• The Journal of Engineering Geology
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• v.28 no.3
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• pp.349-365
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• 2018

The strain and acoustic emission (AE) signals of Pocheon granite were measured during uniaxial compression tests to investigate microcrack formation and damage. Crack closure, initiation, and damage stresses of each sample were determined through an analysis of the crack volumetric strain and stiffness. The samples experienced four damage stages according to stress levels: stage 1 = crack closure stage; stage 2 = elastic stage; stage 3 = crack initiation stage; stage 4 = crack damage stage. At least 75% of all AE signals occurred in stages 3 and 4, and different AE parameters were detected in the four stress stages. Rise time, count, energy, and duration clearly showed a tendency to gradually increase with the damage stress stage. In particular, the rise time, energy, and duration increased by at least 95% in stage 4 as compared with stage 1. However, the maximum amplitude showed a smaller increase, and the average frequency decreased slightly at higher stages. These results indicate that as the degree of rock damage increases, the crack size grows larger. The crack types corresponding to the AE signals were determined using the relationship between RA (Rise time / Amplitude) values and average frequencies. Tension cracking was dominant in all stress stages. Shear cracking was rare in stages 1 and 2, but increased in stages 3 and 4. These results are consistent with previous studies that reported cracking begins after samples have already been damaged. Our study shows that the state of rock damage can be investigated solely through an analysis of AE parameters when rocks are under compressive stress. As such, this methodology is suitable for understanding and monitoring the stress state of bedrock.

• Geomechanics and Engineering
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• v.37 no.2
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• pp.149-166
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• 2024

The acoustic emission (AE) technique is utilized to estimate the rock failure status in underground spaces. Understanding the AE characteristics under loading conditions is essential to ensure the reliability of AE monitoring. The AE characteristics depend on the material properties (p-wave velocity, density, UCS, and Young's modulus) and damage stages (stress ratio) of the target rock mass. In this study, two groups of granite specimens (based on the p-wave velocity regime) were prepared to explore the effect of material properties on AE characteristics. Uniaxial compressive loading tests with an AE measurement system were performed to investigate the effect of the rock properties using AE indices (count index, energy index, and amplitude index). The test results were analyzed according to three damage stages classified by the stress ratio of the specimens. Count index was determined to be the most suitable AE index for evaluating rock mass stability.

• Structural Monitoring and Maintenance
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• v.5 no.3
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• pp.379-390
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• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• Journal of Korean Tunnelling and Underground Space Association
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• v.9 no.3
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• pp.251-261
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• 2007

This study is to investigate the damage assessment of adjacent structures due to tunneling in urban environment. Model tests were carried out with two-story masonry building structures in various shapes and locations. The damage level of adjacent structures were very differently estimated in accordance with the shape ratio (L/h) of structures, construction stages, and various locations. The results of model tests were plotted on the damage level graphs in order to predict the direction of damage levels for the different types of structures (i.e. stiffness of structures, L/h). The progressive crack development mechanism at various construction stages was revealed through model tests and crack size indicated more conservative side of damage level on the damage level graph.

• Smart Structures and Systems
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• v.6 no.1
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• pp.17-27
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• 2010

The approach to damage detection and localization adopted in this paper is based on a statistical comparison of models built from the response time histories collected at different stages during the structure lifetime. Some of these time histories are known to have been recorded when the structural system was undamaged. The consistency of the models associated to two different stages, both undamaged, is first recognized. By contrast, the method detects the discrepancies between the models from measurements collected for a damaged situation and for the undamaged reference situation. The damage detection and localization is pursued by a comparison of the SSE (sum of the squared errors) histograms. The validity of the proposed approach is tested by applying it to the analytical benchmark problem developed by the ASCE Task Group on Structural Health Monitoring (SHM). In the paper, the results of the benchmark studies are presented and the performance of the method is discussed.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.399-418
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• 2015

Automated damage detection through Structural Health Monitoring (SHM) techniques has become an active area of research in the bridge engineering community but widespread implementation on in-service infrastructure still presents some challenges. In the meantime, visual inspection remains as the most common method for condition assessment even though collected information is highly subjective and certain types of damage can be overlooked by the inspector. In this article, a Frequency Response Functions-based model updating algorithm is evaluated using experimentally collected data from the University of Central Florida (UCF)-Benchmark Structure. A protocol for measurement selection and a regularization technique are presented in this work in order to provide the most well-conditioned model updating scenario for the target structure. The proposed technique is composed of two main stages. First, the initial finite element model (FEM) is calibrated through model updating so that it captures the dynamic signature of the UCF Benchmark Structure in its healthy condition. Second, based upon collected data from the damaged condition, the updating process is repeated on the baseline (healthy) FEM. The difference between the updated parameters from subsequent stages revealed both location and extent of damage in a "blind" scenario, without any previous information about type and location of damage.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.10a
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• pp.353-358
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• 2002

The purpose of this paper was to estimate the cumulative damage of pavement concrete by split tension fatigue test. The split tension fatigue test of variable amplitude loading were performed in two and three stages. The results of the fatigue test by variable amplitude loading showed that the sums of damage were greater than 1 in the increasing sequence loading tests, and less than 1 in the decreasing sequence loading tests. The remaining life estimated by equivalent damage theory was almost similar to that of experimental results.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.87-94
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• 1995

In highway bridges robust damage detection exercises are mandatory to secure the safety of the structures from hostile environmental conditions such as fatigue earthquake, wind, and corrosion. This paper presents a damage detection practice in a full-scale highway bridge by utilizing modal response parameters of as-built and damaged states of the structure. first the test structure is described and modal testing procedures are outlined. Next, a damage detection model which yields information on the location of damage directly from changes in mode shapes is outlined. Finally, the damage detection model is implemented to predict the location of damage in the ten structure. From the results, it was found that the damage detection model accurately locates damage in the test structures for which modal parameters of only a single mode are available for pre-damage (as-built) and post-damage stages.

• Journal of the Korean Geotechnical Society
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• v.23 no.10
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• pp.121-131
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• 2007

This study is to investigate the damage assessment of adjacent structures due to excavation in urban environment. Model tests were carried out for 2 story masonry building and frame structures in various shapes and locations. The damage level of adjacent structures were very differently estimated in accordance with the shape ratio (L/h) of structures, construction stages, and various locations. Therefore the most weak part (bay) of structure must be heavily instrumented and monitored in more details at early stage of constructions. The progressive crack development mechanism at various construction stages was revealed through model tests and crack size indicated more conservative side of damage level on the damage level graph.

• Structural Engineering and Mechanics
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• v.70 no.4
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• pp.457-466
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• 2019

This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent.