A health monitoring solution on damage detection of minarets. (May 2022) - Nguyen, Quy Thue; Livaoğlu, Ramazan
Highlights: A 4-sensor network is erected on Hacılar mosque's minaret, making 0.9% DOFs measured. The minaret is simplified into a beam-like system using the Guyan condensation tool. An inverse solution with some presented modifications is utilized to detect damage. Only the changes in terms of modal data of two modes are accounted for. Reliable detection is attained under noise-free and low-level noisy conditions. Abstract: Historical buildings play a monumental role in religious communities around the world. Masonry structures such as minarets built in areas prone to dynamic effects such as earthquakes are vulnerable as a consequence of their brittle materials and slenderness. Structural health monitoring (SHM) has not been carried out in the regular control of minarets' health for early warning of collapse. Meanwhile, the protection and preservation of such a cultural heritage are urgent. Effective techniques to recognize damage occurrence and location in minarets are vital. In this study, a health monitoring solution on damage detection of minarets is presented. In particular, damage detection of the minaret (24.25 m high) of Hacılar mosque that was built using stone and brick in 1467 in Bursa city (Turkey) is aimed. Only four accelerometers are erected along with the height of the minaret to collecte lateral displacements, making an incomplete number of measured degrees of freedom (DOFs). To circumvent the complicated problem, the minaret is transferred into a lumped mass system correspondingly to the erected sensor network considering the similarities in terms of bending behavior at low modes between slender minarets and beam-like structures. Due to the changes in modal data at two bending modes, an eigenvalue problem-based inverse solution is employed to localize damage in the minaret based on the reduction in terms of stiffness of the simplified system. As a result, reliable damage detection is achieved as long as the modal data is noise-free or low-level noise-contaminated (about 0.5%-1% random noise level).