Data-Driven Condition Assessment and Life Cycle Analysis Methods for Dynamically and Fatigue-Loaded Railway Infrastructure Components

IF 2.7 Q2 CONSTRUCTION & BUILDING TECHNOLOGY Infrastructures Pub Date : 2023-11-13 DOI:10.3390/infrastructures8110162
Maximilian Granzner, Alfred Strauss, Michael Reiterer, Maosen Cao, Drahomír Novák
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Abstract

Railway noise barrier constructions are subjected to high aerodynamic loads during the train passages, and the knowledge of their actual structural condition is relevant to assure safety for railway users and to create a basis for forecasting. This paper deals with deterministic and probabilistic approaches for the condition assessment and prediction of the remaining lifetime of railway noise barriers that are embedded in a safety concept that takes into account the damage consequence classes. These approaches are combined into a holistic assessment concept, in other words, a progressive four-stage model in which the information content increases with each model stage and thus successively increases the accuracy of the determined structural conditions at the time of observation and the forecast of the remaining service life of the structure. The analytical methods used in the first stage of the developed holistic framework are based on common static calculations used in engineering practice and, together with expert knowledge and large-scale fatigue test results of noise barrier constructions, form the basis for the subsequent stages. In the second stage of the data-driven condition assessment and life cycle analysis approach, linking routines are implemented that combine the condition assessments from the visual inspections with the additional information from temporary or permanent monitoring systems with the analytical methods. With the application of numerical finite element methods for the development of a digital twin of the noise barrier in the third stage and the probabilistic approaches in the fourth stage, a maximum determination accuracy of the noise barrier condition at the time of observation and prediction accuracy of the remaining service life is achieved. The data-driven condition assessment and life cycle analysis approach enables infrastructure operators to plan their future investments more economically regarding the maintenance, retrofitting, or new construction of railway noise barriers. Ultimately, the aim is to integrate the presented four-stage holistic assessment concept into the specific maintenance and repair planning of infrastructure operators for aerodynamically loaded railway noise barrier constructions.
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铁路基础设施动态疲劳构件状态评估与寿命周期分析方法
铁路隔声屏障结构在列车通行过程中承受较大的气动载荷,了解其实际结构状况对保证铁路用户的安全并为预测提供依据具有重要意义。本文研究了铁路噪声屏障状态评估和剩余寿命预测的确定性和概率方法,这些方法嵌入了考虑损伤后果等级的安全概念。这些方法被组合成一个整体评估概念,换句话说,一个渐进的四阶段模型,其中每个模型阶段的信息含量都在增加,从而在观察时确定结构状况和预测结构剩余使用寿命的准确性不断提高。开发的整体框架第一阶段使用的分析方法基于工程实践中常用的静力计算,并结合专家知识和大型噪声屏障结构疲劳试验结果,为后续阶段奠定了基础。在数据驱动的状态评估和生命周期分析方法的第二阶段,实施连接例程,将视觉检查的状态评估与临时或永久监测系统的附加信息与分析方法相结合。采用数值有限元法和概率法分别在第三阶段和第四阶段建立了隔声屏障的数字孪生模型,实现了观测时隔声屏障状态的最大确定精度和剩余使用寿命的预测精度。数据驱动的状态评估和生命周期分析方法使基础设施运营商能够更经济地规划未来的投资,包括铁路隔音屏障的维护、改造或新建。最终,目的是将提出的四阶段整体评估概念整合到空气动力负载铁路隔音屏障建设的基础设施运营商的具体维护和维修计划中。
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来源期刊
Infrastructures
Infrastructures Engineering-Building and Construction
CiteScore
5.20
自引率
7.70%
发文量
145
审稿时长
11 weeks
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