Wooden Framed House Structural Health Monitoring by System Identification and Damage Detection under Dynamic Motion with Artificial Intelligence Sensor using a Model of House including Braces
Wooden Framed House Structural Health Monitoring by System Identification and Damage Detection under Dynamic Motion with Artificial Intelligence Sensor using a Model of House including Braces
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使用人工智能传感器，使用包括支架的房屋模型，通过动态运动下的系统识别和损坏检测来监测木框架房屋结构健康
DOI：
10.1109/civemsa.2018.8439967
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Takayuki Kawahara
中科院分区：
文献类型：
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作者：
Ryo Tanida;Ryo Oiwa;Takumi Ito;Takayuki Kawahara
我们正试图通过机器学习来区分木材的受损区域。去年，进行了一项确定一块木材损坏位置的实验。这次，进行了房屋支撑损伤位置识别的实验。只有一个支撑被从28个支撑位置的房屋模型中删除，并且假设损坏位置在那里。将振动施加到房屋模型上，并利用压电传感器检测传递的振动波形。使用神经网络分析该振动波形。在固定了隐藏层中的神经元数量后，房屋两侧的分类成功了。在此基础上，采用3层和4层神经网络对房屋整体进行分类。通过改变隐层神经元的数目可以提高分类率。结果，整个房屋的损坏位置的分类率为90.69%。此外，在4层神经网络中的分类率高于3层神经网络。
We are trying to discriminate damage areas of wood by machine learning. Last year, an experiment to identify the damage position of a piece of timber was conducted. This time, an experiment on the identification of the damage position of the house brace was performed. Only one brace was removed from the model of the house with 28 brace positions, and the damage position was assumed to be there. Vibration was applied to the model of the house, and the transferred vibration waveform was detected with a piezoelectric sensor. This vibration waveform was analyzed using a neural network. The classification on each side of the house succeeded after fixing the number of neurons in the hidden layer. After that, classification on the whole side of the house with 3-layer and 4-layer neural networks was conducted. The classification rate could be improved by changing the number of neurons in the hidden layer. As a result, the classification rate of the damage position of the entire house is 90.69%. Also, the classification rate is higher in the 4-layer neural network than in the 3-layer one.