeling of structural mechanics, leveraging machine learning to enhance analytical accuracy. To improve durability and predict carbonation effects, it will explore the application value of intelligent algorithms. With green and low carbon as core goals, it will tap into the potential of machine learning in CO2 emission reduction pathways. Additionally, it will address structural design and optimization as well as structural engineering reliability analysis, helping to boost engineering design efficiency and safety performance. Altogether, this Special Issue aims to synthesize interdisciplinary research outcomes and provide innovative solutions for the intelligent and low-carbon development of structural engineering.

Dr. Enming Li
Dr. Bin Xi
Dr. Chengkai Fan
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

  • mechanical properties
  • durability
  • green and low carbon
  • carbonation
  • structural design and optimization
  • structural engineering reliability analysis
  • CO2 emission reduction

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

Published Papers (4 papers)

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Research

25 pages, 2710 KB  
Article
Effect of Temperature and Binder Composition on Rheological and Mechanical Properties of Fiber-Reinforced Cemented Tailings Backfill: Insights from THMC Multi-Field Coupling
by Yiqiang Li, Shuaigang Liu, Zizheng Zhang, Jianbiao Bai and Xupeng Sun
Buildings 2026, 16(8), 1473; https://doi.org/10.3390/buildings16081473 - 8 Apr 2026
Viewed by 297
Abstract
Fiber-reinforced cemented tailings backfill (FTB) has been widely adopted in underground mining operations as an effective solution for mitigating the brittleness of cemented tailings backfill (CTB) and ensuring compatibility with deep mining environments. Understanding the coupled effects of temperature and binder composition on [...] Read more.
Fiber-reinforced cemented tailings backfill (FTB) has been widely adopted in underground mining operations as an effective solution for mitigating the brittleness of cemented tailings backfill (CTB) and ensuring compatibility with deep mining environments. Understanding the coupled effects of temperature and binder composition on the thermal–hydro–mechanical–chemical (THMC) behavior of FTB is essential for low-carbon mix design and practical application. To address this knowledge gap, this work presents a systematic investigation into the influences of curing temperature, binder type, and cement content on the rheological properties, compressive strength, and THMC-related parameters of FTB. The results demonstrate that elevated temperatures accelerate hydration, reducing flowability while significantly enhancing strength and pore structure refinement. Conversely, low temperatures preserve flowability but impede strength development. The incorporation of slag or fly ash as partial cement substitutes reduces rheological parameters; however, fly ash substitution tends to compromise ultimate strength. Multi-field performance monitoring further reveals the underlying coupling mechanisms among temperature evolution, hydration kinetics, matric suction, and mechanical strength development. Based on these insights, a low-carbon design strategy is proposed, emphasizing dynamic optimization of cement content according to ambient temperature. These findings offer a theoretical foundation for the sustainable proportioning and performance control of mine backfill materials. Full article
(This article belongs to the Special Issue Machine Learning-Driven Modeling and Optimization in Structural Engineering)
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19 pages, 2045 KB  
Article
Mechanical Behavior and Modeling of Polypropylene Fiber-Reinforced Cemented Tailings Interface with Granite Under Shear Loading: Effects of Roughness and Curing Time
by Xiangqian Xu, Yabiao Li and Rui Liang
Buildings 2026, 16(5), 913; https://doi.org/10.3390/buildings16050913 - 25 Feb 2026
Cited by 1 | Viewed by 276
Abstract
Cemented paste backfill (CPB) is widely adopted in underground mines, where the shear resistance of the CPB–rock interface critically governs the integrity of backfill–rock systems. This study investigates the effects of polypropylene fiber reinforcement, surface roughness (Joint Roughness Coefficient, JRC = 0 and [...] Read more.
Cemented paste backfill (CPB) is widely adopted in underground mines, where the shear resistance of the CPB–rock interface critically governs the integrity of backfill–rock systems. This study investigates the effects of polypropylene fiber reinforcement, surface roughness (Joint Roughness Coefficient, JRC = 0 and 1.76), and curing time (1, 3, and 7 days) on the shear strength and deformation characteristics of CPB–rock interfaces. Direct shear tests were performed under normal stresses of 50, 100, and 150 kPa, with synchronous measurements of shear and vertical displacements. Results show that increasing roughness markedly strengthens the interface, with the peak shear stress rising by up to 45% due to enhanced mechanical interlocking and dilation. In contrast, adding 0.5 vol.% PP fibers slightly reduces peak shear capacity but consistently improves post-peak deformability, indicating a transition from brittle interfacial fracture to a more ductile, progressive failure mode. A three-stage mechanical model was established to describe the shear stress–displacement relationship, incorporating elastic, bond degradation, and frictional sliding phases. The model parameters, including the shear stiffness (Ks), bond degradation coefficient (η), and residual strength (τr), were calibrated using the experimental data. Mohr–Coulomb analysis further quantifies the curing-dependent evolution of interfacial strength parameters, highlighting a marked increase in cohesion from 1 to 7 days alongside roughness-governed peak strengthening. This research provides insights into the optimization of the CPB–rock interface design for enhanced geomechanical performance in underground applications. Full article
