地下空间坍塌应急救援现场环境安全风险评价及预测
Assessment and prediction of environmental safety risk at the site of emergency rescue for underground space collapse
谢众智 1李升连 2郑惜雯 1古力 3张国华 4倪晓阳1
作者信息
- 1. 中国地质大学(武汉)工程学院,湖北 武汉 430074
- 2. 云南建设基础设施投资股份有限公司,云南 昆明 650032
- 3. 中国地质大学(武汉)地质探测与评估教育部重点实验室,湖北 武汉 430074
- 4. 中国地质大学(武汉)工程学院,湖北 武汉 430074;中国地质大学(武汉)地质探测与评估教育部重点实验室,湖北 武汉 430074
- 折叠
摘要
为保障地下空间坍塌应急救援现场救援行动的安全开展和设备的正常运转,提高应急救援安全保障能力,首先通过构建地下空间坍塌应急救援现场环境安全风险评价指标体系,结合层次分析法(AHP)和理想点法(TOPSIS),对现场环境的安全状态进行评价;然后结合自回归移动平均模型(ARMA)和指数平滑模型,对未来一定时间内现场环境的安全状态进行预测;最后将该评价及预测模型应用于某煤矿巷道救援案例,并对救援工作提出建议和改进方案.研究结果可为地下空间应急救援现场的安全防护系统、通风设施配置等提供数据及决策依据.
Abstract
To ensure the safety of emergency rescue operations and the normal functioning of equipment in un-derground space collapse incidents,and to enhance the safety assurance capabilities of emergency rescue opera-tions,it is pivotal to first establish an environmental safety risk assessment indicator system for underground space collapse emergency rescue scenes.By integrating the analytic hierarchy process(AHP)and the technique for order preference by similarity to an ideal solution(TOPSIS)method,the safety status of the on-site environ-ment can be evaluated.Subsequently,combining the autoregressive moving average(ARMA)and exponential smoothing models,the future safety status of the site environment within a certain period can be forecasted.Ul-timately,the assessment and prediction models are applied to a rescue case in a coal mine drilling site,with recommendations and improvements proposed for the rescue operation.The research results can provide data and a decision-making basis for the safety protection system and the configuration of ventilation facilities on the emergency rescue sites of underground spaces.
关键词
地下空间坍塌/应急救援现场/安全风险评价/预测预警/层次分析法/理想点法/自回归移动平均Key words
underground space collapse/emergency rescue site/safety risk assessment/predictive early warning/analytic hierarchy process(AHP)/technique for order preference by similarity to an ideal solution(TOPSIS)/au-toregressive moving average(ARMA)引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据