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A platform for research: civil engineering, architecture and urbanism
Pile damage identification method and device based on convolutional neural network, and medium
The invention discloses a pile damage identification method and device based on a convolutional neural network and a medium, and the method comprises the steps: building a plurality of models for a to-be-detected pile structure according to the condition attributes of a to-be-detected pile, solving the models, and generating a speed time history curve of a preset position point on the to-be-detected pile, and generating a speed-time history recursion plot based on the speed-time history curve, inputting the speed-time history recursion plot into the neural network model for detection, and outputting a pile damage parameter evaluation result. The pile structure of the to-be-detected pile is modeled according to the condition attributes of the to-be-detected pile, and the overall condition of the to-be-detected pile can be analyzed and known more comprehensively. And the neural network model has extremely high non-linear large-scale parameter parallel analysis and processing capability, and can better process the complex damage identification problem in the pile-soil structure and improve the accuracy of pile damage identification in combination with the convolutional neural network processing speed time-history recurrence plot.
本发明公开了一种基于卷积神经网络的桩损伤识别方法、设备及介质，其中方法包括:根据待检测桩的条件属性对待检测的桩结构建立多个模型，对模型进行求解生成待检测桩上预设位置点的速度时程曲线，基于速度时程曲线生成速度时程递归图并输入神经网络模型进行检测，输出桩损伤参数评估结果。根据待检测桩条件属性对待检测桩的桩结构进行建模，可以对待检测桩的整体状况有更为全面的分析了解。且神经网络模型，具有极强的非线性大规模参数并行分析处理能力，结合卷积神经网络处理速度时程递归图，能更好地处理桩土结构中复杂的损伤识别问题，提高桩损伤识别的准确性。
Pile damage identification method and device based on convolutional neural network, and medium
The invention discloses a pile damage identification method and device based on a convolutional neural network and a medium, and the method comprises the steps: building a plurality of models for a to-be-detected pile structure according to the condition attributes of a to-be-detected pile, solving the models, and generating a speed time history curve of a preset position point on the to-be-detected pile, and generating a speed-time history recursion plot based on the speed-time history curve, inputting the speed-time history recursion plot into the neural network model for detection, and outputting a pile damage parameter evaluation result. The pile structure of the to-be-detected pile is modeled according to the condition attributes of the to-be-detected pile, and the overall condition of the to-be-detected pile can be analyzed and known more comprehensively. And the neural network model has extremely high non-linear large-scale parameter parallel analysis and processing capability, and can better process the complex damage identification problem in the pile-soil structure and improve the accuracy of pile damage identification in combination with the convolutional neural network processing speed time-history recurrence plot.
本发明公开了一种基于卷积神经网络的桩损伤识别方法、设备及介质，其中方法包括:根据待检测桩的条件属性对待检测的桩结构建立多个模型，对模型进行求解生成待检测桩上预设位置点的速度时程曲线，基于速度时程曲线生成速度时程递归图并输入神经网络模型进行检测，输出桩损伤参数评估结果。根据待检测桩条件属性对待检测桩的桩结构进行建模，可以对待检测桩的整体状况有更为全面的分析了解。且神经网络模型，具有极强的非线性大规模参数并行分析处理能力，结合卷积神经网络处理速度时程递归图，能更好地处理桩土结构中复杂的损伤识别问题，提高桩损伤识别的准确性。
Pile damage identification method and device based on convolutional neural network, and medium
一种基于卷积神经网络的桩损伤识别方法、设备及介质
FU MINGHUI (author) / LIN MEIHONG (author)
2022-04-15
Patent
Electronic Resource
Chinese
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