Predicting the buckling load of smart multilayer columns using soft computing tools

Shahbazi, Yaser; Delavari, Ehsan; Chenaghlou, Mohammad Reza

doi:10.12989/sss.2013.13.1.081

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Because the recent deep-and machine-learning technologies are approaches developed based on existing neural networks, the ANN technique has many advantages that system users can exploit to easily handle these tools with relatively fast operation speed. For this reason, numerous researchers have used ANN techniques in a variety of engineering research fields such as structural health monitoring [29], structural control [30], damage detection [31][32][33], and structural response estimation [34][35][36].…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

Yoon

Kim

et al. 2020

Applied Sciences

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In this study, an artificial neural network (ANN)-based surrogate model is proposed to evaluate the system-level seismic risk of bridge transportation networks efficiently. To estimate the performance of a network, total system travel time (TSTT) was introduced as a performance index, and an ANN-based surrogate model was incorporated to evaluate a high-dimensional network with probabilistic seismic hazard analysis (PSHA) efficiently. To generate training data, the damage states of bridge components were considered as the input training data, and TSTT was selected as output data. An actual bridge transportation network in South Korea was considered as the target network, and the entire network map was reconstructed based on geographic information system data to demonstrate the proposed method. For numerical analysis, the training data were generated based on epicenter location history. By using the surrogate model, the network performance was estimated for various earthquake magnitudes at the trained epicenter with significantly-reduced computational time cost. In addition, 20 historical epicenters were adopted to confirm the robustness of the epicenter. Therefore, it was concluded that the proposed ANN-based surrogate model could be used as an alternative for efficient system-level seismic risk assessment of high-dimensional bridge transportation networks.

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Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

Yoon

Kim

et al. 2020

Applied Sciences

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“…Based on one-dimensional nanofibrous materials for wearable electronics textiles applications, Gheibi et al fabricated a one-step nano-generator, and the piezoelectric properties of fabricated composites were also evaluated on a self-made system as a function of frequency [10]. Recently, the finite element modeling and artificial neural network were presented to study the elastic buckling of smart lightweight column structures integrated with a pair of piezoelectric layers [11]. For PNs, the size-dependent behavior is significantly related with the large surface/interface ratio to the volume.…”

Section: Introductionmentioning

confidence: 99%

Size-dependent effects on electromechanical response of multilayer piezoelectric nano-cylinder under electro-elastic waves

Fang

Liu

Feng

et al. 2015

Composite Structures

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A Hybrid Method for Big Data Analysis Using Fuzzy Clustering, Feature Selection and Adaptive Neuro-Fuzzy Inferences System Techniques: Case of Mecca and Medina Hotels in Saudi Arabia

Alghamdi

2022

Arab J Sci Eng

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Predicting the buckling load of smart multilayer columns using soft computing tools

Cited by 3 publications

References 22 publications

Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

Size-dependent effects on electromechanical response of multilayer piezoelectric nano-cylinder under electro-elastic waves

A Hybrid Method for Big Data Analysis Using Fuzzy Clustering, Feature Selection and Adaptive Neuro-Fuzzy Inferences System Techniques: Case of Mecca and Medina Hotels in Saudi Arabia

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