Machine learning-based seismic assessment of framed structures with soil-structure interaction

Noureldin, Mohamed; Ali, Tabish; Kim, Jinkoo

doi:10.1007/s11709-022-0909-y

Cited by 9 publications

(5 citation statements)

References 51 publications

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“…By substituting Equation (36) in Equation ( 35), the α m ratio for the parabolic loading case is obtained as follows:…”

Section: Moment Contribution Ratio For Parabolic Static Loadmentioning

confidence: 99%

“…Noureldin et al [36] introduced an expert system framework that used supervised machine learning to predict seismic performance in low-to mid-rise structures while considering soil-structure interaction. The framework, which was validated through non-linear time history analysis, incorporated a novel global seismic assessment ratio, resulting in more accurate outcomes compared to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Determination of Moment Contribution Ratio under Foundation Rotation in Shear Wall-Frame Systems

Bozdogan,

Keskin

2024

Buildings

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In shear wall-frame systems, the foundation rotation that may occur under the shear walls changes the displacements and interstory drift ratios and changes the internal force distribution. This study investigates the effect of foundation rotations under shear walls on internal force distribution in shear-frame systems. The originality of the study lies in considering parabolic loads and dynamic analysis (first mode), in addition to static uniform or triangular distributed loads, when determining the shear wall moment contribution ratio under the influence of foundation rotation. The shear wall contribution ratio, a key parameter in many earthquake codes, is defined as the ratio of the sum of bending moments taken by the shear walls at the base to the overturning moment. It plays a crucial role in determining the building’s behavior. Depending on this ratio, the load-reduction coefficient is changed. This study investigates the effect of foundation rotation on the moment distribution at the base for three different static load cases and the first mode in the dynamic analysis. The multi-story building is modeled as an equivalent sandwich beam. The moment contribution ratio (MCR) was calculated with the help of analytical solutions of the differential equations written for three different load cases in static conditions, and graphs were created for practical use directly calculating the MCR. In the methodology of the study, the initial step involves the calculation of the equivalent sandwich beam stiffness parameters and the foundational rotational spring. Subsequent to these calculations, the MCR values can be directly obtained with the help of graphs. This approach facilitates the rapid and practical determination of the MCR and can be used in the preliminary sizing phase to eliminate possible errors in the data entry of software that performs detailed analysis. In addition, in the presented study, it has been shown that taking a single mode into account is sufficient when calculating MCR values in dynamic analysis.

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“…By substituting Equation (36) in Equation ( 35), the α m ratio for the parabolic loading case is obtained as follows:…”

Section: Moment Contribution Ratio For Parabolic Static Loadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Determination of Moment Contribution Ratio under Foundation Rotation in Shear Wall-Frame Systems

Bozdogan,

Keskin

2024

Buildings

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“…Support Vector Machines (SVM), a class of supervised learning algorithms, have steadily emerged as pivotal tools within the seismic community, particularly in the realm of event classification [20]. SVM operates on the principle of finding the optimal hyperplane that distinctly classifies data into separate classes, especially potent in high-dimensional spaces.…”

Section: Svm In Seismic Event Classificationmentioning

confidence: 99%

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Turarbek,

Bektemesov,

Ongarbayeva

et al. 2023

IJACSA

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In recent years, the realm of seismology has witnessed an increased integration of advanced computational techniques, seeking to enhance the precision and timeliness of earthquake predictions. The paper titled "Deep Convolutional Neural Network and Machine Learning Enabled Framework for Analysis and Prediction of Seismic Events" embarks on an ambitious exploration of this interstice, marrying the formidable prowess of Deep Convolutional Neural Networks (CNNs) with an array of machine learning algorithms. At the forefront of our investigation is the Deep CNN, known for its unparalleled capability to process spatial hierarchies and multi-dimensional seismic data. Accompanying this neural behemoth is LightGBM, a gradient boosting framework that offers superior speed and performance, especially with voluminous datasets. Additionally, conventional neural networks, noted for their adeptness in pattern recognition, offer a robust method to gauge the intricacies of seismic data. Our exploration doesn't halt here; the research delves deeper with Random Forest and Support Vector Machines (SVM), both renowned for their resilient performance in classification tasks. By amalgamating these diverse methodologies, this research crafts a multifaceted and synergistic framework. The culmination is a sophisticated tool poised to not only discern the minutiae of seismic activities with heightened accuracy but to predict forthcoming events with a degree of certainty previously deemed elusive. In this era of escalating seismic activities, our research offers a timely beacon, heralding a future where communities are better equipped to respond to the Earth's capricious tremors.

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“…Other studies focused on the application of DLMs to predict the dynamic response of systems (e.g., Feng et al., 2021; Stoffel et al., 2020; Wu et al., 2019; Zhang et al., 2019). Previous studies generally focused on the deterministic prediction of responses (e.g., Ali et al., 2023; Noureldin et al., 2023; Payán et al., 2017). Moreover, predicting the uncertainty of responses is highly affected by the input feature variability, which means that the heteroscedastic assumption needs to be considered, not the homoscedastic one.…”

Section: Introductionmentioning

confidence: 99%

Explainable probabilistic deep learning framework for seismic assessment of structures using distribution‐free prediction intervals

Noureldin

Abuhmed

Saygi

et al. 2023

Computer aided Civil Eng

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A new probabilistic framework is proposed for providing a distribution-free prediction interval (PI) of seismic responses required for various earthquake engineering applications. The framework overcomes the limitation of point prediction models and avoids the complexity of traditional probabilistic methods. The framework utilizes a few assumptions of probability distributions and requires no prior assumed statistical distribution for the PI. Ensemble probabilistic deep learning models (DLMs) are used to provide quality-driven PIs of seismic responses for low-to mid-rise buildings with limited irregularity. Considering these systems and ground motions with the aid of Monte Carlo simulation and nonlinear time-history analysis (NLTHA), huge datasets are generated for training. To have an insight into the probabilistic DLM, explainable artificial intelligence techniques are used. The superiority of the proposed framework in quantifying uncertainties is validated by comparison with the conventional Bayesian method. In addition, its applicability is investigated by providing bounds of seismic fragility curves, life cycle cost, and resilience index obtained by NLTHA for a benchmark case study model. The results showed that the proposed framework is robust and outperforms the conventional Bayesian method in uncertainty quantification for the considered dataset.

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Machine learning-based seismic assessment of framed structures with soil-structure interaction

Cited by 9 publications

References 51 publications

A Method for Determination of Moment Contribution Ratio under Foundation Rotation in Shear Wall-Frame Systems

A Method for Determination of Moment Contribution Ratio under Foundation Rotation in Shear Wall-Frame Systems

Deep Convolutional Neural Network for Accurate Prediction of Seismic Events

Explainable probabilistic deep learning framework for seismic assessment of structures using distribution‐free prediction intervals

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