## 1 Introduction  
Dynamic soil-structure interaction in shallow foundations has generally been modeled using mechanics-based models such as simple spring-dashpot models, beam on nonlinear Winkler foundation models, plasticity-based macro-element models, and continuum-based models. A recent review article summarizes the computational methods generally used to model dynamic soil-foundation-structure interactions during earthquake loading, particularly in the context of geotechnical engineering [1]. As the development of large experimental databases becomes increasingly common, the application of machine learning techniques in geotechnical engineering has been improving and becoming more effective [2]. Machine learning models generalize observed experimental behavior, capture the salient features that may not be captured by mechanics-based models, and can be used with mechanics-based models as complementary measures in engineering applications or can be combined with engineering mechanics using the emerging framework of theory-guided machine learning [3]. Machine learning algorithms such as logistic regression, decision trees, decision tree-based 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]. The earthquake-induced peak acceleration of structures is one of the key seismic design parameters of buildings and bridges, as the seismic performance of these structures depends heavily on the inertial forces experienced by the structural members and non-structural components induced by the acceleration of structures [12; 13]. For instance, base shear force (and bending moment) of structures during earthquake loading, a commonly used seismic design parameter for structures, is directly proportional to the horizontal acceleration at the effective height of the structure [14]. There have been several studies related to floor acceleration demands on structures during seismic loading for structures supported by traditional, fixed-base foundations [15; 16; 17]. Rocking shallow foundations is a recent research phenomenon that has been investigated to some extent, particularly using centrifuge and shaking table experiments [18; 19; 20; 21; 22; 23]. 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. Whereas the previously published research on the application of machine learning algorithms to rocking foundations focused on rocking induced seismic energy dissipation, peak rotation, and tipping-over stability of rocking foundations, the current work focuses on the acceleration amplification ratio (AAR) of rocking foundations.