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The main etiological mechanism for Kashin-Beck disease (KBD) is deep chondrocyte necrosis induced by environmental risk factors (ERFs). The scholars have conducted several epidemiological, cellular, and animal model studies on ERFs. Gradually, four etiological hypotheses have been formed, including water of organic poisoning hypothesis represented by fulvic acid (FA), biogeochemical hypothesis represented by selenium (Se) deficiency, food mycotoxin poisoning hypothesis represented by T-2 toxin poisoning and compound etiology theory hypothesis. The animal models of KBD have been replicated based on the previous etiological hypotheses. The different species of animals (monkey, rat, dog, pig, chicken, and rabbit) were treated with different ERFs interventions, and the clinical manifestations and pathological changes of articular cartilages were observed. The animals in the experimental group were fed with endemic water, endemic grain, low nutrition, thallium sulfate, FA, Se, T-2 toxin, and iodine. The dose of thallium sulfate was 1154 μg/d; the doses range of FA were 5, 50, 150, 200, and 211 mg/kg; the doses range of Se were 0.00035, 0.00175, 0.005, 0.02, 0.031, 0.1, 0.15, 0.314, 0.5, and 10 mg/kg; the doses range of T-2 toxin were 40, 100, 200, 600, 1000, 1500, 3000, 6000, and 9000 ng/g; and the doses range of iodine were 0.04, 0.18, and 0.4-0.5 μg/g. The sample size ranged from 9 to 230 depending on the interventions and grouping; the follow-up duration ranged from 1 week to 18 months. Moreover, the methods and comparisons of different animal models of KBD had been summarized to provide a useful basis for studying the pathogenesis of KBD. In conclusion, the rhesus monkeys administrated endemic water and grain were susceptible animals to replicate KBD. The rats treated with T-2 toxin combined with Se/nutrition deficiency could be a suitable and widely used animal model.

Subgrade filling of high-speed railway will cause additional settlement and deformation of existing lines.In this paper,using the finite element analysis platform (Taxis 2D) and considering the settlement of the existing line itself, This paper analyzes the impact of the new and old subgrade lap type and the layer filled this lesson of the new subgrade on the implementation of the existing subgrade. The results illustrate the following points:(1) Compared with different overlapping surface types, different filling those points have a greater impact on the settlement of the existing line; (2) The height of the steps is 0.6m and the width is 1.0m.In the subsequent layered filling construction process, each level The loading capacity of 30cm is more reasonable, which can effectively control the influence of the existing line on the additional settlement of the existing railway. The research results can provide a reference for the optimization of similar roadbed width construction.

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

Focused on the effects of environmental and operational variability on the structures, a novel procedure for structura l linear and nonlinear damage detection is proposed based on the time series analysis and the higher statistical moments. The higher statistical moments of residual error of AR model, such as skewness and kurtosis, are then defined as the new damage-sensitive feature s to be a complimentary. Six integrated damage-sensitive features are further defined for vibration-based damage detection in terms of arithmetic and geometric mean of the residual errors. A series of experiments on a complicated truss bridge combined with a steel bridge plate have been conducted in laboratory. Damage was simulated by loosening the bolts of joints, and environmental variability wer e introduced by changing the shaker input level. 16 acceleration data of the bridge in each baseline and test state are measured and recorded for the structural damage detection. Based on these time series of acceleration data, the applicability of the proposed proced ure is evaluated. Some valued conclusions are made and discussions suggested as well.

The successful development of structural damage prognosis (SDP) capability requires the further development and integration of many technology areas, such as hardware, software, modeling capabilities, and so forth. By combining the damage-sensitive feature extraction, the higher statistical moments of structural responses and the fuzzy c-means (FCM) clustering algorithm with the traditional time-series analysis-based SDP method, an integrated method is proposed for the SDP of a truss bridge model with end connector bolts under environmental and operational variability simulated in laboratory. The theoretical formulation of the integrated method is developed first. A six-bay truss bridge model is then designed and fabricated in laboratory for assessing the performance of the proposed method. Various damage cases are simulated by partially or completely loosening some end connecter bolts of the truss bridge under the environmental variability. The performance of the integrated SDP method is assessed...

This work aims to predict fatigue life of hybrid integrated circuit (HIC) hermetical metal sealing structure mounted on PCB under random vibration loading. The prediction method consists of following steps. Firstly, finite-element model was developed to obtain model parameters (including natural frequencies and mode shapes) and power spectral density (PSD) of the critical part of sealing structure by ANSYS workbench. Secondly, modal test and random vibration test were conducted to verify the results of simulation. Thirdly, the Von Mises stress PSD was transformed into time-history data through inverse Fourier transform with Matlab program after calculating from the FEA results. The rainflow-counting algorithm was employed to evaluate cumulative damages of the critical part. The material’s S-N curve, Palmgren-Miner’s damage accumulation rule and rainflow-counting algorithm were used to predict fatigue life. A specially designed fixture and board with heat sink were used in the experiment to verify the first five mode shapes and response spectrum of the six critical points with hammer excitation. The calculation result of in this study is 70.3 hours which could be a reference for structural design of hybrid integrated circuit hermetical metal sealing under vibration conditions.

