A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space

Abstract

This study proposes a visualization processing method for the deformation risk level of underground space. The proposed method is based on a BP-Hopfield-RGB (BHR) composite network. Complex environmental factors are integrated in the BP neural network. Dynamic monitoring data are then automatically classified in the Hopfield network. The deformation risk level is combined with the RGB color space model and is displayed visually in real time, after which experiments are conducted with the use of an ultrasonic omnidirectional sensor device for structural deformation monitoring. The proposed method is also compared with some typical methods using a benchmark dataset. Results show that the BHR composite network visualizes the deformation monitoring process in real time and can dynamically indicate dangerous zones.

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“…Unlike the classical regression method, ANNs do not require complex constitutive models to solve these geotechnical problems, thus improving the convenience of obtaining their solutions. Zheng et al [7] used a backpropagation (BP) neural network to integrate complex environmental factors and proposed a visualisation method to evaluate the deformation risk level of underground spaces. e deformation risk level was combined with the red, green, blue color space model to achieve real-time visualisation displays.…”