Masonry heritage structures are often affected by slow irreversible deterioration mechanisms that can jeopardise structural stability in the foreseeable future. Static structural health monitoring (SHM), aimed at the continuous measurement of key slow‐varying parameters, has the potential to identify such mechanisms at a very early stage. This can greatly facilitate the implementation of adequate preventive and remedial measures, which can be critical to ensure that such structures are preserved for generations to come. However, because monitored parameters usually experience reversible seasonal variations of the same order of magnitude as changes caused by active mechanisms, identification of the latter is often a difficult task. This paper presents a fully integrated automated data analysis procedure for complete static SHM systems utilising dynamic linear regression models to filter out the effects caused by environmental variations. The method does not only produce estimated evolution rates but also classifies monitored responses in predefined evolution states. The procedure has successfully been used to identify vulnerable areas in two important medieval heritage structures in Spain, namely, the cathedral of Mallorca and the church of the monastery of Sant Cugat.

中文翻译：

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自动化数据分析，用于砌体遗产结构的静态结构健康监测

砖石结构经常受到不可逆的缓慢变质机制的影响，这种变质机制可能会在可预见的未来危及结构稳定性。旨在连续测量关键缓慢变化参数的静态结构健康监测（SHM）有可能在很早的阶段就识别出这种机制。这可以极大地促进适当预防和补救措施的实施，这对于确保为子孙后代保留这样的结构至关重要。但是，由于受监视的参数通常经历与活动机制引起的变化相同数量级的可逆季节性变化，因此识别后者通常是一项艰巨的任务。本文提出了一个完整的自动化数据分析程序，用于利用动态线性回归模型过滤掉由环境变化引起的影响的完整静态SHM系统。该方法不仅产生估计的进化速率，而且将预定义的进化状态下的监视响应分类。该程序已成功用于识别西班牙两个重要的中世纪遗产结构中的脆弱地区，即马略卡大教堂和圣库加特修道院教堂。