• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.20 no.4
• /
• pp.493-499
• /
• 2007

The Taylor expansion expresses a differentiable function and its coefficients provide good approximations for the given function and its derivatives. In this study, m-th order Taylor Polynomial is constructed and the coefficients are computed by the Moving Least Squares method. The coefficients are applied to the governing partial differential equation for solid problems including crack problems. The discrete system of difference equations are set up based on the concept of point collocation. The developed method effectively overcomes the shortcomings of the finite difference method which is dependent of the grid structure and has no approximation function, and the Galerkin-based meshfree method which involves time-consuming integration of weak form and differentiation of the shape function and cumbersome treatment of essential boundary.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.11
• /
• pp.53-60
• /
• 2020

The bogies of railway vehicles are one of the most critical components for service. Fatigue defects in the bogie can be initiated for various reasons, such as material imperfection, welding defects, and unpredictable and excessive overloads during operation. To prevent the derailment of a railway vehicle, it is necessary to evaluate and detect the defect of a connection weldment between the car body and bogie accurately. The safety of the bogie weldment was checked using an ultrasonic test, and it is necessary to determine the occurrence of defects using a learning method. Recently, studies on deep learning have been performed to identify defects with a high recognition rate with respect to a fine and similar defect. In this paper, the databases of weldment specimens with artificial defects were constructed to detect the defect of a bogie weldment. The ultrasonic inspection using the wedge angle was performed to understand the detection ability of fatigue cracks. In addition, the convolutional neural network was applied to minimize human error during the inspection. The results showed that the defects of connection weldment between the car body and bogie could be classified with more than 99.98% accuracy using CNN, and the effectiveness can be verified in the case of an inspection.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.31 no.4
• /
• pp.351-359
• /
• 2011

In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one mode of sensing. Hence, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study on the pipeline system is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a two-dimensional space using the damage indices extracted from the impedance and guided wave features. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learning-based pattern recognition are optimized. Finally, further research issues will be discussed for real-world implementation of the proposed approach.

• Tunnel and Underground Space
• /
• v.15 no.2 s.55
• /
• pp.119-128
• /
• 2005

Although the evaluation of the mechanical properties and behavior of jointed rock masses is very important for the design of tunnel and underground openings, it has always been considered the most difficult problem. One of the difficulties in describing the rock mass behavior is the selection of the appropriate constitutive model. This limitation may be overcome with the progress in discrete element software such as PFC, which does not need the user to prescribe a constitutive model for rock mass. In this paper, a 30\;m\;\times\;30\;m\;\times\;30\;m m jointed rock mass of road tunnel site was analyzed. h discrete fracture network was developed from the joint geometry obtained from core logging and surface survey. Using the discontinuities geometry from the DFN model, PFC simulations were carried out, starting with the intact rock and systematically adding the joints and the stress-strain response was recorded for each case. With the stress-strain response curves, the mechanical properties of jointed rock masses were determined. As expected, the presence of joints had a pronounced effect on mechanical properties of the rock mass. More importantly, getting the mechanical response of the PFC model doesn't require a user specified constitutive model.

• Journal of Korean Society of Steel Construction
• /
• v.19 no.6
• /
• pp.695-702
• /
• 2007

The Thermal Prestressing Method for continuous composite girder bridges is a new design and construction method developed to induce initial composite stresses in the concrete slab at negative bending regions. Due to the induced initial stresses, prevention of tensile cracks at the concrete slab, reduction of steel girder section, and reduction of reinforcing bars are possible. Thus, the construction efficiency can be improved and the construction can be made more economical. The method for determining the optimum heating region of the thermal prestressing method has not been established although such method is essential for improving the efficiency of the design process. The trial-and-error method used in previous studies is far from efficient, and a more rational method for computing optimal heating region is required. In this study, an efficient method for determining the optimum heating region in using the thermal prestressing method was developed based on the neural network algorithm, which is widely adopted to pattern recognition, optimization, diagnosis, and estimation problems in various fields. Back-propagation algorithm, commonly used as a learning algorithm in neural network problems, was used for the training of the neural network. Through case studies of two-span and three-span continuous composite girder bridges using the developed procedure, the optimal heating regions were obtained.

