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Hybrid physics-based modeling and data-driven method for diagnostics of masonry structures

机译:基于混合物理学的建模和数据驱动方法,用于砌体结构的诊断

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摘要

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 10(5) 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.
机译:在实施监测系统或加强对历史结构的增援之前,必须了解裂缝模式可能产生的裂缝模式以及如何影响结构的稳定性。以前的方法将摄影测量与基于物理学的建模相结合,已经成功地诊断了裂缝形成的原因。但是,现有方法的限制是指比较过程,以确定损坏的起源。本研究概述了组合物理学建模和数据驱动方法的方法,以自动化现有砌体结构的诊断。该方法被证明是定量再现复杂3D结构损坏的原因,并针对实验室规模的实验砌体墙进行验证。与我们的先前方法相比,新自动化程序将吞吐量提高10(5)次,允许测试数量级比以前比以前更能的比例。虽然这里的方法展示了沉降诱导的开裂,但对于更广泛的数据驱动的砌体诊断主题具有重要意义。

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