A comprehensive review on coupling vibrations of train–bridge systems under external excitations

In recent years, high-speed railways in China have developed very rapidly, and the number and span of the railway bridges are keeping increasing. Meanwhile, frequent extreme disasters, such as strong winds, earthquakes and floods, pose a significant threat to the safety of the train–bridge systems. Therefore, it is of paramount importance to evaluate the safety and comfort of trains when crossing a bridge under external excitations. In these aspects, there is abundant research but lacks a literature review. Therefore, this paper provides a comprehensive state-of-the-art review of research works on train–bridge systems under external excitations, which includes crosswinds, waves, collision loads and seismic loads. The characteristics of external excitations, the models of the train–bridge systems under external excitations, and the representative research results are summarized and analyzed. Finally, some suggestions for further research of the coupling vibration of train–bridge system under external excitations are presented.

     

Comprehensive assessment method for environmental impact of railway based on geographic information system

By integrating the merits of the map overlay method and the geographic information system (GIS), a GIS based map overlay method was developed to analyze comprehensively the environmental vulnerability around railway and its impact on the environment, which is adapted for the comprehensive assessment of railway environmental impact and the optimization of railway alignments. The assessment process of the GIS based map overlay method was presented, which includes deciding the system structure and weights of assessment factors, making environmental vulnerability grade maps, and evaluating the alternative alignments comprehensively to obtain the best one. With the GIS functions of spatial analysis, such as overlay analysis and buffer analysis, and functions of handling attribute data, the GIS based map overlay method overcomes the shortcomings of the existing map overlay method and the conclusion is more reasonable. In the end, a detailed case study was illustrated to verify the efficiency of the method.     

Vibration and sound radiation of a rotating train wheel subject to a vertical harmonic wheel–rail force

Railway Engineering Science - The rapid development of high-speed railway networks requires advanced methods for analysing vibration and sound radiation characteristics of a fast rotating train...     

A rigid–flexible coupling finite element model of coupler for analyzing train instability behavior during collision

Rail vehicles generate huge longitudinal impact loads in collisions. If unreasonable matching exists between the compressive strength of the intermediate coupler and the structural strength of the car body, the risk of car body structure damage and train derailment will increase. Herein, a four-stage rigid–flexible coupling finite element model of the coupler is established considering the coupler buckling load. The influence of the coupler buckling load on the train longitudinal–vertical–horizo...     

VV＆A and confidence assessment of a complex giant system simulation model

Modernsocietyisinundatedwithinformation ,networksandsystems .Manycomplexsystemshaveappearedinvariousfieldssuchasengineeringtechnolo gy ,socialeconomyandecologicalenvironment ,etc .,(asshowninFig .1 ) [1 ] .Fig .1　Complexgiantsystem　　Howdowemanagesuchacomplexsystem ?Howdoweanalyze ,predict,programanddesignthecom plexityofsuchasystemsoastoimprovethesystemoperationstateandefficiency ?Peoplearefacingthesemajorproblems .　　Asacomplexsystemmayconcernfieldsincludingengineeringtechnology ,social…     

Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network

An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation（BP） algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small（δ=3.3%） when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.     

Study of fault injection system based on software

A software fault injection system SFIS is designed,which consists of the target system plus a fault injector,fault library,workload,data collector,and data analyzer. A serial communication mechanism is adopted to simulate the factual work environment. Then a fault model is built for single particle event,which can be denoted as FM=(FL,FT). FL stands for fault location,and FT stands for fault type. The fault model supports three temporal faults: transient,intermittent,and permanent. During the experiments implemented by SFIS,the software interruption method is adopted to inject transient faults,and step trace method is adopted to inject permanent faults into the target system. The experiment results indicate that for the injected transient code segment faults,2.8 % of them do not affect the program output,80.1% of them are detected by the built-in error detection in the system,and 17.1% of them are not detected by fault detection mechanism. The experiment results verify the validity of the fault injection method.     

