## 1 Introduction
The results revealed that the ML models can successfully predict the compressive strength of historical constructions. Regarding the damage assessment of masonry, [PERSON] et al. [22] implemented simple ML algorithms for the damage assessment of rubble stone masonry piers and reported the better performance of the K-Nearest Neighbour model. [PERSON] et al. [23] developed an ML-based framework for the performance prediction and damage classification of structural components. They demonstrated the use of the developed framework to classify the reinforced masonry shear walls and to evaluate several key parameters that affect the ultimate drift of walls. Recently, [PERSON] and [PERSON] [24] developed a workflow for crack detection in masonry structures and developed a geometric digital twin of the masonry structures using ML. [PERSON] et al. [25] utilized four machine learning techniques to develop fragility curves that assessed the probability of collapse associated with masonry walls in Malawi. They reported that the Random Forest (RF) technique exhibited efficient performance, with appropriate levels of accuracy. [PERSON] et al. [26] conducted a seismic vulnerability assessment of school buildings in high-intensity seismic zones, employing machine learning methodologies including RF, NN, XGBoost, and the Extremely Randomized Tree Regressor (ETRR). This investigation, which focused on reinforced concrete (RC) and URM school buildings, demonstrated the superior performance of the RF and XGBoost algorithms. Additionally, [PERSON] and [PERSON] [27] devised a straightforward visual screening method aimed at predicting damage to masonry structures using various machine learning algorithms, including Logistic Regression, DT, RF, KNN, SVM, and an Ensemble Learning Method (ELM). Many existing studies have focused on damage assessment through crack detection and capacity estimation in masonry walls rather than entire buildings [28; 29]. Further, studies focusing on seismic response prediction in masonry buildings were found to be scarce in the literature, and the existing studies never attempted the application of a wide range of ML algorithms. More specifically, a study involving both ML-based seismic response and damage prediction for a non-engineered URM building has not been attempted so far. This can be attributed to the need for experimental results and time-consuming numerical analysis procedures required to develop the database. Also, shaking-table tests involving real-scale prototype buildings, particularly URM buildings, would demand large facilities, making the experimental data not readily available for different masonry structures. Hence, the research gap pertains to the ML-based seismic response prediction associated with non-engineered URM buildings and the need for a comprehensive database in order to develop ML models for complete seismic risk assessments of URM buildings. In this context, the present paper aims to examine the effectiveness of various ML techniques used to predict the seismic response and to classify the damage state of a non-engineered URM building, based on the methodology shown in Figure 1. A finite-element (FE) model of a benchmark building based on the nonlinear layered shell elements approach was used to carry out a nonlinear time history analysis in SAP2000 v.22. With the FE model, a database of 1080 numerical results of the analysis of the benchmark URM building was developed, for which the damage levels were identified using the definitions from the literature. The material properties of the URM building and the intensity of ground motion were used as the input variables. ML models for regression and classification were then implemented to identify the structural response and damage state of the URM building. The performance assessments of the prediction and classification models were then presented using standard performance metrics. Finally, an analysis of the relative importance of the predictor variables was also presented.