Reconstruction method of seepage water condition of large-scale civil engineering structures based on deep learning

Abstract

The invention provides a method for reconstructing the water leakage condition of large-scale civil engineering structures based on deep learning, which can measure the received signal strength indicating RSSI data according to the principle of radio wave propagation path loss, and obtain the loss factor distribution through the reconstruction model of the leakage water condition. The image, among which, the model training process is as follows: first, the RSSI sequence positive problem is numerically solved and normalized to obtain the RSSI sequence data set and the path loss factor image data set, and the positive problem scale, attribute dimension and label image are obtained from the data. Dimension to determine the model architecture and initialization parameters, and then obtain the leakage water reconstruction model through training the learning algorithm. By easily obtaining the correlation mapping relationship between the RSSI data and the loss factor distribution image, this method can realize the reconstruction of the seepage water state of large-scale civil engineering structures in a larger area and a larger scale in a more timely manner, and can reduce the leakage caused by water leakage disasters. structural damage, economic loss and casualties.

Description

technical field [0001] The invention relates to a deep learning-based reconstruction method for water leakage of large-scale civil engineering structures, and relates to the field of wireless monitoring of water leakage of large-scale civil engineering structures. Background technique [0002] In the process of infrastructure construction, the structural safety of shield tunnels is an important guarantee for the normal operation of pipe gallery and tunnel construction. Water leakage is the most common and typical shield tunnel structural disaster. Detection and real-time monitoring are especially important. At present, the common solutions for leakage detection of tunnel pipe gallery include manual visual inspection or measurement, infrared thermal imaging detection, laser scanning non-destructive testing, geological radar detection, ultrasonic detection and wireless sensor network data detection. Reconstructing the seepage water condition of an area based on wireless senso...

AI Technical Summary

Problems solved by technology

Reconstructing the water seepage situation in an area based on wireless sensor network data can be abstracted into an inverse problem in mathematics, and the classic inverse problem mostly uses iterative regularization to solve the inverse problem. Although regularization can improve the discomfort of the inverse problem to a certain extent Qualitative, but relying on more prior knowledge, the effect is still limited in the face of complex data, and needs further improvement

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