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Achieving higher operation speeds safely and comfortably is yet a significant challenge in the railway industry. The rail irregularities and wheel-rail interactions in a train running at high speeds may result in large-amplitude vibration in the train’s car body and affect passengers by reducing ride comfort. The train suspension systems have a crucial role in reducing the vibration and improving ride comfort to an acceptable level. In this context, an exclusive semi-active magneto-rheological (MR) damper with a favorable dynamic range was designed and fabricated. The MR dampers were installed in a high-speed train’s secondary lateral suspension system in replacement of original passive hydraulic dampers, with intent to mitigate vibration of the car body and keep the ride comfort in a proper level at low and high running speeds. A unique full-scale experimental investigation on the high-speed train equipped with MR dampers was carried out to evaluate the MR damper functionality in a real operating situation. The full-scale roller experiments were conducted in a vast range of speeds from 80 to 350 km/hr. At each speed, different currents were applied to the MR dampers. The car body dynamic responses were collected to evaluate the ride quality of the train. Ride comfort indices under various train operating conditions are calculated through Sperling and UIC513 rules. This study reveals that the designed MR dampers effectively reduce the car body’s rolling motion. According to Sperling ride comfort index, the car body vibration was “clearly noticeable” at some running speeds when adopting the MR dampers, but it was not unpleasant. Besides, a “very good comfort” was achieved according to the UIC513 criterion. Also, no train instability was whatsoever observed at high speeds. This experimental investigation bears out the capability of the devised MR damper to achieve desirable ride comfort under high running speeds.
S. M. Sajjadi Alehashem; Y. Q. Ni; X. Z. Liu. A Full-Scale Experimental Investigation on Ride Comfort and Rolling Motion of High-Speed Train Equipped with MR Dampers. IEEE Access 2021, 9, 1 -1.
AMA StyleS. M. Sajjadi Alehashem, Y. Q. Ni, X. Z. Liu. A Full-Scale Experimental Investigation on Ride Comfort and Rolling Motion of High-Speed Train Equipped with MR Dampers. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleS. M. Sajjadi Alehashem; Y. Q. Ni; X. Z. Liu. 2021. "A Full-Scale Experimental Investigation on Ride Comfort and Rolling Motion of High-Speed Train Equipped with MR Dampers." IEEE Access 9, no. : 1-1.
Vibration response and amplitude frequency characteristics of a controlled nonlinear meso-scale beam under periodic loading are studied. A method including a general analytical expression for harmonic balance solution to periodic vibration and an updated cycle iteration algorithm for amplitude frequency relation of periodic response is developed. A vibration equation with the general expression of nonlinear terms for periodic response is derived and a general analytical expression for harmonic balance solution is obtained. An updated cycle iteration procedure is proposed to obtain amplitude frequency relation. Periodic vibration response with various frequencies can be calculated uniformly using the method. The method can take into account the effect of higher harmonic components on vibration response, and it is applicable to various periodic vibration analyses including principal resonance, super-harmonic resonance, and multiple stationary responses. Numerical results demonstrate that the developed method has good convergence and accuracy. The response amplitude should be determined by the periodic solution with multiple harmonic terms instead of only the first harmonic term. The damping effect on response illustrates that vibration responses of the nonlinear meso beam can be reduced by feedback control with certain damping gain. The amplitude frequency characteristics including anti-resonance and resonant response variation have potential application to the vibration control design of nonlinear meso-scale structure systems.
Zu-Guang Ying; Yi-Qing Ni. Vibrational Amplitude Frequency Characteristics Analysis of a Controlled Nonlinear Meso-Scale Beam. Actuators 2021, 10, 180 .
AMA StyleZu-Guang Ying, Yi-Qing Ni. Vibrational Amplitude Frequency Characteristics Analysis of a Controlled Nonlinear Meso-Scale Beam. Actuators. 2021; 10 (8):180.
Chicago/Turabian StyleZu-Guang Ying; Yi-Qing Ni. 2021. "Vibrational Amplitude Frequency Characteristics Analysis of a Controlled Nonlinear Meso-Scale Beam." Actuators 10, no. 8: 180.
