Applying BP Neural Network Model to Forecast Peak Velocity of Blasting Ground Vibration

Lv, Shuran; Shujin, Lv

doi:10.1016/j.proeng.2011.11.2166

Cited by 17 publications

(8 citation statements)

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“…Training accuracy can be improved by setting a reasonable number of nodes. The number of hidden nodes is determined by (Shuran and Shujin, 2011;Cheng et al, 2013):…”

Section: Establishment Of Neural Network Modelmentioning

confidence: 99%

Analysis of the Radial Stiffness of Rubber Bush Used in Dynamic Vibration Absorber Based on Artificial Neural Network

Sun

et al. 2018

Neuroquantology

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Rubber bush is used in dynamic vibration absorber as dissipating devices in damping boring bar. These devices actually have to support radial load in compression when chattering occurs. Mastering the behavior of the radial stiffness of the rubber bush implies an accurate understanding of dynamic vibration absorber. The behavior is, however, complex due to the changeable cross-sectional shape and boundary conditions of the rubber bush. By using artificial neural network, the radial stiffness can be predicted efficiently. According to the authors' knowledge, simulations and tests on radial stiffness of the rubber bush under combined different cross-sectional shape and boundary conditions by using artificial neural network have not been performed yet. The purpose of this study is thus to find the law of radial stiffness of rubber bush under different cross-section shapes and axial precompression conditions. In order to achieve this aim, simulations and tests under different chamfering sizes and axial pre-compression by using artificial neural network were first carried out.

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“…Training accuracy can be improved by setting a reasonable number of nodes. The number of hidden nodes is determined by (Shuran and Shujin, 2011;Cheng et al, 2013):…”

Section: Establishment Of Neural Network Modelmentioning

confidence: 99%

Analysis of the Radial Stiffness of Rubber Bush Used in Dynamic Vibration Absorber Based on Artificial Neural Network

Sun

et al. 2018

Neuroquantology

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“…Normal algorithms for prognostics mainly contains two categories (EI-Thalji and Jantunen 2015 ): the one are algorithms based on models, such as the physics model algorithm (Jin et al 2013 ), the auto-regressive and moving average algorithm (ARMA) (Yu et al 2013 ), and the auto-regressive integrated moving average algorithm (ARIMA) (Li and Hu 2012 ). Others are algorithms based on neural intelligence, such as the BP neural network (Shuran and Shujin 2011 ), the recurrent neural network (RNN) (Cao et al 2012 ), the extreme learning machine (ELM) (Wang et al 2011 ), and the echo state networks (ESN) (Park et al 2014 ). The physics model algorithm is capable of making prediction of the operating trends and the possible failures.…”

Section: Introductionmentioning

confidence: 99%

Prognostic for hydraulic pump based upon DCT-composite spectrum and the modified echo state network

Sun

2016

SpringerPlus

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Prognostic is a key step of the condition-based maintenance (CBM). In order to improve the predicting performance, a novel method for prognostic for the hydraulic pump is proposed in this paper. Based on the improvement of the traditional composite spectrum, the DCT-composite spectrum (DCS) fusion algorithm is initially presented to make fusion of multi-channel vibration signals. The DCS composite spectrum entropy is extracted as the feature. Furthermore, the modified echo state networks (ESN) model is established for prognostic using the extracted feature. The reservoir is updated and the elements of the neighboring matrix are redefined for improving predicting accuracy. Analysis of the application in the hydraulic pump degradation experiment demonstrates that the proposed algorithm is feasible and is meaningful for CBM.

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“…Wherever back break has been the blasting problem in a new bench, the lower amount of back break is considered as the blasting pattern evaluation factor Gate et al 2005;Khandelwal and Monjezi 2012;Monjezi et al 2012;Monjezi and Dehghani 2008;Monjezi et al 2010b). In addition, a few endeavours have been made to diminish ground vibration (Erarslan et al 2008;Ak et al 2009;Hudaverdi 2012;Shuran and Shujin 2011;Bakhshandeh Amnieh et al 2012;Dehghani and Ataee-Pour 2011;Monjezi et al 2010a;Guosheng et al 2011;Ak and Konuk 2008;Iphar et al 2008;Monjezi et al 2011b). The essential issue of these examinations is to recognize stand out of the impact criteria in the blasting operation enhancement.…”

Section: Introductionmentioning

confidence: 99%

Hydrology of the Upper Indus Basin Under Potential Climate Change Scenarios

Soncini

Bocchiola

Confortola

et al. 2014

Engineering Geology for Society and Territory - Volume 1

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Applying BP Neural Network Model to Forecast Peak Velocity of Blasting Ground Vibration

Cited by 17 publications

References 0 publications

Analysis of the Radial Stiffness of Rubber Bush Used in Dynamic Vibration Absorber Based on Artificial Neural Network

Analysis of the Radial Stiffness of Rubber Bush Used in Dynamic Vibration Absorber Based on Artificial Neural Network

Prognostic for hydraulic pump based upon DCT-composite spectrum and the modified echo state network

Hydrology of the Upper Indus Basin Under Potential Climate Change Scenarios

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