Model-reference damage tracking and evaluation of hysteretic structures with test validation. (1st March 2019)

• Record Type: Journal Article
• Authors: Shan, Jiazeng; Ouyang, Yuting; Zhang, Hanqing; Shi, Weixing
• Is Part Of: Mechanical systems and signal processing. Volume 118(2019)
• Page Start: 443
• Page End: 460
• Publication Date: 2019-03-01
• Subjects: Hysteretic structure -- Damage evaluation -- Earthquake -- Tracking error -- Shake table test; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.08.016
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A data-driven damage evaluation methodology is proposed with model-reference concept. A damage quantification feature is presented with little prior-knowledge of hysteresis. The tracking and evaluation performance have been illustrated at the element level. Abstract: A data-driven damage tracking and evaluation method for earthquake-excited hysteretic structures is proposed in this study. The model reference tracking concept is introduced to quantify the shifts of the model status due to the damage developments, and a logistic framework is then designed with the employment on monitoring data from both healthy and damaged conditions of the instrumented structures. The linear behavior modeling of the objective structure is adopted for generating the reference signals and calculating the tracking errors of structural responses. A tracking error-based damage sensitive index in a normalized form is then presented for the simultaneous and combined quantification on the peak, cumulative and residual damage effects within the little knowledge on nonlinear characteristics. Comprehensive results from the numerical simulations of a single degree-of-freedom degradation model and the experimental investigations on several large-scale reinforced concrete columns are studied and discussed for the illustration of the tracking and quantification performances of the proposed data-driven evaluation method.