Comfort performance of high-rise structures during strong winds is significant to habitants. Despite the significance, procedures for evaluating occupant comfort in serviceability limit states have not been as well developed as those for strength-based design of high-rise structures. One of the difficulties arises from uncertainties associated with the parameters in occupant comfort assessment, which pertain to the acceleration response magnitude and its relationship to human reaction to the motion. The comfort assessment is in general conducted by examining whether the wind-induced acceleration response satisfies some occupant comfort criteria. Such a deterministic approach, however, fails to account for uncertainty inherent in the wind-induced acceleration response as it is affected by the wind field of stochastic nature and uncertainty about the aerodynamic loads and the structure’s dynamic behavior. In view of this, a Bayesian probabilistic approach is proposed in this study to evaluate the occupant comfort of high-rise structures. First, a Bayesian regression model is formulated for characterizing wind-induced acceleration responses of a structure by use of structural health monitoring (SHM) data acquired during strong winds, thereby enabling to account for the uncertainty contained in the monitored acceleration responses and quantify the uncertainty in modeling and prediction. Based on the predicted acceleration distribution and reliability theory, a safety index is then elicited to perform probabilistic assessment of occupant comfort in wind-induced motion of the structure. In the case study, field monitoring data acquired from a supertall structure of 600 m high during six tropical cyclones are used to illustrate the proposed approach, including the evaluation of occupant comfort of the structure under extreme wind speeds.
Y.W. Wang; C. Zhang; Y.Q. Ni; X.Y. Xu. Bayesian probabilistic assessment of occupant comfort of high-rise structures based on structural health monitoring data. Mechanical Systems and Signal Processing 2021, 163, 108147 .
AMA StyleY.W. Wang, C. Zhang, Y.Q. Ni, X.Y. Xu. Bayesian probabilistic assessment of occupant comfort of high-rise structures based on structural health monitoring data. Mechanical Systems and Signal Processing. 2021; 163 ():108147.
Chicago/Turabian StyleY.W. Wang; C. Zhang; Y.Q. Ni; X.Y. Xu. 2021. "Bayesian probabilistic assessment of occupant comfort of high-rise structures based on structural health monitoring data." Mechanical Systems and Signal Processing 163, no. : 108147.
Self-sensing cementitious composite (SSCC) has been viewed as a promising sensing technology for structural health monitoring and traffic detection on account of its high sensitivity, low cost, long-term stability and compatibility with concrete structures. However, temperature variation effects in the electrical resistance measurements would impede the potential application of SSCC. It is therefore of great significance to understand the temperature effects on the piezoresistive performance of SSCC and eliminate such effects. In this study, temperature effects on the electrical and piezoresistive properties of SSCCs with different contents of carbon nanotube/nano carbon black (CNT/NCB) composite fillers are investigated under varying temperatures ranging from −20 °C to 60 °C and under concurrent temperature and loading variations. Experimental results show that an increase in CNT/NCB composite filler content can decrease the activation energy of SSCC and facilitate the transport of the charge carriers, thus attenuating the sensitivity of SSCC to temperature. Temperature variation has no effect on the piezoresistive repeatability of SSCC due to the stable overall distribution of conductive network in SSCC. However, temperature rise can reduce the piezoresistive sensitivity of SSCC. Aiming to diminish the effect of temperature on the piezoresistive property of SSCC, the SSCC responses to simultaneous temperature and loading excitations are then treated using a Bayesian blind source separation (BSS) method to reconstruct two independent sources. Regardless of the CNT/NCB composite filler content, the reconstructed source in relation to temperature variation always has a high correlation with the measured temperature, indicating that the proposed Bayesian BSS method can well extract and separate the electrical resistance variation induced by temperature variation from that induced by simultaneous temperature and loading excitations.
Siqi Ding; Chi Xu; Yi-Qing Ni; Baoguo Han. Extracting piezoresistive response of self-sensing cementitious composites under temperature effect via Bayesian blind source separation. Smart Materials and Structures 2021, 30, 065010 .
AMA StyleSiqi Ding, Chi Xu, Yi-Qing Ni, Baoguo Han. Extracting piezoresistive response of self-sensing cementitious composites under temperature effect via Bayesian blind source separation. Smart Materials and Structures. 2021; 30 (6):065010.
Chicago/Turabian StyleSiqi Ding; Chi Xu; Yi-Qing Ni; Baoguo Han. 2021. "Extracting piezoresistive response of self-sensing cementitious composites under temperature effect via Bayesian blind source separation." Smart Materials and Structures 30, no. 6: 065010.
Electro-mechanical impedance (EMI) has been proved as an effective non-destructive evaluation indicator in monitoring the looseness of bolted joints. Yet due to the complex electro-mechanical coupling mechanism, EMI-based methods in most cases are considered as qualitative approaches and are only applicable for single-bolt monitoring. These issues limit practical applications of EMI-based methods in industrial and transportation sectors where real-time and reliable monitoring of multiple bolted joints in a localized area is desired. Previous research efforts have integrated various machine learning (ML) algorithms in EMI-based monitoring to enable quantitative diagnosis, but only one-to-one (single sensor single bolt) case was considered, and the EMI–ML integrations are basically unnatural and ingenious by learning the EMI measurements from isolated sensors. This paper presents a novel EMI-based bolt looseness monitoring method incorporating both physical mechanism (acoustic attenuation) and data-driven analysis, by implementing a lead zirconate titanate (PZT) sensor network and a built-in graph convolutional network (GCN) model. The GCN model is constructed in such a way that the structure of the PZT network is fully represented, with the sensor-bolt distance and sweeping frequency encoded in the propagation function. The proposed method takes into account not only the EMI signature but also the relationship between the sensing nodes and the bolted joints and can quantitatively infer the torque loss of multiple bolts through node-level outputs. A proof-of-concept experiment was conducted on a twin-bolt plate, and results show that the proposed method outperforms other baseline models either without a graph network structure or does not consider sensor-bolt distance. The developed hybrid model provides new thinking in interpreting sensor networks which are widely adopted in structural health monitoring, and the approach is expected to be applicable in practical scenarios such as rail insulated joints and aircraft wings where bolt joints are clustered.
