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Traditionally, connected vehicles (CVs) share their own sensor data that relies on the satellite with their surrounding vehicles by vehicle-to-vehicle (V2V) communication. However, the satellite-based signal sometimes may be lost due to environmental factors. Time-delays and packet dropouts may occur randomly by V2V communication. To ensure the reliability and accuracy of localization for CVs, a novel variational Bayesian (VB)-Kalman method is developed for unknown and time varying probabilities of delayed and lost measurements. In this VB-Kalman localization method, two random variables are introduced to indicate whether a measurement is delayed and available, respectively. A hierarchical model is then formulated and its parameters and state are simultaneously estimated by the VB technique. Experimental results validate the proposed method for the localization of CVs in practice.
Hao Zhu; Ji Mi; Yongfu Li; Ka-Veng Yuen; Henry Leung. VB-Kalman Based Localization for Connected Vehicles with Delayed and Lost Measurements: Theory and Experiments. IEEE/ASME Transactions on Mechatronics 2021, PP, 1 -1.
AMA StyleHao Zhu, Ji Mi, Yongfu Li, Ka-Veng Yuen, Henry Leung. VB-Kalman Based Localization for Connected Vehicles with Delayed and Lost Measurements: Theory and Experiments. IEEE/ASME Transactions on Mechatronics. 2021; PP (99):1-1.
Chicago/Turabian StyleHao Zhu; Ji Mi; Yongfu Li; Ka-Veng Yuen; Henry Leung. 2021. "VB-Kalman Based Localization for Connected Vehicles with Delayed and Lost Measurements: Theory and Experiments." IEEE/ASME Transactions on Mechatronics PP, no. 99: 1-1.
We propose a novel algorithm for dynamic response suppression via semi-active control devices, which we refer to as broad learning, robust, semi-active control (BLRSAC). To configure the semi-active controller, a nonparametric reliability-based output feedback control strategy is introduced. In particular, an adaptive broad learning network is developed to formulate the control strategy using the clipped-optimal control technique. The learning network is augmented incrementally to adopt additional training data based on the inherited information of the trained learning network. By utilizing a robust failure probability, the training dataset is obtained adaptively to include the training input–output pairs with optimal structural control performance. The robust failure probability we propose incorporates both predicted failure probability and the uncertainty of the underlying structure. Therefore, the resultant control strategy can handle the inevitable uncertainty of the actual control situation to achieve optimal structural control. To examine the efficacy of the proposed BLRSAC algorithm, illustrative examples of a shear building and a three-dimensional braced frame under various external excitation and structural damaging conditions are presented.
Sin-Chi Kuok; Ka-Veng Yuen; Mark Girolami; Stephen Roberts. Broad learning robust semi-active structural control: A nonparametric approach. Mechanical Systems and Signal Processing 2021, 162, 108012 .
AMA StyleSin-Chi Kuok, Ka-Veng Yuen, Mark Girolami, Stephen Roberts. Broad learning robust semi-active structural control: A nonparametric approach. Mechanical Systems and Signal Processing. 2021; 162 ():108012.
Chicago/Turabian StyleSin-Chi Kuok; Ka-Veng Yuen; Mark Girolami; Stephen Roberts. 2021. "Broad learning robust semi-active structural control: A nonparametric approach." Mechanical Systems and Signal Processing 162, no. : 108012.
As a mathematical representation of the output-to-output relationship, transmissibility function (TF) has been extensively applied in structural damage detection due to its robustness to influences of the input variations. As in most engineering fields, dealing with the problem of uncertainty in TF-based feature detection is an issue of fundamental importance. In this study, a new statistical, data-driven damage detection algorithm is proposed by rigorously modelling the variability of TF without postprocessing with circularly-symmetric complex Gaussian ratio distribution. The probabilistic distance of Symmetric Kullback-Leibler (SKL) divergence between TFs under baseline condition and potential damage scenarios which can measure the dissimilarity of probability distributions for the TFs under different states are computed as a damage index (DI) to detect structural anomaly. Compared against Mahalanobis distance which has the implicit assumption that the normal condition set is governed by Gaussian statistics, the probabilistic distance measure proposed in this study can deal with the deviations in TFs not following Gaussian distribution. A statistically rigorous threshold selection scheme integrating Bayesian inference strategy and Monte Carlo discordancy test is proposed to detect the the presence of damage by accommodating the uncertainties of measurements and the probabilistic model of TF. Numerical, experimental, and field test studies are conducted to validate the potential of probabilistic distance measure of TFs in anomaly detection under ambient vibration instead of forced vibration testing. Results demonstrate satisfactory performance of the proposed approach for detecting the existence and quantify the relative damage severity from a global perspective.
