title: 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.
authors.affiliation: Key Laboratory for Optoelectronic Technology and System of the Education Ministry of China, College of Optoelectronic Engineering, Chongqing University, Chongqing, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zheng
authors.affiliation: Key Laboratory for Optoelectronic Technology and System of the Education Ministry of China, College of Optoelectronic Engineering, Chongqing University, Chongqing, China.
authors.forename: Xiaoya
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Key Laboratory for Optoelectronic Technology and System of the Education Ministry of China, College of Optoelectronic Engineering, Chongqing University, Chongqing, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D003116:Color; D004777:Environment; D004784:Environmental Monitoring / Q000379:methods*; D016571:Neural Networks, Computer; D012306:Risk