Dynamic monitoring and prediction of pavement degradation
Creators: name: Shtayat, Amir
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400512
subject: Transport engineering

description: In 2021, Australia saw an increase of about 1.7 per cent in the number of registered motor vehicles from the previous year. This increase in the number of vehicles on our roadways brings into consideration the need for high-quality riding and comfort for road users. In addition, the increase in vehicular traffic leads to an increase in the pressure on road infrastructure. As an essential component of the road infrastructure system, road pavement suffers from many damages and degradations. Pavement management systems focus on finding appropriate and accurate monitoring techniques to evaluate the current performance of road pavement surfaces and provide appropriate maintenance actions.

Nowadays, due to the need for flexible pavement monitoring techniques and efficient maintenance systems, advanced and accurate technologies are needed to evaluate road surface conditions and assess the levels of pavement deterioration. However, many combined factors affect the accuracy of pavement evaluation and monitoring outcomes.

This research focused on introducing new dynamic pavement monitoring techniques for local roads in Melbourne, Australia. In addition, this research aimed to predict the pavement condition performance by identifying and classifying road pavements' degradation levels. Pavement vibration is one of the modern techniques used to indicate the health status of any road. Moreover, vibration-based methods were used in this research to collect the vibration data during vehicle movement over a road segment. Also, vision-based methods were performed to record the types and quantities of pavement degradations and distress on pavement surfaces.

Regarding the monitoring technique, the research focused on finding the most significant elements with high-quality monitoring results, such as accurate monitoring devices, suitable test vehicles, right test speeds, and appropriate positions to mount the devices inside or outside vehicles. All of the previous elements are the main important factors that need to be considered before monitoring any road pavement condition. Besides, the research focused on finding and introducing an accurate, cost-effective, and simple pavement monitoring system. Two monitoring devices, including a smartphone and an accelerometer sensor, were used to collect vibration data from vehicle chassis while moving over a road pavement segment. These two devices were installed and mounted inside four test vehicles, including a passenger car, e-bike, motorcycle, and regular bicycle.

Moreover, five positions were selected inside and outside a passenger car to mount the accelerometer sensor, including the front dashboard, rear trunk, floorboard, above the upper roof, and above the front wheel on the vehicle chassis. In comparison, the smartphone was installed at a passenger car's front dashboard and back trunk positions. Also, the e-bike, motorcycle, and bicycle handlebars were used to fix the mobile phone for data collection purposes. Test speed is one of the most significant factors that directly affect the pavement monitoring results. Therefore, the study focused on using different low test speeds at 5, 10, 15, and 20 km/hr to identify the speed that provides more consistent and accurate vibration data during riding and to determine the interaction between the speeds and the accuracy of conducted vibrations. In addition, a video camera was used to monitor the pavement condition during the data collection at different test speeds. The main purpose of using a high-quality camera was to determine the type, severity, and quantity of pavement distress.

After a series of analyses and investigations, the monitoring results showed that the significant monitoring device was the accelerometer sensor. In addition, the passenger car was chosen for further data collection at a speed of 20 km/hr. Besides, the front dashboard was selected as the most appropriate position to mount the accelerometer sensor. The monitoring results from the selected factors were examined and compared with international indices such as Pavement Condition Index (PCI), International Roughness Index (IRI), and Present Serviceability Index (PSI). They showed compatible results with high accuracy in identifying the actual condition of the pavement, distress type, distress severity, and distress quantity on road pavement. After that, noise-cancelling and data smoothing techniques were applied to the vibration signals to improve and enhance the consistency of signals and eliminate the noises in vibrations. Four different techniques were used to smooth the data: high pass filter, moving average method, median method, Savitzky-Golay filters, and extracting peak envelop method.

Furthermore, the vibration signals were filtered and well prepared for further analysis using prediction models. Hence, supervised machine learning algorithms were developed to predict pavement surface conditions and determine the type and quantity of pavement distresses. Data processing was done using four steps: 1) data filtering, 2) data tagging, 3) data division, and 4) feature extraction. Furthermore, a high pass filter was applied to filter the raw data from undesirable signals. After that, six different windows were established according to distress type. Then, an auto feature extraction process was applied to extract the peaks representing the actual distress on the road surface.

Moreover, two multi-classification machine learning models were used, including Random Forest (FR) and Decision Tree (DT), for automated classification and detection of different types of pavement distresses. Also, a Support Vector Machine (SVM) was developed as a binary machine learning model for the same classification and detection purposes. The results showed that the developed machine learning models provided significant classification accuracies, with about 93% using the RF and 90% using a DT. Thus, the RF is highly recommended to predict pavement performance using a multi-classification system. While regarding the SVM model, the overall average accuracy of detection and classification of the pavement defects was about 96%.