Pipeline failure due to various threats may contribute to significant adverse impact on human safety, environment, and economy. In order to mitigate the severity of pipeline failure consequences, maintaining the integrity of the vast and aging pipeline structure is crucial. The main concern in offshore risk analysis is the unpredictable and uncertain pipeline conditions. In probability theory, Bayesian network is known as a powerful tool for knowledge presentation and condition inference under uncertainty. Probability analysis of pipeline damages is necessary to prevent unwanted incidents that may cause catastrophic accidents. In this paper, a Bayesian network model was developed to identify and analyze the probability of subsea pipeline condition subjected to corrosion, third party, operational, and material damages. Statistical data and experts' knowledge were integrated in addressing data limitation. Attaining the proposed network model, diagnostic analysis, mutual analysis, and sensitivity analysis were performed to validate and provide a substantial amount of confidence on the outcomes of the proposed model. The analyses have demonstrated that estimation of the developed model is reliable. The outcome obtained can be used to assist the decision maker to prepare preventive safety measures and allocate proper resources to significantly minimize the occurrence probability of the risk factors.

中文翻译：

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海底管道损坏概率分析

由于各种威胁导致的管道故障可能会对人类安全、环境和经济产生重大不利影响。为了减轻管道故障后果的严重性，保持庞大且老化的管道结构的完整性至关重要。海上风险分析的主要关注点是不可预测和不确定的管道条件。在概率论中，贝叶斯网络被称为不确定条件下知识表示和条件推理的有力工具。管道损坏的概率分析对于防止可能导致灾难性事故的意外事件是必要的。在本文中，开发了贝叶斯网络模型来识别和分析海底管道状况遭受腐蚀、第三方、操作和材料损坏的可能性。统计数据和专家知识被整合以解决数据限制问题。获得建议的网络模型后，进行了诊断分析、相互分析和敏感性分析，以验证并提供对建议模型结果的大量置信度。分析表明，所开发模型的估计是可靠的。所获得的结果可用于协助决策者准备预防性安全措施并分配适当的资源，以显着降低风险因素的发生概率。进行了敏感性分析以验证并提供对拟议模型结果的大量置信度。分析表明，所开发模型的估计是可靠的。获得的结果可用于协助决策者准备预防性安全措施并分配适当的资源，以显着降低风险因素的发生概率。进行了敏感性分析以验证并提供对所提议模型结果的大量置信度。分析表明，所开发模型的估计是可靠的。获得的结果可用于协助决策者准备预防性安全措施并分配适当的资源，以显着降低风险因素的发生概率。