The proposed hybrid phase-field–machine learning framework enables rapid parametric studies and optimization of fiber-reinforced concrete systems within a computational engineering context.
Abstract
This paper aims to investigate the effects of steel fiber position, inclination angle and length on the fracture behavior of steel fiber reinforced concrete (SFRC), while reducing the computational burden associated with high-fidelity phase-field fracture simulations.
A computational framework integrating phase-field modeling (PFM) and machine learning (ML) is developed. A strain-orthogonal phase-field formulation is employed to simulate interfacial damage and crack propagation in SFRC across different concrete grades and fiber configurations. Based on these simulations, a dataset of 357 samples is generated, comprising peak load (Pmax), critical displacement (U) and mechanical work (W). Gradient-boosting algorithms, including CatBoost, LightGBM and XGBoost, are trained and optimized using Bayesian optimization with five-fold cross-validation to construct efficient surrogate models.
The results show that CatBoost consistently provides the highest prediction accuracy, achieving test R2 values of 0.999 for peak load, 0.986 for critical displacement, and 0.942 for mechanical work. The developed ML surrogates enable near-instantaneous prediction of fracture responses, offering a substantial reduction in computational cost compared with standalone phase-field simulations. Model interpretation based on SHAP reveals that matrix stiffness and initial crack length dominate peak load, while fiber inclination plays a more significant role in post-peak mechanical work and energy dissipation.
This study proposes a hybrid phase-field–machine learning framework for efficient fracture analysis of SFRC. By exploiting high-fidelity numerical simulations as a data source for surrogate modeling, the proposed approach enables rapid parametric studies and optimization of fiber-reinforced concrete systems within a computational engineering context.
This study investigates the axial behavior of square fibre reinforced polymer–concrete–steel double-skin tubular columns (hybrid DSTCs) by integrating experimental data, nonlinear finite element analysis (NLFEA), and machine learning (ML). A hybrid dataset comprising 174 specimens was developed, including 24 experimental results from the literature and 150 additional specimens generated through validated NLFEA models. The models demonstrated strong agreement with experimental results, achieving R² values of 0.98 for axial load and 0.92 for axial strain. The expanded dataset facilitated parametric studies on cross-sectional dimension, outer FRP tube thickness, inner steel tube thickness, compressive strength of concrete, and FRP modulus of elasticity. Four ML models, including Multi-linear Regression (MLR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were employed to predict axial load (Pu) and axial strain (εcu). Model performance was evaluated using R², RMSE, MAE, and MAPE. The DT model achieved the highest accuracy for axial load (Pu) prediction (R² = 0.97, MAPE = 2.97 %), while XGBoost provided the best overall performance for both axial load (R² = 0.97, MAPE = 3.60 %) and axial strain (R² = 0.83, MAPE = 9.63 %). Feature importance analysis revealed that axial load (Pu) is primarily governed by FRP stiffness, steel yield strength, and concrete strength, whereas ultimate axial strain (εcu) is dominated by steel tube geometry and strength, confirming that axial load and strain are controlled by distinct governing mechanisms. The findings highlight the potential of FEM–ML integration to develop robust predictive tools for square hybrid DSTCs.
P. P. S. Kumar, S. B. Singh, S. Barai· The Indian Concrete Journal· 0 citations
Basalt fiber reinforced concrete (BFRC) has recently attracted increased attention for improving durability, mechanical strength, and chemical resistance concerning harsh environmental conditions. The most noticeable gap is left in being able to predict long-term performance accurately and optimize that performance because of the complexities that arise from the multiscale interactions between fibers, matrix, and environmental stressors. This study, therefore, offers a highly unified and multiscale machine learning framework by pulling together five disaggregated analytical models into a single predictive-optimization pipeline prearranged for basalt fiber reinforced concrete. The physics-augmented graph attention transformer network (P-GATNet) is expected to embed interfacial physics within graph-based message passing to capture load-driven mechanical responses, resulting in highly accurate strength and fracture evolution predictions (e.g., flexural R² ≈ 0.97). The spectral decomposition assisted degradation model uses Hilbert-Huang-based spectral analysis, which then decouples degradation mechanisms for accurately forecasting alkali resistance and damage kinetics with an error of less than 4.5%. The multi-agent physics reinforcement optimizer (MAPRO) jointly optimizes the strength and chemical performance by modeling competing failure mechanisms via cooperative agents. For improved representation of features, the deep morphological encoder with multi-modal fusion (DME-MMF) marries image-derived morphological embeddings with experimental tabular data, thus enhancing the interpretability and accuracy of predictions. Lastly, the transformer-based inverse composite generator enables reverse material design by producing feasible basalt fiber reinforced concrete formulations that satisfy predetermined strength and durability targets at an approximate success rate of 93%. This approach improves predictive fidelity, interpretability, and design in basalt fiber reinforced concrete.
