Arce Fernández,Nilthon2025-12-222025-12-222024-06-24http://hdl.handle.net/20.500.14689/1025In Peru, confined masonry houses are self-built, which makes it crucial to determine their seismic vulnerability. The objective of the research was to estimate the seismic vulnerability of confined masonry dwellings in the Pueblo Libre-Jaén sector using assembly algorithms. A database was constructed with data obtained from the National Institute of Civil Defense (INDECI), scientific articles, and theses. Subsequently, the data set was divided into a training set (80%) and a validation set (20%), employing the stacking method with five combinations CB_1, CB_2, CB_3, CB_4, and CB_5. The basic algorithms Gradient-Boosting, Random-Forest, Extra-Tree, and Decision-Tree were utilized as the base algorithms, with the final estimator being the Random Forest Meta-Learner. The models were trained and validated in Python, achieving accuracies of 94.95, 95.48, 95.39, and 95.66 for the base models and 95.62, 95.23, 95.76, 95.90, and 94.80% for the ensemble models. The most accurate models were the simple Gradient Boosting (95.66%) and the assembled models CB_3 (95.76%) and CB_4 (95.90%). The CB_4 model, which is composed of the Decision Tree and Gradient Boosting algorithms, was applied to the Pueblo Libre sector and yielded a reliability estimate of greater than 95% for the seismic vulnerability of confined masonry. This estimate was classified as high (1.48%), moderate (32.85%), and low (65.67%). It is anticipated that the model implemented will enable engineers and authorities to implement mitigation measures to reinforce housing in the event of a seismic event.application/pdfenginfo:eu-repo/semantics/openAccessautomationensembled algorithmsGradient Boostingmasonry housingseismic vulnerabilityrandom forestinfo:eu-repo/semantics/articlehttps://doi.org/10.18280/ijsse.140327https://purl.org/pe-repo/ocde/ford#2.00.00