Methodology and integration for dynamic safety assessment and early warning of drilling sites for emergency rescue after underground space collapse

Authors

XIE Zhongzhi, Faculty of Engineering, China University of Geosciences , Wuhan 430074, ChinaFollow
ZHANG Guohua, School of Sustainable Energy, China University of Geosciences , Wuhan 430074, China; Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaFollow
WEI Xingtong, China Railway 21st Bureau Group Fourth Engineering Co., Ltd., Lanzhou 730070, China
ZHANG Ning, CCTEG Xi’an Research Institute (Group) Co., Ltd., Xi’an 710077, China
WANG Xiaotian, Faculty of Engineering, China University of Geosciences , Wuhan 430074, China
NI Xiaoyang, Faculty of Engineering, China University of Geosciences , Wuhan 430074, China
ZHOU Zhengwen, Faculty of Engineering, China University of Geosciences , Wuhan 430074, China
WANG Xianze, Kunming Team of China Railway No.2 Engineering Group of National Tunnel Emergency Rescue, Kunming 650500, China

Abstract

Objective IGiven the lack of methods for dynamic safety assessment related to disasters in drilling site environments for emergency rescue after underground space collapse, this study proposed a multisource data-driven methodology for dynamic safety assessment and real-time early warning. Methods By comprehensively considering the disaster-inducing environment and disaster-causing factors at drilling sites for emergency rescue, this study established the safety assessment indices of the environment and their grading system. By integrating multi-source data on surrounding rock deformation, gas concentration, and drilling rig vibration, this study developed a safety assessment method through combination weighting achieved using the analytic hierarchy process (AHP) and entropy weight method (EWM). Moreover, this study constructed a multi-factor time series prediction model based on the autoregressive integrated moving average with an exogenous variable (ARIMAX) model and established a closed-loop mechanism consisting of monitoring, assessment, prediction, and early warning.Results and Conclusions The proposed safety assessment model enabled the dynamic optimization of weight allocation by integrating expert expertise and data distribution characteristics, significantly enhancing the stability of assessment results compared to individual subjective weighting models. This model yielded low prediction errors, with root mean square errors (RMSEs) of less than 0.12. A lightweight software platform was developed, significantly reducing warning response time by efficiently integrating the assessment and early warning methodology with visualization interfaces. Case validation demonstrated the high reliability of the proposed methodology. Overall, the proposed methodology overcomes the limitations of traditional static assessments, providing theoretical support and a technical tool for situational awareness and decision optimization in complex rescue environments.

Keywords

underground space collapse, drilling site for emergency rescue, combination weighting, dynamic safety assessment and early warning platform, ARIMAX model, multi-factor time series prediction

DOI

10.12363/issn.1001-1986.25.05.0309

Recommended Citation

XIE Zhongzhi, ZHANG Guohua, WEI Xingtong, et al. (2025) "Methodology and integration for dynamic safety assessment and early warning of drilling sites for emergency rescue after underground space collapse," Coal Geology & Exploration: Vol. 53: Iss. 8, Article 23.
DOI: 10.12363/issn.1001-1986.25.05.0309
Available at: https://cge.researchcommons.org/journal/vol53/iss8/23

Reference

[1] 石国领，李影平，穆树元. 旋挖钻机快速构建生命救援通道探析[J]. 路基工程，2023(1)：175−178.

SHI Guoling，LI Yingping，MU Shuyuan. Research on rapid construction of life rescue channel with rotary drilling rig[J]. Subgrade Engineering，2023(1)：175−178.

[2] 王献泽，慕开洪，肖敏，等. “一主一辅一备”救援方案高效处置隧道坍塌险情[J]. 路基工程，2023(3)：44−48.

WANG Xianze，MU Kaihong，XIAO Min，et al. Effectively dealing with the danger of tunnel collapse with the rescue plan of “one master，one auxiliary and one standby”[J]. Subgrade Engineering，2023(3)：44−48.

[3] 周以平. 浅谈大数据背景下曼么隧道坍塌与救援[J]. 中国战略新兴产业，2018(24)：136–137

[4] 许超，姚宁平，姜磊，等. 矿山救援大直径多级跟管钻进关键工艺参数[J]. 煤田地质与勘探，2025，53(4)：235−242.

XU Chao，YAO Ningping，JIANG Lei，et al. Critical technical parameters of large–diameter multi–stage casing drilling for mine rescue[J]. Coal Geology & Exploration，2025，53(4)：235−242.

[5] 邹祖杰，凡东，刘庆修，等. 矿山地面大直径钻孔救援提升装备研制[J]. 煤炭科学技术，2017，45(12)：160−165.

ZOU Zujie，FAN Dong，LIU Qingxiu，et al. Research and development on rescue lifting equipment of large diameter borehole at mine ground[J]. Coal Science and Technology，2017，45(12)：160−165.

