Hybrid physics-based modeling and data-driven method for diagnostics of masonry structures
Hybrid physics-based modeling and data-driven method for diagnostics of masonry structures
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DOI：
10.1111/mice.12548
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发表时间：
2020-05-01
影响因子：
9.6
通讯作者：
Glisic, Branko
中科院分区：
文献类型：
--
作者：
Napolitano, Rebecca;Glisic, Branko
在对历史建筑实施监测系统或加固之前，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。以前的方法相结合的摄影测量与基于物理的建模已经成功地诊断裂纹形成的原因。然而，现有方法的一个局限性是确定损坏起源的人工比较过程。这项研究概述了一种方法，结合基于物理的建模和数据驱动的方法来自动诊断现有的砌体结构。这种方法被证明是定量再现复杂的，三维结构的损坏的原因，并验证了对实验室规模的实验石墙。与我们以前的方法相比，新的自动化程序将吞吐量提高了10（5）倍，允许测试比以前可能的数量级更多的假设。虽然这种方法在这里演示沉降引起的开裂，它具有重要的意义，更广泛的主题数据驱动的砌体诊断。
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.