Estimation of Pavement Serviceability Index Through Android-Based Smartphone Application for Local Roads

Abstract

The Wyoming Technology Transfer Center is in the process of developing a pavement management system (PMS) for county paved roads in Wyoming. This PMS uses the present serviceability index (PSI) as a main pavement performance parameter. This PMS depends on pavement condition index, international roughness index, and pavement rutting as explanatory variables to estimate PSI. This study researched new explanatory variables measured by using smartphones’ sensors to estimate PSI. It was found that the variance of the signals (time series acceleration data) acquired by smartphones’ accelerometers could work as a very good explanatory variable to estimate PSI. Two models were developed with high significance ( R2 higher than .9) to predict PSI using the variance of smartphone signals. The initial validation results suggested that using these models could predict, with high certainty, the actual PSI values. The difference between the predicted and the actual PSI values was not statistically different. The study was performed on 20 roadway segments extracted from the Wyoming county roads’ PMS database. In addition, the selected segments had various lengths and geometric features reflecting various roadway segments under any PMS. The proposed methodology is intended to lower the cost of measuring county roads’ pavement conditions by estimating PSI directly without the reliance on the direct measurement of pavement condition parameters.

Developing an Optimization Tool for Selecting Gravel Roads Maintenance Strategies using a Genetic Algorithm

“…Furthermore, the implementation of a cost-effective treatment like TG can significantly increase the effectiveness of the adopted GRMS. In Wyoming, it was proven that using chemical suppressants is very efficient in abating dust, is economical, and can improve the ride quality enormously (23)(24)(25). The implementation of dust chemical treatments can increase the likelihood of treating larger number of roads and abating the impacts of dust simultaneously, as shown in Figure 7.…”

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

“…Methods of data collection based on smartphone accelerometer and GPS readings are the most common. Most studies of smartphone-based data collection methods either focus on pavement performance evaluation and estimation of pavement quality indexes [16,17,[19][20][21], or on anomaly detection [15,18], with a prominent focus on pothole identification. While built-in accelerometer sensors are the most used in smartphone-based systems [14][15][16]20,[22][23][24]26,27], there are systems that use cameras [18,25] and microphones as data collection methods.…”

“…Aleadelat et al [16] resorted to a smartphone accelerometer and GPS to evaluate pavement condition through the estimation of present serviceability index (PSI). More precisely, they proved that the variance of the acceleration data obtained by smartphones could work very effectively as independent variables to estimate PSI.…”

“…Since this analysis is usually carried out in a two-fold action, starting with the identification of surface distresses followed by the determination of quality indexes, the functional category was sub-divided into a second level. This sub-division aimed to distinguish the data analysis methods that focused on the identification and classification of surface distresses, such as superficial cracks [28][29][30]33,40,[62][63][64][65][66][67], potholes [25,32,36], patches [18], and others [37,41], and the estimation of pavement quality indexes, such as IRI [17,19], PCI [16] and other indexes proposed by some researchers [20,22,23,27,68]. These sub-categories were, in turn, divided into the type of adopted approach, either image processing or data-driven models for the case of identification and classification of surface distresses, and model-driven or data-driven for the estimation of pavement quality indexes.…”

Estimation of Gravel Roads Ride Quality Through an Android-Based Smartphone

“…Then, using the quarter car model, Fast Fourier transform (FFT), and statistical analysis, they were able to estimate the international roughness index (IRI) values, locate corrugations within road segments, and infer some information related to surface material characteristics (e.g., gradation). Regarding the use of smartphones in evaluating roads conditions, most of the previous work was related to paved roads, in particular estimating the IRI and locating anomalies within the pavement surface (16)(17)(18)(19)(20)(21)(22)(23).…”