Road network level vehicle overload identification and emergency early warning method

Abstract

The invention discloses a road network level vehicle overload identification and emergency early warning method, and the method comprises the specific implementation steps: installing a corner sensor, a camera and a tachymeter near a bridge support, and measuring the main beam corner and vehicle information in structural deformation at the same time; establishing a typical vehicle database with known vehicle weight by using an RBF (Radial Basis Function) artificial neuron network, and establishing a vehicle weight prediction model; when a vehicle passes through a bridge, transmitting the vehicle information to an input layer of the artificial neuron network, and outputting the vehicle weight data through conversion of a hidden layer; enabling the vehicle weight prediction models of other bridges in the road network to receive the vehicle weight data and input the vehicle weight data into the vehicle weight prediction models of other bridges, and calculating and transmitting the turning angle and the deflection to the grading early warning system. According to the method, the vehicle prediction model is established by adopting the artificial neural network, and before the vehicle gets on the bridge, damage of the vehicle to the bridge is predicted, so that bridge accidents are prevented.

Description

[0001] The invention belongs to the technical field of bridge overload early warning, and in particular relates to a vehicle overload identification and emergency early warning method at the road network level. Background technique [0002] After the bridge is put into use, in addition to bearing various natural loads, it also inevitably bears driving dynamic loads, overloads or impacts, etc., which will inevitably cause changes in the mechanical or structural properties of the bridge. In order to understand the safety status of the bridge, it is necessary to carry out long-term structural safety monitoring at the road network level to prevent bridge accidents caused by vehicle overload. However, the total number of bridges in my country has reached one million, and the traditional monitoring scheme requires huge manpower and material resources. Therefore, it is urgent to establish an economical real-time performance monitoring and collapse early warning scheme to ensure that b...

AI Technical Summary

Problems solved by technology

However, the total number of bridges in my country has reached one million, and the traditional monitoring scheme requires huge manpower and material resources. Therefore, it is urgent to establish an economical real-time performance monitoring and collapse early warning scheme to ensure that bridges can detect diseases and performance degradation rules in time, and realize bridge collapse. Early warning and preventive maintenance

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