Nov 2026· Journal of Structural Design and Construction Practice· Vol 31· 0 citations· 51 references
TL;DR
A computational failure framework that incorporates decision tree classification and machine-learning modeling to identify failure modes in textile-reinforced concrete columns and predict their strength provides practical tools for forensic investigation and reliability-based design of fiber-reinforced confinement systems.
Abstract
This study presents a computational failure framework that incorporates decision tree classification and machine-learning modeling to identify failure modes in textile-reinforced concrete columns and predict their strength. To achieve this, a decision tree classifier was developed using an experimentally derived dataset that considered geometry information, matrix parameters, fiber type (basalt, carbon, steel, glass, PBO), and concrete core properties to detect tensile-jacket rupture and interfacial debonding failure modes. A machine-learning model was subsequently proposed to estimate the ultimate confined compressive strength, using the unconfined concrete strength, confinement ratio, fiber tensile capacity, and matrix characteristics. The decision tree achieved an acceptable classification accuracy, revealing that the fiber ratio and concrete strength were the most important parameters for predicting the failure mode. Moreover, the developed explicit predictive expression for the design applications of the ultimate strength of columns demonstrated a suitable correlation with experimental strength measurements. The proposed methodology not only elucidates the key factors affecting failure behavior but also provides practical tools for forensic investigation and reliability-based design of fiber-reinforced confinement systems.
Accurate prediction of the mechanical strength of Basalt Fiber Reinforced Concrete (BFRC) is critical for structural design, safety assessment, and the advancement of sustainable infrastructure in civil engineering. Traditional prediction methods often fail to capture the nonlinear relationships between BFRC mix proportions and resulting strength characteristics, leading to unreliable estimations. To address this limitation, this study proposes the Optimized Moment Balanced Machine (OMBM), an advanced machine learning model developed to improve the predictive accuracy of BFRC strength parameters. The model was trained and evaluated using key input features, including cement content, silica fume, fly ash, superplasticizer, water, aggregate composition, and fiber property parameters. The performance of the OMBM was benchmarked against four established machine learning models, such as Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Linear Regression (LR). Results from ten-fold cross-validation show that OMBM consistently outperforms the comparison models across five evaluation metrics. It achieved the lowest RMSE (2.411), MAE (1.788), and MAPE (4.08%), along with the highest values for correlation coefficient (R = 0.978), and coefficient of determination (R2 = 0.956). Furthermore, the OMBM achieved a Reference Index (RI) score of 1.000, which confirms its position as the leading predictive model within this comparative framework. These results confirm the robustness and reliability of the proposed OMBM model, making it a highly effective tool for accurate strength prediction of BFRC. This approach offers significant potential for the advancement of sustainable infrastructure by enabling more accurate and efficient use of concrete materials.
R. R. Khasani, Ferry Hermawan, Yuliana Usman· IOP Conference Series: Earth...· 0 citations
The combination of these two approaches demonstrates that flexural strength of SFRC may be forecasted using data-driven models with high reliability and minimises the reliance on the large-scale laboratory testing which makes the assessment process more effective.
Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.
Yuchen Lin· International Conference on...· 0 citations
Ultra-high-performance concrete (UHPC) offers exceptional mechanical and durability properties but often relies on quartz powder, raising sustainability and occupational health concerns. This study introduces an integrated experimental-computational framework for predicting the compressive strength of UHPC and developing quartz-free mixtures. Experimentally, the effects of mixing sequence, sand characteristics, superplasticizer chemistry, and curing regime were investigated, leading to a quartz-free UHPC achieving 136 MPa at 28 days under heat-curing. A dataset of 550 UHPC compressive strength records was compiled, incorporating quantitative mix proportions and categorical variables (cement type, superplasticizer base, fiber type, and specimen geometry). Sixty-three machine learning models from tree-based, boosting, and support vector machine families were optimized using seven meta-heuristic algorithms. The Particle Swarm Optimization-tuned XGBoost model achieved the highest prediction accuracy (R2 = 0.897, RMSE = 7.63 MPa), followed by the Differential Evolution-optimized Random Forest (R2 = 0.867, RMSE = 8.70 MPa). SHapley Additive exPlanations (SHAP) analysis identified curing age as the most influential predictor after optimization. The proposed framework enables accurate and interpretable UHPC strength prediction and supports the design of safer and more sustainable quartz-free UHPC with reduced experimental effort.
Mohamed Ayman, Amr Elnemr· Scientific Reports· 0 citations
Accurate prediction of concrete compressive strength is essential for effective mix design, quality control, and structural performance assessment. Conventional empirical models often exhibit limited accuracy due to the complex and nonlinear interactions among concrete constituents.
This study investigates the applicability of several machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset comprising 1030 concrete mixtures. Linear regression was adopted as a baseline model and compared with support vector regression, random forest regression, and artificial neural networks.
The performance of machine learning models was meticulously assessed using the coefficient of determination, root mean square error, and mean absolute error. Additionally, the models underwent five-fold cross-validation to evaluate their robustness and generalization capabilities. The results unambiguously demonstrate that machine learning models significantly outperform linear regression models.
Cross-validation results confirm the stability and reliability of the developed models. Feature importance analysis reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established concrete material behavior. The findings demonstrate that machine learning models, particularly random forest regression, can serve as effective supporting tools for preliminary concrete mix design and performance evaluation.
S. Rouabah· ITEGAM- Journal of Engineeri...· 0 citations
The ability to predict concrete compressive strength is important in early-stage mix design screening. Thus, the predictions made from these models must match the actual data that was used to train and validate them. Therefore, this study assesses machine learning models using the publicly available UCI concrete compressive strength data set that has 1030 tabular entries. The tabular entries are defined by the following variables; cement, blast furnace slag, fly ash, water, superplasticiser, coarse aggregate, fine aggregate, curing time and measured compressive strength. As such, the study is framed as a transparent tabular prediction benchmark rather than as an experimental evaluation of microsilica and rubber aggregate based concretes. It does not claim to have evaluated any new test pieces, nor does it make any claims regarding SEM imaging or microstructural measurement of those test pieces. Further, there is no claim related to the use of any durability testing procedures. Several machine learning algorithms including support vector regression (SVR), random forest, XGBoost, artificial neural network (ANN) and an optimised tabular ensemble (TE), were each developed under the same leakage-controlled validation methodology. Performance metrics were provided based on both the native test set(s) and a common subset of the test set(s). Metrics included R
2
, RMSE, MAE, MAPE along with residual diagnostic and graphical error analyses. The new framework also emphasizes reproducibility, fairness of comparison and transparency of data domain limitations. In addition to supporting computer-based screening of conventional concrete strength databases, its results indicate what will be required for future studies that contain micro silica, rubber aggregates, microstructural measurements and/or durability measurements in their respective databases.
V. Vairagade· Journal of Materials Science...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
