• Smart Structures and Systems
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• 제32권3호
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• pp.135-151
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• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• Smart Structures and Systems
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• 제32권4호
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• pp.253-266
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• 2023

The time-reversal method is employed to improve the ability of pipeline defect detection, and a new approach of identifying the pipeline defect depth is proposed in this research. When the L(0,2) mode ultrasonic guided wave excited through a lead zirconate titinate (PZT) transduce array propagates along the pipeline with a defect, it will interact with the defect and be partially converted to flexural F(n, m) modes and longitudinal L(0,1) mode. Using a receiving PZT array attached axisymmetrically around the pipeline, the L(0,2) reflection signal as well as the mode conversion signals at the defect are obtained. An appropriate rectangle window is used to intercept the L(0,2) reflection signal and the mode conversion signals from the obtained direct detection signals. The intercepted signals are time reversed and re-excited in the pipeline again, result in the guided wave energy focusing on the pipeline defect, the L(0,2) reflection and the L(0,1) mode conversion signals being enhanced to a higher level, especially for the small defect in the early crack stage. Besides the L(0,2) reflection signal, the L(0,1) mode conversion signal also contains useful pipeline defect information. It is possible to identify the pipeline defect depth by monitoring the variation trend of L(0,2) and L(0,1) reflection coefficients. The finite element method (FEM) simulation and experiment results are given in the paper, the enhancement of pipeline defect reflection signals by time-reversal method is obvious, and the way to identify pipeline defect depth is demonstrated to be effective.

• 한국재료학회지
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• 제33권10호
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• pp.430-438
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• 2023

In this study, we synthesized pH-controlled resorcinol-formaldehyde (RF) gels through the polymerization of two starting materials: resorcinol and formaldehyde. The prepared RF gels were dried using an acetone substitution method, and they were subsequently carbonized under nitrogen atmosphere to obtain carbon xerogels (CX_Y) prepared at different pH (Y). The carbon xerogels were utilized as active materials for coin-type organic supercapacitor electrodes to investigate the influence of pH on the electrochemical properties of the carbon xerogels. The carbon xerogels prepared at lower pH (CX_9.5 and CX_10) exhibited sufficient particle growth, with a three-dimensional network of particles during the RF gel formation, resulting in the development of abundant mesopores. Conversely, the carbon xerogels prepared at higher pH (CX_11 and CX_12) retained densely packed structures of small particles, leading to pore collapse and low specific surface areas. Consequently, CX_9.5 and CX_10 showed high specific surface areas, and provided ample adsorption sites for the formation of electric double layers with electrolyte ions. Moreover, the three-dimensional particle network in CX_9.5 and CX_10 significantly enhanced electrical conductivity. The presence of well-developed mesopores in these materials further facilitated the effective transport of electrolyte ions, contributing to their superior performance as organic supercapacitor electrodes. This study confirmed that pH-controlled carbon xerogels are one of the promising active materials for organic supercapacitor electrodes. Furthermore, we concluded that pH during RF gel formation is a crucial factor determining the electrode performance of the carbon xerogels, highlighting the need for precise pH control to obtain high-performance carbon xerogel electrodes.

• Smart Structures and Systems
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• 제33권1호
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• pp.1-16
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• 2024

The investigation on vehicle-bridge coupling system (VBCS) is crucial in bridge design, bridge condition evaluation, and vehicle overload control. A real-time hybrid testing (RTHT) method for VBCS (RTHT-VBCS) is proposed in this paper for accurately and economically disclosing the dynamic performance of VBCSs. In the proposed method, one of the carriages is chosen as the experimental substructure loaded by servo-hydraulic actuator loading system in the laboratory, and the remaining carriages as well as the bridge structure are chosen as the numerical substructure numerically simulated in one computer. The numerical substructure and the experimental substructure are synchronized at their coupling points in terms of force equilibrium and deformation compatibility. Compared to the traditional iteration experimental method and the numerical simulation method, the proposed RTHT-VBCS method could not only obtain the dynamic response of VBCS, but also economically analyze various working conditions. Firstly, the theory of RTHT-VBCS is proposed. Secondly, numerical models of VBCS for RTHT method are presented. Finally, the feasibility and accuracy of the RTHT-VBCS are preliminarily validated by real-time hybrid simulations (RTHSs). It is shown that, the proposed RTHT-VBCS is feasible and shows great advantages over the traditional methods, and the proposed models can effectively represent the VBCS for RTHT method in terms of the force equilibrium and deformation compatibility at the coupling point. It is shown that the results of the single-degree-of-freedom model and the train vehicle model are match well with the referenced results. The RTHS results preliminarily prove the effectiveness and accuracy of the proposed RTHT-VBCS.