Lu Zhou; Si-Xin Chen; Yi-Qing Ni; Alex Wai-Hing Choy. EMI-GCN: a hybrid model for real-time monitoring of multiple bolt looseness using electromechanical impedance and graph convolutional networks. Smart Materials and Structures 2021, 30, 035032 .
AMA StyleLu Zhou, Si-Xin Chen, Yi-Qing Ni, Alex Wai-Hing Choy. EMI-GCN: a hybrid model for real-time monitoring of multiple bolt looseness using electromechanical impedance and graph convolutional networks. Smart Materials and Structures. 2021; 30 (3):035032.
Chicago/Turabian StyleLu Zhou; Si-Xin Chen; Yi-Qing Ni; Alex Wai-Hing Choy. 2021. "EMI-GCN: a hybrid model for real-time monitoring of multiple bolt looseness using electromechanical impedance and graph convolutional networks." Smart Materials and Structures 30, no. 3: 035032.
Reliable estimation of wind‐induced displacement responses of long‐span bridges is critical to evaluating their wind‐resistant performance. In this study, two Bayesian approaches, Bayesian generalized linear model (BGLM) and sparse Bayesian learning (SBL), are proposed for characterizing the wind‐induced lateral displacement responses of long‐span bridges with structural health monitoring (SHM) data. They are fully model‐free data‐driven approaches, preferable for reckoning the wind‐induced total displacement intended for wind‐resistant performance assessment. With the measured displacement responses and wind speeds, a BGLM is developed to characterize the nonlinear relationship between the total displacement response and wind speed, where the Bayesian model class selection (BMCS) criterion is incorporated to determine the optimal model. In the model formulation by SBL, both wind speed and wind direction are treated as explanatory variables to elicit a probabilistic model with sparse structure. The SBL cleverly makes the resulting model to exempt from overfitting and generalizes well on unseen data. The two formulated models are then utilized to forecast the wind‐induced displacement responses in extreme typhoon events beyond the monitoring scope, and the predicted displacement responses are contrasted to the finite element analysis results and the design maximum allowable displacement under the serviceability limit state (SLS). The proposed methods are demonstrated using the monitoring data acquired by GPS sensors and anemometers instrumented on a long‐span suspension bridge. The results show that the SBL model is superior to the BGLM for wind‐induced displacement response prediction and is amenable to SHM‐based evaluation of wind‐resistant performance under extreme typhoon conditions.
Y.W. Wang; Y.Q. Ni; Q.H. Zhang; C. Zhang. Bayesian approaches for evaluating wind‐resistant performance of long‐span bridges using structural health monitoring data. Structural Control and Health Monitoring 2021, 28, 1 .
AMA StyleY.W. Wang, Y.Q. Ni, Q.H. Zhang, C. Zhang. Bayesian approaches for evaluating wind‐resistant performance of long‐span bridges using structural health monitoring data. Structural Control and Health Monitoring. 2021; 28 (4):1.
Chicago/Turabian StyleY.W. Wang; Y.Q. Ni; Q.H. Zhang; C. Zhang. 2021. "Bayesian approaches for evaluating wind‐resistant performance of long‐span bridges using structural health monitoring data." Structural Control and Health Monitoring 28, no. 4: 1.
This article presents a novel transfer learning approach for evaluating structural conditions of rail in a progressive manner, by using acoustic emission monitoring data and knowledge transferred from an acoustic-related database. Specifically, the low-level layers of a model pre-trained on large audio data are leveraged in our model for feature extraction. Compared with conventional transfer learning approaches that transfer knowledge from models pre-trained on normal images, the proposed approach can handle acoustic emission spectrograms more naturally in terms of both data inner properties and the way of data intaking. The training and testing data used for rail condition evaluation contains two months of acoustic emission recordings, which were acquired from an in situ operating rail turnout with an integrated acoustic emission –based monitoring system. Results show that the evolving stages of rail from intact to critically cracked are successfully revealed using the proposed approach with excellent prediction accuracy and high computation efficiency. More importantly, the study quantitatively shows that audio source data are more relevant to the acoustic emission monitoring data than Image data, by introducing a maximum mean discrepancy metric, and the transfer learning model with smaller maximum mean discrepancy does lead to better performance. As a by-product of the study, it has also been found that the features extracted by the proposed transfer learning model (“bottleneck” features) already exhibit a clustering trend corresponding to the evolving stages of rail conditions even in the training process before any label is annotated, indicating the potential unsupervised learning capability of the proposed approach. Through the study, it is suggested that selecting source data more correspondingly and flexibly would maximize the benefit of transfer learning in structural health monitoring area when facing heterogenous monitoring data under varying practical scenarios.