Fluid–solid coupling analysis of rock pillar stability for concealed karst cave ahead of a roadway based on catastrophic theory

In order to study the mechanism of water inrush from a concealed, confined karst cave, we established a fluid–solid coupling model of water inrush from a concealed karst cave ahead of a roadway and a strength reduction method in a rock pillar for preventing water inrush based on catastrophic theory. Fluid–solid coupling effects and safety margins in a rock pillar were studied. Analysis shows that rock pillar instability, exerted by disturbance stress and seepage stress, is the process of rock pillar catastrophic destabilization induced by nonlinear extension of plastic zones in the rock pillar. Seepage flow emerges in the rock pillar for preventing water inrush, accompanied by mechanical instability of the rock pillar. Taking the accident of a confined karst cave water-inrush of Qiyi Mine as an example, by studying the safety factor of the rock pillar and the relationship between karst cave water pressure and thickness of the rock pillar, it is proposed that rock pillar thickness with a safety factor equal to 1.5 is regarded as the calculated safety thickness of the rock pillar, which should be equal to the sum of the blasthole depth, blasting disturbance depth and the calculated safety thickness of the rock pillar. The cause of the karst water inrush at Qiyi Mine is that the rock pillar was so small that it did not possess a safety margin. Combining fluid–solid coupling theory, catastrophic theory and strength reduction method to study the nonlinear mechanical response of complicated rock engineering, new avenues for quantitative analysis of rock engineering stability evaluation should be forthcoming.     

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model(GM(1,1)). Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system’s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model (GNNM(1,1)) was built. In order to reduce stochastic phenomenon in GNNM(1,1), the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model(MNNGM(1,1)) was proposed by combining Markov chain with GNNM(1,1). Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM(1, 1) and GNNM(1,1) with the proposed model, the results indicate that the absolute mean error of MNNGM(1,1) is about 0.4 times of GNNM(1,1) and 0.2 times of GM(1,1), and the mean square error of MNNGM(1,1) is about 0.25 times of GNNM(1,1) and 0.1 times of GM(1,1).     

Layered learning of soccer robot based on artificial neural network

0　ＩＮＴＲＯＤＵＣＴＩＯＮＲｅｃｅｎｔｌｙ,ｍｕｌｔｉ ａｇｅｎｔｓｙｓｔｅｍｓｈａｖｅｂｅｃｏｍｅａｌａｒｇｅｆｉｅｌｄｏｆａｒｔｉｆｉｃｉａｌｉｎｔｅｌｌｉｇｅｎｃｅ[1 ] ,Ｇｅｎｅｒａｌｌｙ,ｍｕｌｔｉ ａｇｅｎｔｓｙｓｔｅｍｉｓｄｅｆｉｎｅｄａｓｔｈｅｓｙｓｔｅｍｃｏｍｐｏｓｅｄｏｆｍｏｒｅｔｈａｎ 2ｒｏｂｏｔｓａｎｄｐｅｒｆｏｒｍｓｔｈｅｔａｓｋｓｂｙｃｏｏｐｅｒａｔｉｏｎ .Ｔｈｅｓｙｓ ｔｅｍｈａｓｄｉｆｆｅｒｅｎｔｆａｃｔｏｒｓｃｏｍｐａｒｅｄｗｉｔｈｓｉｎｇｌｅｒｏｂｏｔｓｙｓ ｔｅｍ …     

Digital simulation studies on long transmission line protection based on balance of energy

0　ＩＮＴＲＯＤＵＣＴＩＯＮＡｐｐｌｉｃａｔｉｏｎｏｆｎｏｒｍａｌｃｕｒｒｅｎｔｄｉｆｆｅｒｅｎｔｉａｌｒｅｌａｙｏｎｌｏｎｇＵＨＶｔｒａｎｓｍｉｓｓｉｏｎｌｉｎｅｓａｒｅｌｉｍｉｔｅｄｂｅｃａｕｓｅｏｆｄｉｓｔｒｉｂｕｔｅｄｃａｐａｃｉｔａｎｃｅｃｕｒｒｅｎｔ[1,2 ] .Ｔｏｐｒｅｖｅｎｔｔｈｅｓｙｍｐａｔｈｙｔｒｉｐｃａｕｓｅｄｂｙｄｉｓｔｒｉｂｕｔｅｄｃａｐａｃｉｔａｎｃｅｃｕｒ ｒｅｎｔ,ｔｈｅｔｒｉｐｐｉｎｇｃｕｒｒｅｎｔｔｈｒｅｓｈｏｌｄｉｓｕｓｕａｌｌｙｅｌｅｖａｔ ｅｄ .Ｔｈｉｓａｆｆｅ…     

Study on electromechanical coupling nonlinear vibration of flywheel energy storage system

The new type transforming format and storage method of electric energy have become a focus in present. With the breakthrough of high intensity fiber material, low power loss bearing, electric power and electronics technology, the attention is paid to the flywheel energy storage system (FESS) technology by domestic and overseas academia for its high efficiency of electrical energy transforming, quick charge and discharge, no pollu-tion and large energy storage density, etc. The application for…     

The study of fuzzy chaotic neural network based on chaotic method

This paper proposes a type of Fuzzy Chaotic Neural Network (FCNN). Firstly, the model of recurrent fuzzy neural network (RFNN) is considered, which adds a feedback in the second layer to realize dynamic map. Then, the Logistic map is introduced into the recurrent fuzzy neural network, so as to build a Fuzzy Chaotic Neural Network (FCNN). Its chaotic character is analyzed, and then the training algorithm and associate memory ability are studied subsequently. And then, a chaotic system is approximated using FCNN; the simulation results indicate that FCNN could approach dynamic system preferably. And owing to the introducing of chaotic map, the chaotic recollect capacity of FCNN is increased.     

