Selection of ground motion prediction equations for probabilistic seismic hazard analysis based on an improved fuzzy logic

Shayanfar, Mohsen Ali; Barkhordari, Mohammad Ali; Mahmoudi, Mostafa; Jahani, Ehsan

doi:10.21595/jve.2019.20377

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Artificial neural networks have inherent potential fault tolerance and its execution efficiency will not be significantly reduced under certain unfavorable situations, such as neuron disconnection, interfering data, or data loss. e reliability of calculations can be verified by some experience, but it is usually uncontrolled [13].…”

Section: Programming Realization Of Fuzzy Neural Networkmentioning

confidence: 99%

Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic

Liu

Yang³

et al. 2022

Scientific Programming

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The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.

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Section: Programming Realization Of Fuzzy Neural Networkmentioning

confidence: 99%

Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic

Liu

Yang³

et al. 2022

Scientific Programming

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“…In current practice, the seismic hazard curves are generated by combining all possible choices for SM and GMPE models using logic trees as shown in Figure 1. Various approaches such as Backbone models, Mixture models, Fuzzy logic, Bayesian inference have been used in the past to obtain the weights of SM and GMPE models in the logic tree (Bertin et al, 2020;Haendel et al, 2015;Pirchiner et al, 2013;Shayanfar et al, 2019;Viallet et al, 2017). In addition to estimation of weights, a unified approach is needed to integrate uncertainties arising from different sources and propagate them to the hazard curve.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Estimate Weights for GMPE Models in a Logic Tree

Bodda¹,

Keller²,

Gupta³

et al. 2021

Preprint

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In current practice, the seismic hazard curves are generated by combining all possible choices for seismotectonic model (SM), earthquake magnitude recurrence models, and ground motion prediction equation (GMPE) models using logic trees. However, studies have shown that significant uncertainties exist in the evaluation of the seismic hazard, especially in low-to-moderate seismicity regions, such as Central \& Eastern United States and Europe, due to the scarcity of the recorded earthquake data and lack of precise knowledge concerning the earthquake generation and propagation mechanisms. Specifically, these uncertainties lead to conservatisms in the seismic hazard which poses a considerable risk for nuclear facilities. Designing or upgrading facilities with such conservative estimates of seismic hazard results in large capital costs. In standard probabilistic seismic hazard assessment (PSHA) practices, the estimate of branch weights for the GMPE models is based on expert judgment and there is no holistic approach for estimating the weights under uncertainty. In this research, we focus on the GMPE part of the PSHA and we propose a statistical methodology that is based on Bayesian inference, to evaluate the GMPE model weights from a list of GMPE models in the logic tree. In this study, Bayesian model averaging (BMA) approach with Bayesian linear models (BLM) is employed to combine GMPE models and the performance of the BMA model is tested against European Strong-Motion (ESM) database. It is shown that the proposed methodology can calculate the weights at different time periods without any approximations using analytical formulas and it is computationally inexpensive.

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Selection of ground motion prediction equations for probabilistic seismic hazard analysis based on an improved fuzzy logic

Cited by 2 publications

References 24 publications

Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic

Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic

A Bayesian Approach to Estimate Weights for GMPE Models in a Logic Tree

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