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The integrated application of building information modeling (BIM) and big data (BD) has received widespread attention, and has been involved in smart construction sites, construction project management, and construction project budgeting. Nevertheless, research on the integrated implementation of BIM and BD in China mainly concentrates on a project stage or profession, the exploration of technology integration mostly focuses on the theoretical level, and the research distribution is scattered. As such, the intention of this paper is to reveal the research history of BIM and BD in China, as well as to study the research methodologies and research fields for a more thorough knowledge of the BIM and BD research and development status in the Chinese construction sector, which adopts a mixed research method that uses a quantitative research via two analytical software tools, i.e., CiteSpace version of 6.1.R6 and the Statistical Analysis Toolkit on line edition for Informetrics software packages, to conduct a macro bibliometric analysis on BIM and BD research in the China National Knowledge Infrastructure database, and provides a follow-up micro qualitative research with content analysis. To ensure the comprehensiveness of the research, the core articles of the topic in the web of science database have been sorted out and analyzed for fully understanding the development of BIM and BD in the field of Chinese construction, resulting in identifying the current research hotspots and trends in China. The results suggest that the popular research keywords on BIM and BD in China since the year 2015 have mainly been focused on informatization, the internet of things, and rail transportation. Three fruitful research themes in BIM and BD research in China have been identified, including smart construction, smart operation, and bridge informatization. [ABSTRACT FROM AUTHOR]

In this paper, a novel descriptor estimator-based incipient fault estimation scheme is designed for Lipschitz nonlinear descriptor systems with process disturbances and measurement output noises. By using the proposed estimator, incipient sensor faults, abrupt actuator faults, and measurement noises can be estimated asymptotically. Application results conducted on a three-phase inverter system of China railway high-speed trains are given to illustrate the effectiveness of the developed approach. The main contributions are summarized in two aspects: 1) the unified framework designed for actuator and sensor fault reconstruction/estimation problem is capable of dealing with actuator and sensor faults at the same time and 2) a linear matrix inequality optimization method is formulated to achieve optimal performance of signal estimation. [ABSTRACT FROM AUTHOR]

With the rapid development of China high-speed railway, the low frequency oscillation (LFO) of electrical quantities appears more often recently and leads to some severe problems of train operation. To improve the traction line-side converter control and suppress the phenomenon economically and effectively, a model-based predictive direct power control (MPDPC) approach is proposed in this paper. The approach adopts a discrete-time model of traction line-side converter in d–q reference frame to predict the future values of the input active and reactive power. The optimal switching state is selected by minimizing a cost function of power to evaluate the power errors at the next sampling time. Through the theoretical analysis and simulations, the performance of MPDPC is compared with traditional transient direct current control (TDCC) that is widely adopted in China Railway High-Speed 3 electric-multiple-unit. The real-time online simulations based on Real-Time Laboratory (RT-LAB) are also realized to further validate the results. Moreover, two vehicle-grid cascade simulation systems are constructed, and the LFO suppressing capability of MPDPC and TDCC is compared. Finally, the steady-state characteristic, dynamic characteristic, and LFO suppressing capability of MPDPC are demonstrated through the analysis of key performance indexes. [ABSTRACT FROM AUTHOR]

The catenary support components (CSCs) are the most important devices in high-speed railways to support contact lines for powering trains. To estimate the states of CSCs, it is very necessary to locate their positions in a monitoring system based on computer vision. Considering the application scenarios and characteristics of CSCs, an automatic and quick positioning system is designed in this paper to simultaneously position the multiscale CSCs with 12 categories. In the system, an effective framework called CSCs network (CSCNET) is presented, which cascades the coarse positioning network and the fine positioning network to reduce multiscale differences between different CSCs. In the coarse positioning network, a new unsupervised clustering algorithm based on the relative positioning information is proposed to classify the catenary images. Then, a convolutional neural network (CNN) classification network is trained to extract the structural features of catenary images and generate the proposal regions with labels. In the fine positioning network, a modified CNN positioning framework is applied to obtain the accurate positions of CSCs based on the coarse positioning results. Due to the special lightweight structure with a classification network, the relative position information is applied and makes the CSCNET sensitive to small-scale components. The experimental results from some high-speed railway lines in China show that the proposed system has obvious advantages in the CSCs positioning. The mean average precision and frames per second of CSCNET reach 0.837 and 2.17, respectively. Compared with some popular convolutional networks [faster region-based CNN (Faster R-CNN), etc.] and a typical positioning method, the proposed system significantly improves the AP without increasing the computational time. [ABSTRACT FROM AUTHOR]