Wang-Ji Yan; Dimitrios Chronopoulos; Ka-Veng Yuen; Yi-Chen Zhu. Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme. Mechanical Systems and Signal Processing 2021, 162, 108009 .
AMA StyleWang-Ji Yan, Dimitrios Chronopoulos, Ka-Veng Yuen, Yi-Chen Zhu. Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme. Mechanical Systems and Signal Processing. 2021; 162 ():108009.
Chicago/Turabian StyleWang-Ji Yan; Dimitrios Chronopoulos; Ka-Veng Yuen; Yi-Chen Zhu. 2021. "Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme." Mechanical Systems and Signal Processing 162, no. : 108009.
Broad Bayesian learning (BBL), a novel probabilistic Bayesian neural network methodology with optimized architecture configuration, is proposed. It has an expandable feedforward broad learning network. Therefore, the uncertain estimates can be quantified in terms of probability distributions and network architecture augmentation can be adopted incrementally by use of the inherited information from the previously trained network. Furthermore, a learning network architecture configuration optimization scheme is proposed to determine the optimal architecture configuration. Based on the plausibilities of the concerned configurations, the most plausible one can be obtained, and it indicates the proper augmentation to develop the optimal configuration. To demonstrate the proposed methodology, three simulation examples and an application with in‐field structural health monitoring measurement are presented.
Sin‐Chi Kuok; Ka‐Veng Yuen. Broad Bayesian learning (BBL) for nonparametric probabilistic modeling with optimized architecture configuration. Computer-Aided Civil and Infrastructure Engineering 2021, 1 .
AMA StyleSin‐Chi Kuok, Ka‐Veng Yuen. Broad Bayesian learning (BBL) for nonparametric probabilistic modeling with optimized architecture configuration. Computer-Aided Civil and Infrastructure Engineering. 2021; ():1.
Chicago/Turabian StyleSin‐Chi Kuok; Ka‐Veng Yuen. 2021. "Broad Bayesian learning (BBL) for nonparametric probabilistic modeling with optimized architecture configuration." Computer-Aided Civil and Infrastructure Engineering , no. : 1.
This article proposes an innovative unsupervised learning method for early damage detection and long‐term structural health monitoring of civil structures under environmental variability. This method consists of three main parts including a novelty detector based on kernel null Foley–Sammon transform (KNFST), a practical approach to choosing an optimal Gaussian kernel parameter, and a probabilistic method for the threshold estimation. The crux of KNFST is to map all original samples to a kernel feature space and project the kernelized features into a single point in a null space. The proposed threshold estimation method exploits the extreme value theory, the generalized Pareto distribution, and the peak‐over‐threshold. The major contribution of this article is to propose an innovative novelty detection method by a one‐class kernel null space algorithm and a probabilistic threshold estimation approach. Dealing with the problem of environmental variations and estimating a reliable alarming threshold are the main advantages of the proposed method. The effectiveness and reliability of the proposed method are validated by the Wooden Bridge in a laboratory environment and the full‐scale Z24 Bridge. Results demonstrate that the proposed unsupervised learning method highly succeeds in detecting damage even under strong environmental variations.
Hassan Sarmadi; Ka‐Veng Yuen. Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold. Computer-Aided Civil and Infrastructure Engineering 2021, 36, 1150 -1167.
AMA StyleHassan Sarmadi, Ka‐Veng Yuen. Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold. Computer-Aided Civil and Infrastructure Engineering. 2021; 36 (9):1150-1167.
Chicago/Turabian StyleHassan Sarmadi; Ka‐Veng Yuen. 2021. "Early damage detection by an innovative unsupervised learning method based on kernel null space and peak‐over‐threshold." Computer-Aided Civil and Infrastructure Engineering 36, no. 9: 1150-1167.