[6] 麻坦，诸葛雷，赵伟东. 矿山地面救援应急响应与大直径钻孔关键技术[J]. 煤田地质与勘探，2024，52(9)：192−202.

MA Tan，ZHUGE Lei，ZHAO Weidong. Emergency response and critical technologies for large–diameter boreholes in mine rescue through surface drilling[J]. Coal Geology & Exploration，2024，52(9)：192−202.

[7] 袁亮. 我国煤矿安全发展战略研究[J]. 中国煤炭，2021，47(6)：1−6.

YUAN Liang. Study on the development strategy of coal mine safety in China[J]. China Coal，2021，47(6)：1−6.

[8] 谢众智，李升连，郑惜雯，等. 地下空间坍塌应急救援现场环境安全风险评价及预测[J]. 安全与环境工程，2024，31(5)：51−61.

XIE Zhongzhi，LI Shenglian，ZHENG Xiwen，et al. Assessment and prediction of environmental safety risk at the site of emergency rescue for underground space collapse[J]. Safety and Environmental Engineering，2024，31(5)：51−61.

[9] 袁亮. 煤矿典型动力灾害风险判识及监控预警技术研究进展[J]. 煤炭学报，2020，45(5)：1557−1566.

YUAN Liang. Research progress on risk identification，assessment，monitoring and early warning technologies of typical dynamic hazards in coal mines[J]. Journal of China Coal Society，2020，45(5)：1557−1566.

[10] 蒋贵航. 煤矿采掘过程瓦斯动态特征及瓦斯灾害隐患识别[D]. 徐州：中国矿业大学，2023.

JIANG Guihang. Gas dynamic characteristics and gas disaster potential hazard identification in coal mining process[D]. Xuzhou：China University of Mining and Technology，2023.

[11] 赵欢腾. 建筑施工安全风险分级管控体系构建研究[D]. 北京：首都经济贸易大学，2020.

ZHAO Huanteng. Research on the construction of safety risk grading management and control system for building constructions[D]. Beijing：Capital University of Economics and Business，2020.

[12] 杨豫龙，曹卫华，甘超，等. 深部地质钻进过程地层特征参数建模与安全预警研究进展[J]. 煤田地质与勘探，2024，52(10)：195−206.

YANG Yulong，CAO Weihua，GAN Chao，et al. Advances in research on stratigraphic characteristic parameter modeling and safety early warning for deep geological drilling processes[J]. Coal Geology & Exploration，2024，52(10)：195−206.

[13] 宋丙剑，陶昕益，崔堃鹏，等. 山东栖霞笏山金矿“1·10”重大爆炸事故救援战例研究[J]. 城市与减灾，2023(3)：1–6

[14] MALLADI R K，DHEERIYA P L. Time series analysis of Cryptocurrency returns and volatilities[J]. Journal of Economics and Finance，2021，45(1)：75−94.

[15] 李冰. 基于深度学习和多源信息融合的煤与瓦斯突出预警方法研究[D]. 徐州：中国矿业大学，2021.

LI Bing. Research on early warning method of coal and gas outburst based on deep learning and multi–source information fusion[D]. Xuzhou：China University of Mining and Technology，2021.

[16] 刘辉，陈斯涤，朱晓峻，等. 基于D–InSAR技术的煤矿工业广场动态沉降特征研究[J]. 煤田地质与勘探，2023，51(5)：99−112.

LIU Hui，CHEN Sidi，ZHU Xiaojun，et al. Research on dynamic subsidence characteristics of coal mine industrial square based on D–InSAR technology[J]. Coal Geology & Exploration，2023，51(5)：99−112.

[17] WANG Jiaqi，HUANG Yanli，ZHAI Wenrui，et al. Research on coal mine safety management based on digital twin[J]. Heliyon，2023，9(3)：e13608.

[18] ZHANG Guohua，JIAO Yuyong，CHEN Libiao，et al. Analytical model for assessing collapse risk during mountain tunnel construction[J]. Canadian Geotechnical Journal，2016，53(2)：326−342.

[19] 吴书一. 云南省屏边县滑坡地质灾害孕灾环境及致灾因子分析[D]. 昆明：云南大学，2021.

WU Shuyi. Disaster–generating environment and disaster–causing factors of landslide geological disaster in Pingbian County，Yunnan Province[D]. Kunming：Yunnan University，2021.

[20] 《工程地质手册》编委会. 工程地质手册(第5版)[M]. 北京：中国建筑工业出版社，2018.

[21] 国家煤矿安全监察局. 煤矿防治水细则[Z]. 煤安监调查〔2018〕14号，2018-06-01.

[22] ZHONG Chenghao，YANG Qingchun，LIANG Ji，et al. Fuzzy comprehensive evaluation with AHP and entropy methods and health risk assessment of groundwater in Yinchuan Basin，Northwest China[J]. Environmental Research，2022，204：111956.

