Before implementing monitoring systems or reinforcements on a historic structure, it is essential to understand how crack patterns may have originated and how they affect the stability of the structure. Previous methods combining photogrammetry with physics‐based modeling have been successful in diagnosing the cause of crack formation. However, a limitation of existing methods is the manual comparison process to ascertain damage origins. This research outlines a method combining physics‐based modeling and data‐driven approaches to automate diagnostics for existing masonry structures. This method was shown to quantitatively reproduce the cause of damage for complex, 3D structures and was validated against a laboratory‐scale experimental masonry wall. The newly automated procedure increases throughput by 10⁵ times compared to our prior method, allowing for the testing of orders of magnitude more hypotheses than were previously possible. Although the approach is demonstrated here for settlement‐induced cracking, it has important implications for the broader topic of data‐driven masonry diagnostics.

中文翻译：

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基于混合物理的建模和数据驱动方法用于砌体结构诊断

在历史建筑上实施监视系统或加固之前，必须了解裂缝模式是如何产生的以及它们如何影响结构的稳定性。将摄影测量法与基于物理的建模相结合的先前方法已成功诊断出裂纹形成的原因。但是，现有方法的局限性在于手动比较过程以确定损坏的来源。这项研究概述了一种方法，该方法结合了基于物理学的建模和数据驱动的方法，可以对现有砌体结构进行自动诊断。结果表明，该方法可以定量地再现复杂的3D结构的损坏原因，并已通过实验室规模的实验砖石墙进行了验证。新的自动化程序将吞吐量提高了10 ⁵相较于我们先前的方法，它可以节省更多的时间，从而可以比以前更多地验证假设。尽管此处已证明该方法可解决沉降引起的裂缝，但它对数据驱动的砌体诊断这一更广泛的主题具有重要的意义。