Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

In order to shorten construction periods, concrete is often cured using steam and is loaded at an early age. This changes the performance and even the durability of the concrete compared to concrete that has been cured under normal conditions. Thus, the pattern and the mechanism of concrete performance change under different curing conditions, and loading ages are of great significance. The development of brittleness under different curing conditions and loading ages was studied. The evaluation methods that were used to determine concrete brittleness were expounded. Steam, standard, and natural curing conditions were carried out on single-side notched concrete beams as well as on a concrete prism and cubic blocks. The compressive strength and splitting tensile strength of the concrete blocks along with the fracture performance of the concrete beams were tested after 3, 7, 28, and 90 days. The steam curing condition significantly improved the strength of concrete before 28 days had passed, and the standard curing condition improved the strength of concrete after 28 days. Based on the experimental fracture parameters, a two-parameter fracture model was applied to study the development of fracture toughness $K_{\mathit{IC}}^S$, critical crack tip opening displacement ${\mathit{CTOD}}_c$, and critical strain energy release rate $G_{\mathit{IC}}^S$ with hydration age under different curing conditions. With respect to long-term performance, the standard curing condition was better at resisting concrete crack propagations than the steam curing condition was. The characteristic length $l_{\mathit{ch}}$ and the material length $Q$ under the three curing conditions and the long-term development of brittleness in the concrete indicated that steam curing increased the concrete brittleness. Considering the effects of the curing condition and the loading age, a time-dependent concrete fracture toughness model was established, and the predicted value of the model was verified against the measured value. The results indicated that the model was able to accurately predict the fracture toughness with an error rate of less than 16%.

Because concrete-filled steel-tube (CFST) arch bridges usually reside in areas with strong temperature effects, which influence concrete creep behavior considerably, this paper aims to present an approach to evaluate the temperature influence on the creep behavior of CFST arch bridges, which has not been reported until now. Based on the microprestress-solidification theory for concrete creep, the age-adjusted effective modulus method and a proposed temperature estimation method were integrated into a finite-element model to calculate the creep behavior of a half-through long-span CFST arch bridge. The comparison between the predictions and measurements validated the efficiency of this approach and also demonstrated the necessity of considering temperature history in the creep-effect analysis of CFST arch bridges. The numerical method proposed in this paper provides a practical way to evaluate the temperature-involved creep effects on CFST structures.

This paper presents a model-based probabilistic structural identification that uses the deflection of prestressed concrete bridges (PSCBs) as observational information to perform Bayesian inference on the state variables associated with creep, structural rigidity, dead loads, shrinkage and prestress. The creep development is modeled as a stochastic process, and the structural rigidity is modeled as a stochastic field using the Karhunen–Loève transform. By incorporating the stochastic process/field into the inference frame, detailed information on structural states can be extracted. Considering the high dimension of the state variables, their posterior distributions are derived by the Hamiltonian Markov chain Monte Carlo (HMCMC) algorithm. As an illustrative example, two sets of deflection measurement of a case bridge are used to update the state variables in a sequential manner. Bayesian inference can calibrate the state variables, while the uncertainties associated with the state variables can be reduced. A K-means analysis can reveal the typical modes in the joint posterior distribution of the state variables, corresponding to the typical failure modes in the attributive analysis of the excessive deflection. The updated state variables are used in the probabilistic condition assessment associated with the deflection evolution.

Due to their low intrinsic damping, stay cables in cable-stayed bridges are vulnerable to dynamic excitation. The cables have often been exhibiting undesirable and excessive vibrations, which result in increasing maintenance frequency and disruption to normal operations of the entire bridges. Mitigation of undesired cable vibration can be achieved by attaching a traditionally mechanical damper near the anchorage. Although utilizing this type of damper demonstrates vibrating diminishment in cables, the approach is not the most effective method due to geometric constraints. Tuned Mass Damper (TMD) has been used to overcome the deficiency of the traditional mechanical damper since it can be mounted anywhere along a cable. This paper proposes a new type of damper called the Tuned Mass - High Damping Rubber (TM-HDR) damper for increasing damping ratio of the cable. Oscillation parameters of cable with an attached TM-HDR damper are investigated in detail using an analytical formulation of the complex eigenvalue problem. As a result, optimal parameters of the damper can be evaluated and selected in order to reduce vibrations of cable. In addition, this paper demonstrates the effects of TM-HDR damper by using an example.

Crosswind and train-induced wind are major factors influencing the aerodynamic performance of vehicles on single-level rail-cum-road bridges. Based on computational fluid dynamics (CFD) technology, this work developed a numerical model to discuss relevant issues. The flow features of the vehicle-bridge system, the influence of different lanes and vehicles are explored, and the superposition of crosswind and train-induced wind has been analyzed. The results indicate that the crosswind and train-induced wind superpose near the windward side of the concrete wall, producing two high-pressure and low-pressure zones, which are the primary cause of the aerodynamic variations on nearby vehicles. The crosswind decreases the variation amplitude of side force, rolling moment and yawing moment for 10% ∼ 20%, but the influence on the lift force and pitching moment is not significant. Therefore, the aerodynamic forces of the vehicle while meeting the train at different wind speeds can be obtained by superposing the aerodynamic variation caused by the moving train and the aerodynamic forces at its sole-traveling state, which are conservative and can provide references for actual engineering.