Evaluation of emergency management capability for construction safety accidents based on FRAM-BN

doi:10.16265/j.cnki.issn1003-3033.2026.02.1492

Abstract

Abstract:

To scientifically evaluate and enhance construction enterprises' emergency management capabilities for sudden safety incidents, a comprehensive model integrating qualitative analysis and qunatitative evaluation was proposed, addressing the limitations of traditional static assessment methods, which struggle to capture functional coupling and are easily influenced by subiective weighting. First, based on the theory of balanced emergency management throughout the entire process, a complete evaluation indicators system was established by dentifying 12 secondary indicators across four stages, including preparation and prevention, monitoring and early warning, response and disposal, and recovery and learning, and integrating the trajectory intersection theory and catastrophe theory. Subsequently, FRAM was employed to identify key functions and coupling paths among these indicators. An evaluation model was then developed by integrating an improved K-shell algorithm with BN. Finally, the model was applied to a practical engineering case and its effectiveness was validated through expert review and scenario simulations. The results demonstrate that the selected construction enterprise has a comprehensive emergency management capability of 81.682%, indicating its emergency mechanism can effectively respond to and handle various construction safety incidents. Among the capabilities, recovery and learning performs best (90.855%), while monitoring and early warning remains relatively weak (76.616%). Sensitivity analysis shows that professional team development (F₃) and on-site command decision-making (F₇) contributed most significantly to the overall capability.

Key words: functional resonance analysis method (FRAM), Bayesian network (BN), construction safety, emergency, emergency management capability, improved K-shell algorithm

CLC Number:

LI Zhijian, SHE Jianjun, LU Cong, GUO Zihao, ZHOU Yilun. Evaluation of emergency management capability for construction safety accidents based on FRAM-BN[J]. China Safety Science Journal, 2026, 36(2): 199-208.

Figures/Tables 18

Table 1

Fig.1

Table 2

Fig.2

Table 3

Aggregation variability of evaluation indicators for emergency management capability

指标	变异性聚合
F₁	F₅(O), F₁₂(O) $→$ F₁(O) $→$ F₂(I), F₃(I), F₄(I)
F₂	F₁(O), F₃(O), F₁₀(O), F₁₂(O) $→$ F₂(O) $→$ F₆(I), F₇(C) F₈(C), F₉(C), F₁₁(C)
F₃	F₁(O), F₄(O), F₁₂(O) $→$ F₃(O) $→$ F₂(R), F₅(I), F₆(R), F₇(R), F₈(R), F₉(R), F₁₀(R), F₁₁(R)
F₄	F₁(O), F₅(O) $→$ F₄(O) $→$ F₃(C), F₆(C), F₇(C)
F₅	F₃(O), F₆(O), F₁₀(O) $→$ F₅(O) $→$ F₁(I), F₄(C), F₈(P)
F₆	F₂(O), F₃(O), F₄(O), F₉(O), F₁₂(O) $→$ F₆(O) $→$ F₅(C), F₇(I)
F₇	F₂(O), F₃(O), F₄(O), F₆(O), F₁₂(O) $→$ F₇(O) $→$ F₈(I), F₉(I), F₁₀(I)
F₈	F₂(O), F₃(O), F₅(O), F₇(O), F₉(O), F₁₀(O), F₁₂(O) $→$ F₈(O) $→$ F₁₁(P),
F₉	F₂(O), F₃(O), F₇(O), F₁₀(O), F₁₂(O) $→$ F₉(O) $→$ F₆(C), F₈(R), F₁₁(I)
F₁₀	F₃(O), F₇(O) $→$ F₁₀(O) $→$ F₂(C), F₅(P), F₈(P), F₉(P),
F₁₁	F₂(O), F₃(O), F₈(O), F₉(O) $→$ F₁₁(O) $→$ F₁₂(I)
F₁₂	F₁₁(O) $→$ F₁₂(O) $→$ F₁(C), F₂(P), F₃(P), F₆(C), F₇(C), F₈(C), F₉(C)

Table 3

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 4

Table 5

Fig.8

Table 6

Forward analysis under different scenarios

场景	F₃	F₆	F₇	F₁₂	准备与预防能力/%	监测与预警能力/%	响应与处置能力/%	恢复与学习能力/%
基础	—	—	—	—	79.879	76.616	81.301	90.855
场景1	假	—	—	—	44.375 $(↓)$	76.616	81.301	90.855
场景2	假	假	—	—	44.375	33.183 $(↓)$	81.301	90.855
场景3	假	假	假	—	44.375	33.183	58.642 $(↓)$	90.855
场景4	假	假	假	假	44.375	33.183	58.642	32.792 $(↓)$

