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Reinforced concrete is subjected to deterioration due to aging, increased load, and natural hazards. To minimize the maintenance costs and to increase the operation lifetime, researchers and practitioners are increasingly interested in improving current nondestructive evaluation technologies or building advanced structural health monitoring strategies. Acoustic emission methods offer an attractive solution for nondestructive evaluation/structural health monitoring of reinforced concrete structures. In particular, monitoring the development of cracks is of large interest because their properties reflect not only the condition of concrete as material but also the condition of the entire system at structural level. This article presents a new probabilistic approach based on Gaussian mixture modeling of acoustic emission to classify crack modes in reinforced concrete structures. Experimental results obtained in a full-scale reinforced concrete shear wall subjected to reversed cyclic loading are used to demonstrate and validate the proposed approach.

Conventionally, the assessment of reinforced concrete shear walls relies on manual visual assessment which is time-consuming and depends heavily on the skills of the inspectors. The development of automated assessment employing flying and crawling robots equipped with high-resolution cameras and wireless communications to acquire digital images and advance image processing to extract crack patterns has paved the path toward implementing an automated system which determines structural damage based on visual signals acquired from structures. Since there are few, if any, studies to correlate crack patterns to structural integrity, this article proposes to analyze crack patterns using a multifractal analysis. The approach is initially tested on synthetic crack patterns, and then it is applied to a set of experimental data collected during the testing of two large-scale reinforced concrete shear wall subjected to controlled reversed cyclic loading. The structural response data available for each specimen are used to link the multifractal parameters with the structural performance of the two specimens. A relationship between the multifractal parameters and the crack patterns’ evolution and mechanism is noted. The results show that as the crack patterns extend and grow, multifractal parameters move toward higher values. The parameters jump as the mechanical response shows severe stiffness loss. In this study, no attempt is made to automate the process of mapping cracks from images.

No abstract

Reinforced concrete shear walls are critical structural components in gravity and lateral force resisting systems. The objective of this work is to design and validate a monitoring system capable of rapid and automated damage assessment of reinforced concrete shear walls. The proposed system is based on a sparse array of piezoelectric transducers to receive acoustic emissions distributed across the wall and a statistical pattern recognition algorithm capable of identifying critical structural conditions to inform decision makers on the need for repair to ensure safe operation of the structure. The proposed system was validated on a full-scale reinforced concrete shear wall subjected to quasi-static cyclic loading.