A tunnel deformation monitoring system is developed with the use of fiber Bragg grating (FBG) sensing technique, aiming at providing continuous monitoring of railway tunnel deformation in the long term, and early warning for the rail service maintainers and authorities to avoid catastrophic consequences when significant deformation occurs. Specifically, a set of FBG bending gauges with the ability of angle measurement and temperature compensation is designed and manufactured for the purpose of online monitoring of tunnel deformation. An overall profile of lateral tunnel displacement along the longitudinal direction can be obtained by implementing an array of the FBG bending gauges interconnected by rigid rods, in conjunction with a proper algorithm. The devised system is verified in laboratory experiments with a test setup enabling to imitate various patterns of tunnel deformation before the implementation of this system in an in-service high-speed railway (HSR) tunnel.

The dynamic behaviors of the bridge structures have great effects on the comfortability and safety of running high-speed trains, which can also reflect the structural degradation. This paper aims to reveal the characteristics of the dynamic behaviors induced by train loadings for a combined highway and railway bridge. Monitoring-based analysis of the acceleration and dynamic displacement of the bridge girder is carried out. The effects of train loadings on the vertical acceleration of the bridge girder are analyzed; the spatial variability of the train-induced lateral girder displacement is studied; and statistical analysis has been performed for the daily extreme values of the train-induced girder deflections. It is revealed that there are great time and spatial variabilities for the acceleration induced by train loadings for the combined highway and railway cable-stayed bridge. The daily extreme values of the train-induced girder deflections can be well fitted by the general extreme value distribution.

Ground vibration is one of the most undesirable effects induced by blast operation in mountain tunnels, which could cause negative impacts on the residents living nearby and adjacent structures. The ground vibration effects can be well represented by peak particle velocity (PPV) and corner frequency ($f_c$) on the ground. In this research, the PPV and the corner frequency of the mountain surface above the large-span tunnel of the new Badaling tunnel are observed by using the microseismic monitoring technique. A total of 53 sets of monitoring results caused by the blast inside tunnel are recorded. It is found that the measured values of PPV are lower than the allowable value. The measured values of corner frequency are greater than the natural frequencies of the Great Wall, which will not produce resonant vibration of the Great Wall. The vibration effects of associated parameters on the PPV and corner frequency which include blast charge, rock mass condition, and distance from the blast point to mountain surface, are studied by regression analysis. Empirical formulas are proposed to predict the PPV and the corner frequency of the Great Wall and surface structures due to blast, which can be used to determine the suitable blast charge inside the tunnel.

Track irregularities of high-speed Maglev lines have significant influence on ride comfort. Their adjustment is of key importance in the daily maintenance of these lines. In this study, an adjustment method is proposed and track irregularities analysis is performed. This study considers two modules: an inspection module and a vehicle-guideway coupling vibration analysis module. In the inspection module, an inertial reference method is employed for field-measurements of the Shanghai high-speed Maglev demonstration line. Then, a partial filtering, integration method, resampling method, and designed elliptic filter are employed to analyze the detection data, which reveals the required track irregularities. In the analysis module, a vehicle-guideway interaction model and an electromagnetic interaction model were developed. The influence of the measured line irregularities is considered for the calculations of the electromagnetic force. Numerical integration method was employed for the calculations. Based on the actual field detection results and analysis using the numerical model, a threshold analysis method is developed. Several irregularities modalities with different girder end's deviations were considered in the simulations. The inspection results indicated that long-wavelength irregularities with larger girder end's deviations were the dominant irregularities. In addition, the threshold analysis of the girder end's deviation shows that irregularities that have a deviation amplitude larger than 6 mm and certain modalities (e.g., M- and N-shape) are unfavorable. These types of irregularities should be adjusted during the daily maintenance.

Nowadays, many tunnels have been commissioned for several decades, which require effective inspection methods to assess their health conditions. The ambient vibration test has been widely adopted for the damage identification of concrete structures. In this study, the vibration characters of tunnel lining shells built with forepoling method was analyzed based on the analytical solutions of the Donnell-Mushtari shell theory. The broken rock, foreploing, rock-concrete contacts between rock mass and concrete lining, was represented by elastic boundaries with normal and shear stiffness. The stiffness of weak contacts has significant effects on the natural frequency of tunnel lining. Numerical simulations were also carried out to compare with the results of the analytical methods, showing that even though the low nature frequency is difficult to distinguish, the presented approach is convenient, effective and accurate to estimate the natural frequency of tunnel linings. Influences of the void, the lining thickness and the concrete type on natural frequencies were evaluated.

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

Compared with the highway bridges, the relatively higher requirement on the safety and comfort of vehicle makes the high-speed railway (HSR) bridges need to present enhanced dynamic performance. To this end, installing a health monitor system (HMS) on selected key HSR bridges has been widely applied. Typically, the HSR takes fully enclosed operation model and its skylight time is very short, which means that it is not easy to operate the acquisition devices and download data on site. However, current HMS usually involves manual operations, which makes it inconvenient to be used for the HSR. Hence, a HMS named DASP-MTS (Data Acquisition and Signal Processing - Monitoring Test System) that integrates the internet, cloud computing (CC) and virtual instrument (VI) techniques, is developed in this study. DASP-MTS can realize data acquisition and transmission automatically. Furthermore, the acquired data can be timely shared with experts from various locations to deal with the unexpected events. The system works in a Browser/Server frame so that users at any places can obtain real-time data and assess the health situation without installing any software. The developed integrated HMS has been applied to the Xijiang high-speed railway arch bridge. Preliminary analysis results are presented to demonstrate the efficacy of the DASP-MTS as applied to the HSR bridges. This study will provide a reference to design the HMS for other similar bridges.