The Pearl River Delta (PRD) region is located on the southeast coast of mainland China and it is an important economic hub. The high levels of particulate matter (PM) in the atmosphere, however, and poor visibility have become a complex environmental problem for the region. Air quality modeling systems are useful to understand the temporal and spatial distribution of air pollution, making use of atmospheric emission data as inputs. Over the years, several atmospheric emission inventories have been developed for the Asia region. The main purpose of this work is to evaluate the performance of the air quality modeling system for simulating PM concentrations over the PRD using three atmospheric emission inventories (i.e., EDGAR, REAS and MIX) during a winter and a summer period. In general, there is a tendency to underestimate PM levels, but results based on the EDGAR emission inventory show slightly better accuracy. However, improvements in the spatial and temporal disaggregation of emissions are still needed to properly represent PRD air quality. This study’s comparison of the three emission inventories’ data, as well as their PM simulating outcomes, generates recommendations for future improvements to atmospheric emission inventories and our understanding of air pollution problems in the PRD region.
Diogo Lopes; Joana Ferreira; Ka Hoi; Ka-Veng Yuen; Kai Mok; Ana Miranda. Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region. International Journal of Environmental Research and Public Health 2021, 18, 4155 .
AMA StyleDiogo Lopes, Joana Ferreira, Ka Hoi, Ka-Veng Yuen, Kai Mok, Ana Miranda. Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region. International Journal of Environmental Research and Public Health. 2021; 18 (8):4155.
Chicago/Turabian StyleDiogo Lopes; Joana Ferreira; Ka Hoi; Ka-Veng Yuen; Kai Mok; Ana Miranda. 2021. "Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region." International Journal of Environmental Research and Public Health 18, no. 8: 4155.
Since S-wave velocity of the subsurface is an important parameter in near surface applications, many studies have been conducted for its estimation. Among the various methods that use surface waves or body waves, Rayleigh wave inversion is the most popular. In practice, the densities and P-wave velocities of different layers are usually assumed to be known to avoid ill-posed problems, as they have less influence on the dispersion curves. However, improper assignment of these two groups of parameters leads to inaccurate estimation of the S-wave velocity profile. In order to address this problem, the all-parameters Rayleigh wave inversion strategy is proposed in which the S-wave velocities, layer thicknesses, densities and P-wave velocities of different layers are included as the unknown parameters for inversion. Meanwhile, the transitional Markov Chain Monte Carlo (TMCMC) algorithm is applied for the implementation of all-parameters Rayleigh wave inversion. One simulated example and two real-test applications are demonstrated to verify the capability of the proposed method in the estimation of the S-wave velocity profile, the densities and the P-wave velocities. Furthermore, it is verified that the proposed method achieved more accurate S-wave velocity profile estimation than the traditional approach.
Xiao-Hui Yang; Ka-Veng Yuen. All-parameters Rayleigh wave inversion. Earthquake Engineering and Engineering Vibration 2021, 20, 517 -534.
AMA StyleXiao-Hui Yang, Ka-Veng Yuen. All-parameters Rayleigh wave inversion. Earthquake Engineering and Engineering Vibration. 2021; 20 (2):517-534.
Chicago/Turabian StyleXiao-Hui Yang; Ka-Veng Yuen. 2021. "All-parameters Rayleigh wave inversion." Earthquake Engineering and Engineering Vibration 20, no. 2: 517-534.
To investigate the structural dynamics of a container subjected to a vented hydrogen explosion, 48 field tests were conducted in a 40-foot container with roof vents and an end vent. The effects of the hydrogen concentration, ignition position, and obstacles on the evolution of the dynamic responses were investigated. Three stages were generally observed for displacements: (1) At the stage of the vent rupture, the displacement could be approximated as a quasi-static response, and there was a linear relationship between the peaks of positive overpressure and displacement. (2) Structural deformation appeared as reciprocating vibration at the stage of Helmholtz oscillation. (3) The structure exhibited relatively weak irregular fluctuation when high-frequency acoustic oscillation occurred. Two types of the structural acceleration with low and high amplitudes resulting from Helmholtz oscillation and acoustic oscillation, respectively, were clearly observed. For the end-vented explosion, multiple peaks were observed for the displacement at the quasi-static stage due to the rupture, discharge, and external explosion. Moreover, the displacement was sensitive to hydrogen concentration, whereas the number of obstacles and the ignition position had significant influences on the peak acceleration for roof venting. This work conducted the fundamental explanation for the evolution law of structural responses induced by vented hydrogen explosions from the perspective of structural dynamics and enriched the experimental accumulation in a large-scale container with congestion in this field.
