• Smart Structures and Systems
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• v.16 no.1
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• pp.67-79
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• 2015

Acoustic emission analysis is an effective technique for monitoring the evolution of damage in a structure. An experimental analysis on a set of reinforced concrete beams under flexural loading was carried out. A mixed AE analysis method which used both parameter-based and signal-based techniques was presented to characterize and identify different failure mechanisms of damage, where the signal-based analysis was performed by using the Hilbert-Huang transform. The maximum instantaneous energy of typical damage events and the corresponding frequency characteristics were established, which provided a quantitative assessment of reinforced concrete beam using AE technique. In the bending tests, a "pitch-catch" system was mounted on a steel bar to assess bonding state of the steel bar in concrete. To better understand the AE behavior of bond-slip damage between steel bar and concrete, a special bond-slip test called pullout test was also performed. The results provided the basis of quantitative AE to identify both failure mechanisms and level of damages of civil engineering structures.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.383-397
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• 2015

This article identifies and addresses current limitations on the use of numerical models for validation and/or calibration of damage detection methodologies that are based on the analysis of the high frequency response of the structure to identify the occurrence of abrupt anomalies. Distributed-plasticity non-linear fiber-based models in combination with experimental data from a full-scale reinforced concrete column test are used to point out current modeling techniques limitations. It was found that the numerical model was capable of reproducing the global and local response of the structure at a wide range of inelastic demands, including the occurrences of rebar ruptures. However, when abrupt sudden damage occurs, like rebar fracture, a high frequency pulse is detected in the accelerations recorded in the structure that the numerical model is incapable of reproducing. Since the occurrence of such pulse is fundamental on the detection of damage, it is proposed to add this effect to the simulated response before it is used for validation purposes.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.3
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• pp.282-289
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• 2010

Most nondestructive testing techniques require good contact between the sensor and tested concrete surface to obtain reliable data. But the surface preparation is often very time and labor consuming due to the rough surface or limited access of concrete structures. One approach to speed up the data collection process is to eliminate the need for physical contact between the sensor and tested structure. Non-contact air-coupled sensing technique can be a good solution to this problem. An obvious advantage of the non-contact air-coupled sensing technique is which can greatly speed up the data collection in field and thus the damage detection process can be completed very rapidly. In this article, recent developments in non-contact air-coupled sensing technique for rapid detection of damages in concrete structures are summarized to evoke interest, discussion and further developments on this technique to a NDT research community in Korea. It is worth noting that the works in this article have been published in the types of thesis, proceedings, and journals. All published sources are cited in the text and listed in reference.

• Computers and Concrete
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• v.20 no.5
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• pp.563-572
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• 2017

Ordinary reinforced concrete (RC) and prestressed concrete bridges are two popular and typical types of short- and medium-span bridges that accounts for the vast majority of all existing bridges. The cost of maintaining, repairing or replacing degraded existing RC bridges is immense. Detecting the abnormality of RC bridges at an early stage and taking the protective measures in advance are effective ways to improve maintenance practices and reduce the maintenance cost. This study proposes a systematic method from influence line (IL) identification to damage detection with applications to RC bridges. An IL identification method which integrates the cubic B-spline function with Tikhonov regularization is first proposed based on the vehicle information and the corresponding moving vehicle induced bridge response time history. Subsequently, IL change is defined as a damage index for bridge damage detection, and information fusion technique that synthesizes ILs of multiple locations/sensors is used to improve the efficiency and accuracy of damage localization. Finally, the feasibility of the proposed systematic method is verified through experimental tests on a three-span continuous RC beam. The comparison suggests that the identified ILs can well match with the baseline ILs, and it demonstrates that the proposed IL identification method has a high accuracy and a great potential in engineering applications. Results in this case indicate that deflection ILs are superior than strain ILs for damage detection of RC beams, and the performance of damage localization can be significantly improved with the information fusion of multiple ILs.

• Smart Structures and Systems
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• v.6 no.3
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• pp.291-307
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• 2010

A specific type of structural damage that would benefit from remote sensing is discussed and a solution is suggested.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.511-523
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• 2023

Research on the integration of unmanned aerial vehicles and deep learning for reinforced concrete damage detection is actively underway. Convolutional neural networks have a high impact on the performance of image classification, detection, and segmentation as backbones. The MobileNet, a pre-trained convolutional neural network, is efficient as a backbone for an unmanned aerial vehicle-based damage detection model because it can achieve sufficient accuracy with low computational complexity. Analyzing vanilla convolutional neural networks and MobileNet under various conditions, MobileNet was evaluated to have a verification accuracy 6.0~9.0% higher than vanilla convolutional neural networks with 15.9~22.9% lower computational complexity. MobileNetV2, MobileNetV3Large and MobileNetV3Small showed almost identical maximum verification accuracy, and the optimal conditions for MobileNet's reinforced concrete damage image feature extraction were analyzed to be the optimizer RMSprop, no dropout, and average pooling. The maximum validation accuracy of 75.49% for 7 types of damage detection based on MobilenetV2 derived in this study can be improved by image accumulation and continuous learning.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.669-674
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• 2007

To develop a promising hybrid structural health monitoring (SHM) system, a combined use of structural vibration and electro-mechanical (EM) impedance is proposed. The hybrid SHM system is designed to use vibration characteristics as global index and EM impedance as local index. The proposed health monitoring scheme is implemented into prestressed concrete (PSC) girder bridges for which a series of damage scenarios are designed to simulate various prestress-loss situations at which the target bridges car experience during their service life. The measured experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the occurrence of damage and furthermore to indicate its location.

• Journal of Ocean Engineering and Technology
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• v.21 no.6
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• pp.26-33
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• 2007

Finite element simulation of elastic wave propagation in a concrete plate was carried out to investigate its modeling and damage detection procedures. For the numerical stability three criteria were introduced and tested. With a proper element size and time increment, two different kinds of damage scenarios (crack and deterioration) were applied to verify the feasibility of the finite element simulation. It is shown that the severities of those damages are sensitive to the received displacement signals.

• Proceeding of KASS Symposium
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• 2008.05a
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• pp.179-183
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• 2008

Methods of damage detection are used non-destructive test in concrete structures. These are using various sensors, but the most of damage detections are used a visual angle of human. Problems of crack damage detection are occurred to directions and boundary conditions of steel bars using accelerometer in concrete element. In this study, fundamental studies for estimation using 3 axial type of accelerometer and electric resistance property of thermocouple sensors are discussed estimation to effect of arranged steel bars and damage from low strength when they are oscillated elastic wave in concrete specimen.