Damage detection at storey and element levels of high-rise buildings: a hybrid method

Quy Thue Nguyen Q. T. N. , LİVAOĞLU R.

NEURAL COMPUTING & APPLICATIONS, vol.34, no.15, pp.12765-12788, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 15
  • Publication Date: 2022
  • Doi Number: 10.1007/s00521-022-07111-w
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.12765-12788
  • Keywords: Structural health monitoring, High-rise buildings, Vibration-based damage detection, Damage localization, Artificial neural networks (ANN), NEURAL-NETWORK, IDENTIFICATION, MODELS
  • Bursa Uludag University Affiliated: Yes


Storey-level detection of high-rise buildings has become a subject of focus but still inadequate, whereas element-level detection is by far not reached because of the complexity of tall buildings, especially in a three-dimensional (3D) problem. In this study, element-level detection of two 3D 30-storey 90-m-high RC buildings (symmetrical and asymmetrical) composed of 2880 degrees of freedom (DOFs) is aimed. Only one biaxial accelerometer per floor is required to measure lateral displacements, making the number of measured DOFs equal to about 2% of the full system. To circumvent the complicated problem, a two-step procedure is proposed to detect damage at storey and then element levels. The backbone idea lies in the similarities in terms of bending behaviour at low modes between tall buildings and beam-like systems. Particularly, in Step 1, in each direction, a full 3D building is approximately simplified to a beam-like system using the Guyan static condensation procedure based on the measured DOFs. Thereafter, an eigenvalue problem-based inverse solution is implemented directly on the simplified system to detect damaged storeys using only the first two bending modes. In Step 2, an artificial neural network model is designed to indicate ruined shear walls and columns focusing only on the preliminarily identified storeys, effectively reducing the number of desired variables. Only modal data at the lowest three swaying modes are accounted for. As a result, storey- and element-level detection is accurately achieved as long as the identified modal data are noise-free or low-level noise polluted.