Table 6

Fig.9

Fig.10

Fig.11

Table 7

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目标层	一级指标	二级指标	二级指标说明
施工安全突发事件应急管理能力A	准备与预防能力B₁	应急组织建设F₁	构建职责明确的应急组织体系,完善岗位分工、协调机制与通信网络
		应急预案制定F₂	制定涵盖主要风险的应急预案,明确响应流程、职责分配与资源配置策略
		专业队伍建设F₃	建立常设的专业应急队伍,强化岗位培训与跨部门协同能力
		应急教育培训F₄	组织常态化培训与演练,提升员工应急意识、自救互救及逃生能力
	监测与预警能力B₂	危险源识别与监测F₅	实施风险排查与设备监控,构建动态化危险源识别与管理机制
	监测与预警能力B₂	风险预警机制F₆	建立预警系统,完善信号识别、信息传递与响应联动机制
	响应与处置能力B₃	现场指挥与决策F₇	明确指挥体系与响应流程,提升快速决策与调度能力
		应急救援处置F₈	规范应急救援程序,保障人员救护、装备支持与物资供应
		应急资源调配F₉	建立资源储备与调配机制,提升关键资源的可用性与调度效率
		应急信息传递F₁₀	构建多渠道、快速的信息发布与反馈机制,确保信息畅通与精准传达
	恢复与学习能力B₄	灾后恢复重建F₁₁	制定结构修复与秩序重建方案,保障灾后恢复的系统性与及时性
	恢复与学习能力B₄	经验总结改进F₁₂	建立事件复盘与知识更新机制,持续改进应急计划与管理体系

类型	变异性来源	描述
技术	内部	技术内部功能原理可能复杂且难以理解,或随着时间推移,技术设备可能因老化而功能退化
技术	外部	不当的设备维护或极端环境条件可能导致技术性能的变异性
人类	内部	个体心理和生理状态可能引起其功能表现的变异
人类	外部	个体行为可能受到技术、组织和社会环境影响,从而产生变异
组织	内部	组织内部的通信、结构和文化可能导致功能变异性
组织	外部	组织外部的因素,如用户需求变化和监管环境的严格程度,可能导致功能变异性
耦合	下游功能的变异性可能会直接受到上游功能变异的影响,这种现象在不同功能之间形成复杂的依赖关系网络

节点	先验概率
节点	真	假
F₁	0.794	0.206
F₂	0.790	0.210
F₃	0.804	0.196
F₄	0.814	0.186
F₅	0.816	0.184
F₆	0.732	0.268
F₇	0.800	0.200
F₈	0.770	0.230
F₉	0.800	0.200
F₁₀	0.920	0.080
F₁₁	0.842	0.158
F₁₂	0.951	0.049

节点	概率分布
B₁	0.140 4F₁+0.276F₂+0.441 6F₃+0.142F₄
B₂	0.406 6F₅+0.593 3F₆
B₃	0.283 2F₇+0.274 4F₈+0.265 3F₉+0.177F₁₀
B₄	0.389 4F₁₁+0.610 5F₁₂
A	0.337 4B₁+0.132 9B₂+0.374 3B₃+0.155 3B₄

专家编号	主要反馈意见	是否认同模型结果
E₁	模型结构清晰,逻辑合理, 结果基本符合实际	认同
E₂	结果合理,建议加强关键节点权重影响说明	基本认同
E₃	输出趋势与现场情况一致, 建议推广使用	认同
E₄	可进行情景模拟,提升了模型的应用潜力	认同
E₅	建议补充各阶段指标的操作性定义	基本认同
E₆	指标设置完整,风险预警机制评估较为精准	认同
E₇	模型层级科学,具备较高可信度	认同
E₈	结果基本合理,部分节点推理过程建议可视化展示	基本认同
E₉	模型简洁直观,得分结构清晰, 有一定实用性	认同
E₁₀	得分反映企业应急能力短板, 具有参考价值	认同
E₁₁	应用性强,建议在更多工程项目中试点验证	认同
E₁₂	评价结果基本吻合实际,术语建议进一步标准化	基本认同