Si-Xin Chen; Lu Zhou; Yi-Qing Ni; Xiao-Zhou Liu. An acoustic-homologous transfer learning approach for acoustic emission–based rail condition evaluation. Structural Health Monitoring 2020, 20, 2161 -2181.
AMA StyleSi-Xin Chen, Lu Zhou, Yi-Qing Ni, Xiao-Zhou Liu. An acoustic-homologous transfer learning approach for acoustic emission–based rail condition evaluation. Structural Health Monitoring. 2020; 20 (4):2161-2181.
Chicago/Turabian StyleSi-Xin Chen; Lu Zhou; Yi-Qing Ni; Xiao-Zhou Liu. 2020. "An acoustic-homologous transfer learning approach for acoustic emission–based rail condition evaluation." Structural Health Monitoring 20, no. 4: 2161-2181.
A train–rail–bridge (TRB) interaction model of vector mechanics (VM) is developed to predict the derailment of a train traveling over cable-supported bridges under crosswinds. The aerodynamic coefficients measured from the bridge section-model in wind tunnel testing is used to simulate the unsteady wind pressure acting on the train-bridge system by buffeting forces in the time domain. A versatile wheel-rail contact model considering the wheel-rail contact geometry is then formulated to assess the risk of derailment of a running train. The feasibility and effectiveness of the proposed VM-TRB model are verified by comparison with a conventional finite element procedure. To assess the running safety of the train, a two-phase plot of derailment factors for each pair of wheelsets is generated. The plots indicate that both wind velocity and train speed are critical factors that lead the train cars to potential derailment. Nevertheless, the linking railcar couplers play a holding role in reducing the separation or jumping of the moving wheels from the rail. The case study well demonstrates the capability of the VM-TRB model in dealing with train derailment.
Su-Mei Wang; Jong-Dar Yau; Yuan-Feng Duan; Yi-Qing Ni; Hua-Ping Wan; Si-Kai Wu; Edward C. Ting. Prediction of Crosswind-Induced Derailment of Train–Rail–Bridge System by Vector Mechanics. Journal of Engineering Mechanics 2020, 146, 04020132 .
AMA StyleSu-Mei Wang, Jong-Dar Yau, Yuan-Feng Duan, Yi-Qing Ni, Hua-Ping Wan, Si-Kai Wu, Edward C. Ting. Prediction of Crosswind-Induced Derailment of Train–Rail–Bridge System by Vector Mechanics. Journal of Engineering Mechanics. 2020; 146 (12):04020132.
Chicago/Turabian StyleSu-Mei Wang; Jong-Dar Yau; Yuan-Feng Duan; Yi-Qing Ni; Hua-Ping Wan; Si-Kai Wu; Edward C. Ting. 2020. "Prediction of Crosswind-Induced Derailment of Train–Rail–Bridge System by Vector Mechanics." Journal of Engineering Mechanics 146, no. 12: 04020132.
Bridge condition assessment by use of structural health monitoring (SHM) data has been recognized as a promising approach towards the condition-based preventive maintenance. In-service bridges are normally subjected to multiple types of loads such as highway traffic, railway traffic, wind and thermal effect, resulting in heterogeneous and multimodal data structure of strain/stress responses. This study aims to develop an SHM-based bridge reliability assessment procedure in terms of parametric Bayesian mixture modelling. The parametric mixture model admits representation of multimodal structural responses, while the Bayesian paradigm enables both aleatory and epistemic uncertainties to be accounted for in modelling. By defining appropriate priors for the mixture parameters that are viewed as random variables to interpret the model uncertainty, an analytical form of the full conditional posteriors is derived. A Markov chain Monte Carlo (MCMC) algorithm in conjunction with Bayes factor is explored to determine the optimal model order and estimate the joint posterior of the mixture parameters. In full compliance with the Bayesian framework, a conditional reliability index is elicited with the parametric Bayesian mixture model by using the first-order reliability method. The estimated value of the reliability index, which serves as a quantitative measure of health condition for the in-service bridge, can be successively updated with the accumulation of monitoring data. The proposed method is exemplified by using one-year strain monitoring data acquired from the instrumented Tsing Ma Suspension Bridge, in which the evolution of the estimated reliability index is obtained.
