Damage growth detection in steel plates: Numerical and experimental studies. (1st December 2016)

• Record Type: Journal Article
• Authors: Alavi, Amir H.; Hasni, Hassene; Lajnef, Nizar; Chatti, Karim
• Is Part Of: Engineering structures. Volume 128(2016:Dec. 01)
• Page Start: 124
• Page End: 138
• Publication Date: 2016-12-01
• Subjects: Steel plate -- Piezoelectricity -- Dynamic strain -- Probability density function -- Data fusion -- Sensor group effect; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2016.09.026
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A self-powered sensing paradigm is proposed for damage detection in steel plates. The signal sensed by the PZT transducers is used for both empowering the sensor and SHM. A new damage detection concept termed as "group effect" is introduced based on the data fusion from multi-sensors. Abstract: This paper presents a new method for crack growth detection in steel plates through the analysis of cumulative duration of strain events measured by a self-powered sensing paradigm at preselected level discretization. In order to evaluate the proposed method, experimental and numerical studies were performed on a thin steel plate subjected to in-plane tension. Damage was introduced by making notches with different sizes. Piezoelectric transducers were placed on the surface of the plate to measure the delivered voltage in each damage growth phase. Three-dimensional finite element models were developed to extract the strains induced by the dynamic loading. Thereafter, features extracted from the dynamic strain data for a number of sensing nodes were used to detect the damage progression. Furthermore, a new data fusion concept based on the effect of group of sensors was proposed to improve the damage detection performance. The results indicate that the proposed method is efficient in detecting different damage states in steel plates.