Research On Damage Identification Method Of High Fill Channel Cement Slope Based On Multi-feature Fusion

High fill channels refer to fill channels with a height of more than 6m.The high fill canal is widely distributed in the middle route of the South-to-North Water Transfer Project.Due to the high filling height,wide distribution range and complicated engineering geological conditions of the Middle Route of the South-to-North Water Transfer Project,the lining panel is cracked or the slope of the channel slope is damaged.Although the effect of leakage is small,the leakage will still be large due to the long line.Therefore,the seepage monitoring technology of the high fill channel section is one of the key technologies for the safety technology of the middle line project of the South-to-North Water Transfer Project.In order to ensure the safe operation of the channels after water supply and to ensure the safety of people's lives and property,it is necessary to carry out special leakage monitoring design for the high fill channel sections.At home and abroad,the research on the detection of hidden dangers of dam leakage mainly includes geological radar method,resistivity method,ultrasonic wave,image processing and other detection methods.The algorithm models for leak detection include channel seepage model,multi-source data fusion model,and multi-objective inverse modeling.However,there is currently no method for leak detection of high fill channels.With the continuous improvement of image processing technology,the detection of channel slope image damage has become a research content for finding channel leakage.This paper proposes a multi-feature fusion based high-fill channel cement slope damage monitoring model,and performs various feature extraction and fusion processing on the slope image to determine whether the high fill channel cement slope is normal,crack,fracture and hole.Four situations,in order to prevent and find out whether the high fill channel has leakage effect,play a vigilant role.The main research work of this paper is as follows:1.Design the leakage monitoring model of the high fill channel section of the South-water diversion centerline project,and extract the feature extraction of the cement surface image in the spatial and frequency domains respectively,where the spatial domain includes color histogram color feature extraction and Hu invariant moment.The shape feature extraction and the texture feature extraction of the gray level co-occurrence matrix;respectively analyze the various features,improve the extraction method,and finally use SVM to classify the three spatial domain features to obtain various evaluation results.2.The frequency domain feature extraction model of high fill channel based on Gabor wavelet is established.The multi-scale multi-directional selection characteristics of Gabor wavelet are studied.The features extracted from each scale and direction are analyzed to find the best scale and direction parameter group.The SVM is classified to obtain the recognition result,and the optimal scale and direction parameter set are verified by the experimental results,so that the classification effect obtained under the same performance of the classifier is better.3.A high-fill channel channel leakage monitoring model based on supervised feature selection is proposed.Algorithm optimization and method fusion study on convolutional neural networks,convolution kernel size and channel number and fully connected layer in convolutional and pooled layers The activation function selection and other parameters are analyzed and designed;and the designed convolutional neural network is applied to the feature extraction and classification identification of the high fill channel image to determine whether there is a leakage hazard in the high fill channel,which will be adjusted for the South-North Water Transfer.Provide technical support for finding hidden leaks in the fill channel.