Y.Q. Ni; R. Chen. Strain monitoring based bridge reliability assessment using parametric Bayesian mixture model. Engineering Structures 2020, 226, 111406 .
AMA StyleY.Q. Ni, R. Chen. Strain monitoring based bridge reliability assessment using parametric Bayesian mixture model. Engineering Structures. 2020; 226 ():111406.
Chicago/Turabian StyleY.Q. Ni; R. Chen. 2020. "Strain monitoring based bridge reliability assessment using parametric Bayesian mixture model." Engineering Structures 226, no. : 111406.
Because of long distance of railway lines, it is difficult to find an appropriate method to inspect the rail track condition efficiently and accurately. In this paper, a machine vision system based on driving recorder and image signal processing is proposed to evaluate the rail curvature automatically. The proposed machine vision system consists of four modules including the video acquisition module, the image extraction module, the image processing module, and the track condition assessment module. Three classic edge detection methods are adopted and compared for rail edge detection. In line with the videos of driving recorder, coordinate systems for train and rail are defined in the Lagrangian space, and the track curvature is estimated using the proposed chord offset method and double measurement method. For evaluating the track condition, an index describing the concordance between the train and track is defined. In the case study, a set of videos from the driving recorders of trains during their in-service operations are analyzed by the proposed technique, and the obtained results are verified by comparison with those obtained by a track geometry inspection vehicle. It is shown that the proposed technique can evaluate the track curvature accurately. Moreover, the influence of the position of deployed driving recorder, the focal length and anti-shake of camera on the accuracy of evaluation results is discussed. It is testified that the proposed technique provides a simple and reliable way to inspect the track curvature.
Su-Mei Wang; Ching-Lung Liao; Yi-Qing Ni. A Machine Vision System based on Driving Recorder for Automatic Inspection of Rail Curvature. IEEE Sensors Journal 2020, PP, 1 -1.
AMA StyleSu-Mei Wang, Ching-Lung Liao, Yi-Qing Ni. A Machine Vision System based on Driving Recorder for Automatic Inspection of Rail Curvature. IEEE Sensors Journal. 2020; PP (99):1-1.
Chicago/Turabian StyleSu-Mei Wang; Ching-Lung Liao; Yi-Qing Ni. 2020. "A Machine Vision System based on Driving Recorder for Automatic Inspection of Rail Curvature." IEEE Sensors Journal PP, no. 99: 1-1.
Wheel condition assessment is of great significance to ensure the operation safety of trains and metro systems. This study is intended to develop a Bayesian probabilistic method for online and quantitative assessment of railway wheel conditions using track-side strain-monitoring data. The proposed method is a fully data-driven, nonparametric approach without the need of a physical model. To enable defect identification using only response measurement, the measured dynamic strain responses of rail tracks during the passage of trains are processed to elicit the normalized cumulative distribution function values representative of the effect of individual wheels, which in conjunction with the frequency points are used to formulate a probabilistic reference model in terms of sparse Bayesian learning. Through cleverly realizing sparsity by introducing hyper-parameters and their priors, the sparse Bayesian learning makes the resulting model to exempt from overfitting and generalize well on unseen data. Only the monitoring data in healthy state are needed in formulating the reference model. A novel Bayesian null hypothesis significance testing in terms of scale-invariant intrinsic Bayes factor, which does not suffer from the Jeffreys–Lindley paradox, is then pursued in the presence of new monitoring data collected from possibly defective wheel(s) to detect wheel defects and quantitatively assess wheel condition. The proposed method in fully Bayesian inference framework is verified by utilizing the real-world monitoring data acquired by a distributed fiber Bragg grating–based track-side monitoring system and comparing with the offline inspection results.
Yi-Qing Ni; Qiu-Hu Zhang. A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring. Structural Health Monitoring 2020, 20, 1536 -1550.
AMA StyleYi-Qing Ni, Qiu-Hu Zhang. A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring. Structural Health Monitoring. 2020; 20 (4):1536-1550.
Chicago/Turabian StyleYi-Qing Ni; Qiu-Hu Zhang. 2020. "A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring." Structural Health Monitoring 20, no. 4: 1536-1550.
