Detection and Imaging of Debonding in Adhesive Joints of Concrete Beams Strengthened with Steel Plates Using Guided Waves and Weighted Root Mean Square

Strengthening of engineering structures is an important issue, especially for elements subjected to variable loads. In the case of concrete beams or slabs, one of the most popular approaches assumes mounting an external reinforcement in the form of steel or composite elements by structural adhesives. A significant disadvantage of adhesive joints is the lack of access to the adhesive film for visual condition assessment, thus, there is a need for non-destructive diagnostics of these kinds of connections. The aim of this paper was the identification and visualization of defects in adhesive joints between concrete beams and steel plates using the guided wave propagation technique. The initial theoretical and numerical analyses were performed. The experimental wave field was excited and measured by the scanning laser Doppler vibrometry. The collected signals were processed by the weighted root mean square (WRMS) calculation. As a result, 2-D damage maps were obtained. The numerical simulations were performed to corroborate the experimental results. The results showed that the guided waves could be successfully applied in non-destructive diagnostics of adhesive joints between concrete and steel elements. However, the quality of damage visualizations strongly depended on the location of excitation.

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Non-Destructive Diagnostics of Concrete Beams Strengthened with Steel Plates Using Modal Analysis and Wavelet Transform

Materials ◽

10.3390/ma14113014 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3014

Author(s):

Magdalena Knak ◽

Erwin Wojtczak ◽

Magdalena Rucka

Keyword(s):

Wavelet Transform ◽

Modal Analysis ◽

Adhesive Bonding ◽

Concrete Beams ◽

Load Capacity ◽

Adhesive Joints ◽

Steel Plates ◽

Vanishing Moments ◽

Term Operation ◽

Non Destructive

Externally bonded reinforcements are commonly and widely used in civil engineering objects made of concrete to increase the structure load capacity or to minimize the negative effects of long-term operation and possible defects. The quality of adhesive bonding between a strengthened structure and steel or composite elements is essential for effective reinforcement; therefore, there is a need for non-destructive diagnostics of adhesive joints. The aim of this paper is the detection of debonding defects in adhesive joints between concrete beams and steel plates using the modal analysis approach. The inspection was based on modal shapes and their further processing with the use of continuous wavelet transform (CWT) for precise debonding localization and imaging. The influence of the number of wavelet vanishing moments and the mode shape interpolation on damage imaging maps was studied. The results showed that the integrated modal analysis and wavelet transform could be successfully applied to determine the exact shape and position of the debonding in the adhesive joints of composite beams.

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Application of ultrasonic guided waves for non-destructive testing of large and complex geometry engineering structures

Vibroengineering PROCEDIA ◽

10.21595/vp.2017.19193 ◽

2017 ◽

Vol 14 ◽

pp. 87-90 ◽

Cited By ~ 2

Author(s):

Renaldas Raišutis ◽

Rymantas Kažys ◽

Liudas Mažeika ◽

Egidijus Žukauskas ◽

Reimondas Šliteris ◽

...

Keyword(s):

Guided Waves ◽

Complex Geometry ◽

Ultrasonic Guided Waves ◽

Non Destructive Testing ◽

Destructive Testing ◽

Engineering Structures ◽

Non Destructive

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Resistência ao cisalhamento de vigas de concreto autoadensável com adição de fibras de aço

Semina Ciências Exatas e Tecnológicas ◽

10.5433/1679-0375.2021v42n1p45 ◽

2021 ◽

Vol 42 (1) ◽

pp. 45

Author(s):

Gustavo Savaris ◽

Isabela de Gois Laufer

Keyword(s):

Shear Strength ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Concrete Beams ◽