Teng-Teng Hao; Chang-Jian Wang; Wang-Ji Yan; Wei-Xin Ren; Ka-Veng Yuen. Experimental investigation on the dynamic responses of vented hydrogen explosion in a 40-foot container. International Journal of Hydrogen Energy 2021, 46, 19229 -19243.
AMA StyleTeng-Teng Hao, Chang-Jian Wang, Wang-Ji Yan, Wei-Xin Ren, Ka-Veng Yuen. Experimental investigation on the dynamic responses of vented hydrogen explosion in a 40-foot container. International Journal of Hydrogen Energy. 2021; 46 (36):19229-19243.
Chicago/Turabian StyleTeng-Teng Hao; Chang-Jian Wang; Wang-Ji Yan; Wei-Xin Ren; Ka-Veng Yuen. 2021. "Experimental investigation on the dynamic responses of vented hydrogen explosion in a 40-foot container." International Journal of Hydrogen Energy 46, no. 36: 19229-19243.
With the development of deep learning, object detection algorithms based on horizontal box are widely used in the field of damage identification. However, damages can be in any direction and position, and they are not necessarily horizontal or vertical. This article proposes a bolt damage identification network, namely, orientation-aware center point estimation network, which models a damage as a center point of its rotated bounding box. The proposed orientation-aware center point estimation network uses deep layer aggregation network to search center points and regress to all other damage properties, such as size and angle. A loss function is designed to improve the optimization efficiency of network. Orientation-aware center point estimation network is applied to bolt damage detection, and comparison with the well-known Faster Region-Convolutional Neural Network (a benchmark using horizontal bounding box) demonstrates the accuracy of the proposed method. Finally, videos were utilized to verify the capability of the proposed orientation-aware center point estimation network in real-time detection of bolt damages.
Yang Zhang; Ka-Veng Yuen. Bolt damage identification based on orientation-aware center point estimation network. Structural Health Monitoring 2021, 1 .
AMA StyleYang Zhang, Ka-Veng Yuen. Bolt damage identification based on orientation-aware center point estimation network. Structural Health Monitoring. 2021; ():1.
Chicago/Turabian StyleYang Zhang; Ka-Veng Yuen. 2021. "Bolt damage identification based on orientation-aware center point estimation network." Structural Health Monitoring , no. : 1.
Because the vibration of buildings and bridges is often small in amplitude, low in frequency, and wide in frequency bandwidth, designing a conventional energy harvester with the natural frequency low enough to coincide with the low excitation usually accompany with very large mass and volume. However, it is very important to use portable energy harvester (usually with higher natural frequency) to obtain energy for wireless devices. If portable energy harvesters were used directly on high‐rise buildings, the efficiency would be very low. To solve this problem, we propose a simple device to implement portable energy harvester in high‐rise building application environment with a high efficiency. It is an intermediate spring‐mass system to be installed between the energy harvester and the underlying building. The crux of this device is to enhance the vibration amplitude of the oscillating mass of energy harvesters, so that more energy can be harvested. By changing the degree of freedom (DOF) and stiffness of the intermediate device, the performance of energy harvester can be drastically enhanced. The effects of the parameters of the device for the harvesting performance have been identified using the Lyapunov method, and the conclusion has been verified using four earthquake ground motions. Applications to 100‐story buildings are used to demonstrate the efficiency of the proposed intermediate device. In the illustrative examples, this device can increase the generated energy of linear electromagnetic energy harvester (EMEH) up by several hundred times.
Ka‐Veng Yuen; Lishu Xu. Efficiency enhancement of electromagnetic energy harvesters for high‐rise buildings. Structural Control and Health Monitoring 2021, 28, e2722 .
AMA StyleKa‐Veng Yuen, Lishu Xu. Efficiency enhancement of electromagnetic energy harvesters for high‐rise buildings. Structural Control and Health Monitoring. 2021; 28 (6):e2722.
Chicago/Turabian StyleKa‐Veng Yuen; Lishu Xu. 2021. "Efficiency enhancement of electromagnetic energy harvesters for high‐rise buildings." Structural Control and Health Monitoring 28, no. 6: e2722.
