Sponsored
AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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...
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, Dong-Zhan Jin et al.
· 0 citations
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.
Xiang-Sheng 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 envi...
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 propor...
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