<p>  </p> <p>For decades, agencies have collected infrastructure condition assessment data using dedicated equipment that require substantial capital investments and staff time to operate/drive. However, these techniques are challenging to scale network wide. The United States has over 8 million lane miles of roadways which generate almost 3 trillion vehicle miles annually.  Connected vehicles can now provide real-time data on a wide range of parameters such as vehicle speed, location, lane markings, and 3 axis acceleration. This dissertation develops techniques to validate, utilize and leverage connected vehicle data for infrastructure assessment and monitoring.  </p> <p><br></p> <p>Opportunities to employ connected vehicle data were examined in the following areas: quality of lane marking edge lines, width of lanes (particularly temporary lanes in construction zones), and pavement roughness. Quality of lane markings was evaluated using embedded lane keep assist data and equipment. In 2020 and 2021 over 5000 miles of pavement markings were evaluated on Indiana interstates. Comparisons between 2020 and 2021 data showed detection increase from 80.2% to 92.3%.  Although there are no industry standards for lane keep assist data, this study demonstrated both the importance and utility of partnering with the automotive industry to develop shared vision on acceptable lane quality. </p> <p><br></p> <p>A follow-up quantitative study was performed using a LiDAR vehicle to compare LiDAR values with those that are obtained from traditional retroreflectivity measurements used for contract acceptance and maintenance decisions. A comparison of LiDAR intensity to retroreflectivity (the industry standard) on 70 miles of US-52 and US-41 in Indiana was assessed and a linear regression found that the intensity values are comparable to retroreflectivity readings with an R2 of 0.87 and 0.63 for right edge and center skip lines respectively. These results suggest that LiDAR is a viable tool for monitoring of retroreflectivity of pavement markings that are strongly correlated with existing standards, but scale much better than traditional retroreflectivity measurement techniques.</p> <p><br></p> <p>The LiDAR data also provided the opportunity to evaluate how well modern vehicles measure lane width. This dissertation reports on over 200 miles of roadway and when compared to LiDAR and field measurements had a root mean square error of 0.24 feet. This data is valuable for agencies to quickly identify system wide where lane widths fall below acceptable design standards, typically 11-feet. </p> <p> </p> <p>The final connected vehicle data set evaluated was pavement roughness and compared with traditional dedicated vehicles collecting international roughness index (IRI) data. The study evaluated a 20-mile segment in 2022, and showed a linear regression between these data sets had an R2 of over 0.7, suggesting that connected vehicle roughness data can be utilized for network level monitoring of pavement quality. Scalability of these techniques is also illustrated with graphics characterizing IRI values obtained from almost 6 million records to evaluate improvements in Indiana construction zones and over 5,800 miles of I-80 in April of 2022 and October 2022.</p> <p><br></p> <p>Although connected vehicle data for infrastructure assessment is still in its infancy, these case studies demonstrate significant opportunities for public agencies to collect selected system wide infrastructure condition in near real-time, and in many cases at a lower cost than traditional techniques. </p>

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Pavements in Canada that are built on top of frost susceptible soil experience loss of support in early spring as the frozen structure begins thawing. To minimize pavement damage, low- volume roads rely on Spring Load Restrictions (SLR), since building these roads to withstand spring thaw is usually not feasible. However, implementing SLR increases operational costs to commercial transporters and impacts local economies. The Ministry of Transportation Ontario (MTO) is routinely faced with requests from the truck industry to lift restrictions on certain roads, and questioning from municipalities that seek understanding on the needs for SLR in their roads. To help answer these questions, a comprehensive study was performed at 15 Seasonal Load Adjustment (SLA) stations across Ontario. The data collected included Falling Weight Deflectometer (FWD) testing, borehole investigation, climatic data, traffic volumes, and pavement rehabilitation data. A backcalculation using the FWD data estimated pavement structural capacity and remaining service life for each SLA for different dates throughout spring, followed by a life cycle analysis using the rehabilitation data. The SLAs were divided in three groups of similar pavement support conditions based on the service life analysis results. Results indicate that none of Group 1 SLAs need load restrictions, with the calculated remaining service life being greater than 25 years for all test dates. Within Group 2, pavement recovery throughout spring suggests that SLR could extend service life, although generally not necessary for the intended life cycle. Results indicate most SLAs in Group 2 achieving a full life cycle from the last rehabilitation activities for estimates based on early spring parameters, suggesting that these roads were likely designed with spring conditions accounted for. For Group 3, the lack of structural support and low service life values indicate the need for strict load restrictions to avoid excessive damage and maintain serviceability. Pavements with high-quality subgrades, granular structures and non-frost susceptible materials, thick asphalt layers and major rehabilitation activities were found to generally perform well for spring conditions. However, with several site-specific conditions, an overall recommendation for implementation of SLR cannot be generalized based on the pavement structure and subgrade soil type alone. The structural condition and thawing behaviour of individual sites must be thoroughly understood before a decision is made, as investigation might indicate that some roads can withstand full traffic year-round and would not need SLR, while others might need more rigorous restrictions. In addition to the service life analysis, approximately 600 lane km of deflection data was collected using a Multi-Speed Deflectometer in Southern Ontario. Recommendations were made for potential applications of the equipment as a network screening device, able to identify weak road sections in a time and cost-effective manner prior to a detailed investigation using FWD; and for regular monitoring of road conditions at a network level, including the monitoring of seasonal variations. / Thesis / Master of Applied Science (MASc)

This study focused on the material characterization of granular materials containing different percentages of “RAP”. A series of laboratory tests results were carried out to determine the physical and mechanical properties of natural aggregates and various aggregate-RAP blends. The results were used to evaluate methods to develop high-performance granular layer for pavement construction through proper compaction and control of RAP usage. The resilient modulus and accumulative deformation characteristics were determined in relation to RAP content, relative density, compaction method, stress level, stress state and the number of load applications. The effects of RAP content and density on the CBR values of aggregate-RAP blends under various conditions were also investigated. In addition, the effect of small strain cyclic loading on shear strength of aggregate-RAP blends was observed in laboratory tests. Results from this investigation demonstrated that: 1) adding RAP to natural aggregates may increase the resilient modulus of natural aggregates, and optimum content can be found to achieve the highest resilient modulus; 2) resilient modulus generally increases with density; higher density of aggregate-RAP blends can be achieved by using methods combining vibration and static loading. 3) deviatoric stress has more pronounced influence on accumulative deformation than confining pressure. 4) proper compaction method can reduce accumulative deformation of samples. 5) addition of RAP into aggregates results in little change in accumulative deformation when the RAP content is less than a threshold. 6) CBR value decreases with increasing RAP content and decreasing compaction effort or compacted dry density. 7) shear strength of an aggregate-RAP blend tends to increase after small strain cyclic loading. / Thesis / Master of Applied Science (MASc)