Flexural strength prediction of steel fiber reinforced concrete with machine learning

Synergistic experimental and ANN-based prediction of mechanical properties of basalt fiber reinforced high-strength concrete

This research focuses on how basalt macrofibers and microfibres affect the durability and Mechanical properties of M60 high-strength concrete. Compressive, split tensile, flexural strength, and acid resistance tests were employed to assess the performance of fiber-reinforced concrete at various fibre volume fractions. The findings demonstrate that the addition basalt fibres greatly enhanced the overall behaviour of concrete compared with the conventional control mix. The greatest compressive strengths were attained at a fibre dosage of 0.75%, reaching 68.42 MPa for microfibres and 63.86 MPa for macrofibres, compared to 58.81 MPa for conventional concrete. Additionally, split tensile strength increased as the fibre content increased to 0.75%, beyond which a slight reduction was observed, likely due to reduced matrix uniformity. Flexural performance showed substantial enhancement, with peak values of 8.78 and 7.88 MPa for microfibre- and macrofibre-reinforced concrete, respectively, demonstrating improved crack resistance and ductile behaviour. Durability evaluation through acid resistance testing revealed comparable performance at the optimum fibre dosage, although a marginal increase in weight loss was observed at higher fibre contents. Microstructural investigations confirmed effective fibre–matrix interaction and crack-bridging mechanisms, while XRD analysis verified the presence of stable crystalline phases that contributed to matrix integrity. Overall, basalt fibre reinforcement enhanced strength, toughness and durability characteristics of concrete. Furthermore, the experimental dataset was integrated with a model of an artificial neural network that accomplished high prediction accuracy. To validate and extend the predictive capability, additional Machine learning approaches were used, and strong agreement was shown with the experimental observations.

Machine Learning-Based Prediction of Compressive Strength in Basalt Fiber Reinforced Concrete

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.

Dynamics of Compressive Performance in Textile-Reinforced Concrete: A Machine-Learning Approach for Failure Mode and Strength Prediction

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.

Prediction of compressive and flexural strength of modified concrete using machine learning

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.

Experimental investigation of the mechanical and durability performance of hybrid basalt fiber-reinforced concrete using gaussian process regression-based prediction

Machine Learning–Based Prediction of Mechanical Properties of Sustainable Concrete

There has been a rise in the need of concrete leading to high consumption of cement and emission of carbon. Sustainable concrete incorporating supplementary cementitious materials (SCMs) is an alternative that is eco-friendly, but the mechanical behavior is complicated and hard to predict using the traditional tests. The work presents a framework involving machine learning because of predicting the mechanical properties of sustainable concrete, namely, compressive, split tensile, and flexural strength. Four models such as Linear Regression, Support Vector Regression, Random Forest, and Artificial Neural Network were constructed based on a data of nearly 1000 sustainable concrete mixes. To estimate the model performance, R 2, RMSE and MAE were used. The findings indicated that ANN and RF had the greatest prediction accuracy. The feature analysis established the most influential factors to be water content, cement dosage, replacement ratio of SCM, and curing age. The mix design approach proposed here is a fast, economical, and sustainable approach to the design of concrete mix.