• Steel and Composite Structures
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• 제19권3호
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• pp.569-583
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• 2015

Recent years, explosive welding structures have been widely used in many engineering fields. The bonding state detection of explosive welding structures is significant to prevent unscheduled failures and even catastrophic accidents. However, this task still faces challenges due to the complexity of the bonding interface. In this paper, a new method called dual-tree complex wavelet transform based permutation entropy (DTCWT-PE) is proposed to detect bonding state of such structures. Benefiting from the complex analytical wavelet function, the dual-tree complex wavelet transform (DTCWT) has better shift invariance and reduced spectral aliasing compared with the traditional wavelet transform. All those characters are good for characterizing the vibration response signals. Furthermore, as a statistical measure, permutation entropy (PE) quantifies the complexity of non-stationary signals through phase space reconstruction, and thus it can be used as a viable tool to detect the change of bonding state. In order to more accurate identification and detection of bonding state, PE values derived from DTCWT coefficients are proposed to extract the state information from the vibration response signal of explosive welding structure, and then the extracted PE values serve as input vectors of support vector machine (SVM) to identify the bonding state of the structure. The experiments on bonding state detection of explosive welding pipes are presented to illustrate the feasibility and effectiveness of the proposed method.

• Journal of KIBIM
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• 제13권3호
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• pp.1-11
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• 2023

In order to use BIM as a tool for improving the productivity and quality of products in the construction industry, a BIM model must be created from the design stage first. Infrastructure structures such as bridges and tunnels are mainly created based on three-dimensional alignment in the generation of BIM models. Especially, generation of BIM models based on three-dimensional linearity has high task difficulty and algorithms for automating BIM modeling for railway infra structures have been suggested in previous studies. This study improved the BIM modeling automation algorithm of railway infrastructures and developed a system based on the algorithm so that it can be easily used by ordinary users. The system was built as an add-in system of Autodesk's Revit. As an improvement first, it is possible to arrange different libraries for each pattern, enabling various uses. In addition, it can be created models of several members with a single process and the system can automatically places structures that are added periodically, such as Rock Bolt and Fore Polling. Finally, 3D length information and volume for each pattern are automatically calculated for more accurate 3D-based volume calculation. This study contributes to increasing user accessibility by building a BIM modeling automation algorithm into a system. The system is expected to improve the efficiency of BIM modeling creation of linear-based infra structures, including railway infrastructure.

• The Korean Journal of Food & Health Convergence
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• 제4권3호
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• pp.1-5
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• 2018

In today's technological development of human society more and more influence on the lives of biological organisms different electromagnetic radiation. Therefore, the study and analysis of the mechanisms of their effects is an urgent task. The purpose of research - the study of the primary mechanisms of interaction of photons of optical radiation with the structures of biological objects, using the laws of quantum mechanics and biophysics. Photobiological basis of the mechanism of action of EMR optical range is the energy absorption of light quanta (photons) by atoms and molecules of biological structures (law Grotgus-Draper), which resulted in the formation of electronically excited states of these molecules with the transfer of photon energy (internal photoeffect). This is accompanied by electrolytic dissociation and ionization of biological molecules. The degree of manifestation of photobiological effects in the body depends on the intensity of the optical radiation, which is inversely proportional to the square of the distance from the source to the irradiated surface. Accordingly, in practice, determine not the intensity and irradiation dose at a certain distance from the source of exposure by the exposure time.

• Structural Engineering and Mechanics
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• 제63권1호
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• pp.115-124
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• 2017

In this paper, a different technique to predict the effects of soil-structure interaction (SSI) on seismic response of building systems is investigated. The technique use a machine learning algorithm called Support Vector Regression (SVR) with technical and analytical results as input features. Normally, the effects of SSI on seismic response of existing building systems can be identified by different types of large data sets. Therefore, predicting and estimating the seismic response of building is a difficult task. It is possible to approximate a real valued function of the seismic response and make accurate investing choices regarding the design of building system and reduce the risk involved, by giving the right experimental and/or numerical data to a machine learning regression, such as SVR. The seismic response of both single-degree-of-freedom system and six-storey RC frame which can be represent of a broad range of existing structures, is estimated using proposed SVR model, while allowing flexibility of the soil-foundation system and SSI effects. The seismic response of both single-degree-of-freedom system and six-storey RC frame which can be represent of a broad range of existing structures, is estimated using proposed SVR model, while allowing flexibility of the soil-foundation system and SSI effects. The results show that the performance of the technique can be predicted by reducing the number of real data input features. Further, performance enhancement was achieved by optimizing the RBF kernel and SVR parameters through grid search.

