Fracture Mechanics of Corrosion Cracking in Concrete by Acoustic Emission

Uddin, Farid A. K. M.; Shigeishi, Mitsuhiro; Ohtsu, Masayasu

doi:10.1007/s11012-006-0004-9

Cited by 22 publications

(4 citation statements)

References 5 publications

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“…Such transitory coexistence is well represented by the corresponding decrease in SE which puts in evidence a distinctive feature of mixed-mode deformations when compared to tensile and shear damages. [11,13,67,69,81].…”

Section: Spectral Entropymentioning

confidence: 99%

Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

et al. 2021

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In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

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Section: Spectral Entropymentioning

confidence: 99%

Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

et al. 2021

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“…The modulus of elasticity of the tested concrete was employed in the analysis. The critical stress intensity factor: K IC = 0.723MPa m ( Uddin et al 2006). Along the crack orientation determined, two new crack-tip elements were created, separating a node on the interface.…”

Section: Bem Analysismentioning

confidence: 99%

Mechanisms of Corrosion-Induced Cracks in Concrete at Meso- and Macro-Scales

Ohtsu

Uddin²

2008

ACT

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Following the initiation of corrosion in reinforcement, expansion of corrosion products generates corrosion-induced cracks in concrete. At the meso-scale, accumulation and coalescence of micro-cracks create the fracture process zone. Since nucleation of an individual micro-crack can be detected by acoustic emission (AE) techniques, the moment tensor analysis of AE waves is applied to quantitatively identify cracking kinematics of a location, a crack-type and a crack orientation. At the macro-scale, initiation and extension of the corrosion-induced cracks are analyzed by means of classical fracture mechanics, applying the boundary element method (BEM). The generation of corrosion-induced cracks was simulated in expansion tests on concrete specimens. Kinematical mechanisms of micro-cracks were identified by the SiGMA (Simplified Green's functions for Moment tensor Analysis) analysis. By applying the two-domain BEM analysis, extension of the corrosion-induced crack in an arbitrary direction was analyzed. With respect to the orientations of crack extension, results of the BEM analysis were compared with those of the SiGMA analysis, introducing the normalized stress intensity factors. It is demonstrated that extension of the corrosion-induced crack is governed by the mode-I failure both at the meso-scale and at the macro-scale.

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“…The premise of AE refers to the generation of transient elastic waves during the rapid release of energy from a localized source within a material. AE is a non-destructive testing technique with several unique features including real time monitoring capability, high sensitivity, global monitoring capability, source location, sensitivity to processes or mechanisms that generates stress waves, and passive nature (utilizing energy generated by damage source omits the need for supplying external energy) [20][21][22][23]. These features allow employment of AE as an informative online assessment technique for a wide range of structures and for various purposes.…”

Section: Introductionmentioning

confidence: 99%

A novel damage index for online monitoring of RC slabs under monotonic loading by integration of process controlling into acoustic emission technique

Behnia

Chai

Mousa

et al. 2019

Mechanical Systems and Signal Processing

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This study introduces a novel structural health monitoring scheme for cementitious composite slabs with the aid of acoustic emission (AE) technique coupled with statistical process controlling (SPC) method. The adopted framework is an integrated monitoring solution that effectively relates current state (damaged) to reference state of the structure. Evaluation of the latter was made possible using autoregressive model incorporating a set of damage-sensitive feature. In order to provide a benchmark damage indicator, the collected data were processed using control chart analysis. The damage indicators for the former was similarly obtained and then compared with the benchmark to gauge the structural damage. These control charts offer a robust framework meticulously identifying inconsistency in the damage-sensitive feature imposed over the monitoring period. Linear and quadratic projections were also incorporated into SPC model to enhance identification of system transition to other damage states.

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Fracture Mechanics of Corrosion Cracking in Concrete by Acoustic Emission

Cited by 22 publications

References 5 publications

Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning

Mechanisms of Corrosion-Induced Cracks in Concrete at Meso- and Macro-Scales

A novel damage index for online monitoring of RC slabs under monotonic loading by integration of process controlling into acoustic emission technique

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