Comprehensive Investigation of the Long-term Performance of Internally Integrated Concrete Pavement with Sodium Acetate

The research carried out in this study presents the effectiveness of using sodium acetate as a protective material for concrete pavement. A newly developed freeze-thaw method that depends on the alteration of temperature and humidity is introduced in this research to investigate the efficacy of integrating sodium acetate with concrete with different water to cement ratios (w/c). Results from the introduced freeze-thaw method were compared with the outcomes of a standard freeze-thaw testing method. The distressed concrete was tested for water absorption and compressive strength after finishing six months of freeze-thaw testing. Additionally, the morphology of sodium acetate and its interaction with concrete were investigated by using Scanning Electron Microscope (SEM). Results demonstrated the effectiveness of sodium acetate in protecting concrete

Traffic Monitoring System Using In-Pavement Fiber Bragg Grating Sensors

Recently, adding more lanes becomes less and less feasible, which is no longer an applicable solution for the traffic congestion problem due to the increment of vehicles. Using the existing infrastructure more efficiently with better traffic control and management is the realistic solution. An effective traffic management requires the use of monitoring technologies to extract traffic parameters that describe the characteristics of vehicles and their movement on the road. A three-dimension glass fiber-reinforced polymer packaged fiber Bragg grating sensor (3D GFRP-FBG) is introduced for the traffic monitoring system. The proposed sensor network was installed for validation at the Cold Weather Road Research Facility in Minnesota (MnROAD) facility of Minnesota Department of Transportation (MnDOT) in MN. A vehicle classification system based on the proposed sensor network has been validated. The vehicle classification system uses support vector machine (SVM), Neural Network (NN), and K-Nearest Neighbour (KNN) learning algorithms to classify vehicles into categories ranging from small vehicles to combination trucks. The field-testing results from real traffic show that the developed system can accurately estimate the vehicle classifications with 98.5 % of accuracy. Also, the proposed sensor network has been validated for low-speed and high-speed WIM measurements in flexible pavement. Field testing validated that the longitudinal component of the sensor has a measurement accuracy of 86.3% and 89.5% at 5 mph and 45 mph vehicle speed, respectively. A performed parametric study on the stability of the WIM system shows that the loading position is the most significant parameter affecting the WIM measurements accuracy compared to the vehicle speed and pavement temperature. Also the system shows the capability to estimate the location of the loading position to enhance the system accuracy

Weigh-In-Motion System in Flexible Pavements Using Fiber Bragg Grating Sensors Part A: Concept

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Weight data of vehicles play an important role in traffic planning, weight enforcement, and pavement condition assessment. In this paper, a weigh-in-motion (WIM) system that functions at both low-speeds and high-speeds in flexible pavements is developed based on in-pavement, three-dimensional glass-fiber-reinforced, polymer-packaged fiber Bragg grating sensors (3D GFRP-FBG). Vehicles passing over the pavement produce strains that the system monitors by measuring the center wavelength changes of the embedded 3D GFRP-FBG sensors. The FBG sensor can estimate the weight of vehicles because of the direct relationship between the loading on the pavement and the strain inside the pavement. A sensitivity study shows that the developed sensor is very sensitive to sensor installation depth, pavement property, and load location. Testing in the field validated that the longitudinal component of the sensor if not corrected by location has a measurement accuracy of 86.3% and 89.5% at 5 mph and 45 mph vehicle speed, respectively. However, the system also has the capability to estimate the location of the loading position, which can enhance the system accuracy to more than 94.5%.U.S. Department of Transportation (USDOT) under the agreement No.69A35517477108 through Mountain-Plains Consortium Project No. MPC-547, a NSF Award under Agreement No.1750316, and NSF ND EPSCoR DDA Program.https://www.ugpti.org/about/staff/viewbio.php?id=7