Similar papers
Dec 2026
Explainable Machine Learning Framework for Predicting Bond Strength of FRP Bars Embedded in UHPC
The interfacial performance of advanced composites bars embedded in Ultra-High Performance Concrete (UHPC) is an important factor that controls load transfer and the performance of structural elements. Predicting bond strength is still difficult because it is affected by several factors, such as rebar type, bar profile, bar diameter, bonded length, cover depth, fiber content, UHPC compressive strength, and FRP tensile strength. Therefore, this study uses machine-learning models to estimate the the bonding capacity of FRP bars placed in UHPC Using a collected experimental database of 183 specimens from previous studies. Four machine-learning models were developed and compared, including Linear Regression, Random Trees, Multi-Layer Perceptron, and Locally Weighted Learning. The MLP model gave the best prediction performance, with a correlation coefficient of 0.9466, MAE of 2.3083 MPa, and RMSE of 3.0631 MPa. SHAP analysis showed that embedment length was the most influential variable, followed by bar surface condition, FRP tensile strength, and concrete cover. This confirms that FRP–UHPC bond behavior is controlled by the interaction between bonded length, surface condition, mechanical interlock, and confinement provided by UHPC. Overall, the developed explainable ML framework provides a useful tool for predicting FRP–UHPC bond strength and supporting future UHPC-specific bond models.
Abdulaziz Alqurashi
· Islamic University Journal o... · 0 citations
Preprint
Aug 2026
Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
An interpretable machine-learning framework for predicting the splitting strength of asphalt concrete and supporting data-driven mixture design and a GUI platform integrating prediction and SHAP-based explanation was developed to improve the accessibility and practical applicability of the proposed framework.
J. Xing, Xiao Tan, Dongzhan Jin et al.
· 0 citations
#reinforcement learning
Oct 2026
Interpretable and Uncertainty-Aware Machine Learning for Shear Strength Prediction of FRCM-Strengthened RC Beams
An interpretable and uncertainty-aware machine-learning framework for estimating the shear capacity of FRCM-strengthened beams enables accurate, transparent, and uncertainty-aware assessment of shear capacity in FRCM-strengthened concrete beams.
Xiangsheng Liu, G. Figueredo, G. Gordon et al.
· Journal of composites for co... · 0 citations
Open access
Jul 2026
Predicting Time-Dependent Durability of FRP–Timber Bonds in Harsh Environments: A Comparative Study of Machine Learning Models
This study presents a comparative evaluation of three machine learning models, XGBoost, AdaBoost, and LightGBM, for predicting the time-dependent bond strength between fiber-reinforced polymer (FRP) and timber in both normal and harsh environments. A dataset was compiled (79 for normal conditions and 265 for harsh environments) incorporating material properties, geometric parameters, exposure time, and solution pH as input features. Hyperparameter optimization was performed for each model, and performance was evaluated using R2, RMSE, MAE, and MSE metrics. SHAP analysis and Partial Dependence Plots were employed to interpret feature importance and model behavior. Under normal conditions, XGBoost achieved the highest predictive accuracy (testing R2 = 0.944, RMSE = 2.170, and MAE = 1.775), outperforming AdaBoost and LightGBM. However, in harsh environments, LightGBM demonstrated superior generalization, with the highest testing R2 of 0.797 and the lowest RMSE of 0.549 and MAE of 0.431, outperforming XGBoost and AdaBoost. AdaBoost exhibited severe overfitting under harsh conditions, with a training-to-testing R2 drop of 0.341. Feature importance analysis by SHAP analysis identified fiber tensile strength and exposure time as the most influential parameters governing bond performance. SHAP force plots demonstrated that fiber properties predominantly enhance bond strength, while pH consistently acts as a decreasing factor under harsh conditions. This research provides a robust predictive framework for FRP–timber bond durability, offering valuable insights for structural design and service life prediction in harsh environments.
Bahareh Mehdizadeh, Younes Nouri, Atiye Farahani et al.
· Buildings · 0 citations
Conference
Open access
Jul 2026
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.
R. R. Khasani, Ferry Hermawan, Yuliana Usman
· IOP Conference Series: Earth... · 0 citations
Open access
Aug 2026
Machine Learning-Driven Prediction and Design Guidance for Asphalt Concrete Using Marshall Stability and Indirect Tensile Strength
A Streamlit-based graphical user interface was developed to enable real-time prediction and MS–ITS trade-off visualization, providing a reference for preliminary mix design of asphalt concrete, and results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS.
J. Xing, Xiao Tan, Mu Guo et al.
· Materials · 0 citations