This study was devoted to investigating stochastic model updating in a Bayesian inference framework based on a frequency response function (FRF) vector without any post-processing such as smoothing and windowing. The statistics of raw FRFs were inferred with a multivariate complex-valued Gaussian ratio distribution. The likelihood function was formulated by embedding the theoretical FRFs that contained the model parameters to be updated in the class of the probability model of the raw FRFs. The Transitional Markov chain Monte Carlo (TMCMC) used to sample the posterior probability density function implies considerable computational toll because of the large batch of repetitive analyses of the forward model and the increasing expense of the likelihood function calculations with large-scale loop operations. The vectorized formula was derived analytically to avoid time-consuming loop operations involved in the likelihood function evaluation. Furthermore, a distributed parallel computing scheme was developed to allow the TMCMC stochastic simulation to run across multiple CPU cores on multiple computers in a network. The case studies demonstrated that the fast-computational scheme could exploit the availability of high-performance computing facilities to drastically reduce the time-to-solution. Finally, parametric analysis was utilized to illustrate the uncertainty propagation properties of the model parameters with the variations of the noise level, sampling time, and frequency bandwidth.
Wang-Ji Yan; Shi-Ze Cao; Wei-Xin Ren; Ka-Veng Yuen; Dan Li; Lambros Katafygiotis. Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions. Mechanical Systems and Signal Processing 2021, 156, 107615 .
AMA StyleWang-Ji Yan, Shi-Ze Cao, Wei-Xin Ren, Ka-Veng Yuen, Dan Li, Lambros Katafygiotis. Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions. Mechanical Systems and Signal Processing. 2021; 156 ():107615.
Chicago/Turabian StyleWang-Ji Yan; Shi-Ze Cao; Wei-Xin Ren; Ka-Veng Yuen; Dan Li; Lambros Katafygiotis. 2021. "Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions." Mechanical Systems and Signal Processing 156, no. : 107615.
Uncertainty quantification for the experimental estimations of dynamic characterization functions, including frequency response functions (FRFs) and transmissibility functions (TFs), is of practical importance in improving the robustness of the real applications of these functions for system identification and structural health monitoring. Interval analysis is an appealing tool for dealing with the uncertainties of engineering problems in which only the bounds of uncertain parameters are available. FRFs and TFs are complex-valued random variables. However, due to the negligence of the dependencies of complex-valued variables, the existing complex ratio interval arithmetic operation can be overly conservative. In this study, the polar representation of complex ratio numbers was extended to complex ratio polar intervals and a multidimensional parallelepiped (MP) interval model was introduced to accommodate the dependence between the numerator and the denominator. Based on the explicit expressions of the MP model through a dependence matrix, two new global extrema searching schemes with and without the regularization of the uncertainty domain of the MP model were proposed in order to derive the explicit formulas of the upper and lower bounds of the magnitudes and phases of the FRFs and TFs. The new schemes were then applied to the uncertainty propagation for a numerically simulated beam and a bridge subjected to a single excitation. The results showed that the interval overestimation problem could be significantly alleviated by using the new complex-valued ratio interval arithmetic operation of the parallelepiped model.
Meng-Yun Zhao; Wang-Ji Yan; Ka-Veng Yuen; Michael Beer. Non-probabilistic uncertainty quantification for dynamic characterization functions using complex ratio interval arithmetic operation of multidimensional parallelepiped model. Mechanical Systems and Signal Processing 2021, 156, 107559 .
AMA StyleMeng-Yun Zhao, Wang-Ji Yan, Ka-Veng Yuen, Michael Beer. Non-probabilistic uncertainty quantification for dynamic characterization functions using complex ratio interval arithmetic operation of multidimensional parallelepiped model. Mechanical Systems and Signal Processing. 2021; 156 ():107559.
Chicago/Turabian StyleMeng-Yun Zhao; Wang-Ji Yan; Ka-Veng Yuen; Michael Beer. 2021. "Non-probabilistic uncertainty quantification for dynamic characterization functions using complex ratio interval arithmetic operation of multidimensional parallelepiped model." Mechanical Systems and Signal Processing 156, no. : 107559.
Ka‐Veng Yuen. A unique journal by a unique editor in chief. Computer-Aided Civil and Infrastructure Engineering 2020, 35, 1312 -1314.
