Modeling of Seismic Energy Dissipation of Rocking Foundations Using Nonparametric Machine Learning Algorithms

Abstract

The objective of this study is to develop data-driven predictive models for seismic energy dissipation of rocking shallow foundations during earthquake loading using multiple machine learning (ML) algorithms and experimental data from a rocking foundations database. Three nonlinear, nonparametric ML algorithms are considered: k-nearest neighbors regression (KNN), support vector regression (SVR) and decision tree regression (DTR). The input features to ML algorithms include critical contact area ratio, slenderness ratio and rocking coefficient of rocking system, and peak ground acceleration and Arias intensity of earthquake motion. A randomly split pair of training and testing datasets is used for initial evaluation of the models and hyperparameter tuning. Repeated k-fold cross validation technique is used to further evaluate the performance of ML models in terms of bias and variance using mean absolute percentage error. It is found that all three ML models perform better than multivariate linear regression model, and that both KNN and SVR models consistently outperform DTR model. On average, the accuracy of KNN model is about 16% higher than that of SVR model, while the variance of SVR model is about 27% smaller than that of KNN model, making them both excellent candidates for modeling the problem considered.

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

“…The input features for machine learning models have been selected based on their theoretical and experimentally observed close relationships with AAR, presented in Gajan et al (2021) [26]. In addition, in order to predict other performance parameters of rocking foundations (namely, seismic energy dissipation, maximum rotation of rocking foundation and factor of safety for tipping over failure), the same set of input features have been found to be appropriate and successful [10,11]. The input features include three nondimensional rocking system capacity parameters (A/A c , h/B and C r ), and two earthquake loading demand parameters (a max and Arias intensity of earthquake (I a )).…”

“…Machine learning algorithms such as logistic regression, decision trees, decision treebased ensemble models, and artificial neural networks have been used in a variety of geotechnical engineering applications that include mechanical properties of soils, strength of soils, soil slope stability, bearing capacity of foundations, and dynamic response of soils during earthquake loading [4][5][6][7][8][9]. Recently, in dynamic soil-foundation-structure interactions, machine learning algorithms have been used to develop data-driven models for rocking-induced seismic energy dissipation in soil, peak rotation of foundation, and factor of safety for tipping-over failure of rocking shallow foundations [10,11].…”

High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis

“…KNN considers the similarity factor between new and available data to classify an object into predefined categories. KNN has been widely used in many fields such as industry [7][8][9], machine engineering [10], health [11][12][13], marketing [14], electrical engineering [15], security [16][17][18], manufacturing [19], energy [20][21][22], aerial [23], environment [24], geology [25,26], maritime [27,28], geographical information systems (GIS) [29], and transportation [30].…”

Data-Driven Modeling of Peak Rotation and Tipping-Over Stability of Rocking Shallow Foundations Using Machine Learning Algorithms

“…Previous studies related to the application of machine learning algorithms for performance prediction of rocking foundations considered seismic energy dissipation, permanent settlement, and acceleration amplification ratio (acceleration transmitted to the structure) as performance (or prediction) parameters [25,26]. The novelty and originality of the present study include the following: (i) this is the first study that applies machine learning algorithms to develop data-driven predictive models for rocking-induced peak rotation and factor of safety for tipping-over failure of rocking foundations and (ii) the present study develops a new ensemble machine learning model for rocking foundations, namely, a random forest model consisting of multiple decision trees.…”