Construction accident occurrences are essentially rare, stochastic, and dynamic. This study proposes a method for accident prediction that fully captures these natures based on historical data and prior knowledge. The method utilizes the relatively high occurrence frequency of precursor events and the dependency between precursors and accidents. The modeling approach consists of three steps: (1) characterize the stochastic occurrences of precursor events over time based on precursor data; (2) estimate the failure rate of the Poisson model which is assumed to be a prior distribution of accident occurrences; and (3) elicit the expert knowledge about the stochastic dependency between near miss occurrences and accident occurrences. A copula-based Markov model is used to develop the time series model of precursors while a copula-based protocol is proposed to aid expert judgment elicitation and quantification. The probability of accident occurrence is then dynamically updated according to the observed historical near miss numbers. The proposed method is applied to a metro construction project. A five-year long near miss data were collected and used as accident precursor data, while three experts were invited to provide relevant information. The developed accident model is used to predict the accident-prone periods, which are consistent with the months that the observed near miss occurrence frequency deviates significantly from normality. Thus, the model can be used to support the planning of necessary safety improvement programs before the accident risk increased.

施工事故的发生本质上是罕见的，随机的和动态的。这项研究提出了一种用于事故预测的方法，该方法可以根据历史数据和先验知识充分捕获这些性质。该方法利用了较高的前兆事件发生频率以及前兆与事故之间的依赖性。建模方法包括三个步骤：（1）根据前体数据表征随时间变化的前体事件的随机发生；（2）估计泊松模型的失效率，该模型被假定为事故发生的先验分布；（3）激发出专家的知识，了解临近事故发生与事故发生之间的随机相关性。使用基于copula的马尔可夫模型来开发前体的时间序列模型，同时提出基于copula的协议以帮助专家判断和量化。然后，根据观察到的历史未命中次数动态更新事故发生的可能性。所提出的方法被应用于地铁建设项目。收集了一个为期五年的近乎未命中的数据，并将其用作事故先兆数据，同时邀请了三名专家提供相关信息。所开发的事故模型用于预测容易发生事故的时期，这与观察到的接近未命中发生频率显着偏离正常值的月份一致。因此，该模型可用于支持在事故风险增加之前制定必要的安全改进计划。