Numerical Study on the Impact of Out-of-Plane Eccentricity on Lateral Behavior of Concentrically Braced Frames

Zahrai, Seyed Mehdi; Bolandi, Hamed

doi:10.1007/s13296-018-0119-6

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Gusset plates connect beams and columns to braces in steel structures. The behavior and analysis of these components are critical since various reports have observed failures of gusset plates subject to lateral loads [37][38][39][40]. The boundary conditions and time-history load cases are considered to simulate similar conditions in common gusset plate structures under external loading.…”

Section: Data Generationmentioning

confidence: 99%

Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components

Bolandi¹,

Sreekumar²,

Li³

et al. 2023

Preprint

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Structural components are typically exposed to dynamic loading, such as earthquakes, wind, and explosions. Structural engineers should be able to conduct real-time analysis in the aftermath or during extreme disaster events requiring immediate corrections to avoid fatal failures. As a result, it is crucial to predict dynamic stress distributions during highly disruptive events in real time. Currently available high-fidelity methods, such as Finite Element Models (FEMs), suffer from their inherent high complexity and are computationally prohibitive. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, Neuro-DynaStress, is proposed to predict the entire sequence of stress distribution based on finite element simulations using a partial differential equation (PDE) solver. The model was designed and trained to use the geometry, boundary conditions and sequence of loads as input and predict the sequences of high-resolution stress contours. The proposed framework's performance is compared to finite element simulations using a PDE solver.

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Section: Data Generationmentioning

confidence: 99%

Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components

Bolandi¹,

Sreekumar²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The boundary conditions and loading angles simulate conditions that are similar to those affecting common gusset plate structures under external loading. Analysis of the behavior of these components is essential since various reports have observed failures of gusset plates subject to lateral loads [55][56][57][58]. The distributed static loads applied to the plates in this study range from 1 to 5 kN with intervals of 1 kN.…”

Section: Data Generationmentioning

confidence: 99%

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components

Bolandi

Salem

et al. 2022

Front. Struct. Civ. Eng.

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Finite-element analysis (FEA) for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures. Conventional methods, such as FEA, provide high fidelity results but require the solution of large linear systems that can be computationally intensive. Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-time analysis. This can prove extremely valuable in real-time structural assessment applications. Our proposed method uses deep neural networks in the form of convolutional neural networks (CNN) to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary conditions. The CNN was designed and trained to use the geometry, boundary conditions, and load as input to predict the stress contours. The proposed technique’s performance was compared to finite-element simulations using a partial differential equation (PDE) solver. The trained DL model can predict the stress distributions with a mean absolute error of 0.9% and an absolute peak error of 0.46% for the von Mises stress distribution. This study shows the feasibility and potential of using DL techniques to bypass FEA for stress analysis applications.

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Physics informed neural network for dynamic stress prediction

Bolandi,

Sreekumar,

et al. 2023

Appl Intell

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Numerical Study on the Impact of Out-of-Plane Eccentricity on Lateral Behavior of Concentrically Braced Frames

Cited by 5 publications

References 16 publications

Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components

Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components

Physics informed neural network for dynamic stress prediction

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