Record-based simulation of three-component long-period ground motions: Hybrid of surface wave separation and multivariate empirical mode decomposition

Liu, Shuoyu; Jiang, Yan; Qiao, Ke; Peng, Liuliu; Liu, Duote

doi:10.1016/j.soildyn.2023.108037

Cited by 6 publications

(6 citation statements)

References 42 publications

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“…The maximum acceleration of 3.518 cm/s 2 occurs in the Y direction, and the one in the X direction is 2.602 cm/s 2 . They are less than the specified value of 24.5 cm/s 2 recommended in design codes [ 34 , 35 , 36 ], which means the SWFC meets the provisional requirements under Typhoon Lekima.…”

Section: Vibration Response Characteristics Of the Swfc Under Typhoonmentioning

confidence: 84%

Field Measurements of Wind-Induced Responses of the Shanghai World Financial Center during Super Typhoon Lekima

Wang

Zhang

et al. 2023

Sensors

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In this paper, the wind-induced responses of the Shanghai World Financial Center (SWFC) under Super Typhoon Lekima are measured using the health monitoring system. Based on the measurements, the characteristics of vibration, including probability density distribution of accelerations, power spectra, and mode shapes are studied. The curve method and the standard deviation method are used to analyze the relationship of the first- and second-order natural frequencies and damping ratios with amplitudes and the mean wind speed. The results show the following: (1) The structural wind-induced responses in the X and Y directions have high consistencies, and the vibration signals exhibit a peak state; moreover, response amplitudes and acceleration signals disperse when the floor height increases. (2) The first- and second-order natural frequencies in the X and Y directions decrease with the increasing amplitudes and are negatively correlated with mean wind speed; the maximum decrease in natural frequency is 5.794%. The first- and second-order damping ratios in the X and Y directions increase with the increasing amplitudes and are positively correlated with the mean wind speed; the maximum increase in damping ratio is 95.7%. (3) The curve method and the standard deviation method are similar in identifying dynamic characteristic parameters, but the discreteness of the natural frequencies obtained by the curve method is lesser. (4) Under excitations of various typhoons, the mode shapes of SWFC are basically the same, and the mode shapes in the X and Y directions increase with the height and have nonlinearity.

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Section: Vibration Response Characteristics Of the Swfc Under Typhoonmentioning

confidence: 84%

Field Measurements of Wind-Induced Responses of the Shanghai World Financial Center during Super Typhoon Lekima

Wang

Zhang

et al. 2023

Sensors

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“…When dealing with multi-source data, a key-value pair collection approach is employed to represent and store each individual bridge entity. The former specifies the specific features, while the latter assigns corresponding data to these features [26]. Bridge entity attributes should encompass information from three aspects: the route level, bridge level, and component level.…”

Section: Data Preprocessingmentioning

confidence: 99%

Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine

Jiang,

Tang,

Jiang

et al. 2023

Buildings

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To address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset consisting of 539 sets of bridge inspection data and determined the necessary influencing factors through correlation analysis. Subsequently, the WOA-ELM algorithm was applied to establish a nonlinear mapping relationship between each influencing factor and the bridge condition indicators. Furthermore, the extreme learning machine (ELM), back-propagation neural network (BPNN), decision trees (DT), and support vector machine (SVM) were employed for comparison to validate the superiority of the proposed method. In addition, this paper provides further substantiation of the model’s exceptional predictive capabilities across diverse bridge components. The results demonstrate the accurate predictive capability of the proposed method for bridge conditions. Compared with ELM, BPNN, DT, and SVM, the proposed method exhibits significant improvements in predictive accuracy, i.e., the correlation coefficient is increased by 4.1%, 11.4%, 24.5%, and 33.6%, and the root mean square error is reduced by 7.3%, 18.0%, 14.8%, and 18.1%, respectively. Moreover, the proposed method presents considerably enhanced generalization capabilities, resulting in the reduction in mean relative error by 11.6%, 15.3%, 6%, and 16.2%. The proposed method presents a robust framework for proactive bridge maintenance.

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“…At present, the goals of real-time monitoring, synchronous analysis, and data network sharing of monitoring systems have been gradually achieved [ 1 , 2 , 3 , 4 , 5 ]; however, the accurate diagnosis of bridge status, as the main functional goal of health-monitoring systems [ 6 ], is still difficult due to the complexity of the bridges, the randomness of external loads, and the uncertainty of the service environment. Therefore, most of the existing studies focus on the accumulation of historical data [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ], the identification of external load and internal damage [ 14 , 15 , 16 ], and the assessment of overall safety [ 17 ]. For example, Jian et al [ 18 ] automatically detected and classified a large number of fault monitoring data points by using the histogram of the relative frequency distribution of the data and the one-dimensional convolutional neural network and then comprehensively verified the selected acceleration data of the two long-span bridges.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Framework for Construction Quality of Bridge Monitoring System Using the DHGF Method

Xin

Wang

Tang

et al. 2023

Sensors

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Aiming at comprehensively evaluating the status of a bridge monitoring system, an evaluation framework based on the improved Delphi, analytic Hierarchy process, Grey relations analysis and Fuzzy integrated evaluation (DHGF) is selected. Firstly, the evaluation indexes for the bridge monitoring system are determined by an anonymous group discussion and expert questionnaire using the improved Delphi method. Secondly, a comparison matrix of the evaluation indexes is constructed to determine the comprehensive weight via the analytic hierarchy process. Then, based on the gray relations analysis, the albino weight function is constructed, the evaluation gray class is determined, and the single-factor fuzzy evaluation matrix is obtained. Finally, the final evaluation result was obtained by the fuzzy comprehensive evaluation. The evaluation results of a real bridge monitoring system show that the evaluation level of the monitoring system was level II, and the proposed framework could better reflect the construction and operation status of the monitoring system.

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Record-based simulation of three-component long-period ground motions: Hybrid of surface wave separation and multivariate empirical mode decomposition

Cited by 6 publications

References 42 publications

Field Measurements of Wind-Induced Responses of the Shanghai World Financial Center during Super Typhoon Lekima

Field Measurements of Wind-Induced Responses of the Shanghai World Financial Center during Super Typhoon Lekima

Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine

An Evaluation Framework for Construction Quality of Bridge Monitoring System Using the DHGF Method

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