Steel Fibers ◽

Strength Prediction ◽

Failure Behavior ◽

Mean Square ◽

Self Compacting Concrete

The use of self-compacting concrete has increased for several reasons over recent decades but, mainly due to its high fluidity, which dispenses of the need for concrete vibrators, ease of casting, higher quality and better compacting, allowing the production of slender pieces, with a higher reinforcement ratio. However, even self-compacting concrete exhibits brittle failure behavior and low tensile and shear strength, issues that can be mitigated with the use of steel fibers. Aiming to investigate the shear strength in self-compacting concrete beams with steel fibers, this study presents a database collected from 113 experimental tests in the literature. Using the Root Mean Square Error (RMSE) and the Collins’ Demerit Points Classification (DPC), five code-based equations and ten experimental based equations for the prediction of the shear capacity of SFRC beams were evaluated. The results show that, unlike concrete without the addition of fibers, increase in aggregate dimensions decreases the shear strength with the use of steel fibers in SCC beams. Additionally, the increase in fiber volume corresponds to an increase in concrete shear strength with a maximum compressive strength of 50 MPa. The results also demonstrate that the Root Mean Square Error (RMSE) is better for evaluating the precision but not the safety of the shear strength prediction equations, which are better determined by Collins’ Demerit Points Classification (DPC). Code-based equations for ultimate shear strength prediction of fiber reinforced concrete beams presented results with satisfactory safety and economy.

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Non-Destructive Testing of Moisture and Nitrogen Content in Pinus Massoniana Seedling Leaves with NIRS Based on MS-SC-CNN

Applied Sciences ◽

10.3390/app11062754 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2754

Author(s):

Zhuo Huang ◽

Tingting Zhu ◽

Zhenye Li ◽

Chao Ni

Keyword(s):

Root Mean Square Error ◽

Nitrogen Content ◽

Mean Square Error ◽

Root Mean Square ◽

Pinus Massoniana ◽

Mean Square ◽

Non Destructive Testing ◽

Destructive Testing ◽

Testing Method ◽

Non Destructive

Pinus massoniana is a pioneer reforestation tree species in China. It is crucial to evaluate the seedling vigor of Pinus massoniana for reforestation work, and leaf moisture and nitrogen content are key factors used to achieve it. In this paper, we proposed a non-destructive testing method based on the multi-scale short cut convolutional neural network (MS-SC-CNN) to measure moisture and nitrogen content in leaves of Pinus massoniana seedlings. By designing a reasonable short cut structure, the method realized the transmission of loss function gradient across the multi-layer structure in the network and reduced the information loss caused by the multi-layer transmission in the forward propagation. Meanwhile, in the back propagation stage, the loss caused by the multi-layer transmission of gradient was reduced. Thus, the gradient vanishing problem in training was avoided. Since the method realized cross-layer transmission error, the convolutional layer could be increased appropriately to obtain higher measurement accuracy. To verify the performance of the proposed MS-SC-CNN non-destructive measurement method, the near-infrared hyperspectral data of sample leaves of 219 Pinus massoniana seedlings were collected from the Huangping Forest Farm in Guizhou Province. The correlation coefficient between the measured and real values of the prediction was as high as 0.977 and the root mean square error was 0.242 for the moisture content of Pinus massoniana seedling leaves. For the nitrogen content of Pinus massoniana seedling leaves, the correlation coefficient between the measured and real values of the prediction was 0.906 and the root-mean-square error was 0.061. The results showed that the non-destructive testing method based on MS-SC-CNN that we proposed can accurately measure the moisture and nitrogen content in leaves of Pinus massoniana seedlings.

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Anomaly detection in the concrete arc girder subjected to fatigue test

MATEC Web of Conferences ◽

10.1051/matecconf/201928500025 ◽

2019 ◽

Vol 285 ◽

pp. 00025

Author(s):

Dominika Ziaja ◽

Piotr Nazarko

Keyword(s):

Anomaly Detection ◽

Elastic Waves ◽

Guided Waves ◽

Diagnostic System ◽

Fatigue Tests ◽

Piezoelectric Transducers ◽

Engineering Structures ◽

Steel Reinforced Concrete ◽

Bridge Girder ◽

Non Destructive

A steel reinforced concrete arch of a bridge girder has been subjected to static and fatigue tests. The aim of this study is the application of guided waves in non-destructive diagnostics of civil engineering structures and early damage detection. Two piezoelectric transducers were mounted at a distance of 1 m to monitor area of the arch keystone. After every 500 000 cycles the signals of elastic waves have been measured and the girder visual examination was carried out. It turned out that both the load magnitude and the appearance of cracks have affected the signal changes. The obtained signal database has been used to train artificial neural networks and establish a diagnostic system. The results of the conducted tests have showed good sensitivity of anomaly detection and satisfying accuracy of load identification.

