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 Eur...
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.
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 g...
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...
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.
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