AMA StyleKa‐Veng Yuen. A unique journal by a unique editor in chief. Computer-Aided Civil and Infrastructure Engineering. 2020; 35 (12):1312-1314.
Chicago/Turabian StyleKa‐Veng Yuen. 2020. "A unique journal by a unique editor in chief." Computer-Aided Civil and Infrastructure Engineering 35, no. 12: 1312-1314.
This paper presents a case study of curtain grouting to prevent water and mud inrush disasters for a weathered granite tunnel in Guangxi, China. First, the basic information of the Junchang tunnel is given, including ground conditions, water and mud inrush disasters, and the subsequent prevention countermeasures. Then, we present the original grouting design and procedure, and the field operation of 25 grouting cycles and excavations for the evaluation of the grouting effect and suggestion for future grouting work. Based on the field investigation, the values of the major grouting parameters are suggested, including grouting thickness of 5– 8 m, grouting length of 15– 18 m (3– 3.6 times of thickness), and per-meter grouting volume of 34.2 m3. Furthermore, the guidelines for composite materials selection are proposed for the various flowing water environments. Moreover, the space positions and water quality of the inspection boreholes and the original water richness of the ground should be taken into consideration in the grouting evaluation criteria. The optimized grouting parameters, materials selection and evaluation criteria provide important references for disasters prevention in similar projects.
Jin-Quan Liu; Ka-Veng Yuen; Wei-Zhong Chen; Xiao-Sheng Zhou; Wei- Wang. Grouting for water and mud inrush control in weathered granite tunnel: A case study. Engineering Geology 2020, 279, 105896 .
AMA StyleJin-Quan Liu, Ka-Veng Yuen, Wei-Zhong Chen, Xiao-Sheng Zhou, Wei- Wang. Grouting for water and mud inrush control in weathered granite tunnel: A case study. Engineering Geology. 2020; 279 ():105896.
Chicago/Turabian StyleJin-Quan Liu; Ka-Veng Yuen; Wei-Zhong Chen; Xiao-Sheng Zhou; Wei- Wang. 2020. "Grouting for water and mud inrush control in weathered granite tunnel: A case study." Engineering Geology 279, no. : 105896.
Environmental variability is still a major challenge in structural health monitoring. Due to the similarity of changes caused by environmental variations to damage, false positive and false negative errors are prevalent in detecting damage that cause serious economic and safety issues. To address this challenge and its disadvantages, this article proposes a novel ensemble learning‐based method in a nongenerative sequential algorithm for structural health monitoring under varying environmental conditions by three kinds of Mahalanobis distance metrics in three main levels. At each level, one attempts to find a few and adequate nearest neighbors of each feature to remove environmental variability via an innovative approach. The major contribution of this article is to develop a novel data‐based method by the concepts of ensemble learning and unsupervised learning. The great advantages of the proposed method include developing a nonparametric data‐based framework without estimating any unknown parameter, dealing with the negative effects of environmental variability, improving the performance of Mahalanobis distance, and increasing damage detectability. The performance and effectiveness of this method are validated by modal features of two real bridge structures along with several comparisons. Results demonstrate that the proposed ensemble learning‐based method highly succeeds in detecting damage under environmental variability, and it is superior to some state‐of‐the‐art techniques.
Hassan Sarmadi; Alireza Entezami; Behzad Saeedi Razavi; Ka‐Veng Yuen. Ensemble learning‐based structural health monitoring by Mahalanobis distance metrics. Structural Control and Health Monitoring 2020, 28, 1 .
AMA StyleHassan Sarmadi, Alireza Entezami, Behzad Saeedi Razavi, Ka‐Veng Yuen. Ensemble learning‐based structural health monitoring by Mahalanobis distance metrics. Structural Control and Health Monitoring. 2020; 28 (2):1.
Chicago/Turabian StyleHassan Sarmadi; Alireza Entezami; Behzad Saeedi Razavi; Ka‐Veng Yuen. 2020. "Ensemble learning‐based structural health monitoring by Mahalanobis distance metrics." Structural Control and Health Monitoring 28, no. 2: 1.
