Department of Architecture and Civil Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Aichi, Japan.
Sensors (Basel). 2022 Aug 25;22(17):6426. doi: 10.3390/s22176426.
It is necessary to detect the structural damage condition of essential buildings immediately after an earthquake to identify safe structures, evacuate, or resume crucial activities. For this reason, a CNN methodology proposed to detect the structural damage condition of a building is here improved and validated for two currently instrumented essential buildings (Tahara City Hall and Toyohashi Fire Station). Three-dimensional frames instead of lumped mass models are used for the buildings. Besides this, a methodology to select records is introduced to reduce the variability of the structural responses. The maximum inter-storey drift and absolute acceleration of each storey are used as damage indicators. The accuracy is evaluated by the usability of the building, total damage condition, storey damage condition, and total comparison of the damage indicators. Finally, the maximum accuracy and R of the responses are obtained as follows: for the Tahara City Hall building, 90.0% and 0.825, respectively; for the Toyohashi Fire Station building, 100% and 0.909, respectively.
震后有必要立即检测重要建筑物的结构损坏情况，以识别安全结构，进行疏散或恢复关键活动。为此，本文对一种用于检测建筑物结构损坏情况的 CNN 方法进行了改进，并对两座现有仪器化的重要建筑物（田原市厅和豊桥消防局）进行了验证。该方法使用三维框架而不是集中质量模型来表示建筑物。此外，还引入了一种记录选择方法，以减少结构响应的可变性。最大层间位移和每层的绝对加速度被用作损伤指标。通过建筑物的可用性、整体损伤状况、楼层损伤状况以及损伤指标的总体比较来评估准确性。最后，得到了响应的最大精度和 R，对于田原市厅建筑，分别为 90.0%和 0.825；对于豊桥消防局建筑，分别为 100%和 0.909。
