Performance of different membership functions in stress classification with fuzzy logic

Bülbül, Mehmet Akif

doi:10.17678/beuscitech.1190436

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Artificial neural networks (ANN), which is one of the machine learning methods, and particle swarm optimization (PSO), which is used in solving multidimensional problems, are frequently used in many areas [27][28][29][30][31][32]. In structural and earthquake engineering applications, these techniques have a critical impact on areas such as simulation, modeling, optimization, regression, and classification [33,34].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

et al. 2023

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The realistic determination of damage estimation and building performance depends on target displacements in performance-based earthquake engineering. In this study, target displacements were obtained by performing pushover analysis for a sample reinforced-concrete building model, taking into account 60 different peak ground accelerations for each of the five different stories. Three different target displacements were obtained for damage estimation, such as damage limitation (DL), significant damage (SD), and near collapse (NC), obtained for each peak ground acceleration for five different numbers of stories, respectively. It aims to develop an artificial neural network (ANN)-based sustainable model to predict target displacements under different seismic risks for mid-rise regular reinforced-concrete buildings, which make up a large part of the existing building stock, using all the data obtained. For this purpose, a hybrid structure was established with the particle swarm optimization algorithm (PSO), and the network structure’s hyper parameters were optimized. Three different hybrid models were created in order to predict the target displacements most successfully. It was found that the ANN established with particles with the best position revealed by the hybrid models produced successful results in the calculation of the performance score. The created hybrid models produced 99% successful results in DL estimation, 99% in SD estimation, and 99% in NC estimation in determining target displacements in mid-rise regular reinforced-concrete buildings. The hybrid model also revealed which parameters should be used in ANN for estimating target displacements under different seismic risks.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

et al. 2023

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Urinary Bladder Inflammation Prediction with the Gray Wolf Optimization Algorithm and Multi-Layer Perceptron-Based Hybrid Architecture

BÜLBÜL

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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In this study, a decision support system for bladder inflammation prediction is presented. The proposed decision support system is built by establishing a hybrid architecture with Gray wolf optimization algorithm (GWO) and Multi-layer perceptron (MLP) networks. In addition to optimizing the hyperparameters in the MLP structure with GWO, the hybrid architecture also optimizes the order of input values to be presented to the MLP structure. The Acute Inflammations data set in the UCI Machine Learning repository was used as the data set in the study. Classification operations were carried out on this data set with the models obtained with hybrid architecture, Decision trees, k-Nearest Neighbors and Support Vector Machines methods. The controversial findings presented as a result of experimental studies have shown that the proposed hybrid architecture produces more successful results than other machine learning methods used in the study. In addition, the MLP network structure optimized with the hybrid architecture offers a new diagnostic method in terms of patient decision support systems.

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Performance of different membership functions in stress classification with fuzzy logic

Cited by 2 publications

References 17 publications

A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

Urinary Bladder Inflammation Prediction with the Gray Wolf Optimization Algorithm and Multi-Layer Perceptron-Based Hybrid Architecture

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