(This article belongs to the Special Issue Machine Learning-Driven Modeling and Optimization in Structural Engineering)
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27 pages, 5760 KB  
Article
An Interpretable Hybrid Machine Learning Approach for Predicting the Compressive Strength of Internal-Curing Concrete Incorporating Recycled Roof-Tile Waste
by Duy Dung Khuat, Dam Duc Nguyen, May Huu Nguyen, Binh Thai Pham and Kenichiro Nakarai
Buildings 2026, 16(3), 674; https://doi.org/10.3390/buildings16030674 - 6 Feb 2026
Viewed by 414
Abstract
The use of recycled materials as internal curing (IC) agents offers substantial benefits to the concrete industry by improving performance and enhancing environmental sustainability. However, the design of IC concrete has grown intricate due to the nonlinear interactions among many input variables. Previous [...] Read more.
The use of recycled materials as internal curing (IC) agents offers substantial benefits to the concrete industry by improving performance and enhancing environmental sustainability. However, the design of IC concrete has grown intricate due to the nonlinear interactions among many input variables. Previous research on IC is mostly experimental, with only a few studies focusing on predicting the compressive strength (CS) of IC concrete. In particular, machine learning has not been applied to quantify the effect of roof-tile waste (RTW) on the CS of IC concrete. This research presents an innovative hybrid model that combines random forest and particle swarm optimization (RF-PSO) to predict the CS of IC concrete using RTW as an IC aggregate. Before model building, a comparative analysis of potential methodologies was conducted, highlighting the key characteristics, benefits, and drawbacks. RF-PSO was then chosen, achieving enhanced accuracy with a coefficient of determination (R2) of 0.961, a root mean square error (RMSE) of 5.361 MPa, and a mean absolute error (MAE) of 4.001 MPa. The RF-PSO model improved prediction accuracy by increasing R2 from 0.906 to 0.961 and reducing statistical errors by nearly 30% compared with conventional machine learning models. A Shapley Additive exPlanations (SHAP) analysis was performed to interpret the model results. The analysis identified the water-to-cement ratio and curing age as the dominant predictors, while IC water contributed a secondary, age-dependent effect. The proposed framework makes contributions: it integrates SHAP-based interpretability into a high-accuracy RF-PSO model and provides a viable tool for reducing empirical trial mixes in sustainable design workflows. Despite the limited dataset, the findings provide a reproducible baseline for future expansion and highlight the potential of combining RTW with IC to improve early and long-term strength. Full article
(This article belongs to the Special Issue Machine Learning-Driven Modeling and Optimization in Structural Engineering)
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25 pages, 5552 KB  
Article
Predicting Carbonation Depth of Recycled Aggregate Concrete Using Optuna-Optimized Explainable Machine Learning
by Yuxin Chen, Xiaoyuan Li, Enming Li and Jian Zhou
Buildings 2026, 16(2), 349; https://doi.org/10.3390/buildings16020349 - 14 Jan 2026
Cited by 2 | Viewed by 650
Abstract
Accurately predicting the carbonation depth of recycled aggregate (RA) concrete is essential for durability assessment. Based on a dataset of 682 experimental samples, this study employed seven machine learning algorithms to develop prediction models for the carbonation depth of RA concrete. The Optuna [...] Read more.
Accurately predicting the carbonation depth of recycled aggregate (RA) concrete is essential for durability assessment. Based on a dataset of 682 experimental samples, this study employed seven machine learning algorithms to develop prediction models for the carbonation depth of RA concrete. The Optuna framework was utilized to conduct 500 trials of hyperparameter optimization for these models, with the objective of minimizing the 5-fold cross-validated mean squared error. Results indicate that model performance improved significantly after optimization. Among them, the XGBoost model achieved the best performance, with a coefficient of determination (R2) of 0.9789, root mean squared error (RMSE) of 1.0811, mean absolute error (MAE) of 0.6972, mean absolute percentage error (MAPE) of 8.7932%, variance accounted for (VAF) of 97.8966%, and mean bias error (MBE) of 0.0641 on the test set. Explainability analysis using SHapley Additive exPlanations (SHAP) further revealed that exposure time is the most significant factor influencing the carbonation depth prediction. Additionally, considering that the database incorporates both natural and accelerated carbonation conditions, the samples were partitioned based on CO2 concentration and conducts a stratified performance evaluation. The results demonstrate that the model maintains high predictive accuracy under natural carbonation as well as across different accelerated carbonation intervals, indicating that, within the scope covered by the current dataset, the proposed approach provides a highly accurate and interpretable tool for predicting the carbonation depth of recycled aggregate concrete. Full article
(This article belongs to the Special Issue Machine Learning-Driven Modeling and Optimization in Structural Engineering)
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