Functional Link Neural Network Learning for Response Prediction of Tall Shear Buildings With Respect to Earthquake Data

Sahoo, Deepti Moyi; Chakraverty, Snehashish

doi:10.1109/tsmc.2017.2700334

Cited by 30 publications

(13 citation statements)

References 38 publications

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“…An NN trained using one historical EQ was verified via a numerical study in which one artificial GM that was not used for training was employed. Similarly, Sahoo and Chakraverty proposed a method of predicting the time history of acceleration responses of buildings subject to EQs. They proposed a new NN with faster learning and reduced computational cost by removing hidden layers to improve the prediction performance of the existing multilayer NN.…”

Section: Introductionmentioning

confidence: 99%

“…In some cases, the validity of the seismic damage prediction method was examined numerically using structural modeling information for a number of building cases as well as damage indexes obtained through FEM‐based analysis of the model, without introduction of the SHM concept, which is a practical and reasonable approach to structural condition evaluation. In other studies, although the methods were implemented in the SHM framework based on recorded structural responses, only numerical validation was presented. Thus, support for practical application of the developed methods using the measured structural responses was not provided.…”

Section: Introductionmentioning

confidence: 99%

“…The EQs used for NN training and verification were not distinguished in some previous works, 9,18 and prediction performance exhibited considerable errors when EQs that had not been used for training were used for verification. 7 In addition, the verifications of the methods proposed in some of the aforementioned studies [7][8][9]14,15,17 were limited to numerical studies only. In some cases, 7-9 the validity of the seismic damage prediction method was examined numerically using structural modeling information for a number of building cases as well as damage indexes obtained through FEM-based analysis of the model, without introduction of the SHM concept, which is a practical and reasonable approach to structural condition evaluation.…”

Section: Introductionmentioning

confidence: 99%

“…In the aforementioned literature, the following broad limitations exist: The NN construction is limited to an FEM model that cannot reflect the uncertainties of actual structures; no emphasis is placed on displacements that are more meaningful responses from a seismic performance evaluation perspective; and measured displacement responses to EQs are required for application of the trained NN . For some of the proposed methods, the selection methods of the seismic loads used for verification of the trained NNs were inappropriate.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Introductionmentioning

confidence: 99%

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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show abstract

“…An intelligent neural system, which combines competitive ANNs and radial basis function ANNs, was developed to enhance accuracy, generality, and speed (Gholizadeh et al, 2009). A functional link ANN that incorporates polynomial functions was also developed by Sahoo and Chakraverty (2018) for improved accuracy and efficiency. Recently, a DL approach, named as the long short-term memory (LSTM) network, was proposed by Zhang et al (2019b) to model and predict the seismic responses of a nonlinear hysteretic system, a real-world building with field sensing data, and a steel moment resisting frame.…”

Section: System Identification and Damage Detectionmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

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Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation

Nizami,

Gangula,

Udumula

et al. 2024

Engineering Reports

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This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network‐based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady‐state performance. The stability of the entire closed‐loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start‐up, step‐reference tracking, and external step‐load torque disturbances. The real‐time experimentation is conducted on a laboratory prototype of power converter‐driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

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Functional Link Neural Network Learning for Response Prediction of Tall Shear Buildings With Respect to Earthquake Data

Cited by 30 publications

References 38 publications

Seismic response prediction method for building structures using convolutional neural network

Seismic response prediction method for building structures using convolutional neural network

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation

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