Least-Square-Support-Vector-Machine-based approach to obtain displacement from measured acceleration

Tezcan, Jale; Marin-Artieda, Claudia

doi:10.1016/j.advengsoft.2017.10.011

Cited by 19 publications

(5 citation statements)

References 15 publications

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“…In the method, a limitation existed in that displacement responses measured during EQs were required in the prediction process as well as for the NN training. In addition, Tezcan and Marin‐Artieda proposed a method of predicting the time history of displacement response from the time history of acceleration response measured from building structures subject to EQs. For displacement prediction, the least squares support vector machine method was introduced.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, these EQs are not considered sufficient for NN performance verification. The EQs used for NN training and verification were not distinguished in some previous works, and prediction performance exhibited considerable errors when EQs that had not been used for training were used for verification . In addition, the verifications of the methods proposed in some of the aforementioned studies were limited to numerical studies only.…”

Section: Introductionmentioning

confidence: 99%

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Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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Summary In this study, a method of predicting the seismic responses of building structures based on a convolutional neural network (CNN) is proposed. In the method, the time histories of acceleration responses previously measured in a building during earthquakes are used in the CNN input layer, with the corresponding time histories of the displacement responses being used in the CNN output layer. The correlations between the features automatically extracted from the acceleration responses by the convolution and pooling operations in the various CNN layers and the displacement responses in the CNN output layer are established through CNN training. The trained CNN predicts the displacement responses for a future earthquake event using only the measured acceleration responses. To verify the validity of the proposed method, a numerical study on the ASCE benchmark model and an experimental study on a reinforced concrete frame structure are conducted. In the numerical study, the structural responses of the ASCE model subject to artificial earthquakes are utilized for CNN training, and the performance of the trained CNN is verified through seismic response prediction. In the experimental study, shaking table tests are performed for various earthquakes to measure the seismic responses of the reinforced concrete test structure. The seismic response prediction performance of the proposed method is examined using CNN trained with the measured seismic responses. Furthermore, the performances of CNNs trained with different numbers of datasets generated by the proposed data generation method based on data overlapping with the same data pool are discussed with related to the number of CNN training iteration number.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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“…K600 Krypton camera and LED sensors were used to capture the displacement time histories. The accuracy of the displacement time histories defined by the Krypton camera is 1:5000±1 mm [17].…”

Section: Validationmentioning

confidence: 96%

“…In the experiment, an input acceleration to simulate the 120 % of the recorded roof acceleration at San Bernardino 5-story hospital, station CSMIP23634, channels 9 under the Landers earthquake (California Integrated Seismic Network, CISN) was applied to a seismic isolated system that support a simulated equipment [17]. The acceleration and displacement responses of the seismic isolated system were recorded by sensors.…”

Section: Validationmentioning

confidence: 99%

The Eigenfunction Method for Determining Displacement Time History in Structural Dynamic Analysis

Pandey

et al. 2019

Period. Polytech. Civil Eng.

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In condition monitoring of structures, acceleration time histories are usually recorded due to ease of instrumentation. In cases where the information about a displacement time history is required, the acceleration data needs to be integrated to obtain the velocity and then the velocity needs to be integrated to obtain the displacements. However, the numerical integration of the acceleration data usually introduces an unrealistic drift component to the velocity as well as displacement. This paper presents an eigenfunction method to derive velocity and displacement time histories from a given acceleration time history. The paper analyzes displacements in two case studies using the numerical integration as well as the proposed eigenfunction method. It is concluded that the eigenfunction method is a viable approach to derive the displacement information from the acceleration data.

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“…Dong [3] proposed a two-stage method based on SVMs to simulate and predict the nonlinear dynamic response of structures, achieving good accuracy. Jalet [4] proposed a regression method based on least-squares-optimized SVMs to obtain displacement time series from acceleration data in order to reconstruct displacement data when displacement sensor data corresponding to acceleration sensors are missing. Bao [5] proposed a novel compressed sampling technique for wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring

Zhu,

Miao,

Chen

et al. 2024

Buildings

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For structural health monitoring (SHM), a complete dataset is crucial for further modal identification analysis and risk warning. Unfortunately, data loss can occur due to sensor failure, transmission system interruption, or hardware failure, which can lead to missing data. Therefore, this study proposes a bidirectional long short-term memory neural network (Bi-LSTM) response recovery method based on variational mode decomposition (VMD) and sparrow search algorithm (SSA) optimization that utilizes the structural response data between multiple sensors and can simultaneously consider temporal and spatial correlations. A dataset containing approximately half a month of monitoring data was collected from a certain project for training, validation, and testing. A publicly available dataset was also referenced to validate the proposed method in this paper. Using the public dataset, under 13 different data loss rates, the VMD + SSA + Bi-LSTM model reduced the RMSE of data reconstruction by an average of 65.01% and 45.35% compared to the Bi-LSTM model and the VMD + Bi-LSTM models, respectively, while the coefficient of determination increased by 62.21% and 11.19%. The data reconstruction method proposed in this paper can accurately reconstruct the variation trends of missing data without the manual optimization of hyperparameters, and the reconstruction results are close to the real data.

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Least-Square-Support-Vector-Machine-based approach to obtain displacement from measured acceleration

Cited by 19 publications

References 15 publications

Seismic response prediction method for building structures using convolutional neural network

Seismic response prediction method for building structures using convolutional neural network

The Eigenfunction Method for Determining Displacement Time History in Structural Dynamic Analysis

Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring

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