Identification of corrosion in a prestressed concrete pipe utilizing acoustic emission technique

Goldaran, Reza; Turer, Ahmet; Kouhdaragh, Mehdi; Ozlutas, Kezban

doi:10.1016/j.conbuildmat.2020.118053

Cited by 38 publications

(4 citation statements)

References 22 publications

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“…Therefore, cement hydration was less likely to occur and the main AE source was expected to be absorption of the sodium chloride solution. AE hits caused by absorption of the electrolyte were also reported by Goldaran et al 19 and Zheng et al 11 . The concrete prism was completely dry and placed in a sodium chloride solution.…”

Section: Ae Source Characterisationsupporting

confidence: 60%

Acoustic emission source characterisation of chloride-induced corrosion damage in reinforced concrete

Steen

Nasser

Verstrynge

et al. 2021

Structural Health Monitoring

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Worldwide, asset managers are struggling with the management of ageing infrastructure in reinforced concrete. Early detection of reinforcement corrosion, which is generally considered as the major problem, can help to perform dedicated maintenance and repair. The acoustic emission technique is promising to reach this goal. However, research on the characterisation of the different damage sources during corrosion in reinforced concrete remains scarce. In this article, the characterisation of damage processes is investigated on small reinforced concrete prisms and upscaled to reinforced concrete beams under accelerated conditions in a laboratory environment. Damage sources are assigned based on careful validation with crack width measurements and dummy samples. Signals originating from different acoustic emission sources are compared in the time and frequency domain. Moreover, the continuous wavelet transform is applied to provide information on time–frequency characteristics. The results show that the moment of concrete macro-cracking can be derived from a sudden increase of the cumulative acoustic emission events and cumulative acoustic emission energy. However, validation with crack measurements is required. The shift in both peak and centre frequency of the acoustic emission signals is found to be a better indicator. Wavelet transform allows to distinguish acoustic emission sources when frequency ranges are overlapping. Possible acoustic emission sources such as the corrosion process and concrete cover cracking, are successfully assigned. The major contributions of this article are the characterisation of acoustic emission sources from corrosion damage in reinforced concrete, validation with crack measurements and dummy samples, as well as a dedicated wavelet analysis.

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Section: Ae Source Characterisationsupporting

confidence: 60%

Acoustic emission source characterisation of chloride-induced corrosion damage in reinforced concrete

Steen

Nasser

Verstrynge

et al. 2021

Structural Health Monitoring

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“…When th loading cycles reached higher stress in point D, the material entered an unstable phase in which the previous microcracks expanded significantly and serious damage occurred. AE signals could be seen even before reaching point D, which means that the Kaiser effect a the higher stress level tends to decrease [37,38]. Linear (one-dimensional) localisation of acoustic sources was performed considering the wave propagation velocity for each material type separately (determined from ultrasonic waves); see Figure 4.…”

Section: Acoustic Emission Methodsmentioning

confidence: 99%

The Effect of Specimen Size on Acoustic Emission Parameters and Approximate Position of Defects Obtained during Destructive Testing of Cementitious and Alkali-Activated Degraded Fine-Grained Materials

et al. 2023

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Two sizes of test samples were selected to investigate the effect of size on the level of degradation. The smaller test specimens had dimensions of 40 × 40 × 160 mm, and the larger ones had dimensions of 100 × 100 × 400 mm. Both sizes of test specimens were always made of the same mortar. In one case, Blast Furnace Cement was chosen as the binder. In the other case, it was an alkali-activated material as a possibly more environmentally economical substitute. Both types of material were deposited in three degrading solutions: magnesium sulphate, ammonium nitrate and acetic acid. The reference set was stored in a water bath. After six months in the degradation solutions, a static elastic modulus was determined for the specimens during this test, and the acoustic emission was measured. Acoustic emission parameters were evaluated: the number of hits, the amplitude magnitude and a slope from the amplitude magnitude versus time (this slope should correspond to the Kaiser effect). For most of the parameters studied, the size effect was more evident for the more degraded specimens, i.e., those placed in aggressive solutions. The approximate location of emerging defects was also determined using linear localisation for smaller specimens where the degradation effect was more significant. In more aggressive environments (acetic acid, ammonium nitrate), the higher resistance of materials based on alkaline-activated slag was more evident, even in the case of larger test bodies. The experiments show that the acoustic emission results agree with the results of the static modulus of elasticity.

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“…In recent years, artificial intelligence (AI) systems have been increasingly used for solving regression and classification problems. This is due to the fact that more reliable results are obtained than when using conventional methods [37][38][39][40][41][42][43][44][45]. AI systems have generally shown a lot of potential for solving real-life tasks, particularly non-linear problems.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Prediction of the Compressive Strength of Environmentally Friendly Concrete Modified with Eggshell Powder Using the Hybrid ANN-SFL Optimization Algorithm

et al. 2021

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The aim of this article is to predict the compressive strength of environmentally friendly concrete modified with eggshell powder. For this purpose, an optimized artificial neural network, combined with a novel metaheuristic shuffled frog leaping optimization algorithm, was employed and compared with a well-known genetic algorithm and multiple linear regression. The presented results confirm that the highest compressive strength (46 MPa on average) can be achieved for mix designs containing 7 to 9% of eggshell powder. This means that the strength increased by 55% when compared to conventional Portland cement-based concrete. The comparative results also show that the proposed artificial neural network, combined with the novel metaheuristic shuffled frog leaping optimization algorithm, offers satisfactory results of compressive strength predictions for concrete modified using eggshell powder concrete. Moreover, it has a higher accuracy than the genetic algorithm and the multiple linear regression. This finding makes the present method useful for construction practice because it enables a concrete mix with a specific compressive strength to be developed based on industrial waste that is locally available.

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Identification of corrosion in a prestressed concrete pipe utilizing acoustic emission technique

Cited by 38 publications

References 22 publications

Acoustic emission source characterisation of chloride-induced corrosion damage in reinforced concrete

Acoustic emission source characterisation of chloride-induced corrosion damage in reinforced concrete

The Effect of Specimen Size on Acoustic Emission Parameters and Approximate Position of Defects Obtained during Destructive Testing of Cementitious and Alkali-Activated Degraded Fine-Grained Materials

Metaheuristic Prediction of the Compressive Strength of Environmentally Friendly Concrete Modified with Eggshell Powder Using the Hybrid ANN-SFL Optimization Algorithm

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