Machine learning-based analysis of historical towers

Determining frequency of buildings has a crucial impact on proper analysis and design of structures. Conventional methods (i.e., in situ and numerical analysis) are generally costly and time-consuming. This study aims at developing Machine Learning (ML)-based methods for obtaining frequency of structures with the focus on masonry towers (i.e., bell towers). To this end, a database including the results of either in situ or numerical analysis on over 90 masonry towers available in literature is collected. Additionally, frequencies of 18 towers located in Venice, Italy are measured by site survey, and added to the database. Parameters with the highest influence on tower's frequency, namely height, plan dimensions and modulus of elasticity are defined as input variables for predicting natural frequency of a tower by ML-based techniques including Decision Tree (DT), Random Forest (RF), XGBoost and K-Nearest Neighbors (KNN). The models' performance is analyzed by comparing the correlation between the predicted and real values. Moreover, the models' accuracy is assessed through common performance metrics and Taylor diagram, and the most accurate model is introduced accordingly. Results highlighted the high capability of ML-based approaches for predicting towers' frequency, and the XGBoost model exhibited the highest accuracy. In the second part of the paper, the values predicted by the most accurate model are compared to those calculated by the equations proposed by design Codes (i.e., NTC008, NCSE, ASCE) and literature. Lastly, for deeper investigating the performance of masonry towers a sensitivity analysis is performed by the proposed XGBoost model, and an equation is suggested for calculating their natural frequency based on their height and plan width.