A novel method for evaluating load restraint assemblies to ensure the safety of railway freight transportation.

The violent goods vibration during curve negotiation is a huge threat to the vehicle running safety. Qualified load restraint assemblies that can significantly suppress the cargo vibration are necessary. This study proposes a novel method for evaluating the essential restraint strength, focusing on the relative motion between cargo and wagon. In the beginning, as a comparison, current methods are used to calculate the necessary stiffness of lashings, which are adopted to restrain the cargo vibration on the wagon. Based on the data of the field test, the accuracy of the established wagon-cargo coupled dynamics model is validated. The loaded wagon model negotiates the curve under different running and loading conditions. The simulation results and analysis demonstrate effective strategies for suppressing the vibration of the cargo and reveal the necessary lashing stiffness. The comparison among the results of different evaluation methods shows that the stability of the cargo can be improved by optimizing the lashing stiffness with the method of dynamics simulations. We hope this study will make a positive contribution to the safety of railway freight transportation.

Self-Sensing Cementitious Composites with Hierarchical Carbon Fiber-Carbon Nanotube Composite Fillers for Crack Development Monitoring of a Maglev Girder.

In view of high-performance, multifunctional, and low-carbon development of infrastructures, there is a growing demand for smart engineering materials, making infrastructures intelligent. This paper reports a new-generation self-sensing cementitious composite (SSCC) incorporated with a hierarchically structured carbon fiber (CF)-carbon nanotube (CNT) composite filler (CF-CNT), which is in situ synthesized by directly growing CNT on CF. Various important factors including catalyst, temperature, and gas composition are considered to investigate their kinetic and thermodynamic influence on CF-CNT synthesis. The reciprocal architecture of CF-CNT not only alleviates the CNT aggregation, but also significantly improves the interfacial bonding between CF-CNT and matrix. Due to the synergic and spatially morphological effects of CF-CNT, that is, the formation of widely distributed multiscale reinforcement networks, SSCCs with CF-CNTs exhibit high mechanical properties and electrical conductivity as well as excellent self-sensing performances, particularly enhanced sensing repeatability. Moreover, the SSCCs with CF-CNTs are integrated into a full-scale maglev girder to devise a smart system for crack development monitoring. The system demonstrates high sensitivity and fidelity to capture the initiation of cracks/damage, as well as progressive and sudden damage events until the complete failure of the maglev girder, indicating its considerable potential for structural health monitoring of infrastructures.

An Adaptive and Robust Control Strategy for Real-Time Hybrid Simulation.

A real-time hybrid simulation (RTHS) is a promising technique to investigate a complicated or large-scale structure by dividing it into numerical and physical substructures and conducting cyber-physical tests on it. The control system design of an RTHS is a challenging topic due to the additional feedback between the physical and numerical substructures, and the complexity of the physical control plant. This paper proposes a novel RTHS control strategy by combining the theories of adaptive control and robust control, where a reformed plant which is highly simplified compared to the physical plant can be used to design the control system without compromising the control performance. The adaptation and robustness features of the control system are realized by the bounded-gain forgetting least-squares estimator and the sliding mode controller, respectively. The control strategy is validated by investigating an RTHS benchmark problem of a nonlinear three-story steel frame The proposed control strategy could simplify the control system design and does not require a precise physical plant; thus, it is an efficient and practical option for an RTHS.

Preliminary Study on Assessing Delaminated Cracks in Cement Asphalt Mortar Layer of High-Speed Rail Track Using Traditional and Normalized Impact-Echo Methods.

The severe deterioration of a cement asphalt (CA) mortar layer may lead to the movement of the upper concrete slab and impair the safety of the speedy train. In this study, a test specimen simulating the structure of high-speed rail track slabs was embedded with delaminated cracks in various lateral sizes inside the CA mortar layer. Impact-echo tests (IE) were performed above the flawed and flawless locations. In present study, the IE method is chosen to assess defects in the CA mortar layer. Both traditional IE and normalized IE are used for data interpolation. The normalized IE are the simulated transfer function of the original IE response. The peak amplitudes in the normalized amplitude spectrum and the peak frequency in the traditional amplitude spectrum for the top concrete overlay were used to develop simple indicators for identifying the integrity of the CA mortar layer. The index was based on the difference of the experimental peak amplitude and frequency of the ones calculated from previously developed formulas for plates without substrates. As a result, the technique does not require an experimental baseline for the crack assessment. A field test and analysis procedure for evaluating high-speed rail slab systems are proposed.

Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method.

For high-speed trains, out-of-roundness (OOR)/defects on wheel tread with small radius deviation may suffice to give rise to severe damage on both vehicle components and track structure when they run at high speeds. It is thus highly desirable to detect the defects in a timely manner and then conduct wheel re-profiling for the defective wheels. This paper presents a wayside fiber Bragg grating (FBG)-based wheel condition monitoring system which can detect wheel tread defects online during train passage. A defect identification algorithm is developed to identify potential wheel defects with the monitoring data of rail strain response collected by the devised system. In view that minor wheel defects can only generate anomalies with low amplitude compared with the wheel load effect, advanced signal processing methods are needed to extract the defect-sensitive feature from the monitoring data. This paper explores a Bayesian blind source separation (BSS) method to decompose the rail response signal and to obtain the component that contains defect-sensitive features. After that, the potential defects are identified by analyzing anomalies in the time history based on the Chauvenet's criterion. To verify the proposed defect detection method, a blind test is conducted using a new train equipped with defective wheels. The results show that all the defects are identified and they concur well with offline wheel radius deviation measurement results. Minor defects with a radius deviation of only 0.06 mm are successfully detected.

Measurement and Forecasting of High-Speed Rail Track Slab Deformation under Uncertain SHM Data Using Variational Heteroscedastic Gaussian Process.

Uncertainty in sensor data complicates the construction of baseline models for the measurement and forecasting (M&F) of high-speed rail (HSR) track slab deformation. Standard Gaussian process (GP) assumes a uniform noise throughout the input space. However, in the application to modelling of HSR structural health monitoring (SHM) data, this assumption can be unrealistic, because of its unique heteroscedastic uncertainty that is induced by dynamic train loading, electromagnetic interference, large temperature variation, and daily maintenance actions of railway track infrastructure. Therefore, this study firstly develops a novel online SHM system enabled by fiber Bragg grating (FBG) technology to eliminate electromagnetic interference on SHM data for continuous and long-term monitoring of track slab deformation, with the capacity of temperature self-compensation. To deal with different sources of uncertainty, the study explores Variational Heteroscedastic Gaussian Process (VHGP) approach while using variational Bayesian and Gaussian approximation for data modelling, estimation of the monitoring data uncertainty, and further data forecasting. The results demonstrate that the VHGP framework yields more robust regression results and the estimated confidence level can better depict the heteroscedastic variances of the noise in HSR data. Higher accuracy for both regression and forecasting is gained through VHGP and the position with maximum noise can be more accurately forecasted with a smooth varying confidence interval. Based on in-situ measurement data, the uncertainty levels for all sensors are estimated together with corresponding deformation profiles for the instrumented segment and three typical types of uncertainty are summarized during the M&F process of HSR track slab deformation.