Fundamental mode shape-based normalization scheme for damage detection of minarets: A non-model-based approach


Nguyen Q. T., LİVAOĞLU R.

Engineering Failure Analysis, vol.147, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 147
  • Publication Date: 2023
  • Doi Number: 10.1016/j.engfailanal.2023.107160
  • Journal Name: Engineering Failure Analysis
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Keywords: Structural health monitoring, Non-model-based-damage detection, Minarets, Masonry structures, Beam-like structures, Historical structures, DYNAMIC-BEHAVIOR, SENSOR PLACEMENT, IDENTIFICATION, TOWER, LOCALIZATION
  • Bursa Uludag University Affiliated: Yes

Abstract

Historical, cultural, and artistic masonry structures like minarets constructed in regions prone to dynamic effects such as strong winds and earthquakes are vulnerable as a consequence of their slenderness and brittle materials. Structural health monitoring (SHM) has not been employed adequately in the regular control of minarets’ integrity for early warning of disaster. Meanwhile, the protection and preservation of such a cultural inheritance are acute. A non-model-based normalization scheme is proposed to detect damage in minarets based only on the changes in terms of the fundamental mode shape generated by a modest quantity of accelerometers. The backbone idea lies in the similarities in terms of the fundamental mode shape between slender minarets and beam-like structures. Particularly, the modal amplitudes below a specific normalizing node change significantly if damage occurs below this point. Thereby, moving upward the location of this point straightforwardly leads to damage detection of minarets. The proposed technique is implemented on Hacılar mosque's minaret (24.25 m high) built in 1467 in Bursa city, Türkiye. Reliable damage prediction is attained as long as the modal information is noise-free or reasonable-level noise-polluted.