V. Vairagade· Journal of Materials Science...· 0 citations
Dynamic modulus is a key stiffness parameter in the mechanistic–empirical design of asphalt pavements. Traditional laboratory tests are time-consuming and costly, while conventional empirical models fail to characterize the nonlinear viscoelasticity introduced by fibers, and existing machine learning methods suffer from premature hyperparameter convergence and limited interpretability. To address these issues, this study employs an improved black-winged kite algorithm (IBKA) to optimize eXtreme Gradient Boosting (XGBoost) for establishing a dynamic modulus prediction model. Gaussian chaotic mapping, guided pool strategy, and adaptive step size are introduced to enhance global hyperparameter optimization capability. A dataset of 288 samples involving temperature, frequency, strain, and fiber categories is compiled from multi-condition tests. Nested cross-validation and an independent test set are adopted for internal optimization and generalization assessment, with permutation testing (1000 Monte Carlo, p < 0.001) confirming the statistical reliability of the model. The results demonstrate that IBKA–XGBoost delivers excellent accuracy and robustness, achieving an RMSE of 355.1248 MPa and an R2 of 0.9966 in NCV and 373.5450 MPa and 0.9955 on the independent test set. It outperforms BKA–XGBoost, four metaheuristic algorithms, and three conventional tuning strategies across nine evaluation metrics; compared with BKA–XGBoost, RMSE decreases by 23.9% and prediction uncertainty U95 narrows by 23.7%. SHAP and PDP analyses identify temperature as the dominant factor, reveal fiber-type differentiation governed by modulus matching and interfacial compatibility, and confirm asymmetric temperature–frequency interactions consistent with the time–temperature superposition principle. The proposed framework facilitates fiber screening and the intelligent refined design of pavement materials.
This study aims to develop a high-performance three-phase conductive asphalt concrete (TP-CAC) composed of steel slag, graphite, and carbon fiber, and build a machine learning model for predicting damage from electrical resistance changes in beam specimens. A complete specimen preparation process was first designed to address carbon fiber agglomeration, followed by electrical resistance measurement and mechanical tests (Marshall stability, rutting, and low-temperature bending) to determine the optimal mix ratio. Using fracture energy (
G
f
) as a damage indicator, gradient boosting models [CatBoost, Light gradient boosting machine (GBM), extreme gradient boosting (XGBoost)] and their ensemble model were built to analyze the relationship between electrical resistance and
G
f
, with CatBoost selected as the optimal predictive model. The results showed that TP-CAC with 50% steel slag (by volume), 30% graphite (by mass), and 0.2% carbon fiber (by mass) exhibits excellent smart properties (resistance-sensitive to damage) and mechanical performance. All four models achieved a coefficient of determination
(
R
2
)
>
0.9
, confirming the feasibility of monitoring damage via resistance changes. Although the initial root-mean square error (RMSE) values were relatively high, feature engineering (sliding window extraction) and hyperparameter optimization (Optuna) improved the CatBoost model to
R
2
=
0.97
, with a 25% reduction in RMSE. This study integrates experimental optimization with data-driven modeling to advance smart materials.
Cai Xu, Yang Liu, Zhendong Qian et al.· Journal of Transportation En...· 0 citations
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.
M. Mirrashid, N. Okasha, H. Naderpour et al.· Journal of Structural Design...· 0 citations
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