High-frequency harmonic resonance has occurred frequently in the traction power-supply system (TPSS) when high-speed trains that are equipped with pulsewidth-modulation converters were serviced in the China High-Speed Railway (HSR). This phenomenon can result in serious voltage distortion, overheating of electrical components, and electromagnetic interference (EMI) of communication circuits. In order to address this issue, a complete mathematical model of the 2\,\times\,27.5-kV ac railway system, by means of the nodal admittance matrix, is presented to attain equal modeling of TPSS and modern train for harmonic studies in this paper. The impact of the train on resonance behavior of TPSS by employing resonance-mode assessment and modal sensitivity analysis are investigated. Finally, numerical simulation results using the presented model are compared to measurements results of the China HSR to verify the accuracy of the TPSS model and Norton model of the train, and proved that the method of resonance frequency shifts described is doable. The comparison results illustrate that the proposed model and harmonic resonance assessment can be useful for determining and mitigating the harmonic resonance problems in TPSS as well as the power system. [ABSTRACT FROM AUTHOR]

Train stations are one of the largest and most unavoidable obstructions for electromagnetic wave propagation on a high-speed railway. They can bring about severe extra propagation loss, and therefore, lead to poor coverage or handover failure. However, their influence has been rarely investigated before. Based on rich experimental results of 930 MHz measurements conducted on train stations of high-speed railway in China, this paper proposes two empirical models for the extra propagation loss owing to train stations for the first time. The extra loss depends on four conditions: the distance between the transmitter (Tx) and the train station, the type of the train station, the track carrying the train, and the propagation mechanism zones. Hence, the models are established for every case of all the combinations of these four conditions. The validation shows that the proposed models accurately predict the extra propagation loss and support an effective way to involve the influence of the train station in the simulation and design of the signaling and train control communications systems. [ABSTRACT FROM PUBLISHER]

The Qinghai-Tibet railway, the highest railway in terms of elevation, was built under the harshest of weather conditions in the world. A new technique, called the ventiduct embankment, was used for the first time in railway construction in the Qinghai-Tibet permafrost region of China to overcome the specific difficulties of the permafrost plateau. To evaluate its effectiveness, several full-scale test embankments were constructed at a representative field site, the Qingshuihe experimental section. The effectiveness of the ventiduct embankment structure has been verified by comparing it with the performance of the nearby control embankment in terms of permafrost table, settlement, and thaw depth. These observations are presented in this paper. The ventiduct embankment structure based on this study has been adopted in Qinghai-Tibet permafrost railway construction. [ABSTRACT FROM AUTHOR]

This paper proposes a dc voltage incipient sensor fault isolation method for single-phase three-level rectifier devices in high-speed railway electrical traction systems. Different incipient fault modes characterizing locations and incipient fault types are parameterized nonlinearly by unknown fault parameters. A new incipient fault isolation method is developed by combining sliding mode technique with nonlinear parameterization adaptive estimation technique. A bank of particular adaptive sliding mode estimators is proposed, which facilitates to derive new isolation residuals and adaptive threshold intervals. The isolability is studied, and the isolable sufficient condition is derived using new functions. For the practical electrical traction system in China Railway High-Speed 2, simulation and experiment based on TDCS-FIB (a software) are presented to verify the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of high-speed and high-power railway system in China, power quality such as the negative sequence current and harmonic current caused by electric locomotives becomes more and more critical. This paper proposes a half-bridge-converter-based railway static power conditioner (RPC) (HBRPC) which consists of two half-bridge converters connected by two capacitors in series. Compared with the traditional RPC, the HBRPC requires only a pair of power switch legs and two capacitors. Under the premise of accomplishing the same function of RPC, the proposed conditioner can reduce half of the power switches, which can make it with lower cost and hardware complexity. A double-loop control is proposed for HBRPC to keep the dc-link voltage stable and achieve the dynamic tracking of the current reference signals, while a balanced voltage control is proposed to eliminate the error of two capacitor voltages and maintain the normal operation of HBRPC. Finally, simulation and experiment results have verified the proposed structure and its control method effectively. [ABSTRACT FROM PUBLISHER]

Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too small in the inspection images. Instead, the BS is first localized for detecting the BS fastener. This leads to a new problem that the localized BS boxes may not contain the complete BS fasteners due to low localization accuracy, making it infeasible to further diagnose the fastener conditions. To tackle this problem, this article proposes a novel pipeline for BS fastener looseness diagnosis. First, the competitive deep learning model Faster RCNN ResNet101 is used to coarsely localize BSs. Second, an action-driven reinforcement learning agent is adopted to refine the coarse-localized boxes through a dynamic position searching process. Then, BS fasteners are extracted from the refined localized BS image by the deep segmentation model YOLACT++, which is fast and interpretable. Finally, a looseness diagnosis criterion based on segmented information are proposed. We evaluate the performance of submodels independently and the overall performance of the whole model on a real-life catenary image dataset collected from a high-speed line in China. The test results show that the proposed method is effective for BS looseness detection in railway catenary. [ABSTRACT FROM AUTHOR]