Bing Han

To further enhance the convenience, economic efficiency, and safety of damage detection for small- and medium-span bridges, this article presents a damage identification method founded on the bridge deflection at the vehicle position. This method is developed through theoretical derivations, numerical simulations, and laboratory validation using scaled samples, and it offers potential applications for bridge structures. The sum of the fourth-order coefficients of the bridge deflection at the vehicle position is employed as the damage identification index. Theoretical derivation indicates that this index can locate the damage position and quantitatively describe the damage degree without relying on undamaged bridge information. Each damaged part is independent of the others and is not influenced by undamaged parts. Through numerical simulation of the vehicle–bridge interaction system, the effectiveness of this index in identifying single or multiple bridge damage points on the beam is analyzed. The impacts of factors such as damage degrees, measurement noise, and road surface roughness on the recognition effect are also examined. Using high-precision laser displacement sensors, a dual-track synchronous model test is conducted. A “secondary difference” method is proposed to process the test data, which once again verifies the effectiveness of the method. It is pointed out that reducing the vehicle speed and increasing the vehicle weight can improve the accuracy of damage identification. The research presented in this article is founded on theoretical derivations, numerical simulations, and laboratory validation with scaled samples.

System performance functions constructed using traditional methods are generally non-differentiable, which requires the use of finite difference methods or virtual stochastic processes when implementing the probability density evolution method (PDEM). This approach not only compromises computational accuracy but also diminishes efficiency due to the implementation of additional computational procedures. To address this issue, this study introduces a new method to construct system performance functions using sign functions. These functions allow for derivative operations on parameters, making them more suitable for integration with PDEMs, as well as for calculations using integration methods and Monte Carlo simulations (MCSs). Furthermore, the study establishes analytical expressions for the performance functions of systems in series, parallel, and mixed configurations. Case studies on system reliability using the constructed functions demonstrate that the proposed method enhances both accuracy and efficiency in PDEM applications.

Time-dependent reliability assessment of structural systems is challenging when degradation and multiple interacting failure modes govern failure. Under these conditions, the system limit state function (LSF) may be highly nonlinear, non-smooth, and available only implicitly through high-fidelity analysis. This paper proposes a system decomposition and hierarchical learning (DHL) framework to construct an evaluable surrogate system LSF for degradation-driven, time-variant reliability analysis. The structural system is decomposed into dominant failure modes and their connectivity. Artificial neural networks are trained hierarchically to learn the decomposed relationships. Mode-level surrogates approximate the LSF of each failure mode. A system-level surrogate then integrates the mode-level performance quantities and time to capture mode interaction and mechanism switching. The resulting surrogate is combined with Monte Carlo simulation and the probability density evolution method to compute time-dependent failure probabilities and, when required, the evolution of the system performance probability density. Two benchmark problems—a highly nonlinear parallel system and a rigid–plastic portal frame with correlated collapse mechanisms under degrading capacities—are used to evaluate the approach. DHL improves system-level surrogate fidelity relative to direct system-level ANN learning, with mean reliability prediction errors below 3.1% and 1.23% in the two benchmarks, respectively, while remaining compatible with both sampling-based and density-evolution propagation schemes.

The steel–concrete composite beam, as a structural form that combines the advantages of steel and concrete, has been applied in railway engineering. However, with the increase in railway operation time, the degradation pattern of the service performance of steel–concrete composite bridges remains unclear. This paper proposes a method for calculating the long-term service performance of railway steel–concrete composite beams, considering the cumulative fatigue damage of steel beams and studs, based on the vehicle–bridge coupling theory and Miner’s linear cumulative damage criterion. The proposed method is validated using measured data from an in-service steel–concrete composite railway bridge with spans of 40 + 50 + 40 m. The calculated mid-span vertical displacement and the first two natural frequencies of the composite beam deviated from the measured results by only 2.1%, 7.7%, and 9.5%, respectively. The research results can provide a basis for extending the service life of composite beams and preventing the occurrence of safety accidents.

