The purpose of the paper is to improve the efficiency of vehicle based sensing technology in highway pavement condition assessment by evaluating the effect of four factors (sensor placement, pavement temperature, drive speed, and threshold for pavement distress classification) and providing suggestions to better improve the accuracy of pavement condition detection and minimize the interruption of pavement sensing operation. Two I-10 corridors in the Phoenix region were selected for vibration data collection and data analysis. A series of statistical analyses were performed to determine if each one of the factors has a significant impact on the pavement distress detection. The results of Analysis of Variance (ANOVA) tests and Analysis of Covariance (ANCOVA) tests show that the placement of sensors have a significant effect in the pavement condition assessments. The significant differences occurred in the group of sensors that were placed on the same side of the vehicle, as well as, in either front wheels or rear wheels of the vehicle. The effect of pavement temperature on the vehicle based sensing implementation is significant while the mean drive speed is not seen as a significant factor in the pavement condition survey. The two thresholds were determined to select points of interest (POI; cracks, potholes) for the pavement distress classification and these POIs are in good agreement with international roughness index (IRI) data in an ArcGIS map. The findings of the paper can be used to better improve the computing algorithms of vehicle based sensing techniques.

This paper provides a five-year performance evaluation of an application of geogrid reinforcement in low-volume unpaved roads using dynamic cone penetrometer (DCP), plate load tests (PLT), and roadway sensing method. A Forest Service unpaved road located in northern Arizona, USA, exhibited severe deterioration on the surface, creating an unsafe traffic environment for vehicles. A total of four structural sections (1–4; 4.3 m wide) were installed in the 40 m long test area. One additional section of existing subgrade/roadbed with native soil adjacent to the test sections was used for comparison purposes. The project was originally completed in November 2015, followed by five annual field visits to observe surface conditions of the five test sections. Based on DCP and PLT results (both conducted in 2015), and roadway sensing tests conducted in 2020, the section made of 30 cm thick aggregate with one geogrid layer appeared to have a better capacity for resisting traffic loading as compared with the other four sections. This paper concludes that, from a long-term point of view, the geogrid reinforcement improves the capacity of the unpaved roads, with significantly reduced rutting and damage from both roadway traffic loads and weathering effects.