Residual displacement estimation of the bilinear SDOF systems under the near-fault ground motions using the BP neural network

Wei, Mingkang; Hu, Xiaobin; Yuan, Hongyong

doi:10.1177/13694332211058530

Cited by 27 publications

(16 citation statements)

References 43 publications

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“…The application of neural networks to compute multi-parameter tting problems has been widely used in structural engineering [33] , where the nonlinear computational power can effectively reduce the time to analyze the mechanism while ensuring computational accuracy. Lin Jyh-Woei [34] used the BPNN to predict the probability of earthquakes in Taiwan, a method that can be commercialized at a relatively low cost and with minimal resources and equipment using only earthquake catalogs.…”

Section: Bpnn-based Pga Predictionmentioning

confidence: 99%

Probabilistic seismic hazard analysis of BPNN predicting PGA

Guo

Zhang

et al. 2023

Preprint

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Probabilistic seismic hazard analysis (PSHA) is recognized as a reasonable method for quantifying seismic threats. Traditionally, the method ignores the effect of focal depth and in which the ground motion prediction equations (GMPEs) are applied to estimate the probability distribution associated with the possible motion levels induced by the site earthquakes, but it is limited by the unclear geological conditions, which makes it difficult to give a uniform equation, and the equation cannot express the nonlinear relationship in geological conditions. Hence, this paper proposes a method to consider the seismic focal depth for the PSHA with the example of California, and use a back propagation neural network (BPNN) to predict peak ground acceleration (PGA) instead of the GMPEs. Firstly, the measured PGA and unknown PGA seismic data applicable to this method are collected separately. Secondly, the unknown PGA data are supplemented by applying the BPNN based on the measured PGA data. Lastly, based on the full-probability equation PSHA considering the focal depth is completed and compared with the current California seismic zoning results. The results show that using the BPNN in the PSHA can ensure computational accuracy and universality, making it more suitable for regions with the unclear geological structures and providing the possibility of adding other parameters to be considered for the influence of PSHA.

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Section: Bpnn-based Pga Predictionmentioning

confidence: 99%

Probabilistic seismic hazard analysis of BPNN predicting PGA

Guo

Zhang

et al. 2023

Preprint

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“…Model structure Irregular data will have a large impact on the coded data of the model input data, affecting the distribution of the input data, which is not conducive to model training and causing a large error in the final fitted model. Using smoothing to reduce the impact of irregular data, the input data is corrected by [14], and the function is shown below.…”

Section: Input Layermentioning

confidence: 99%

Distribution substation heavy overload data processing method based on BP neural network

Chen,

Xu,

et al. 2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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The traditional processing of distribution substation heavy overload data uses manual method or association rule method, but its processing efficiency is slow and processing accuracy is low. In this regard, a BP neural network method for processing distribution substation heavy overload data is proposed. The BP neural network is constructed to build a model, extract data features, and train the model for data processing using large-scale data. In the experiments, the processing accuracy of the proposed model is verified. The analysis of the experimental results shows that the model processing efficiency is high and the data processing accuracy is high when the proposed model is used for data accuracy detection.

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“…The techniques and theories related to backpropagation neural networks (BPNNs) have been widely utilized. Mingkang et al conveniently used BPNNs to predict the residual displacement of bilinear single level of freedom systems [3]. Hu et al combined a genetic algorithm and BP to build a new prediction model for solar ultra-short-term radiation, and tested the constructed model in practical situations [4].…”

Section: Related Wordsmentioning

confidence: 99%

The evaluation of marketing competitiveness of B2B E-commerce enterprises based on optimized deep learning networks

Zhang

2023

Int. J. Simul. Multidisci. Des. Optim.

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In the context of e-commerce trade between enterprises, there are problems such as price wars, false advertising, and unreasonable operations aimed at seizing market share. However, traditional estimation methods cannot provide a reasonable evaluation of the marketing competitiveness of e-commerce enterprises from an overall perspective. To help enterprises better clarify their own position and quantify their marketing competitiveness, a competitiveness evaluation model based on optimized deep learning networks is proposed. By combining subjective and objective evaluation methods, the indicators that affect the marketing competitiveness of enterprises are assigned to obtain the final competitiveness value. The research outcomes expressed that the max absolute error of the model constructed by the research institute was 0.0006, the max relative error was 0.0045, and the model accuracy was 99.85%. In the secondary indicator experiment of marketing competition, the model determined that the turnover rate of fixed assets had the greatest impact, with a weight value of 0.1263. Nine companies were randomly selected for market value estimation, and the average relative error of the model was 14.90%, which was lower than the mean relative error of the relative valuation, cash flow and absolute valuation methods, with numerical differences of 8.03%, 2.94%, and 0.12%, respectively. The research findings illustrated that the model constructed by the research institute had good performance and certain reference values for evaluating the marketing competitiveness of e-commerce enterprises.

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Residual displacement estimation of the bilinear SDOF systems under the near-fault ground motions using the BP neural network

Cited by 27 publications

References 43 publications

Probabilistic seismic hazard analysis of BPNN predicting PGA

Probabilistic seismic hazard analysis of BPNN predicting PGA

Distribution substation heavy overload data processing method based on BP neural network

The evaluation of marketing competitiveness of B2B E-commerce enterprises based on optimized deep learning networks

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