Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.

Overloaded vehicles have a variety of adverse effects; they not only damage pavements, bridges, and other infrastructure but also threaten the safety of human life. Therefore, it is necessary to address the problem of overloading, and this requires the accurate identification of the vehicle weight. Many methods have been used to identify vehicle weights. Most of them use contact methods that require sensors attached to or embedded in the road or bridge, which have disadvantages such as high cost, low accuracy, and poor durability. The authors have developed a vehicle weight identification method based on computer vision. The methodology identifies the tire–road contact force by establishing the relationship using the tire vertical deflection, which is extracted using computer vision techniques from the tire image. The focus of the present paper is to study the tire–road contact mechanism and develop tire contact force equations. Theoretical derivations and numerical simulations were conducted first to establish the tire force–deformation equations. The proposed vision-based vehicle weight identification method was then validated with field experiments using two passenger cars and two trucks. The effects of different tire specifications, loads, and inflation pressures were studied as well. The experiment showed that the results predicted by the proposed method agreed well with the measured results. Compared with the traditional method, the developed method based on tire mechanics and computer vision has the advantages of high accuracy and efficiency, easy operation, low cost, and there is no need to lay out sensors; thus, it provides a new approach to vehicle weighing.

Nowadays, the comfort of drivers and passengers is drawing more and more attention. The interaction between vehicles and the asphalt road can cause coupled vibration and reduce the comfort of passengers. Therefore, the research on human–vehicle–road coupled vibration and the comfort of passengers is of great importance. In this paper, the three-dimensional human–bus–road coupled vibration system is established, including the bus model, the parallel biomechanical human model with 2 degrees-of-freedom (DOF), and road surface roughness condition. The proposed coupled model was then used to study the dynamic response of the system and the comfort evaluation of the human body. In the comfort evaluation, the annoyance rate based method was proposed to consider the randomness of passenger vibration, the difference of the psychosensory vibration, and the fuzziness of evaluation indicators. Compared to the fuzzy evaluation based on the ISO 2631 standard, the proposed annoyance rate based method gives a quantitative evaluation of human comfort. Not only the degree of comfort can be evaluated, but the percentage of people feeling uncomfortable can also be obtained. Finally, parametric studies were conducted to investigate the effects of road surface roughness, interlayer bonding condition, bus weight, bus speed, and sitting position. It is found that the road surface roughness has the most significant effect on human comfort.

The ride comfort of vehicles traveling on the road has always been a concern. In addition, the braking force of vehicles will aggravate the damage to road structures and reduce the comfort of drivers and passengers. In the present study, the dynamic response of pavement, road friendliness, and human comfort are investigated by setting up the human-bus-road coupled vibration system considering the braking forces. Firstly, the three-dimensional finite element model of asphalt pavement with interlayer contact and the human-bus model with multiple degrees of freedom (2n + 7) were established to analyze the dynamic responses of asphalt pavement, human body, and bus under different braking conditions. Then, the dynamic load coefficients of wheels under different combined conditions were investigated to evaluate the road friendliness and the human annoyance rate was adopted to evaluate the human comfort. The results show that for the asphalt pavement, the shear stress of the pavement is larger than the normal stress due to the bus braking, and the combined effect of road surface roughness and braking force on the road friendliness is the largest. For the human comfort, the pitch acceleration of human body increases more than the roll acceleration due to bus braking, resulting in the decreased human comfort and increased annoyance rate.