Acoustic emission monitoring of containment structures during post-tensioning. (15th April 2020)

• Record Type: Journal Article
• Authors: Ebrahimkhanlou, Arvin; Choi, Jongkwon; Hrynyk, Trevor D.; Salamone, Salvatore; Bayrak, Oguzhan
• Is Part Of: Engineering structures. Volume 209(2020)
• Publication Date: 2020-04-15
• Subjects: Acoustic emission -- K-means clustering -- Delamination -- Containment structures -- Pattern recognition -- Concrete structures -- Post-tensioning -- Structural health monitoring; 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.2019.109930
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Acoustic emission (AE) monitoring during post-tensioning of a containment structure. Delamination cracking in a curved, concrete containment structure. K-mean clustering of AE features and interpreting the mechanism causing them. Explaining AE based on the stress state and modified Mohr–Coulomb failure criteria. Introducing a novel approach to visualize AE features. Abstract: This paper introduces a method based on acoustic emission (AE) to monitor the onset of delamination in post-tensioned concrete containment structures. The method is based on clustering AE occurring during post-tensioning and/or re-tensioning such structures. In particular, the investigation is focused on AE of a large-scale, curved concrete wall subject to monotonically increasing prestressing forces. This specimen is a representative of typical cylindrical concrete structures, such as water storage tanks, silos, bins, and nuclear containment structures. To analyze AE data, this paper uses both time-driven and hit-driven features extracted from AE. To this end, a novel approach is proposed to analyze and visualize hit-driven features. To detect and localize such defects, the proposed approach identifies an optimal number of clusters in AE data and interprets each cluster based on the physical mechanism that generates it. Such interpretations are compared with the state of stresses and modified Mohr–Coulomb failure criteria. The results show that the AE events are due to three categories of source mechanisms, micro shear cracking, micro tensile cracking, and macro delamination cracking. To validate the results, comparisons are made with through-thickness expansion measurements of the wall. The results demonstrate that the proposed approach can detect delamination defects and enable decision makers to take remedial and preventive actions.