TL;DR
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.
Accurate estimation of coal pillar strength is essential for ensuring safety and operational efficiency in underground mining. Current assessment methods often face limitations in addressing time-dependent failure mechanisms, geological discontinuities, and dynamic loading conditions. This paper identifies future resea...
Predicting underground mining rockbursts remains challenging because geotechnical data sets are often small, sparse, incomplete, and noisy, resulting from inconsistent testing standards, high acquisition costs, measurement errors, and geological variability. Consequently, traditional machine learning (ML) models fr...
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.
This paper aims to provide theoretical references and methodological guidance for safety early warning, intelligent construction, and full life-cycle health management of underground engineering by highlighting cross-project generalization and probabilistic prediction as important methodological challenges.
As industrialization expands, modern infrastructure increasingly collides with volatile geological environments, driving a sharp escalation of complex, cascading geohazards. These destructive hazard chains represent a critical global threat to structural and economic resilience. Despite this escalating risk, current ea...
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