Empirical models for the nonlinear rocking response of shallow foundations

Hamidpour, Sara; Shakib, Hamzeh; Paolucci, Roberto; Correia, António A.; Soltani, Mohammad Reza

doi:10.1007/s10518-022-01449-1

Cited by 2 publications

(1 citation statement)

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“…Research on rocking foundations reveals that they dissipate seismic energy in soil, reduce acceleration, force and flexural drift demands transferred to the structures, and effectively perform as a geotechnical seismic isolation mechanism [24][25][26]. Numerical modeling methods and empirical methods are available to quantify the moment-rotation response, rotational stiffness, damping ratio and settlement-rotation relationships of rocking foundations [27,28]. This paper presents the development of models to predict the rocking induced, reduced peak acceleration demands on structures using machine learning algorithms that are trained and tested on experimental results from a rocking foundation database that covers a wide range of soil properties, foundation geometry, and structural configurations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Acceleration Amplification Ratio of Rocking Foundations Using Machine Learning and Deep Learning Models

Gajan

2023

Applied Sciences

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Experimental results reveal that rocking shallow foundations reduce earthquake-induced force and flexural displacement demands transmitted to structures and can be used as an effective geotechnical seismic isolation mechanism. This paper presents data-driven predictive models for maximum acceleration transmitted to structures founded on rocking shallow foundations during earthquake loading. Results from base-shaking experiments on rocking foundations have been utilized for the development of artificial neural network regression (ANN), k-nearest neighbors regression, support vector regression, random forest regression, adaptive boosting regression, and gradient boosting regression models. Acceleration amplification ratio, defined as the maximum acceleration at the center of gravity of a structure divided by the peak ground acceleration of the earthquake, is considered as the prediction parameter. For five out of six models developed in this study, the overall mean absolute percentage error in predictions in repeated k-fold cross validation tests vary between 0.128 and 0.145, with the ANN model being the most accurate and most consistent. The cross validation mean absolute error in predictions of all six models vary between 0.08 and 0.1, indicating that the maximum acceleration of structures supported by rocking foundations can be predicted within an average error limit of 8% to 10% of the peak ground acceleration of the earthquake.

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Section: Introductionmentioning

confidence: 99%

Prediction of Acceleration Amplification Ratio of Rocking Foundations Using Machine Learning and Deep Learning Models

Gajan

2023

Applied Sciences

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Predictive modeling of rocking-induced settlement in shallow foundations using ensemble machine learning and neural networks

Gajan

2024

Front. Built Environ.

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IntroductionThe objective of this study is to develop predictive models for rocking-induced permanent settlement in shallow foundations during earthquake loading using stacking, bagging and boosting ensemble machine learning (ML) and artificial neural network (ANN) models.MethodsThe ML models are developed using supervised learning technique and results obtained from rocking foundation experiments conducted on shaking tables and centrifuges. The overall performance of ML models are evaluated using k-fold cross validation tests and mean absolute percentage error (MAPE) and mean absolute error (MAE) in their predictions.ResultsThe performances of all six nonlinear ML models developed in this study are relatively consistent in terms of prediction accuracy with their average MAPE varying between 0.64 and 0.86 in final k-fold cross validation tests.DiscussionThe overall average MAE in predictions of all nonlinear ML models are smaller than 0.006, implying that the ML models developed in this study have the potential to predict permanent settlement of rocking foundations with reasonable accuracy in practical applications.

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Empirical models for the nonlinear rocking response of shallow foundations

Cited by 2 publications

References 38 publications

Prediction of Acceleration Amplification Ratio of Rocking Foundations Using Machine Learning and Deep Learning Models

Prediction of Acceleration Amplification Ratio of Rocking Foundations Using Machine Learning and Deep Learning Models

Predictive modeling of rocking-induced settlement in shallow foundations using ensemble machine learning and neural networks

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