Fuzzy Probability Approach in Seismic Hazard Analysis

Hiremath, Prasad; Khan, Mohammad Muzzaffar; Kumar, G. Kalyan

doi:10.1007/978-981-13-0562-7_5

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…FL has applications in various fields, including control systems, pattern recognition, and decision making. In structural studies, FL aids in handling imprecise or uncertain data, allowing for nuanced reasoning and analysis of complex systems [8][9][10][11][12][13]. NN, another supervised technique, has been increasingly applied in structural health assessment and system optimisation.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic, Neural Network, and Adaptive Neuro-Fuzzy Inference System in Delegation of Standard Concrete Beam Calculations

Baghdadi,

Babovic,

Kloft

2023

Buildings

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Machine learning (ML) has been proven effective in various scientific and industrial domains. Nevertheless, its practical application in the construction industry requires further investigation. Leveraging ML capabilities conserves human resources, reduces errors, and speeds up computation and interpretation tasks. The efficacy of ML algorithms depends on factors like ability, result accuracy, analysis cost, and sensitivity to parameter count and available data volume. This study explores the potential of using ML to delegate structural calculation processes, which is an aspect with limited attention. Concrete beam section calculations based on the American Concrete Institute (ACI) standards were chosen as a case study to assess ML’s capacity to emulate a structural designer’s role. Initially, manual design steps and standard considerations for a concrete beam section were parametrically coded in MATLAB. Validation against structural design references ensured code accuracy in calculating shear and bending capacities. The parametric results served as initial data (lookup table) for training ML operators. Various ML techniques, including fuzzy logic (FL), neural network (NN), and adaptive neuro-fuzzy inference system (ANFIS), were coded in MATLAB. A comparative analysis of the three ML operators assessed their performance in replacing standard calculations. Parametric examples illustrated each operator’s precision in delegation compared to direct calculations. The study also explored the impact of the number of parameters and lookup table size on the accuracy of each ML operator. The findings revealed that while all three operators could delegate standard calculations, their precision varied. Notably, when the lookup table was optimal, ANFIS operators demonstrated the ability to represent standard calculations with varying parameter counts and high precision. Focused on beam calculations, this study provides insights into ML operator performance. The outcomes, including selecting the most capable operator and their sensitivity to parameters and lookup table size, offer valuable guidance for researchers interpreting experimental and numerical analysis results.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Logic, Neural Network, and Adaptive Neuro-Fuzzy Inference System in Delegation of Standard Concrete Beam Calculations

Baghdadi,

Babovic,

Kloft

2023

Buildings

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“…The basic parameters ( = ln10) and (the number of events with magnitudes equal to or greater than a defined magnitude level) was also fuzzified in the methods of FPSHA [23]. The variables and was first converted into Gaussian fuzzy sets using -cut method.…”

Section: Introductionmentioning

confidence: 99%

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

Shayanfar¹,

Barkhordari²,

Mahmoudi³

et al. 2019

J. vibroeng.

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The fuzzy logic method has been used widely in civil and earthquake engineering, but there is no comprehensive point of view for utilizing fuzzy approach in order to obtain ground motion prediction equations (GMPEs) for probabilistic seismic hazard analysis (PSHA). Hence, fuzzy magnitude-distance method as a new approach for choosing GMPEs in the process of PSHA, is developed in this research through the selection of the ruling peak ground acceleration (PGA) of each common cell (the combined cell of earthquake intensity and site to source distance). The presented method reduces the need for engineering judgments in seismic analysis based on a newly developed benchmark. It enables designers to not only determine the range of acceptable fuzzy results but also introduces a concept which ensures the selection of initial well-suited GMPEs for the analysis.

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“…(2012),Boostanetal. (2015, Hiremath(2016)andAndric&Lu (2017)denotetheimportanceanddevelopmentsintheSHAusingthefuzzylogicapproach.Itis notedthattheseismichazardstudiesconsideringtheearthquakeeventsafter2011ADhavenotbeen accountedforinthestudyarea,Chennaicity,southIndia.Theexistingseismichazardanalysisfor thestudyregionhavenotincludedthefuzzyuncertainties;therefore,theavailablehazardresults lackthereliability.Hence,thepresentstudyfocusesoncarryingouttheseismichazardanalysisby applyingtheproposedfuzzy-probabilisticapproachbysystematicallycompilingtheearthquake datafrom1800to2020ADandaccountingfortheprobabilisticandfuzzyuncertaintiesassociated withtheparametersoftheseismichazard.AfewconventionalhazardanalysescarriedoutinIndia andsouthernPI (Nayaketal.,2015;Mridulaetal.,2016;Ghosh&Chakraborty,2017;Patiletal., 2018;MuzzaffarKhan&KalyanKumar,2020;Sharmaetal.,2020&2021)arealsoutilisedinthis studyforcompilingtheearthquakecatalogueandthehazardanalysisprocedureisimprovedwith theadditionoffuzzyuncertainty.…”

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confidence: 99%

Seismic Hazard Analysis Using Fuzzy-Probabilistic Approach for Chennai City, South India

Menaka

Dodagoudar

2022

International Journal of Geotechnical Earthquake Engineering

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Fuzzy-Probabilistic Seismic Hazard Analysis (FPSHA) is performed for Chennai city, south India incorporating both the random and fuzzy uncertainties. Randomness is handled using Monte-Carlo simulation technique, and fuzziness is accounted in the hazard analysis using fuzzy logic. The magnitude of earthquakes and epicentral distances are fuzzified and used as inputs in the fuzzy inference rules. Fuzzy attenuation relationships are developed as consequence of the inference rules with the help of ground-motion models and ANFIS. The proposed FPSHA method has the advantage over the conventional hazard analysis methods in respect of its preciseness, efficiency, practicability and reliability. The ground motions compatible with the target spectrum (UHS) of 475 years return period are selected from the recorded accelerograms with appropriate scaling. The established spectrum compatible accelerograms are vital in the seismic analysis and design of infrastructure facilities, rehabilitation and strengthening of historical and critical structures.

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Fuzzy Probability Approach in Seismic Hazard Analysis

Cited by 3 publications

References 5 publications

Fuzzy Logic, Neural Network, and Adaptive Neuro-Fuzzy Inference System in Delegation of Standard Concrete Beam Calculations

Fuzzy Logic, Neural Network, and Adaptive Neuro-Fuzzy Inference System in Delegation of Standard Concrete Beam Calculations

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

Seismic Hazard Analysis Using Fuzzy-Probabilistic Approach for Chennai City, South India

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