Research on structural health monitoring (SHM) is nowadays evolving from SHM‐based diagnosis towards SHM‐based prognosis. The structural strain response, as a localized response, has gained growing attention for application to structural condition assessment and prognosis in that continuous strain measurement can offer information about the stress experienced by an in‐service structure and is better suited to characterize local deficiency and damage of the structure than global responses. As such, accurate forecasting of the structural strain response in real time is essential for both structural condition diagnosis and prognosis. In this paper, a Bayesian modeling approach embedding model class selection is proposed for dynamic forecasting purpose, which enables the probabilistic forecasting of structural strain response and bears a strong capability of modeling the underlying non‐stationary dynamic process. As opposed to the classical time series models, the proposed Bayesian dynamic linear model (BDLM) accommodates both stationary and non‐stationary time series data and delineates the time‐dependent structural strain response through invoking different hidden components, such as overall trend, seasonal (cyclical), and regressive components. It in turn paves an effective way for incorporating the newly observed time‐variant data into the model framework for structural response prediction. By embedding a novel model class selection paradigm into the BDLM, the proposed algorithm enables simultaneous model class selection and probabilistic forecasting of strain responses in a real‐time manner. The utility of the proposed approach and its forecasting accuracy are examined by using the real‐world monitoring data successively collected from a three‐tower cable‐stayed bridge.
Y.W. Wang; Y.Q. Ni. Bayesian dynamic forecasting of structural strain response using structural health monitoring data. Structural Control and Health Monitoring 2020, 27, 1 .
AMA StyleY.W. Wang, Y.Q. Ni. Bayesian dynamic forecasting of structural strain response using structural health monitoring data. Structural Control and Health Monitoring. 2020; 27 (8):1.
Chicago/Turabian StyleY.W. Wang; Y.Q. Ni. 2020. "Bayesian dynamic forecasting of structural strain response using structural health monitoring data." Structural Control and Health Monitoring 27, no. 8: 1.
This paper proposes an improved most likely heteroscedastic Gaussian process (MLHGP) algorithm to handle a kind of nonlinear regression problems involving input-dependent noise. The improved MLHGP follows the same learning scheme as the current algorithm by use of two Gaussian processes (GPs), with the first GP for recovering the unknown function and the second GP for modeling the input-dependent noise. Unlike the current MLHGP pursuing an empirical estimate of the noise level which is provably biased in most of local noise cases, the improved algorithm gives rise to an approximately unbiased estimate of the input-dependent noise. The approximately unbiased noise estimate is elicited from Bayesian residuals by the method of moments. As a by-product of this improvement, the expectation maximization (EM)-like procedure in the current MLHGP is avoided such that the improved algorithm requires only standard GP learnings to be performed twice. Four benchmark experiments, consisting of two synthetic cases and two real-world datasets, demonstrate that the improved MLHGP algorithm outperforms the current version not only in accuracy and stability, but also in computational efficiency.
Qiu-Hu Zhang; Yi-Qing Ni. Improved Most Likely Heteroscedastic Gaussian Process Regression via Bayesian Residual Moment Estimator. IEEE Transactions on Signal Processing 2020, 68, 3450 -3460.
AMA StyleQiu-Hu Zhang, Yi-Qing Ni. Improved Most Likely Heteroscedastic Gaussian Process Regression via Bayesian Residual Moment Estimator. IEEE Transactions on Signal Processing. 2020; 68 (99):3450-3460.
Chicago/Turabian StyleQiu-Hu Zhang; Yi-Qing Ni. 2020. "Improved Most Likely Heteroscedastic Gaussian Process Regression via Bayesian Residual Moment Estimator." IEEE Transactions on Signal Processing 68, no. 99: 3450-3460.
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.
Ying Tzu Ke; Chia-Chi Cheng; Yung-Chiang Lin; Yi-Qing Ni; Keng-Tsang Hsu; Tai-Tung Wai. Preliminary Study on Assessing Delaminated Cracks in Cement Asphalt Mortar Layer of High-Speed Rail Track Using Traditional and Normalized Impact–Echo Methods. Sensors 2020, 20, 3022 .
AMA StyleYing Tzu Ke, Chia-Chi Cheng, Yung-Chiang Lin, Yi-Qing Ni, Keng-Tsang Hsu, Tai-Tung Wai. Preliminary Study on Assessing Delaminated Cracks in Cement Asphalt Mortar Layer of High-Speed Rail Track Using Traditional and Normalized Impact–Echo Methods. Sensors. 2020; 20 (11):3022.
Chicago/Turabian StyleYing Tzu Ke; Chia-Chi Cheng; Yung-Chiang Lin; Yi-Qing Ni; Keng-Tsang Hsu; Tai-Tung Wai. 2020. "Preliminary Study on Assessing Delaminated Cracks in Cement Asphalt Mortar Layer of High-Speed Rail Track Using Traditional and Normalized Impact–Echo Methods." Sensors 20, no. 11: 3022.
