Browsing by Author "Zhang, Zhiming"

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An Integrated System for Road Condition and Weigh-in-Motion Measurements using In-Pavement Strain Sensors
(North Dakota State University, 2016) Zhang, Zhiming
The United States has the world’s largest road network with over 4.1 million miles of roads supporting more than 260 million of registered automobiles including around 11 million of heavy trucks. Such a large road network challenges the road and traffic management systems such as condition assessment and traffic monitoring. To assess the road conditions and track the traffic, currently, multiple facilities are required simultaneously. For instance, vehicle-based image techniques are available for pavements’ mechanical behavior detection such as cracks, high-speed vehicle-based profilers are used upon request for the road ride quality evaluation, and inductive loops or strain sensors are deployed inside pavements for traffic data collection. Having multiple facilities and systems for the road conditions and traffic information monitoring raises the cost for the assessment and complicates the process. In this study, an integrated system is developed to simultaneously monitor the road condition and traffic using in-pavement strain-based sensors, which will phenomenally simplify the road condition and traffic monitoring. To accomplish such a superior system, this dissertation designs an innovative integrated sensing system, installs the integrated system in Minnesota's Cold Weather Road Research Facility (MnROAD), monitors the early health conditions of the pavements and ride quality evaluation, investigates algorithms by using the developed system for traffic data collection especially weigh-in-motion measurements, and optimizes the system through optimal system design. The developed integrated system is promising to use one system for multiple purposes, which gains a considerable efficiency increase as well as a potential significant cost reduction for intelligent transportation system.
Road Profile Reconstruction Using Connected Vehicle Responses and Wavelet Analysis
(2018) Zhang, Zhiming; Sun, Chao; Bridgelall, Raj; Sun, Mingxuan; Upper Great Plains Transportation Institute
Practitioners analyze the elevation profile of a roadway to detect localized defects and to produce the international roughness index. The prevailing method of measuring road profiles uses a specially instrumented vehicle and trained technicians, which usually leads to a high cost and an insufficient measurement frequency. The recent availability of probe data from connected vehicles provides a method that is cost-effective, continuous, and covers the entire roadway network. However, no method currently exists that can reproduce the elevation profile from multi-resolution features of the vehicle inertial response signal. This research uses the wavelet decomposition of the vehicle inertial responses and a nonlinear autoregressive artificial neural network with exogenous inputs to reconstruct the elevation profile. The vehicle inertial responses are a function of both the vehicle suspension characteristics and its speed. Therefore, the authors normalized the vehicle response models by the traveling speed and then numerically solved their inertial response equations to simulate the vehicle dynamic responses. The results demonstrate that applying the artificial neural network to the wavelet decomposed inertial response signals provides an effective estimation of the road profile.
Road Roughness Evaluation Using In-Pavement Strain Sensors
(2015) Zhang, Zhiming; Deng, Fodan; Huang, Ying; Bridgelall, Raj; Upper Great Plains Transportation Institute
The international roughness index (IRI) is a characterization of road roughness or ride quality that transportation agencies most often report. The prevalent method of acquiring IRI data requires instrumented vehicles and technicians with specialized training to interpret the results. The extensive labor and high cost requirements associated with the existing approaches limit data collection to at most once per year for portions of the national highway system. Agencies characterize roughness only for some secondary roads but much less frequently, such as once every five years, resulting in outdated roughness information. This research developed a real-time roughness evaluation approach that links the output of durable in-pavement strain sensors to prevailing indices that summarize road roughness. Field experiments validated the high consistency of the approach by showing that it is within 3.3% of relative IRI estimates. After their installation and calibration during road construction, the ruggedized strain sensors will report road roughness continuously. Thus, the solution will provide agencies a real-time roughness monitoring solution over the remaining service life of road assets.
Vehicle Axle Detection from Under-Sampled Signal through Compressed-Sensing-Based Signal Recovery
(2022) Zhang, Zhiming; Huang, Ying; Bridgelall, Raj; Upper Great Plains Transportation Institute
In traffic data collection, sampling design should satisfy the requirements of identifying prominent pulses corresponding to vehicle axle passage. Insufficient measurement leads to signal distortion and attenuation, reducing the quality of signal pulses. This study exploits the value of under-sampled data by applying compressed sensing (CS) methods to recover signal components that are critical for vehicle axle detection. Two CS methods are investigated in this study to recover the strain signal pulses from inside-pavement instrumented sensors at high-speed traversals. The CS methods successfully recovered the signal pulses from all axles of the truck used for testing. A comparison of the measured axle distances with the reference measurements validated the effectiveness of signal recovery methods. Therefore, the CS methods have the potential of reducing the cost, energy consumption, and data storage space, and improving the data transmission efficiency in practical implementations by enabling sampling devices designed for static measurements to achieve dynamic measurements.