Vibration-Based Damaged Road Classification Using Artificial Neural Network
Yudy Purnama, Fergyanto E. Gunawan
It is necessary to develop an automated method to detect damaged road because manually monitoring the road condition is not practical. Many previous studies had demonstrated that the vibration-based technique has potential to detect damages on roads. This research explores the potential use of Artificial Neural Network (ANN) for detecting road anomalies based on vehicle accelerometer data. The vehicle is equipped with a smart-phone that has a 3D accelerometer and geo-location sensors. Then, the vehicle is used to scan road network having several road anomalies, such as, potholes, speedbump, and expansion joints. An ANN model consisting of three layers is developed to classify the road anomalies. The first layer is the input layer containing six neurons. The numbers of neurons in the hidden layer is varied between one and ten neurons, and its optimal number is sought numerically. The prediction accuracy of 84.9% is obtained by using three neurons in conjunction with the maximum acceleration data in x, y, and z-axis. The accuracy increases slightly to 86.5%, 85.2%, and 85.9% when the dominant frequencies in x, y, and z-axis, respectively, are taken into account beside the previous data.