###### Abstract  
The seismic bearing capacity of a shallow strip footing above a void displays a complex dependence on several characteristics, linked to geometric problems and to the soil properties. Hence, setting analytical models to estimate such bearing capacity is extremely challenging. In this work, machine learning (ML) techniques have been employed to predict the seismic bearing capacity of a shallow strip footing located over a single unsupported rectangular void in heterogeneous soil. A dataset consisting of 38,000 finite element limit analysis simulations has been created, and the mean value between the upper and lower bounds of the bearing capacity has been computed at the varying undrained shear strength and internal friction angle of the soil, horizontal earthquake accelerations, and position, shape, and size of the void. Three machine learning techniques have been adopted to learn the link between the aforementioned parameters and the bearing capacity: multilayer perceptron neural networks; a group method of data handling; and a combined adaptive-network-based fuzzy inference system and particle swarm optimization. The performances of these ML techniques have been compared with each other, in terms of the following statistical performance indices: coefficient of determination (R\({}^{2}\)); root mean square error (RMSE); mean absolute percentage error; scatter index; and standard bias. Results have shown that all the ML techniques perform well, though the multilayer perceptron has a slightly superior accuracy featuring noteworthy results (R\({}^{2}=0.9955\) and RMSE \(=0.0158\)). s 202  
void existence may adversely affect the stability of constructions, leading to a differential settlement of superstructures or the collapse of foundations. To date, various studies have been conducted to investigate the bearing capacity of strip footings above voids, by using either theoretical or experimental methods. Preliminary studies examined the fundamental issues of the problem, see examples [3; 4; 5; 6; 7; 8]. In those works, the focus was on the bearing capacity of strip footings over a single void. Later, [PERSON] et al. [9] carried out a set of centrifuge tests over multiple voids, and the results were assessed through the PLAXIS finite element code. [PERSON], [PERSON], and [PERSON] [10] then analyzed the bearing capacity of a strip footing on stiff grounds with voids, and they classified the failure patterns for a single hole into three categories: roof failure; sidewall failure; and combined failure. [PERSON] et al. [11], in a case characterized by multiple voids, demonstrated that closely spaced tunnels showed a good correlation between the results of finite element simulations and upper-bound limit analysis. This problem was investigated using small strain kinematics. [PERSON], [PERSON], and [PERSON] [12] quantified the influence of single and dual continuous voids, allowing for a wide variety of geometries and material combinations on the undrained vertical load bearing capacity of strip footings. [PERSON], [PERSON], and [PERSON] [13] also studied the effect of load inclination. [PERSON], [PERSON], and [PERSON] [14] examined the undrained bearing capacity factors of ring footings, to assess the impact of the friction angle in heterogeneous soils. The results showed that, by increasing the inside-to-outside-radius ratio, the value of the soil bearing capacity factor is reduced, whereas when the ratio is reduced, the size of the plastic zones gets enhanced--the bearing capacity also rises with the soil heterogeneity. [PERSON] et al. [15] offered a new framework for estimating the bearing capacity of a strip footing over voids, and showed that the bearing capacity is significantly affected by the void's geometry and position. [PERSON] and [PERSON] [16] focused on the impact of pseudo-static horizontal earthquake forces on the bearing capacity of strip footings placed over an unsupported circular tunnel in undrained clay.