Structural damage assessment method based on distributed vibration data and convolutional self-encoding deep learning

Abstract

The invention discloses a structural damage assessment method based on distributed vibration data and convolutional self-encoding deep learning, and the method comprises the following steps: S1, selecting acceleration response monitoring points, and arranging an acceleration sensor at each monitoring point; s2, acquiring monitoring data of n acceleration sensors in a normal use state of the structure, and performing data preprocessing to form a data set for deep learning network training; s3, constructing a convolutional self-encoding deep learning network suitable for the data set in the step S2; s4, preprocessing massive structure monitoring data in a normal use state according to the step S2, and inputting the preprocessed data into a convolutional auto-encoder for training to obtain a deep learning network file; and S5, evaluating a structural damage state through a data reconstruction correlation function. According to the method, the data does not need to be pre-classified, the structure damage state is quantified in real time by using the real-time vibration monitoring data, and the score is given.

Description

technical field [0001] The invention belongs to the field of structural health monitoring vibration data damage assessment, in particular to a structural damage assessment method based on distributed vibration data and convolutional self-encoding deep learning. Background technique [0002] The structural safety and damage assessment of buildings and bridges has always been a problem that has attracted much attention in the field of structural safety and disaster prevention and mitigation engineering at home and abroad. Due to the dynamic extreme effects of vehicles, pedestrians, cargo loads, typhoons, and earthquakes during the service of bridges or buildings / structures, various macroscopic or microscopic damages will inevitably occur on the interior and surface of the structure, resulting in internal materials and structures. Deterioration or changes in stress distribution, etc. If these invisible damages (such as cracks, fatigue fractures, etc.) and the internal changes ...

AI Technical Summary

Problems solved by technology

Although this supervised learning method can classify the type and degree of structural damage, this method is based on the known or predicted damage state of the vibration data, and the monitoring vibration of the unknown damage state data, this approach often exhibits weaker damage identification and assessment capabilities

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