Pavement disease rapid inspection method based on mechanical indexes and artificial neural network

Abstract

The invention relates to a pavement disease rapid inspection method based on mechanical indexes and an artificial neural network, and the method comprises the following steps: taking VMI values, vehicle speeds, vehicle elevation information and amplitudes on roads with different disease types in a vehicle driving process as a group of data, and forming an input data set by a plurality of groups of data; clustering the data in the input data set by using the SOM neural network, and outputting a corresponding clustering result; determining real disease classification level information corresponding to the acceleration characteristic information in each clustering center range according to the data in the clustering center range corresponding to the corresponding time information and latitude and longitude information, and establishing a sample disease database; and training a decision tree by using the established sample disease database, establishing a decision tree model, and performing disease classification and hierarchical decision on the damaged road. The method has the advantages of being short in detection period, low in cost, high in precision and not affected by subjective factors of people, and rapid recognition, classification, grading and positioning of pavement diseases are achieved.

Description

technical field [0001] The invention relates to the technical field of pavement disease detection, in particular to a method for rapid pavement disease inspection based on mechanical indexes and artificial neural networks. Background technique [0002] In order to ensure the safety and comfort of vehicles on the road and prolong the service life of the road, it is necessary to regularly inspect and maintain the road, and it is very important and necessary to quickly detect and identify the disease during driving. . The main diseases of the pavement mainly include potholes, ruts, cracks, subsidence and wave wrapping. [0003] At present, the traditional detection methods of pavement diseases mainly use road detection vehicles and manual detection. The road inspection vehicle is equipped with laser sensing equipment such as a laser deflection tester and a vehicle-mounted bump accumulator. The on-board equipment detects road surface deflection, flatness, water seepage coeffi...

AI Technical Summary

Problems solved by technology

[0004] Both inspection vehicles and manual inspections will affect the normal use of roads, especially on expressways. Manual inspections may even require road closures. At the same time, it will consume a lot of manpower, material and financial resources, and it is difficult to guarantee the inspection efficiency.

Manual detection is easily affected by subjective judgment and lacks consistent judgment standards

Images