Gauge-meter model building based on the effect of elastic deformation of rolls in a plate mill

The calculation error of the gauge-meter model will affect the gap setting precision and the self-learn precision of rolling force. The precision of the gauge-meter model is strongly influenced by plate width, working roll radius, backup roll radius, working roll crown, backup roll crown, and rolling force. The influence rules are hard to get by measuring. Taking a conventional 4-h plate mill as the research subject, these influences were transferred into the calculation of roll deflection and flattening deformation. To calculate these deformations, the theory of the influence function method was adopted. By modifying the traditional gauge-meter model, a novel model of the effect of roll elastic deformation on the gap setting was built by data fitting. By this model, it was convenient to analyze the variation caused by the rolling condition. Combining the elastic deformation model of rolls with the kiss-rolls method, a gauge-meter model was put forward for plate thickness prediction. The prediction precision of thickness was greatly improved by the new gauge-meter model.     

Multi-objective intelligent coordinating optimization blending system based on qualitative and quantitative synthetic model

1 INTRODUCTIONDuring Pb-Zn sintering blending process ,theproper mixture ratio of Pb-Zn concentrate fromdif-ferent mines and the returning powder is necessaryto obtain the desired sintering materials . The ma-terials are sintered to produce the agglomerate forthe smelting. The agglomerate compositions andthe sintering permeability are thei mportant param-eters of Pb-Zn sintering process , whose accuracyand stabilization strictly affect the smeltingprocess . The mixture ratio is one of e…     

Modal identification of system driven by lévy random excitation based on continuous time AR model

Based on the continuous time AR model, this paper presents a new time-domain modal identification method of LTI system driven by the uniformly modulated lévy random excitation. The structural dynamic equation is first transformed into the observation equation and the state equation (namely, stochastic differential equation). Based on the property of the strong solution of the stochastic differential equation, the uniformly modulated function is identified piecewise. Then by virtue of the Girsanov theorem, we present the exact maximum likelihood estimators of parameters. Finally, the modal parameters are identified by eigen analysis. Numerical results show that the method not only has high precision and robustness but also has very high computing efficiency.     

Vertical bearing capacity of pile based on load transfer model

The load transfer analytical method is applied to study the bearing mechanism of piles with vertical load in this paper. According to the different hardening rules of soil or rock around the pile shaft, such as work-softening, ideal elasto-plastic and work-hardening, a universal tri-linear load transfer model is suggested for the development of side and tip resistance by various types of soil （rock） with the consideration of sediment at the bottom of the pile. Based on the model, a formula is derived for the relationship between the settlement and load on the pile top to determine the vertical bearing capacity, taking into account such factors as the characteristics of the stratum, the side resistance along the shaft, and tip resistance under the pile tip. A close agreement of the calculated results with the measured data from a field test pile lends confidence to the future application of the present approach in engineering practice.     

Application of functional-link neural network in evaluation of sublayer suspension based on FWD test

Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflec-tormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural net-work was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learn-ing method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 Na-tional Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.0001, while the optimum chosen 4-8-1 BP needs over 10000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network, the functional-link neural network adopts single layer structure to learn and calculate, which simpli-fies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be adopted to directly evaluate the sublayer suspension based on FWD test data on the site. En-gineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.     

Galvanic coupling and its effect on origin potential flotation system of sulfide minerals

The galvanic coupling formed in origin potential flotation systems of sulfide minerals can be divided into three types: sulfide mineral-sulfide mineral-water system; sulfide mineral-steel ball-water system; and sulfide mineral-sulfide mineral-collector system. In this paper, taking lead, zinc, iron sulfide mineral systems for examples,several models of galvanic coupling were proposed and the effects of galvanic coupling on flotation were discussed. A galvanic contact between galena (or sphalerite) and pyrite contributes to decreasing the content of zinc in lead concentrate, and enhances remarkably the absorption of collector on the galena surface. During grinding, due to galvanic interactions between minerals and steel medium, Fe(OH)3 formed covers on the cathodic mineral surface, affecting its floatability.