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Error analysis for approximate structural life-cycle reliability and risk using machine learning methods
Structural Safety ( IF 5.7 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.strusafe.2020.102033
David Y. Yang , Dan M. Frangopol , Xu Han

Abstract Life-cycle management under uncertainty relies on the determination of life-cycle failure probability and risk. Usually, both life-cycle failure probability and risk are approximated by adding up the annual quantities during the service life of a structure. This approximation is based on the assumptions of (a) low annual failure probabilities and (b) independence of annual failure events. As a structure continuously deteriorates over time, both assumptions are likely to be violated. Therefore, it is crucial to investigate the error associated with this approximation so that the downstream management decisions such as inspection planning and maintenance optimization can be well grounded. In this paper, the error of approximate life-cycle failure probability and risk is analyzed considering different distribution types of structural capacity and demand as well as various deterioration mechanisms. Several machine learning methods are used for this purpose. Specifically, conditions under which the error is acceptable are identified in qualitative analysis using patient rule-induction method (PRIM). Quantitative error analysis based on polynomial and support vector regression is conducted to develop error correction techniques suitable for life-cycle analysis and management. The results of the error analysis are applied in the life-cycle analysis of a deteriorating structure to demonstrate the magnitude of approximation error and the importance of error correction.

中文翻译:

使用机器学习方法进行近似结构生命周期可靠性和风险的误差分析

摘要 不确定性下的生命周期管理依赖于生命周期失效概率和风险的确定。通常,生命周期失效概率和风险都是通过将结构使用寿命期间的年数量相加来估算的。该近似值基于 (a) 低年度故障概率和 (b) 年度故障事件的独立性的假设。随着时间的推移,结构不断恶化,这两个假设都可能被违反。因此,调查与此近似相关的误差至关重要,以便下游管理决策(例如检查计划和维护优化)能够很好地扎根。在本文中,考虑结构容量和需求的不同分布类型以及各种劣化机制,分析了近似全寿命周期失效概率和风险的误差。为此目的使用了几种机器学习方法。具体而言,在使用患者规则归纳法 (PRIM) 的定性分析中识别可接受误差的条件。进行基于多项式和支持向量回归的定量误差分析,以开发适用于生命周期分析和管理的误差校正技术。误差分析的结果应用于老化结构的生命周期分析,以证明近似误差的大小和误差校正的重要性。为此目的使用了几种机器学习方法。具体而言,在使用患者规则归纳法 (PRIM) 的定性分析中识别可接受误差的条件。进行基于多项式和支持向量回归的定量误差分析,以开发适用于生命周期分析和管理的误差校正技术。误差分析的结果应用于老化结构的生命周期分析,以证明近似误差的大小和误差校正的重要性。为此目的使用了几种机器学习方法。具体而言,在使用患者规则归纳法 (PRIM) 的定性分析中识别可接受误差的条件。进行基于多项式和支持向量回归的定量误差分析,以开发适用于生命周期分析和管理的误差校正技术。误差分析的结果应用于老化结构的生命周期分析,以证明近似误差的大小和误差校正的重要性。进行基于多项式和支持向量回归的定量误差分析,以开发适用于生命周期分析和管理的误差校正技术。误差分析的结果应用于老化结构的生命周期分析,以证明近似误差的大小和误差校正的重要性。进行基于多项式和支持向量回归的定量误差分析,以开发适用于生命周期分析和管理的误差校正技术。误差分析的结果应用于老化结构的生命周期分析,以证明近似误差的大小和误差校正的重要性。
更新日期:2021-03-01
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