A train-bridge system (TBS) is inevitably subjected to parameter uncertainty, which leads to variability in its dynamic responses. In practice, it is difficult to characterize parameter uncertainty using precise probability density functions due to lack of sufficient statistical information. In such situations, uncertain parameters are usually modeled as uncertain-but-bounded parameters; this is also known as interval uncertainty. This paper aims to determine the dynamic response bounds of a TBS subjected to interval uncertainty. In mathematics, estimation of dynamic response bounds can be pursued in the context of optimization, that is, the minimization or maximization of an objective function. The solver in this context shares common features of a black-box function, such as high computational cost and no closed-form solution. In view of this, the present study proposes an efficient Bayesian optimization approach for estimating the dynamic response bounds of a TBS. Specifically, a Bayesian modeling approach employing a Gaussian process prior is proposed to replace the current expensive-to-run original model solver, along with an acquisition function that trades off exploration and exploitation of the search space. By doing so, the optimization of a complex, intractable black-box function is converted to the maximization of a computationally efficient acquisition function that has a closed-form expression and is differentiable. Two test functions are provided in order to demonstrate the applicability of the proposed Bayesian optimization methodology for finding the global minimum. It is demonstrated that the Bayesian optimization methodology is efficient and effective in solving the optimization problem with a limited number of function evaluations. Next, the proposed Bayesian optimization approach is utilized for interval dynamic analysis (IDA) of the TBS. The computational accuracy and efficiency of the proposed method is compared with a direct Monte Carlo simulation (MCS) estimator, which is used as a reference solution because of its generality, robustness, and easy implementation. The comparison results show that the proposed Bayesian optimization method is feasible and reliable for IDA of the TBS in terms of both computational accuracy and efficiency. Last, the influence of the interval change ratios of the system parameters on dynamic responses is investigated. The results reveal that an increase in the parameter uncertainty level results in a higher uncertainty bound on the dynamic responses.
Hua-Ping Wan; Yi-Qing Ni. A New Approach for Interval Dynamic Analysis of Train-Bridge System Based on Bayesian Optimization. Journal of Engineering Mechanics 2020, 146, 04020029 .
AMA StyleHua-Ping Wan, Yi-Qing Ni. A New Approach for Interval Dynamic Analysis of Train-Bridge System Based on Bayesian Optimization. Journal of Engineering Mechanics. 2020; 146 (5):04020029.
Chicago/Turabian StyleHua-Ping Wan; Yi-Qing Ni. 2020. "A New Approach for Interval Dynamic Analysis of Train-Bridge System Based on Bayesian Optimization." Journal of Engineering Mechanics 146, no. 5: 04020029.
Recently self-sensing cementitious composite has demonstrated its strong potentiality for structural health monitoring of civil infrastructures because of its low-cost, long-term stability and compatibility with concrete structures. In this paper, we propose novel hybrid nanocarbon materials engineered cement-based sensors (HNCSs) with high-sensitivity, which are fabricated with self-sensing cementitious composites containing electrostatic self-assembled CNT/NCB composite fillers. The mechanical property and sensing performance of the HNCSs are pre-characterized under static and dynamic compressive loadings. The HNCSs are then integrated into a five-story building model via custom-made clamps to verify the feasibility for dynamic response measurements. Results show that the developed sensors have satisfactory mechanical property and excellent pressure-sensitive reproducibility and stability. With clamps holding on the building model, the HNCSs perform satisfactorily under sinusoidal excitations in the frequency range from 2 to 40 Hz. In addition, the modal frequencies and their changes of the building model caused by 'damage' simulated through adding additional masses identified by the HNCSs are favorably consistent with the counterparts acquired by accelerometers and strain gauges, indicating that the developed HNCSs have great potential for structural modal identification and damage detection applications.
Siqi Ding; You-Wu Wang; Yi-Qing Ni; Baoguo Han. Structural modal identification and health monitoring of building structures using self-sensing cementitious composites. Smart Materials and Structures 2020, 29, 055013 .
AMA StyleSiqi Ding, You-Wu Wang, Yi-Qing Ni, Baoguo Han. Structural modal identification and health monitoring of building structures using self-sensing cementitious composites. Smart Materials and Structures. 2020; 29 (5):055013.
Chicago/Turabian StyleSiqi Ding; You-Wu Wang; Yi-Qing Ni; Baoguo Han. 2020. "Structural modal identification and health monitoring of building structures using self-sensing cementitious composites." Smart Materials and Structures 29, no. 5: 055013.
Premature failure of bridge expansion joints has been increasingly observed in recent years, and nowadays it becomes a major concern of bridge owners. A better understanding of their performance in service is highly desired. Deterministic linear regression models between bridge temperature and expansion joint displacement have widely been adopted to characterize the in-service performance of bridge expansion joints. When such a regression pattern is elicited using real-time monitoring data, the deterministic models fail to account for uncertainty inherent in the monitoring data and interpret the model error. In this study, a probabilistic approach for characterization of the regression pattern between bridge temperature and expansion joint displacement by use of Structural Health Monitoring (SHM) data and for SHM-based condition assessment and damage alarm of bridge expansion joints is developed in the Bayesian context. The proposed approach enables to account for the uncertainty contained in the monitoring data and quantify the model error and the prediction uncertainty. By combining the Bayesian regression model and reliability theory, an anomaly index is formulated to evaluate the health condition of the expansion joint when newly collected monitoring data are available and to provide damage alarm once the probability of damage exceeds a certain threshold. In the case study, real-world monitoring data acquired from a cable-stayed bridge are used to illustrate the proposed approach, including examining the appropriateness of the design values of expansion joint displacements under extreme temperatures in serviceability limit state.
Y.Q. Ni; You-Wu Wang (Y.W. Wang); C. Zhang. A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data. Engineering Structures 2020, 212, 110520 .
AMA StyleY.Q. Ni, You-Wu Wang (Y.W. Wang), C. Zhang. A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data. Engineering Structures. 2020; 212 ():110520.
Chicago/Turabian StyleY.Q. Ni; You-Wu Wang (Y.W. Wang); C. Zhang. 2020. "A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data." Engineering Structures 212, no. : 110520.
High-speed rail (HSR) is being developed in Asian and European countries to satisfy the rapidly growing demand for intercity services and to shore up economic growth. The rapid growth of HSR, however, has posed great challenges regarding operation safety, reliability and ride comfort. Irregular wheel defects can induce high-magnitude impact forces hindering safety and ride comfort of HSR and may also cause damage to rail tracks and vehicles. The focus of this study is to develop a real-time defect detection methodology based on Bayesian dynamic linear model (DLM) enabling to detect potentially defective wheels in real time. The proposed methodology embraces logics for (i) prognosis, (ii) potential outlier detection, (iii) identification of change occurrence (change-point detection), and (iv) quantification of damage extent and uncertainty. Relying on the strain monitoring data acquired from high-speed train bogies, the Bayesian DLM for characterizing the actual stress ranges is established, by which one-step forecast distribution is elicited before proceeding to the next observation. The detection of change-point is executed by comparing the routine model (forecast distribution generated by the Bayesian DLM) and an alternative model (the mean value is shifted by a prescribed offset) to determine which better fits the actual observation. If the comparison results are in favor of the alternative model, it is claimed that a potential change has occurred. Whether such an observation is an outlier or the beginning of a genuine change (change-point), three metrics (i.e., Bayes factor, maximum cumulative Bayes factor and run length) are performed for further identification. Once a change-point is confirmed, Bayesian hypothesis testing is conducted for the purpose of damage extent assessment and uncertainty quantification. A severe change, if identified, implies that the quality of train wheels has suffered from a significant alteration due to defects. In the case study, two cases making use of strain monitoring data acquired by fiber Bragg grating (FBG) sensors affixed on bogies are illustrated to verify the performance of the proposed methodology for real-time wheel defect detection of in-service high-speed trains.
You-Wu Wang (Y.W. Wang); Y.Q. Ni; X. Wang. Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model. Mechanical Systems and Signal Processing 2020, 139, 106654 .
AMA StyleYou-Wu Wang (Y.W. Wang), Y.Q. Ni, X. Wang. Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model. Mechanical Systems and Signal Processing. 2020; 139 ():106654.
Chicago/Turabian StyleYou-Wu Wang (Y.W. Wang); Y.Q. Ni; X. Wang. 2020. "Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model." Mechanical Systems and Signal Processing 139, no. : 106654.
Hossein Vatandoost; Seyed Masoud Sajjadi Alehashem; Mahmood Norouzi; Hamid Taghavifar; Yi-Qing Ni. A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode. IEEE Transactions on Magnetics 2019, 55, 1 -8.
AMA StyleHossein Vatandoost, Seyed Masoud Sajjadi Alehashem, Mahmood Norouzi, Hamid Taghavifar, Yi-Qing Ni. A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode. IEEE Transactions on Magnetics. 2019; 55 (12):1-8.
Chicago/Turabian StyleHossein Vatandoost; Seyed Masoud Sajjadi Alehashem; Mahmood Norouzi; Hamid Taghavifar; Yi-Qing Ni. 2019. "A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode." IEEE Transactions on Magnetics 55, no. 12: 1-8.
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.
Xiao-Zhou Liu; Chi Xu; Yi-Qing Ni. Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method. Sensors 2019, 19, 3981 .
AMA StyleXiao-Zhou Liu, Chi Xu, Yi-Qing Ni. Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method. Sensors. 2019; 19 (18):3981.
Chicago/Turabian StyleXiao-Zhou Liu; Chi Xu; Yi-Qing Ni. 2019. "Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method." Sensors 19, no. 18: 3981.