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Analysis of failure in concrete and reinforced-concrete beams for the smart aggregate–based monitoring system

Structural Health Monitoring ◽

10.1177/1475921719854151 ◽

2019 ◽

Vol 19 (2) ◽

pp. 463-480 ◽

Cited By ~ 3

Author(s):

Azadeh Noori Hoshyar ◽

Bijan Samali ◽

Ranjith Liyanapathirana ◽

Saber Taghavipour

Keyword(s):

Reinforced Concrete ◽

Root Mean Square ◽

Concrete Beams ◽

Wavelet Packet ◽

Damage Index ◽

Reinforced Concrete Beams ◽

Mean Square ◽

Mean Square Deviation ◽

Damage Indices ◽

Smart Aggregate

Monitoring of structures and defining the severity of damages that occur under loading are essential in practical applications of civil infrastructure. In this article, we analyze failure using a smart aggregate sensor–based approach. The signals captured by smart aggregate sensors mounted on the structure under loading are de-noised using wavelet de-noising technique to prevent misdirection of the event interpretation of what is happening in the material. The performance of different mother wavelets on the de-noising process was investigated and analyzed. The objective is to identify the optimal mother wavelet for assessing and potentially reducing the effects of existing noise on signal properties for structural damage detection. In addition, we propose two innovative damage indices, entropy-based dispersion and entropy-based beta, for diagnostic purposes. The proposed entropy-based dispersion damage index is based on the modified wavelet packet tree and root mean square deviation, whereas the entropy-based beta damage index is based on the modified wavelet packet tree and slope of linear regression (beta). In both damage indices, the modified wavelet packet tree uses entropy as a high-level feature. Theoretical and experimental analyses are derived by computing indices on smart aggregate–based sensor data for concrete and reinforced-concrete beams. Validity assessment of the proposed indices was addressed through a comparative analysis with root mean square deviation damage index (benchmark) and the loading history. The proposed indices recognized the cracks faster than other measures and well before major cracking incurs in the structure. This article is expected to be beneficial for smart aggregate–based structural health monitoring applications particularly when damages occurred under loading.

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Evaluation of detectability of differential type probe using directional eddy current for fibre waviness in CFRP

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2019.0587 ◽

2020 ◽

Vol 378 (2182) ◽

pp. 20190587

Author(s):

Hiroyuki Kosukegawa ◽

Yuta Kiso ◽

Mitsuo Hashimoto ◽

Tetsuya Uchimoto ◽

Toshiyuki Takagi

Keyword(s):

Standard Deviation ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Eddy Current ◽

Mean Square ◽

Reinforced Plastic ◽

Differential Type ◽

Carbon Fibre Reinforced Plastic ◽

Non Destructive

This paper describes the detectability of eddy current testing (ECT) using directional eddy current for detection of in-plane fibre waviness in unidirectional carbon fibre reinforced plastic (CFRP) laminate. Three different types of probes, such as circular driving, symmetrical driving and uniform driving probe, were proposed, and the waviness angle was extracted from the contour map of the ECT signal by applying a Canny filter and a Hough transform. By comparing both the waviness angle estimated by ECT and that obtained by an X-ray CT image, the standard deviation (precision) and root mean square error (accuracy) were evaluated to discuss the detectability of these probes. The directional uniform driving probe shows the best detectability and can detect fibre waviness with a waviness angle of more than 2° in unidirectional CFRP. The probe shows a root mean square error of 1.90° and a standard deviation of 4.49° between the actual waviness angle and the angle estimated by ECT. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

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