• The Journal of Engineering Geology
• /
• v.28 no.1
• /
• pp.61-67
• /
• 2018

Grouting is reinforcement or cutoff method which uses the hardening agent which is typically represented by portland cement and injected into the ground or the structure. When mixing the cement in powder form with water, the particles tend to cohere each other. Once they cohered, the particle size tends to become larger while injection efficiency becomes lower. This study, in a bid to reduce the cohesion of cement, the screen was set inside the grout mixer so that the cement particles are separated while vibrating them. To validate the effect of vibration screen, comparison test was conducted by using ordinary portland cement, slag cement and micro cement. Viscosity test, bleeding test and grain-size analysis indicated that the characteristics varied significantly after passing through the vibration filter. It is expected that the vibration filter installed inside the grout mixer will reduce the cement cohesion when mixing with water.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.5 s.176
• /
• pp.1331-1342
• /
• 2000

In power generation systems a variety of structural components typically operate at high temperature and pressure. Therefore a life assessment methodology accounting for gradual creep fracture is increasingly needed for these components. The most critical defects in such structure are generally found in the form of semi-elliptical surface cracks in the welded tubular joints. Therefore the analysis of a semi-elliptical surface crack in a plate or a shell is an important problem in engineering fracture mechanics. On this background, via shell/line-spring finite element analyses of such surface cracks in the welded T and L joints under various loadings, we investigate J-integral along the crack front We first develop T and L joints auto mesh generation program providing ABAQUS input file composed of shell/line-spring finite elements. We then further develop a T and L joints life assessment program based on the experimental creep crack growth law and auto mesh generation program in a graphical user interface format Finally the remaining life of T and L joints for various analytical parameters are assessed using the developed life assessment program.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.6 no.2
• /
• pp.225-231
• /
• 2002

Severe cracks on Reinforced Concrete (RC) structures caused by structural displacement can be often one of the main reasons for the degradation of tensile and flexural rigidities of RC structures and for the deterioration of durability and serviceability of RC structures through accelerated steel corrosion. These combined factors adversely affect the performance of RC concrete, leading to shortened life time of RC structures. In consideration of these problems, we conducted 3 point bending experiments by employing three different types of concrete specimens: fiber-net reinforced concrete (FNRC), polypropylene-fiber reinforced concrete (PFRC), and plain concrete (PC). FNRC is well known for its strong corrosion resistance, light self-weight, and excellent tensile strength, while PFRC is known to be effective in crack control. FNRC was found to have the best first and final crack resistances followed by PFRC and PC, as evidenced by the highest initial crack load and the smallest final crack width, respectively. The FNRC specimens with various tensile strength of fiber net exhibited greater ultimate strengths than those for PFRC and PC. Furthermore, the crack widths of FNRC specimens were smaller than those calculated by the crack-width estimation equation of the KCI and ACI code. Therefore, we conclude that fiber net reinforcement is effective not only on crack control, but also on loading share.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2013.10a
• /
• pp.320-324
• /
• 2013

A crack identification method using an equivalent bending stiffness for cracked beam and committee of neural networks is presented. The equivalent bending stiffness is constructed based on an energy method for a straight thin-walled pipe, which has a through-the-thickness crack, subjected to bending. Several numerical analysis for a steel cantilever pipe using the equivalent bending stiffness are carried out to extract the natural frequencies and mode shapes of the cracked beam. The extracted modal properties are used in constructing a training patterns of a neural network. The input to the neural network consists of the modal properties and the output is composed of the crack location and size. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated crack locations and sizes from different neural networks are averaged. Experimental crack detection is carried out for 3 damage cases using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.

• Journal of Korean Association for Spatial Structures
• /
• v.22 no.2
• /
• pp.19-27
• /
• 2022

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.