This study deals with vibration-based damage detection in a truss bridge model and suggests a novel methodology based on fuzzy clustering and measured frequency response function (FRF) data reduced by principal component projection. A six-bay truss bridge model is designed and fabricated in laboratory, various connection damages are simulated by loosening the end connecter bolts, and the environmental effects are taken into account by changing in excitation force levels of a shaker. The FRFs of the healthy and the damaged structure are used as initial data. The FRF data normalization is performed for eliminating the effects caused by the environmental and operational variability. Two data projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA) are adopted for data compression and the median values of principal components are defined for damage feature extraction. The fuzzy c-means (FCM) clustering algorithm is used to categorize these features for structural damage detection. The illustrated results show that the proposed method can effectively identify the bridge damages simulated by loosening the bolted joints of the truss bridge structure. It is sensitive to the structural damage but it is non-sensitive to the effect of the environmental and operational variations. This makes it quite generic and permits its potential development for real and complex truss bridges in site.

Effect of computational patterns of principle component analysis (PCA) on moving force identification (MFI) is studied in this paper. The motion equation of bridge due to moving vehicles are formed, the relationship between moving axle loads and caused bridge responses are established for the PCA-based MFI method in time domain. The measured bridge responses are rearranged in a matrix form for easily performing PCA and are adopted for obtaining an acceptable solution to the MFI problem. A laboratory experimental study was conducted to assess effectiveness and robustness of the PCA-based MFI method. The illustrated results show that the PCA-based method is an easy executive and more effective method for the MFI problem. The PCA computational patterns should be appropriately considered due to its higher sensitivity on response catalogues.

The moving vehicle loads on a bridge deck is one of the most important live loads of bridges. They should be understood, monitored and controlled before the bridge design as well as when the bridge is open for traffic. A MOM-based algorithm (MOMA) is proposed for identifying the timevarying moving vehicle loads from the responses of bridge deck in this paper. It aims at an acceptable solution to the ill-conditioning problem that often exists in the inverse problem of moving force identification. The moving vehicle loads are described as a combination of whole basis functions, such as orthogonal Legendre polynomials or Fourier series, and further estimated by solving the new system equations developed with the basis functions. A number of responses have been combined, some numerical simulations on single axle, two axle and multiple-axle loads, being either constant or timevarying, have been carried out and compared with the existing time domain method (TDM) in this paper. The illustrated results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent when it is used to identify the moving force from bridge responses.

A MOM-based algorithm (MOMA) has been developed for moving force identification from dynamic responses of bridge in the companion paper. This paper further evaluates and investigates the properties of the developed MOMA by experiment in laboratory. A simply supported bridge model and a few vehicle models were designed and constructed in laboratory. A series of experiments have then been conducted for moving force identification. The bending moment and acceleration responses at several measurement stations of the bridge model are simultaneously measured when the model vehicle moves across the bridge deck at different speeds. In order to compare with the existing time domain method (TDM), the best method for moving force identification to date, a carefully comparative study scheme was planned and conducted, which includes considering the effect of a few main parameters, such as basis function terms, mode number involved in the identification calculation, measurement stations, executive CPU time, Nyquist fraction of digital filter, and two different solutions to the ill-posed system equation of moving force identification. It was observed that the MOMA has many good properties same as the TDM, but its CPU execution time is just less than one tenth of the TDM, which indicates an achievement in which the MOMA can be used directly for real-time analysis of moving force identification in field.

A MOM-based algorithm (MOMA) is proposed for identifying of the time-varying moving vehicle loads on a bridge in this paper. A series of numerical simulations and experiments in laboratory have been studied and the proposed MOMA are compared with the existing time domain method (TDM). A few main parameters, such as basis function terms, executive CPU time, Nyquist fraction of digital filter, two different solutions to the ill-posed system equation, etc, have been investigated. Both the numerical simulation and experimental results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent, but its CPU execution time is just less than one tenth of the TDM.

Using the measured frequency response function (FRF) data, a novel algorithm for structural health monitoring is proposed based on the principal component analysis (PCA) in this paper. First, the measured FRF data for a structure in both the healthy and the damaged states are used as the initial data. A PCA transformation is performed to obtain the features of an intact structure, in which an orthogonal transformation matrix packed by the first few eigenvectors of covariance matrix can be found. Further, the orthogonal transformation matrix is applied to the FRF data of damage structures to extract the features of structure in the damage state. By comparing the features of structure in the two states, a new damage feature index, namely the median values of the principal components (PCs), is proposed for structural health monitoring. Finally, the efficiency and robustness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. This paper aims at investigating the effect of parameters, such as training data selection, sample points and so on. The illustrated results show that the PCA-based algorithm is correct and effective for structural health monitoring. The new damage feature index can reflect the structure state as well. The algorithm is purely based on the FRF data and independent of structure model, which makes it applicable for structural health monitoring in situ.

An improved time domain method (ITDM) is proposed for moving force identification using bridge responses, which aims at an acceptable solution to the ill-conditioning problem that often exists in the inverse problem of moving force identification. Based on the method of moments (MOM) and the theory of moving force identification, the moving forces were described as a combination of whole basis functions, such as orthogonal Legendre polynomials or Fourier series, and were then estimated by solving the new system equations developed based on the basis functions. Under a number of response combination cases, the moving vehicle loads are identified using the ITDM and compared with the existing time domain method (TDM). Further a laboratory study was conducted to evaluate the effect of various parameters on the ITDM. Those parameters include basis function number, mode number, number of measured stations, and CPU executive time of the ITDM. Simulation and experiment results show that the ITDM has higher identification accuracy and robust noise immunity as well as being able to generate an acceptable solution to the ill-conditioning problem to some extent when it is used to identify the moving forces from bridge responses. Meanwhile, the ITDM can lessen the executive CPU time as well as being more flexible when compared with the TDM. This is beneficial to real time analysis of moving force identification in field.

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.