Reliable estimation of the confined compressive strength of fiber-reinforced polymer (FRP)-wrapped concrete is essential for the safe design and assessment of strengthened structural members. This study proposes an adaptive neuro-fuzzy inference system (ANFIS) model to predict the confined compressive strength of FRP-confined circular concrete cylinders. The model is trained using the Levenberg-Marquardt backpropagation algorithm, combined with an early-stopping strategy, to enhance generalization and prevent overfitting. Four physically meaningful parameters-unconfined compressive strength, cylinder diameter, FRP thickness, and FRP elastic modulus-are employed as input variables, while the confined compressive strength is taken as the output. A comprehensive database of 812 experimental results from the literature was compiled and used for model training, validation, and testing. The predictive capability of the proposed ANFIS framework was evaluated against five widely used analytical confinement models using statistical performance indicators. The developed model demonstrated superior predictive consistency and reduced scatter relative to existing confinement equations, indicating improved reliability across a broad range of strengths. The results confirm that the proposed ANFIS approach provides a stable and practical tool for estimating the confined compressive strength of FRP-wrapped concrete, supporting preliminary structural assessment and strengthening design applications.
T. A. Tawfik, Z. Akbulut, M. A. Arvas et al.· Scientific Reports· 0 citations
The rapid rise in global population and industrial activity has intensified environmental challenges, particularly carbon dioxide (CO₂) emissions from the cement and concrete industry. Biochar, a carbon-rich byproduct of biomass pyrolysis, has emerged as a promising solution for sustainable construction by enhancing carbon sequestration and improving mechanical performance when partially substituting cement. This study integrates experimental evidence with advanced machine learning (ML) techniques to evaluate the compressive strength, cost-efficiency, and carbon footprint of biochar-incorporated concrete. A comprehensive dataset of nine input parameters, including cement, aggregates, silica fume, fly ash, biochar, water, superplasticizer, and curing age was modeled using multiple ML approaches. Among the models tested, the hybrid XGB-Histogram Gradient Boosting (XGB-HistGB) model consistently achieved the best overall performance, with a testing R2 of 0.958, the lowest mean absolute error (3.03), and minimal prediction bias. This model outperformed standalone algorithms and other hybrids, providing reliable accuracy across compressive strength, cost, and embodied CO₂ predictions. SHAP and partial dependence analyses confirmed fine aggregate, curing age, and superplasticizer as the most influential parameters, while biochar dosage required careful optimization to balance strength retention with sustainability benefits. A user-friendly graphical interface was also developed, enabling real-time prediction of compressive strength, material cost, and CO₂ emissions based on user-defined mix proportions. Overall, the findings demonstrate that biochar can be effectively integrated into sustainable concrete formulations, and the XGB-HistGB model offers a powerful AI-driven predictive framework to optimize both structural performance and environmental outcomes.
M. Uddin, Md. Samsuzzaman Sobuz, Mohamed Ghalla et al.· Scientific Reports· 0 citations
The rapid rate of urbanization and industrialization has driven the excessive use of natural resources like river sand and gravel, raising significant sustainability concerns. Waste foundry sand (WFS), a discarded by-product of ferrous and nonferrous metal casting industries, offers a promising substitute for natural sand in concrete. This study focuses on predicting the compressive strength (CS) of WFS-infused concrete by analyzing the impact of various factors, such as cement content, WFS proportion, supplementary cementitious materials (SCMs), water, aggregate composition, and superplasticizer (SP) usage. A data set comprising 401 mix ratios and their corresponding strengths was developed using systematic literature review approach and analyzed using advanced machine-learning (ML) models, including extreme gradient boosting (XGB), categorial boosting (CatB), light gradient boosting, gradient boosting, decision tree,
k
-nearest neighbor, adaptive boosting, bagging regressor, and random forest. The data set was divided into training and testing subsets, and statistical evaluations were performed to determine correlations between input parameters and strength. Among the models, XGB and CatB demonstrated the highest accuracy (
R
2
=
0.98
and 0.97 for training data;
R
2
=
0.83
and 0.86 for testing data, respectively). Shapley additive explanations (SHAP) and partial dependence plot (PDP) analysis revealed that water content and curing age significantly enhanced compressive strength. Furthermore, the developed graphical user interface will help to practically estimate the compressive strength of WFS concrete without any experimental trials.
M. H. R. Sobuz, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi et al.· Journal of Structural Design...· 0 citations