AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed f-PICNN-CPOA model provides an effective and robust approach for predicting the shear strength of RC deep beams, and makes it a valuable decision-support tool for structural engineers, contributing to safer design practices and improved performance of reinforced concrete structures in the construction industry.
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.
This study investigates the application of artificial neural networks (ANNs) for estimating the compressive strength of clay masonry walls based on the mechanical and geometrical properties of their constituent materials. A multilayer perceptron (MLP) neural network was developed using a hybrid dataset derived from Eurocode 6 empirical formulations and representative commercially available masonry units and mortars, enabling systematic generation of realistic input–output relationships. Input parameters included masonry unit dimensions and compressive strength, mortar compressive strength, masonry unit classification group, and mortar type. Different ANN topologies with ReLU, tanh, and logistic activation functions were analyzed, while training was performed using the Adam optimization algorithm. Model performance was evaluated using MSE, MAE, RMSE, R2, and 5-fold cross-validation. The proposed ANN model achieved high prediction accuracy, with R2 values approaching 0.98 for the optimal configuration. Sensitivity, SHAP, and partial dependence analyses confirmed that the constituent material strengths, together with the masonry unit classification and mortar type, are the most influential inputs, in agreement with the Eurocode 6 formulation. The developed model provides a practical tool for preliminary engineering assessment and rapid comparative analysis of masonry wall configurations, reducing reliance on repetitive empirical calculations. The model is based on a Eurocode 6 synthetic dataset and is intended for predictive approximation and engineering support rather than replacement of experimental testing. In this study, the ANN is explicitly framed as a surrogate model of the Eurocode 6 formulation rather than as a replacement for it: its added value lies in providing a fast, continuously differentiable, and interpretable approximation that enables large-scale parameter exploration, interpretability analysis, and deployment as a real-time decision-support web service. To confirm its robustness, the surrogate was benchmarked against Linear Regression, Random Forest, and XGBoost models on the identical dataset.
This paper proposes a hybrid model (Bayes-CNN-LSTM) integrating Bayesian optimization, a convolutional neural network (CNN), and a long short-term memory (LSTM) network for the intelligent prediction of the compressive strength of carbon fiber-reinforced polymer (CFRP)-confined concrete. Based on 518 sets of experimental data, the model uses CNN to extract local correlations among features and LSTM to capture underlying dynamic evolution, while Bayesian optimization is employed for adaptive hyperparameter tuning. The results show that the optimized model significantly outperforms traditional methods in prediction accuracy, achieving a coefficient of determination (R2) of 0.957 and a root mean square error (RMSE) of 7.42 MPa on the test set. This study provides an efficient and reliable intelligent framework for predicting the mechanical performance of FRP-confined concrete.
A comprehensive data-driven framework integrating ensemble machine learning models with systematic hyperparameter sensitivity analysis and explainable artificial intelligence techniques is proposed, demonstrating that the XGB model significantly outperforms the other approaches, achieving superior accuracy and robust generalization.
The compressive strength of ultra-high-performance concrete (UHPC) is jointly influenced by multiple factors, including material composition, mixture proportion parameters, and curing regime. Conventional empirical methods are therefore insufficient to accurately characterize the highly nonlinear relationships involved. To improve the prediction accuracy of UHPC compressive strength and to achieve mixture proportion optimization that simultaneously considers mechanical performance, economic efficiency, and environmental impact, this study developed random forest (RF), artificial neural network (ANN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) models based on 810 publicly available UHPC experimental datasets. Model performance was evaluated using R2, RMSE, MAE, and MAPE. To enhance the robustness of model validation, repeated K-fold cross-validation, sensitivity analysis with different random seed splits, and benchmark model comparisons were further introduced. The results indicate that the XGBoost model achieved superior predictive performance on both the test set and robustness validation, with test-set R2, RMSE, MAE, and MAPE values of 0.9604, 7.77, 5.58, and 4.80, respectively. The model was further interpreted using SHAP, PDP, and ICE methods, and the results revealed that curing age, fiber content, silica fume content, and water-to-binder ratio were important variables affecting the compressive strength of UHPC. Furthermore, XGBoost was used as a surrogate model and coupled with NSGA-II and TOPSIS methods for multi-objective optimization. Under the constraints of compressive strength, water-to-binder ratio, superplasticizer-to-binder ratio, and absolute volume, a computationally recommended UHPC mixture proportion balancing strength, cost, and carbon emissions was obtained. This study provides a reproducible machine-learning-assisted approach for UHPC compressive strength prediction and low-carbon, cost-effective mixture proportion design.
A unified ML-based shear strength prediction model was developed that simultaneously captures the shear behaviour of both NC and UHPC beams, incorporating physically meaningful input features derived from the data set, thereby overcoming the limitations of separate empirical formulations.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.