In order to study the mechanical behavior of shield tunnel segments during assembly stage, the in-situ tests and FDM numerical simulation were conducted based on the Foguan Shiziyang Tunnel with large cross-section. Analysis for the load state of the assembling segments in different assembly steps as well as the investigation for the changing of inner forces and longitudinal stress of segments with assembling steps were carried out in this paper. By comparing the tested results with the simulated results, the conclusions and suggestions could be drawn as follows: (1) It is the most significant for the effects on axial force and bending moment caused by the assembly of adjacent segment, followed by the insertion of key segment while the effects in the other assembly steps are relative smaller. With the increasing value of axial force, the negative bending moment turns into positive and remains increasing in most monitored sections, while the bending moment of segment B1and B6 are negative and keeping increasing; (2) The closer the monitored section to the adjacent segments or the key segment, the more significant the internal forces response, and the monitored effects of key segment insertion are more obvious than that of calculation; (3) The axial forces are all in compression during assembling and the monitored values are about 1.5~1.75 times larger than the calculated values, and the monitored values of bending moment are about 2 times the numerical calculation. The bending moment is more sensitive to the segments assembly process compared with axial force, and it will result in the large bending moment of segments during assembling when the construction parameters are not suitable or the assembly error is too large. However, the internal forces in assembly stage are less than those in normal service stage; (4) The distribution of longitudinal stress has strong influence on the changing of the internal forces. The segment side surface and intrados in the middle of two adjacent jacks are the crack-sensitive positions in the early assembly stage, and subsequently segment corners far away from the jacks become the crack-sensitive parts either.

This paper presents a comparative study of displacement measurement using four sensors that are being used in the field: they are a ring gauge, a laser Doppler vibrometer (LDV), a vision-based displacement measurement system (VDMS), and an optoelectronic displacement meter (ODM). The comparative study was carried out on a brand-new high-speed railroad bridge designed to produce displacements within a couple of millimeters under the loading of a high-speed train. The tests were carried out on a single-span steel plate girder bridge two times with different train loadings: KTX and HEMU. The measured displacement is compared as raw and further discussion was made on the measurement noise, peak displacement, and frequency response of four sensors. The comparisonsare summarized to show the pros and cons of the used sensors in measuring displacement at a typical high-speed railroad bridge.

This work proposes and investigates re-centering negative stiffness dampers (NSDs) for vibration suppression in high-speed trains. The merit of the negative stiffness feature is demonstrated by active controllers on a high-speed train. This merit inspires the replacement of active controllers with re-centering NSDs, which are more reliable and robust than active controllers. The proposed damper design consists of a passive magnetic negative stiffness spring and a semi-active positioning shaft for re-centering function. The former produces negative stiffness control forces, and the latter prevents the amplification of quasi-static spring deflection. Numerical investigations verify that the proposed re-centering NSD can improve ride comfort significantly without amplifying spring deflection.

Under braking forces of a freight train, there are great longitudinal structural responses of a large freight railway cable-stayed bridge. To alleviate such adverse reactions, viscous dampers are required, whose parametric selection is one of important and arduous researches. Based on the longitudinal dynamics vehicle model, responses of a cable-stayed bridge are investigated under various cases. It shows that there is a notable effect of initial braking speeds and locations of a freight train on the structural responses. Under the most unfavorable braking condition, the parameter sensitivity analyses of viscous dampers are systematically performed. Meanwhile, a mixing method called BPNN-NSGA-II, combining the Back Propagation neural network (BPNN) and Non-Dominated Sorting Genetic Algorithm With Elitist Strategy (NSGA-II), is employed to optimize parameters of viscous dampers. The result shows that: 1. the relationships between the parameters of viscous dampers and the key longitudinal responses of the bridge are high nonlinear, which are completely different from each other; 2. the longitudinal displacement of the bridge main girder significantly decreases by the optimized viscous dampers.

The structural responses are often used to identify the structural local damages. However, it is usually difficult to gain the responses of the track, as the sensors cannot be installed on the track directly. The vehicles running on a track excite track vibration and can also serve as response receivers because the vehicle dynamic response contains the vibration information of the track. A damage identification method using the vehicle responses and sensitivity analysis is proposed for the vehicle-track coupling system in this paper. Different from most damage identification methods of vehicle-track coupling system, which require the structural responses, only the vehicle responses are required in the proposed method. The local damages are identified by a sensitivity-based model updating process. In the vehicle-track coupling system, the track is modeled as a discrete point supported Euler-Bernoulli beam, and two vehicle models are proposed to investigate the accuracy and efficiency of damage identification. The measured track irregularity is considered in the calculation of vehicle dynamic responses. The measurement noises are also considered to study their effects to the damage identification results. The identified results demonstrate that the proposed method is capable to identify the local damages of the track accurately in different noise levels with only the vehicle responses.

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

Based on monitoring data collected from the Nanjing Dashengguan Bridge over the last five years, this paper systematically investigates the effects of temperature field and train loadings on the structural responses of this long-span high-speed railway bridge, and establishes the early warning thresholds for various structural responses. Then, some lessons drawn from the structural health monitoring system of this bridge are summarized. The main context includes: (1) Polynomial regression models are established for monitoring temperature effects on modal frequencies of the main girder and hangers, longitudinal displacements of the bearings, and static strains of the truss members; (2) The correlation between structural vibration accelerations and train speeds is investigated, focusing on the resonance characteristics of the bridge at the specific train speeds; (3) With regard to various static and dynamic responses of the bridge, early warning thresholds are established by using mean control chart analysis and probabilistic analysis; (4) Two lessons are drawn from the experiences in the bridge operation, which involves the lacks of the health monitoring for telescopic devices on the beam-end and bolt fractures in key members of the main truss.

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.