The invention discloses a concrete dam damage identification method based on multi-
modal parameter
deep learning, and the method comprises the steps: extracting the actual measurement
modal parameters of a concrete dam through distributed
optical fiber vibration sensing data, building a high-precision dam body dynamic model based on
finite element simulation, randomly generating different material parameter combinations, obtaining the corresponding
modal parameters, and carrying out the recognition of the damage of the concrete dam. And the
simulation model is optimized and corrected, so that the
simulation model better conforms to the actual dynamic characteristics of the dam body. In the corrected
simulation model, randomly generating different damage positions, ranges and degrees, and constructing a training sample set covering multiple damage
modes; and establishing a one-dimensional
convolutional neural network, performing
deep learning model training by using the training sample, and substituting the actually measured distributed vibration data to realize intelligent identification of the damage position, range and degree of the concrete dam. According to the method, the accuracy and adaptability of concrete dam damage identification are improved, the method can be widely applied to long-term health monitoring of hydraulic structures, and efficient and accurate
technical support is provided for dam safety assessment.