Modelling the interactions between defect mechanisms on metal bridges
Modelling the interactions between defect mechanisms on metal bridges
复制标题
模拟金属桥缺陷机制之间的相互作用
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. Hamer
中科院分区：
文献类型：
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作者：
G. Calvert;L. Neves;J. Andrews;M. Hamer
：桥梁资产管理公司拥有fiNite资源，可用于将结构故障风险降至最低，并确保桥梁维护到合适的安全门槛。任何维护策略都必须有效地利用资源，并提供最佳的全生命周期成本。准确的寿命周期费用的计算取决于是否有准确的劣化模型来预测未来的资产状况，以及是否有足够的fi性能指标来评估决策模型中的目标维修策略。典型地，预测桥梁劣化模型输出单个状态指示器随时间的概率分布。然而，桥梁的劣化是一个不同的物理过程，具有不同的退化机制。例如，金属桥梁构件可能遭受涂层或油漆的腐蚀和损失，以及结构构件的失效模式，如屈曲、永久变形、撕裂和断裂。本文提出了一种基于多缺陷的桥梁资产管理建模方法。利用动态贝叶斯网络(DBN)实现了多缺陷劣化模型。该模型可以预测多个桥梁缺陷的同时发展。决策模型可以利用多个状态指标来应用最合适的维护干预，以在状态中提供提升。本研究中使用的行业数据采用纵向研究的形式，这是许多交通机构常见的。这种数据的使用限制了劣化模型使用假设故障率恒定的无记忆分布。然而，经验表明，桥梁的劣化是一个非持续的过程。通过将桥梁劣化建模为相互作用的缺陷的组合，即使模型本身是使用指数分布进行参数化的，也可以对非恒定行为进行建模。本文介绍了利用英国13,569座金属铁路桥的数据对模型进行校核的案例研究。
: Bridge asset managers have finite resources at their disposal to minimise the risk of structural failure and ensure bridges are maintained to a suitable safety threshold. Any maintenance strategy must be efficient in its use of resources and deliver an optimal Whole Life Cycle Cost (WLCC). The calculation of an accurate WLCC is contingent on having an accurate deterioration model to predict future asset condition and sufficient performance indicators to appraise targeted maintenance strategies in a decision model. Typically predictive bridge deterioration models output a probability distribution for a single condition indicator over time. However, bridge deterioration is a diverse physical process with different degradation mechanisms. For example, metallic bridge elements may undergo corrosion and loss of coating or paintwork, as well as suffer from structural component failure modes such as buckling, permanent distortion, tearing and fracture. This paper presents a multi-defect approach to modelling bridge asset management. A multi-defect deterioration model is implemented using a Dynamic Bayesian Network (DBN). The model can predict the simultaneous progression of multiple bridge defects. The decision model can utilise the multiple condition indicators to apply the most appropriate maintenance intervention to provide an uplift in condition. The industrial data used in this research takes the format of a longitudinal study, which is common for many transportation agencies. The use of such data restricts the deterioration model to use a memoryless distribution which assumes a constant failure rate. However, bridge deterioration has been empirically shown to be a non-constant process. By modelling bridge deterioration as a combination of interacting defects, non-constant behaviour can be modelled, even when the model itself is parametrised using an exponential distribution. The paper presents a case study of the model calibrated using data from 13,569 metallic railway bridge girders in the United Kingdom.