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Engineering Structure Risk Assessment and Decision-Making Support

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 1 June 2027 | Viewed by 751

Editor

Special Issue Information

Dear Colleagues,

The safety and serviceability of engineering structures is paramount to public safety and economic stability. Engineering structure risk assessment and decision-making support is a systematic and critical process that aims to evaluate the potential risks these structures face throughout their lifecycle; it involves identifying potential hazards (e.g., natural disasters, material degradation, operational loads), analyzing their probability of occurrence and potential consequences, and then quantifying the resulting risk. The ultimate goal of these assessments is not merely to identify risks, but to provide a robust, data-driven foundation for informed decision-making. This support system empowers management departments to prioritize inspections, allocate maintenance resources efficiently, plan for repairs or reinforcements, and develop effective risk mitigation strategies. It is an approach that can significantly enhance structural resilience, optimize life-cycle costs, and ensure the long-term safety and reliability of critical infrastructure.

Dr. Qi-Ang Wang
Guest Editor

Manuscript Submission Information

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Keywords

  • structural risk assessment
  • decision support systems
  • quantitative analysis
  • risk mitigation strategies
  • lifecycle management

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Published Papers (1 paper)

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Research

26 pages, 11719 KB  
Article
Multi-Level Spatial Design Decision-Making Model for Block Caving Systems in Super-Large Open-Pit Mines
by Qi-Ang Wang, Gao-Yu Cui, Guo-Quan Sun, Bei-Dou Ding, Zhan-Guo Ma, Jia-Mian Yang, Peng Gong, Ji Liu and Hao-Yu Zhu
Appl. Sci. 2026, 16(13), 6753; https://doi.org/10.3390/app16136753 - 6 Jul 2026
Viewed by 324
Abstract
As global super-large open-pit mines expand in scale and extraction depth, conventional single-stage planning cannot meet the combined demands of productivity and resource recovery, making the shift to underground block caving inevitable. This study outlines the systemic challenges of block-scale extraction and the [...] Read more.
As global super-large open-pit mines expand in scale and extraction depth, conventional single-stage planning cannot meet the combined demands of productivity and resource recovery, making the shift to underground block caving inevitable. This study outlines the systemic challenges of block-scale extraction and the rationale for adopting multi-level spatial design decision-making. Four core model categories are briefly proposed: ultimate pit limit optimization, gravity flow simulation for draw strategy, long-term production scheduling for large-scale computation, and probabilistic frameworks addressing geological and market uncertainty. A Bayesian network-based block decision model is then proposed and decoupled into three physical decision tiers. The first tier incorporates energy prices, transport costs, and ore prices to establish an economic boundary rating robust to market volatility. The second tier aggregates mining units with discrete-event perturbations to produce a reliability-oriented production rating. The third tier integrates rock mechanics parameters with in situ monitoring data to derive a physics-informed safety rating. The three ratings are synthesized via Bayesian inference and evaluated within a multi-attribute utility function encompassing net present value, safety index, downside risk, and information risk. A feedback module quantifies the economic benefit of uncertainty reduction, yielding a closed-loop intelligent system spanning macroeconomic boundary definition to operational safety alerting. Finally, the main conclusion of this study is that integrating macro-economic volatility with rock mechanics through a dynamic Bayesian framework is essential for managing the open-pit to underground transition. The results indicate that leveraging the Value of Information for real-time risk diagnosis significantly reduces conservative design losses, providing a quantifiable and robust decision-making paradigm for super-large mining systems. Full article
(This article belongs to the Special Issue Engineering Structure Risk Assessment and Decision-Making Support)
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