Application of Levenberg Marquardt Method for Calibration of Unsteady Friction Model for a Pipeline System

Park, Jo Eun; Kim, Sang Hyun

doi:10.3741/jkwra.2013.46.4.389

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Training neural network samples is a process of constantly searching for the best threshold and weight value, that is, the output error of the network becomes smaller and smaller through training. In this section, the Levenberg-Marquardt algorithm [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] is used to train the neural network samples.…”

Section: Training Of Neural Network Samplesmentioning

confidence: 99%

Mechanical Parameter Inversion in Sandstone Diversion Tunnel and Stability Analysis during Operation Period

et al. 2019

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A large number of experimental studies show that the mechanical parameters of deep buried surrounding rock show significant attenuation characteristics with the increase of strain from the rheological acceleration stage to the attenuation stage. However, the existing numerical models all take mechanical parameters as constants when describing the rheological behavior of surrounding rocks, which can only be applied to the stability analysis of the shallowly buried tunnel. Therefore, this work proceeding from the actual project, improved the sandstone rheological constitutive model and optimized the algorithm of parameter inversion, and put forward a long-term stability analysis model that can accurately reflect the rheological characteristics of surrounding rocks under the complex geological condition including high stress induced by great depth and high seepage pressure. In the process, a three-dimensional nonlinear rheological damage model was established based on Burgers rheological model by introducing damage factors into the derivation of the sandstone rheological constitutive model to accurately describe the rheological behaviors of the deep buried tunnel. And BP (Back Propagation) neural network optimized by the multi-descendant genetic algorithm is used to invert the mechanical parameters in the model, which improves the efficiency and precision of parameter inversion. Finally, the rheological equation was written by using parametric programming language and incorporated into the general finite element software ANSYS to simulate the rheological behavior of the tunnel rock mass at runtime. The results of the model analysis are in good agreement with the monitoring data in the later stage. The research results can provide a reference for the stability analysis of similar projects.

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Section: Training Of Neural Network Samplesmentioning

confidence: 99%

Mechanical Parameter Inversion in Sandstone Diversion Tunnel and Stability Analysis during Operation Period

et al. 2019

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A Uniform Improvement of Bonferroni-Type Tests by Sequential Tests

Posch

Futschik

2008

Journal of the American Statistical Association

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We propose a new method to uniformly improve cutoff tests (as, e.g., the Bonferroni test) by replacing them with a sequential testing procedure. The proposed procedure provides a uniform improvement-that is, it rejects the null hypothesis at least in all cases where the original procedure does, has level α, and never requires a sample size larger than that of the original procedure. Information on the oftenunknown joint distribution of the test statistics across the hypotheses is not required. We discuss the applications to one-and two-sided tests for the intersection of multiple hypotheses, as well as to tests of elementary hypotheses. For the wide class of asymptotically linear test statistics and under unknown nuisance parameters, we propose an asymptotic approximation to our improved procedure and show that it exhausts the nominal type I error rate asymptotically. We present an application in a clinical study with multiple endpoints where the null hypothesis of no effect is tested against the alternative that at least one endpoint shows an effect, and give a real data example.

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Estimation of river discharge using satellite-derived flow signals and artificial neural network model: application to imjin river

Li¹,

Kim²,

Jun³

et al. 2016

Journal of Korea Water Resources Association

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Application of Levenberg Marquardt Method for Calibration of Unsteady Friction Model for a Pipeline System

Cited by 3 publications

References 16 publications

Mechanical Parameter Inversion in Sandstone Diversion Tunnel and Stability Analysis during Operation Period

Mechanical Parameter Inversion in Sandstone Diversion Tunnel and Stability Analysis during Operation Period

A Uniform Improvement of Bonferroni-Type Tests by Sequential Tests

Estimation of river discharge using satellite-derived flow signals and artificial neural network model: application to imjin river

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