• 한국지반공학회논문집
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• 제40권2호
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• pp.41-53
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• 2024

마이크로파일은 직경 300mm이하인 소구경 현장타설말뚝을 칭하며, 기존 구조물의 보강과 신축 구조물의 지지 목적으로 주로 활용되어 왔다. 또한 파일의 활용이 증가함에 따라 마이크로파일 또는 마이크로파일-레프트의 지지특성에 대한 연구가 활발히 진행되어왔으며, 연구결과를 통해 기초에 대한 파일 보강효과가 입증되었다. 그러나 대부분 기존 연구는 마이크로파일-레프트가 파일조건에 따라 기존 파일-레프트와 다른 거동을 보일 수 있고, 기초에 대한 파일의 보강효과가 기초하부 토사층의 점착특성에 따라 좌우될 수 있음을 고려하지 않았다. 따라서 본 연구에서는 수치해석을 통해 파일조건과 기초하부 토사층의 점착특성에 따른 마이크로파일의 보강효과를 평가하기 위해 3차원 수치해석을 수행하였다. 연구결과, 비 점착특성을 가진 토사층 조건에서의 기초에 대한 마이크로파일의 보강효과가 점착력을 가진 토사층인 경우보다 좀 더 효과적이고, 최대 마이크로파일의 보강효과는 전면기초의 지지력보다 3.7배 정도 증가시킬 수 있는 것으로 나타났다.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.53-58
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• 2024

This paper presents a novel miniaturized 3-D cubic antenna for use in wireless sensor network (WSN) application. The geometry of this antenna is designed as a cube including a meander dipole antenna. A truly omnidirectional pattern is produced by this antenna in both E-plane and H-plane, which allows for non-intermittent communication that is orientation independent. The operating frequency lies in the ISM band (centered in 2.45 GHz). The dimensions of this ultra-compact cubic antenna are 1.25*1.12*1cm3 which features a length dimension λ/11. The coefficient which presents the overall antenna structure is Ka=0.44. The cubic shape of the antenna is allowing for smart packaging, as sensor equipment may be easily integrated into the cube hallow interior. The major constraint of WSN is the energy consumption. The power consumption of radio communication unit is relatively high. So it is necessary to design an antenna which improves the energy efficiency. The parameters considered in this work are the resonant frequency, return loss, efficiency, bandwidth, radiation pattern, gain and the electromagnetic field of the proposed antenna. The specificity of this geometry is that its size is relatively small with an excellent gain and efficiency compared to previously structures (reported in the literature). All results of the simulations were performed by CST Microwave Studio simulation software and validated with HFSS. We used Advanced Design System (ADS) to validate the equivalent scheme of our conception. Input here the part of summary.

• Smart Structures and Systems
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• 제33권2호
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.

• Smart Structures and Systems
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• 제33권2호
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• pp.145-163
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• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Smart Structures and Systems
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• 제33권2호
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• pp.165-175
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• 2024

Rotating beams play a crucial role in representing complex mechanical components that are prevalent in vital sectors like energy and transportation industries. These components are susceptible to the initiation and propagation of cracks, posing a substantial risk to their structural integrity. This study presents a two-stage methodology for detecting the location and estimating the size of an open-edge transverse crack in a rotating Euler-Bernoulli beam with a uniform cross-section. Understanding the dynamic behavior of beams is vital for the effective design and evaluation of their operational performance. In this regard, modal parameters such as natural frequencies and eigenmodes are frequently employed to detect and identify damages in mechanical components. In this instance, the Frobenius method has been employed to determine the first two natural frequencies and corresponding eigenmodes associated with flapwise bending vibration. These calculations have been performed by solving the governing differential equation that describes the motion of the beam. Various parameters have been considered, such as rotational speed, beam slenderness, hub radius, and crack size and location. The effect of the crack has been replaced by a rotational spring whose stiffness represents the increase in local flexibility as a result of the damage presence. In the initial phase of the proposed methodology, a damage index utilizing the slope of the beam's eigenmode has been employed to estimate the location of the crack. After detecting the presence of damage, the size of the crack is determined using a Genetic Algorithm optimization technique. The ultimate goal of the proposed methodology is to enable the development of more suitable and reliable maintenance plans.

• 문화기술의 융합
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• 제10권1호
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• pp.471-476
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• 2024

다양한 스마트 기기 및 컴퓨팅 디바이스의 보급에 따라 빅데이터 생성이 광범위하게 일어나고 있다. 기계학습은 데이터의 패턴을 학습하여 추론을 수행하는 알고리즘이다. 다양한 기계학습 알고리즘 중에서 주목을 받는 알고리즘은 신경망 기반의 딥러닝 학습이다. 딥러닝은 다양한 응용이 발표되면서 빠른 성능 향상을 달성하고 있다. 최근 딥러닝 알고리즘 중에서 그래프 구조를 활용하여 데이터를 분석하려는 시도가 증가하고 있다. 본 연구에서는 그래프 구조를 활용하여 딥러닝 네트워크에 전달하기 위한 그래프 생성 방법을 제시한다. 본 논문은 그래프 생성 과정에서 노드의 속성과 간선의 가중치를 일반화하고 행렬화 과정을 제시하여 딥러닝 입력에 필요한 구조로 전환하는 방법을 제시한다. 그래프 생성 과정에서 속성과 가중치 정보를 보전할 수 있는 선형변환 매트릭스 적용 방법을 제시한다. 마지막으로 일반 그래프의 딥러닝 입력 구조를 제시하고 성능 분석을 위한 접근법을 제시한다.