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Principles and guidelines of deterioration modelling for water and waste water assets

机译:水和废水资产恶化模型的原理和准则

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Deterioration modelling is an important analytical component in risk-informed infrastructure asset management. Many asset managers find it very challenging because of its technicality, paucity of deterioration data and difficulty in model selection. Traditional approaches emphasised the mean deterioration trend and heeded too little the characterisation of uncertainty involved. This paper attempts to revert this trend and bring stochastic deterioration modelling back to focus. Following a systems approach, the author argues that deterioration modelling involves not only the data-driven process that asset managers have traditionally perceived, but also a system analysis that carries the empirical deterioration modelling at the level of performance data up to the level of the performance hierarchy at which decisions are made. In addition, deterioration modelling is an important and integral component of risk analysis, and therefore, the characterisation and quantification of aleatory uncertainty and epistemic uncertainty become an essential component of deterioration modelling. Moreover, deterioration data include not only hard data collected from inspection and condition assessment, but also soft data that can be gleaned from expert opinions, design manuals and professional judgements. Although mainly for water and waste water assets, the principles, guidelines and model selection flow chart are equally applicable to other infrastructure assets.
机译:恶化建模是风险告知基础架构资产管理中的重要分析组件。由于其技术性,劣化数据的匮乏以及模型选择的困难,许多资产管理者发现它非常具有挑战性。传统方法强调平均恶化趋势,而很少注意所涉及不确定性的特征。本文试图扭转这种趋势,使随机劣化模型重新成为关注的焦点。遵循系统方法,作者认为,劣化建模不仅涉及资产经理传统上认为的数据驱动过程,而且还涉及系统分析,该分析在绩效数据级别到绩效级别之间进行经验性劣化建模决策层次。此外,恶化建模是风险分析的重要组成部分,因此,不确定性和认知不确定性的表征和量化成为恶化建模的重要组成部分。此外,劣化数据不仅包括从检查和状态评估中收集的硬数据,还包括可以从专家意见,设计手册和专业判断中收集的软数据。尽管主要针对水和废水资产,但其原理,指南和模型选择流程图同样适用于其他基础设施资产。

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