[23] SONG Yan，ZHANG Xinmin，LIU Shaobo. Comprehensive evaluation of geological conditions for coalbed methane development[M]//Coalbed methane in China. Singapore：Springer Singapore，2021：329–362.

[24] 王莉芳. 基于组合赋权与灰色改进TOPSIS方法的受灾点应急物质需求紧迫性分级评价[J]. 安全与环境工程，2017，24(6)：94−100.

WANG Lifang. Classification evaluation of urgency of disaster point emergency material demand based on combination weighting and improved TOPSIS method[J]. Safety and Environmental Engineering，2017，24(6)：94−100.

[25] 刘义庆，卢厚清. 基于组合赋权与灰色模糊综合评价的特战分队作战能力评估[J/OL]. 指挥控制与仿真，2025：1–6 [2025-04-15]. https://kns.cnki.net/KCMS/detail/detail.aspx?filename=QBZH20250414003&dbname=CJFD&dbcode=CJFQ.

LIU Yiqing，LU Houqing. Evaluation of the combat capability of special operations teams based on combined weighting and grey fuzzy comprehensive evaluation[J/OL]. Command Control & Simulation，2025：1–6 [2025-04-15]. https://kns.cnki.net/KCMS/detail/detail.aspx?filename=QBZH20250414003&dbname=CJFD&dbcode=CJFQ.

[26] LEE S. Determination of priority weights under multiattribute decision–making situations：AHP versus fuzzy AHP[J]. Journal of Construction Engineering and Management，2015，141(2)：05014015.

[27] 丁百川. 我国煤矿主要灾害事故特点及防治对策[J]. 煤炭科学技术，2017，45(5)：109−114.

DING Baichuan. Features and prevention countermeasures of major disasters occurred in China coal mine[J]. Coal Science and Technology，2017，45(5)：109−114.

[28] 曹文霞. 基于组合赋权法的交口县地质灾害易发性评价研究[D]. 太原：太原理工大学，2021.

CAO Wenxia. Study on geohazard susceptibility evaluation based on combination weighting method in Jiaokou County[D]. Taiyuan：Taiyuan University of Technology，2021.

[29] 李刚，李建平，孙晓蕾，等. 主客观权重的组合方式及其合理性研究[J]. 管理评论，2017，29(12)：17−26.

LI Gang，LI Jianping，SUN Xiaolei，et al. Research on a combined method of subjective–objective weighing and the its rationality[J]. Management Review，2017，29(12)：17−26.

[30] 周章宁. 基于深度学习的石油钻机微电网故障预测与维护研究[D]. 成都：电子科技大学，2021.

ZHOU Zhangning. Research on fault prediction and maintenance of oil drilling rig microgrid based on deep learning[D]. Chengdu：University of Electronic Science and Technology of China，2021.

[31] 国家安全生产监督管理总局. 煤矿生产安全事故报告和调查处理规定[Z]. 安监总政法〔2008〕212号，2008.

[32] 煤炭工业部. 煤炭工业企业职工伤亡事故报告和统计规定[Z]. 煤安字〔1995〕50号，1995.

[33] 白贤栖. 华砚矿井冲击矿压多源信息融合的时空监测预警研究[D]. 徐州：中国矿业大学，2021.

BAI Xianqi. Study on spatiotemporal monitoring and early warning model of multi–source information fusion for rock burst in Huayan mine[D]. Xuzhou：China University of Mining and Technology，2021.

[34] CHEN Shikuan，DU Wenli，WANG Bing，et al. Dynamic prediction of multisensor gas concentration in semi–closed spaces：A unified spatiotemporal inter–dependencies approach[J]. Journal of Loss Prevention in the Process Industries，2025，94：105569.

[35] WEN Hu，YAN Li，JIN Yongfei，et al. Coalbed methane concentration prediction and early–warning in fully mechanized mining face based on deep learning[J]. Energy，2023，264：126208.

[36] DICKEY D，FULLER W A. Likelihood ratio statistics for autoregressive time series with a unit root[J]. Econometrica，1981，49(4)：1057−1072.

[37] BOX G E P，JENKINS G M，REINSEL G C. Time series analysis：Forecasting and control[M]. Hoboken：Wiley，1746.

[38] BROCKWELL P J，DAVIS R A. Introduction to time series and forecasting[M]. Cham：Springer International Publishing，2016.

[39] CHAI T，DRAXLER R R. Root mean square error (RMSE) or mean absolute error (MAE)[J]. Geoscientific Model Development Discussions，2014，7：1525−1534.

[40] 青海省市场监督管理局. 公路硫化氢隧道设计与施工技术规范：DB63/T 2384—2024[S]. 2024.