• Journal of the Korean Society for Advanced Composite Structures
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• 제6권2호
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• pp.97-104
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• 2015

Compared with a strong axial rigidity due to large intial tension, cable has a weak laterally flexural rigidity. A variety of dynamic loads such as traffic loads and wind loads etc. cause the cables to vibrate significantly and affect the mechanical properties and the performance of cables. Therefore, vibration reduction design is an urgent task to control the vibration of cable-supported bridges. Because a various kind of dampers have shown to reduce the amplitude and duration time of vibration of cable from measured date in field test, damper can be considered that it is effective device significantly to reduce the amplitude and duration time in vibration of cable. Vibration characteristics of cable can change according to manufacturing method and type of established form, and damper has been designed according to distribution of natural frequencies and vibration modes. In this study, numerical analysis is used to show the reduction effects of vibrations and present the design of damper for vibration reduction of cable.

• Smart Structures and Systems
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• 제20권2호
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• pp.219-229
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• 2017

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.

• Steel and Composite Structures
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• 제3권3호
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• pp.185-198
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• 2003

The concept of performance based seismic design has been gradually accepted by the earthquake engineering profession recently, in which the cost-effectiveness criterion is one of the most important principles and more attention is paid to the structural performance at the inelastic stage. Since there are many uncertainties in seismic design, reliability analysis is a major task in performance based seismic design. However, structural reliability analysis may be very costly and time consuming because the limit state function is usually a highly nonlinear implicit function with respect to the basic design variables, especially for the complex large-scale structures for dynamic and nonlinear analysis. Understanding statistical properties of the structural inelastic deformation, which is the aim of the present paper, is helpful to develop an efficient approximate approach of reliability analysis. The present paper studies the statistical properties of the maximum elastoplastic story drift of steel frames subjected to earthquake load. The randomness of earthquake load, dead load, live load, steel elastic modulus, yield strength and structural member dimensions are considered. Possible probability distributions for the maximum story are evaluated using K-S test. The results show that the choice of the probability distribution for the maximum elastoplastic story drift of steel frames is related to the mean value of the maximum elastoplastic story drift. When the mean drift is small (less than 0.3%), an extreme value type I distribution is the best choice. However, for large drifts (more than 0.35%), an extreme value type II distribution is best.

• Smart Structures and Systems
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• 제29권1호
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2307-2310
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• 2005

A pressurizer is one of the major safety related equipment of nuclear power plants. In order to inspect and maintain the internal structures of a pressurizer, jumpers have to enter the pressurizer, in spite of the high dose exposure. Therefore, a tele-operated robotic manipulator has been developed, which consists of four parts with 2DOFs arms, a gripper, base frame, and control console. The task of this robotic manipulator is to extract the damaged electric heaters and inspect the internal structure of the pressurizer. The gripper hanging from the manipulator approaches the heaters and extracts the damaged heater by using a self-locking mechanism. In order to investigate the structural stability of the robotic manipulator, a stress analysis has been performed by using the ANSYS code. The results of this paper include the position control and vibration control of robotic gripper and the development of processing visual information for a vision sensor.

• Smart Structures and Systems
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• 제5권4호
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• pp.483-494
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• 2009

Damage detection has been proven to be a challenging task in structural health monitoring (SHM) due to the fact that damage cannot be measured. The difficulty associated with damage detection is related to electing a feature that is sensitive to damage occurrence and evolution. This difficulty increases as the damage size decreases limiting the ability to detect damage occurrence at the micron and submicron length scale. Damage detection at this length scale is of interest for sensitive structures such as aircrafts and nuclear facilities. In this paper a new photonic sensor based on photonic crystal (PhC) technology that can be synthesized at the nanoscale is introduced. PhCs are synthetic materials that are capable of controlling light propagation by creating a photonic bandgap where light is forbidden to propagate. The interesting feature of PhC is that its photonic signature is strongly tied to its microstructure periodicity. This study demonstrates that when a PhC sensor adhered to polymer substrate experiences micron or submicron damage, it will experience changes in its microstructural periodicity thereby creating a photonic signature that can be related to damage severity. This concept is validated here using a three-dimensional integrated numerical simulation.