AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A critical review of recent advances in ML for assessing the fire performance of bridges is presented and current challenges, including the limited availability of bridge fire databases, model generalization, interpretability, uncertainty quantification, and practical implementation are critically discussed.
A conceptual cyber-physical system (CPS) framework designed to integrate ML-driven classification with sensing, cloud infrastructure, digital twins, and visualization technologies is introduced to guide the transition toward more adaptive rock mass classification in modern underground mining.
Abstract: Wheel damage remains a major constraint on railway safety, dynamic performance, asset life, and maintenance efficiency. Despite extensive studies of individual damage modes, understanding remains fragmented across mechanisms, monitoring, prediction, and maintenance application. This review provides an integra...
Accurate prediction of the residual drift ratio of reinforced concrete bridge piers is challenging because conventional methods are computationally expensive, time-consuming, and unable to effectively capture complex nonlinear interactions among multiple influencing factors. To address these limitations, this study pro...
Accurate assessment of the flow behaviour of fresh concrete is essential for ensuring stability, mechanical performance, and durability. Flow behaviour governs key construction processes including mixing, pumping, and placement. While rheometers provide precise measurements, they are costly, complex, and impractical fo...
This systematic review synthesizes AI-driven prognostic methods, data challenges, and deployment considerations specific to off-highway operation, and contrasts the primary prognostic frameworks—data-driven, physics-based, and hybrid—and the role of knowledge-based expert systems in delivering interpretable alerts.
Fire-induced spalling remains an ongoing concern for concrete structures exposed to fire conditions as existing predictive methods struggle to achieve reliable predictions. To bridge this knowledge gap, this study proposes a methodology to develop empirically driven indices to predict the spalling phenomenon. The metho...
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