Heavy vehicle weights must be closely monitored to prevent fatigue-induced deterioration and critical fracture to civil infrastructure, among many other purposes. Developing a cost-effective weigh-in-motion (WIM) system remains challenging. This doctoral research describes the creation and experimental validations of a computer vision-based non-contact vehicle WIM system. The underlining physics is that the force exerted by each tire onto the roadway is the product of the two key vehicle parameters: tire-roadway contact pressure and contact area. Computer vision is applied (1) to measure the several tire parameters so that the tire-roadway contact area can be accurately estimated; and (2) to recognize the marking texts on the tire sidewall so that the manufacturer-recommended tire pneumatic pressure can be found. Consequently, a computer vision system is developed in this research. The computer vision system comprises a camera and computer vision software/techniques for measuring the tire parameters and recognizing the tire sidewall markings from individual tire images of a moving vehicle. Computer vision techniques, such as edge detection and optical character recognition (OCR), are applied to enhance the measurements and recognition accuracy. Numerous laboratory and field experiments were conducted on a sport utility vehicle and fully loaded or empty concrete trucks to demonstrate the feasibility of this novel method. The vehicle weights estimated by this novel computer vision-based non-contact vehicle WIM system agreed well with the curb weights verified by static weighing, demonstrating the potential of this computer vision-based method as a non-contact means for weighing vehicles in motion. To further illustrate and exemplify the versatility of this novel computer vision-based WIM system, this research explores the potential application capability of the system for structural health monitoring (SHM) in civil engineering. This work aims to investigate the potential of this proposed and prototyped computer vision-based vehicle WIM system to acquire vehicle weight and location information as well as to obtain corresponding bridge responses simultaneously for later structural model updating analysis and damage detection and identification framework. In order to validate the concept, a laboratory vehicle-bridge model was constructed. Subsequently, numerous experiments were carried out to demonstrate how the computer vision-based WIM system can be utilized as a resourceful application to (1) extract bridge responses, (2) estimate vehicle weight, and (3) localize the input force simultaneously. This doctoral research delivers a unique, pioneering, and innovative design and development of a computer vision-based non-contact vehicle WIM method and system that can remotely perform vehicle weight estimation. It also demonstrates a novel application of computer vision technology to solve challenging weigh-in-motion (WIM) and civil engineering problems.

All vehicles have changing payloads that affect their dynamic response. Compared to passenger vehicles, heavy machinery have larger and more greatly varying payload masses, higher centers of mass, and encounter larger disturbances. These factors lead to significant increases in the amount of vibration experienced by heavy machinery operators. This fact, when coupled with the large amount of exposure time that a typical heavy machinery operator incurs, leads to much greater vibration dosage values for the heavy machinery operator. In addition, the heavy machinery operator faces equal or greater opportunity for accident. The chance of accident, along with the increased vibration dosage, leads to an operating condition with significant safety risks, both short and long term. It has been shown that payloads affect both the stability and vibration isolation properties of a vehicle. Large payloads reduce vehicle stability while increasing the amount of vibration transmitted to the operator. A method to compensate for these loading affects would prove to be a useful technique to increase the safety of the vehicle, both in terms of accident avoidance and long term health effects of vibration. This thesis provides such payload compensation techniques. Improved vehicle dynamics were accomplished with the use of both active and semi-active suspension control. The active systems used are optimal control based, and provided the greatest improvements in vehicle performance. An optimal controller designed around a nominal payload, however, proved insufficient for operation over the entire payload range due to too large peak actuator forces at low payloads. A multiple model approach was used to remedy this problem. Semi-active systems based on a Linear Quadratic Regulator with output feedback and damping selection via static deflection were developed. The semi-active systems would require far less power than the active systems, with the need for knowledge of fewer systems states. It was shown that despite these lower demands, the semi-active systems closely approach the performance of the fully active systems.