Vibration-based classification of road damages: gyroscope data and a simple neural network model

2nd International Conference on Eco Engineering Development 2018

生态环境科学

Gunawan, Fergyanto E.^1 ; Herriyandi^2 ; Soewito, Benfano^2 ; Mauritsius, Tuga^3 ; Surantha, Nico^2

Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta

11480, Indonesia^1

Computer Science Department, BINUS Graduate Program - Master of Computer Science, Bina Nusantara University, Jakarta

11480, Indonesia^2

Information Systems Management Department, BINUS Graduate Program - Master of Information Systems Management, Bina Nusantara University, Jakarta

11480, Indonesia^3

关键词: Automatic Detection;    Empirical data;    Feature vectors;    Logistics management;    Monte Carlo approach;    Neural network model;    Probe vehicles;    Three-layer neural networks;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/195/1/012068/pdf
DOI  :  10.1088/1755-1315/195/1/012068

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In the current proposal, the algorithm receives data of the probe vehicle rate of rotations, extracts statistics descriptive from the data to produce a feature vector, and finally, feeds the vector to a simple-three-layer neural network to classify the damages. The types of damages are limited to four cases: normal, pothole, speed bump, and expansion joint. For the development, a probe vehicle is used to produce 400 empirical data involving those four damage cases. By using Monte Carlo approach, the established neural network model is evaluated. The results show that the approach is able to classify the cases with 85% accuracy. The study also finds that the rates of pitch and roll to be the determining factors.