In this article, a novel algorithm is proposed for real‐time system identification using hierarchical interhealing model classes. One major difficulty in the system identification for large structures is to determine the complexity of the structural model and the number of unknown parameters. One can detect finer damages with more unknown stiffness parameters, but this may cause fluctuating or even unidentifiable results. Although Bayesian model class selection allows one to choose among some prescribed model classes, the number of possible model classes for large structures is huge. In this paper, we propose a new method using hierarchical interhealing model classes. The modeling errors of these model classes can be corrected adaptively according to the data and the results from the optimal model class. This includes not only the errors in the parameters but also the deficiencies of the parametric models. Furthermore, the model classes are established in a hierarchical manner so that the proposed strategy requires only a small number of model classes, yet being able to explore a large solution space. Consequently, the proposed algorithm can handle a large number of damage possibilities while it maintains relatively low computational cost. Two examples are presented, and the results show that the proposed algorithm can detect, locate, and quantify damages reliably and efficiently.
Ka‐Veng Yuen; Le Dong. Real‐time system identification using hierarchical interhealing model classes. Structural Control and Health Monitoring 2020, 27, 1 .
AMA StyleKa‐Veng Yuen, Le Dong. Real‐time system identification using hierarchical interhealing model classes. Structural Control and Health Monitoring. 2020; 27 (12):1.
Chicago/Turabian StyleKa‐Veng Yuen; Le Dong. 2020. "Real‐time system identification using hierarchical interhealing model classes." Structural Control and Health Monitoring 27, no. 12: 1.
In this paper, a hierarchical outlier detection approach is proposed for online distributed structural identification. In contrast to centralized identification, distributed identification extracts important features from the raw response data at the sensor nodes and transmits only them to the base station. Therefore, outlier detection is substantially more complicated than the traditional approach. In the proposed method, the local outliers in the raw data are detected directly at the corresponding sensor node, and they are excluded from further processing. However, if a sensor node is biased or exhibits other patterned outliers, these outliers will be undetectable at the sensor node level. It is necessary to conduct another level of outlier detection at the base station, namely, global outlier detection, before fusion. These two levels of outlier detection are of different nature. Local outlier detection concerns directly with the raw response data, whereas the targets of global outlier detection are the local estimation results of the stiffness parameters. Therefore, they require different mathematical tools. The proposed hierarchical outlier detection approach detects the local outliers according to the outlier probability of the data points at the sensor nodes, whereas it detects the global outliers according to the outlier probability of the local estimation results. By excluding both types of outliers, reliable online distributed structural identification can be achieved. Two examples are presented to demonstrate the proposed method.
Ke Huang; Ka-Veng Yuen. Hierarchical outlier detection approach for online distributed structural identification. Structural Control and Health Monitoring 2020, 1 .
AMA StyleKe Huang, Ka-Veng Yuen. Hierarchical outlier detection approach for online distributed structural identification. Structural Control and Health Monitoring. 2020; ():1.
Chicago/Turabian StyleKe Huang; Ka-Veng Yuen. 2020. "Hierarchical outlier detection approach for online distributed structural identification." Structural Control and Health Monitoring , no. : 1.
The Junchang tunnel is a part of a major project in the Censhui highway in Guangxi province, China. The weathered granite part of the tunnel is approximately 180 m long with two lines and four lanes. Numerous water and mud inrush disasters frequently occurred in the weathered granite area, leading to serious economic loss and challenge to the constructors. Therefore, continuous grouting was carried out for the entire weathered part of the tunnel. This paper describes its curtain grouting for disasters prevention. Detailed discussion is presented on the safety thickness after grouting and the challenges encountered under different hydraulic-geological conditions. Specifically, a water and mud inrush model for the nonlinear flow and mass transfer behavior of weathered granite was used to evaluate the safety thickness in this project. Then, field studies with twenty-five grouting cycles and excavations were conducted to investigate the effective safety thickness. Negative correlation was observed between the safety thickness and the rock classification index [BQ]. Numerical simulation and field tests demonstrated that the thickness l should be larger than 3 m, and, in most cases, 5 m was sufficient when [BQ] < 72.5. When [BQ] ≥ 72.5, l should be between 1.5 m and 3 m. Based on the field data, an empirical formula was developed to determine the thickness under different values of [BQ]. The research findings serve as an important reference for future projects in similar stratum.