When the arch rib of the mid-bearing through and lower-bearing through arch bridges undergoes out-of-plane deformation, it is usually subject to the resilience force provided by the flexible hanger, which is known as the “non-conservative force effect” of the suspender. In contrast to the static condition, in the dynamic scenario, the time-varying non-conservative force exerted by the flexible suspender becomes more complex due to dynamic changes in external load. Moreover, the difference in fundamental frequency and vibration period between the bridge system and arch rib may influence the stress distribution within the arch rib during ground motion. This paper investigates the impact of time-varying non-conservative forces on the dynamic stability of arch ribs in concrete-filled steel tube (CFST) bridges under seismic loads. Specifically, it examines the influence of different seismic waveforms, frequency disparities between bridge slabs and arch ribs, and suspender stiffness on the non-conservative effect. The results reveal significant disparities in the impact of non-conservative forces exerted by the suspender during seismic events with identical intensity but varying frequency characteristics. The influence of non-conservative forces on the dynamic stability of bridges escalates as deck stiffness increases, while it remains relatively unaffected by changes in suspender stiffness.

In this study, we investigated the settling velocity and distribution between steel fiber and aggregate during vibrating compaction of ultra-high performance concrete (UHPC) mixtures. In addition, we explored the effect of fiber distribution characteristics on the UHPC strength. Based on Stokes’ theorem and considering the vibration effect, a method for calculating the settling velocity of steel fiber and aggregate in UHPC mixtures during vibrating compaction was proposed. The fiber/aggregate dispersion coefficient based on the proposed method decreased gradually with time. The experimentally obtained fiber dispersion coefficient first increased and then decreased, with the maximum value appearing at 5 s. In contrast, the fiber orientation coefficient gradually increased with time. Moreover, following vibration excitations at different times, the fiber dispersion coefficient increased and the fiber orientation coefficient decreased as the yield stress and plastic viscosity of the UHPC mixtures increased. In the vibration range of 0–15 s, the compressive and flexural strength of UHPC first increased and then decreased, with the maximum values appearing at 5 and 10 s, respectively. The compressive strength was affected by the fiber dispersion coefficient, whereas the flexural strength was affected by the fiber dispersion and orientation coefficients. Furthermore, the relational expression between compressive/flexural strengths of UHPC and fiber dispersion and orientation coefficients were presented.

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.

Concrete structure performance continuously deteriorates during operation, and the performance is simultaneously affected by chloride corrosion and repeated traffic loading. Repeated-loading-induced cracks have an impact on the rate of chloride corrosion. Chloride-induced concrete corrosion also affect the stress level of the structure under loading. Therefore, the coupled effect of repeated loading and chloride corrosion on the structural performance needs to be investigated. An upgraded test device was developed for chloride corrosion testing of unsaturated concrete structures under repeated loading. Based on the experimental results, considering the influence of repeated loading on the moisture diffusion coefficient and the chloride diffusion coefficient, a chloride transport model for unsaturated concrete under the coupled effect of repeated uniaxial compressive loading and corrosion was established. The chloride concentration under coupled loading was determined by the Crank–Nicolson finite difference method and the Thomas algorithm, and then chloride transport under the coupled effect of repeated loading and corrosion was analyzed. The results showed that the stress level and the repeated loading cycles directly affect the relative volumetric water content and chloride concentration in unsaturated concrete. The effect of chloride corrosion is more severe in unsaturated concrete compared to saturated concrete.

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.

To comprehensively consider and study the seismic performance of the self-center mortise-tenon segmental pier, its vulnerability under earthquake is evaluated. The reliability of the model is verified by comparing the cyclic pseudo-static test results with the finite element models. The model is used as the basis for vulnerability analysis. Taking the maximum displacement of the pier top as a damage index. Different damage states are described. The damage index limits μcy1, μcy, μc2, and μcmax of different damage levels are determined. The time history analysis of cast-in-place pier and mortise-tenon segmental piers under different earthquake actions is carried out by using finite element software. The probabilistic seismic demand model is established through IDA curves. And the seismic vulnerability analysis is carried out. The influence of various parameters on the seismic vulnerability of mortise-tenon segmental pier is studied by changing the longitudinal reinforcement ratio, axial compression ratio, and aspect ratio. The results show that the seismic vulnerability of the two types of piers designed according to the principle of equivalence is similar under different damage states. It can be considered that it has better seismic performance. The longitudinal reinforcement ratio, axial compression ratio, and aspect ratio have a great impact on the vulnerability of piers.