• Geomechanics and Engineering
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• v.14 no.3
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• pp.271-281
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• 2018

A jointed rock slope stability evaluation was simulated by a discontinuous deformation analysis numerical method to investigate the process and safety factors for different crack distributions and different overloading situations. An optimized method using Discontinuous Deformation Analysis for Rock Failure (DDARF) is presented to perform numerical investigations on the jointed rock slope stability evaluation of the Dagangshan hydropower station. During the pre-processing of establishing the numerical model, an integrated software system including AutoCAD, Screen Capture, and Excel is adopted to facilitate the implementation of the numerical model with random joint network. These optimizations during the pre-processing stage of DDARF can remarkably improve the simulation efficiency, making it possible for complex model calculation. In the numerical investigations on the jointed rock slope stability evaluations using the optimized DDARF, three calculation schemes have been taken into account in the numerical model: (I) no joint; (II) two sets of regular parallel joints; and (III) multiple sets of random joints. This model is capable of replicating the entire processes including crack initiation, propagation, formation of shear zones, and local failures, and thus is able to provide constructive suggestions to supporting schemes for the slope. Meanwhile, the overloading numerical simulations under the same three schemes have also been performed. Overloading safety factors of the three schemes are 5.68, 2.42 and 1.39, respectively, which are obtained by analyzing the displacement evolutions of key monitoring points during overloading.

• Polymer(Korea)
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• v.28 no.2
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• pp.185-193
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• 2004

Elastomeric nanocomposites were prepared by employing zinc dimethacrylate into an acrylonitrile-butadiene rubber, and their network structures, mechanical properties, and fracture morphologies were investigated according to the adding methods and contents of zinc dimethacrylate. The total crosslink density increased with increasing the zinc dimethacrylate level, due to increased ionic bonds. Both the tensile strength and tear strength increased with increasing zinc dimethacrylate loadings, and then decreased after reaching a maximum value. It was found that the tear strength and crack resistance were greatly affected by the mixing method of zinc dimethacrylate. The in-situ nanocomposites, where zinc dimethacrylate particles were formed by the reaction of zinc oxide and methacrylic acid, showed much improved tear strength and crack resistance compared to those of the nanocomposites based on the direct mixing of zinc dimetacrylate powders. This was because of the finer zinc dimethacrylate particles and improved dispersion of the in-situ nanocomposites.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.205-210
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• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.149-161
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• 2019

In this study, the effect of the fluid flow characteristics on the length distribution of the fracture elements composing the fracture network is analyzed numerically using the 3D fracture crack network model. The truncated power-law distribution is applied to generate the length distribution of the fracture elements and the simulations of fluid flow are carried out with the exponent ${\beta}_l$ from 1.0 to 6.0. As a result of simulations, when the exponent ${\beta}_l$ increases, the length distribution of the fracture elements gradually decreases, and the connectivity between the fracture elements affecting the permeability of the fracture network becomes weak. When we analyzed the distributions of flow rate calculated at each fracture element with the exponent ${\beta}_l$, the mean flow rate at ${\beta}_l=1.0$ was estimated to be about 447 times larger than that at ${\beta}_l=6.0$ and for the flow calculated at the outflow boundary of the fracture network, the case of ${\beta}_l=1.0$ was estimated to be 6,440 times larger than that of ${\beta}_l=6.0$.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2A
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• pp.297-304
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• 2008

It is well known that cracks in concrete decrease permeability and durability of concrete because cracks enhance the penetration of water or corrosive chemicals like as chlorides, carbon dioxides, sulfates and some others. But some of cracks in hardened cements may be sealed in case of contacting water. This phenomenon is called "self healing" and it has a close relation to hydration products newly formed on surfaces of cracks. Many studies on self healing in concretes commonly showed that CSH gel has been observed on crack surfaces. And some studies have reported that calcium hydroxides and ettringite were observed as well as CSH gel on crack surfaces. This study was carried out to investigate hydration products formed by self healing process and also examine the influence of waterproof admixture for concretes on self healing of cement. As a result of XRD, DSC, SEM and EDX analysis of crack surfaces, it was found that self healing of cement was related to CSH gel, calcium hydroxides and ettringite. And waterproof admixture increased fibrous (needle-like) hydration products which were in network form. It is estimated that such fibrous products are effective for self healing process of cement system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• Nano
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• v.13 no.11
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• pp.1850126.1-1850126.10
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• 2018

Flexible strain sensors, as the core member of the family of smart electronic devices, along with reasonable sensing range and sensitivity plus low cost, have rose a huge consumer market and also immense interests in fundamental studies and technological applications, especially in the field of biomimetic robots movement detection and human health condition monitoring. In this paper, we propose a new flexible strain sensor based on thick CVD graphene film and its low-cost fabrication strategy by using the commercial adhesive tape as flexible substrate. The tensile tests in a strain range of ~30% were implemented, and a gage factor of 30 was achieved under high strain condition. The optical microscopic observation with different strains showed the evolution of cracks in graphene film. Together with commonly used platelet overlap theory and percolation network theory for sensor resistance modeling, we established an overlap destructive resistance model to analyze the sensing mechanism of our devices, which fitted the experimental data very well. The finding of difference of fitting parameters in small and large strain ranges revealed the multiple stage feature of graphene crack evolution. The resistance fallback phenomenon due to the viscoelasticity of flexible substrate was analyzed. Our flexible strain sensor with low cost and simple fabrication process exhibits great potential for commercial applications.

• Structural Engineering and Mechanics
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• v.72 no.3
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• pp.383-393
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• 2019

Horizontal openings in reinforced concrete (RC) beams are quite often used to accommodate service pipelines. Several research papers are available in the literature describing their effect. RC beams with vertical openings are commonly used to accommodate service lines in residential buildings in Kuwait. However, there are lack of design guidelines and best practices reported in the literature for RC beams with vertical openings, whereas the detailed guidelines are available for beams with horizontal openings. In the present paper, laboratory experiments are conducted on nine RC beams with and without vertical openings. Parametric study has been carried out using nonlinear finite element analysis (FEA) with changes in the diameter of the opening, various positions of the opening along the length and width of the beam, edge distance, etc. 50 finite element simulations were conducted. The FEA results are verified using the results from the laboratory experiments. The study showed that the load carrying capacity of the beam is reduced by 20% for the RC beam with vertical openings placed near the center of the beam compared to a solid beam without an opening. Significant reduction in load carrying capacity is observed for beams with an opening near the support (${\approx}15%$). The overall stiffness of the beam, crack pattern and failure modes were not affected due to the presence of the vertical opening. Furthermore, an artificial neural network (ANN) analysis is carried out using the FEA generated data. The results and observations from the ANN and FEA are in good agreement with experimental results.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.123-130
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• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.