Jin-Quan Liu; Wei-Zhong Chen; Ka-Veng Yuen; Xiao-Sheng Zhou. Groundwater-mud control and safety thickness of curtain grouting for the Junchang Tunnel: A case study. Tunnelling and Underground Space Technology 2020, 103, 103429 .
AMA StyleJin-Quan Liu, Wei-Zhong Chen, Ka-Veng Yuen, Xiao-Sheng Zhou. Groundwater-mud control and safety thickness of curtain grouting for the Junchang Tunnel: A case study. Tunnelling and Underground Space Technology. 2020; 103 ():103429.
Chicago/Turabian StyleJin-Quan Liu; Wei-Zhong Chen; Ka-Veng Yuen; Xiao-Sheng Zhou. 2020. "Groundwater-mud control and safety thickness of curtain grouting for the Junchang Tunnel: A case study." Tunnelling and Underground Space Technology 103, no. : 103429.
A novel multi‐resolution broad learning (MRBL) approach is proposed for model updating using identified modal data. Due to measurement noise and limited monitoring locations, the identified modal data are incomplete and noise corrupted. Besides, it is inevitable to have modeling errors in finite element models. Therefore, it is nontrivial to establish simple explicit relationship to represent the nonlinear mapping from modal data to structural model parameters. The proposed approach aims to model this implicit mapping using a nonparametric approach. For this purpose, the nonlinear relationship is learnt based on a multi‐resolution recursive procedure with expandable broad learning networks. In contrast to conventional deep learning, the proposed approach is computationally very economical. Instead of requiring large volumes of training data, the multi‐resolution approach adaptively zooms into the important region for sampling. Hence, satisfactory accuracy in model updating can be achieved by using a feasible amount of training data. Moreover, the broad learning network is expandable to adopt architectural modification, so it can be reconfigured incrementally based on the inherit information from the trained network. To demonstrate the efficacy of the proposed approach, illustrative examples of a shear building and a three‐dimensional braced frame with unobserved torsional mode are presented. Finally, an application using real data of Canton Tower is also presented.
Sin‐Chi Kuok; Ka‐Veng Yuen. Multi‐resolution broad learning for model updating using incomplete modal data. Structural Control and Health Monitoring 2020, 27, 1 .
AMA StyleSin‐Chi Kuok, Ka‐Veng Yuen. Multi‐resolution broad learning for model updating using incomplete modal data. Structural Control and Health Monitoring. 2020; 27 (8):1.
Chicago/Turabian StyleSin‐Chi Kuok; Ka‐Veng Yuen. 2020. "Multi‐resolution broad learning for model updating using incomplete modal data." Structural Control and Health Monitoring 27, no. 8: 1.
In this article, a novel propagative broad learning approach is proposed for nonparametric modeling of the ambient effects on structural health indicators. Structural health indicators interpret the structural health condition of the underlying dynamical system. Long-term structural health monitoring on in-service civil engineering infrastructures has demonstrated that commonly used structural health indicators, such as modal frequencies, depend on the ambient conditions. Therefore, it is crucial to detrend the ambient effects on the structural health indicators for reliable judgment on the variation of structural integrity. However, two major challenging problems are encountered. First, it is not trivial to formulate an appropriate parametric expression for the complicated relationship between the operating conditions and the structural health indicators. Second, since continuous data stream is generated during long-term structural health monitoring, it is required to handle the growing data efficiently. The proposed propagative broad learning provides an effective tool to address these problems. In particular, it is a model-free data-driven machine learning approach for nonparametric modeling of the ambient-influenced structural health indicators. Moreover, the learning network can be updated and reconfigured incrementally to adapt newly available data as well as network architecture modifications. The proposed approach is applied to develop the ambient-influenced structural health indicator model based on the measurements of 3-year full-scale continuous monitoring on a reinforced concrete building.
Sin-Chi Kuok; Ka-Veng Yuen; Stephen Roberts; Mark A Girolami. Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators. Structural Health Monitoring 2020, 1 .
AMA StyleSin-Chi Kuok, Ka-Veng Yuen, Stephen Roberts, Mark A Girolami. Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators. Structural Health Monitoring. 2020; ():1.
Chicago/Turabian StyleSin-Chi Kuok; Ka-Veng Yuen; Stephen Roberts; Mark A Girolami. 2020. "Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators." Structural Health Monitoring , no. : 1.