Artificial Neural Network-based robust technique for period prediction of Ottoman minarets in Türkiye

• Published in: Structures (Oxford) Vol. 61; p. 106087
• Main Authors: Nguyen, Quy Thue, Vu, Vu Truong, Livaoğlu, Ramazan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2024
• Subjects: Artificial neural network; Fast assessment; Masonry minarets; Ottoman minarets; Period estimation; Ottoman minarets; Masonry minarets; Fast assessment; Artificial neural network; Period estimation
• ISSN: 2352-0124; 2352-0124
• DOI: 10.1016/j.istruc.2024.106087

Minarets are crucial as symbolic and indispensable elements in mosques across religious communities worldwide. Beyond architectural culture, hazardous conditions have shaped the minaret structural formations in Islamic geography. Among these, masonry minarets built during the Ottoman Empire faced significant vulnerability to lateral forces due to their slender design, brittle materials, and distinctive shapes. After many earthquakes experienced in the 20th and 21st centuries, the collapse of numerous minarets highlights the urgent need to preserve the remaining structures in high seismicity regions of the historical Ottoman lands, moreover, identifying and fortifying the most vulnerable ones takes precedence. This study proposes an innovative approach, departing from previous research that solely focused on the correlation between geometrical and material properties such as height, cross-section area, moment of inertia, Young’s modulus, and material density using approximate formulas to predict the fundamental periods. Instead, it introduces a novel technique based on Artificial Neural Networks (ANNs) to predict the first three periods of Ottoman masonry minarets. Accurate measurements from 18 minarets located in Bursa City, Türkiye, were meticulously collected under ambient conditions, serving as the foundation for the neural network's comprehensive output database. The distinct parameters (geometrical and material properties) of these minarets form the input dataset. To mitigate the influence of random measurement noise, an effective averaging scheme was implemented. As a result, the proposed ANN technique demonstrates its robustness and holds great promise for practical applications, as it enables accurate estimation of the desired modal information for both the 18 minarets used to train the networks and the remaining three minarets, achieving high levels of accuracy.