• Smart Structures and Systems
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• 제19권1호
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• pp.1-10
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• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

• 항공우주시스템공학회지
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• 제12권6호
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• pp.32-40
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• 2018

본 연구에서는 error-outlier 기반의 충격위치탐지 알고리즘과 다중화된 FBG 센서를 이용하여 탄소섬유 강화 플라스틱 복합재 평판 구조물에 대한 충격위치탐지를 수행하였다. 알고리즘의 주요 변수인 오차 임계값(ET)이 0.3 nm, 상수 수준(CL)이 110일 때 최적의 충격위치탐지 결과(최대 오차= 31.82 mm, 평균 오차= 6.31 mm)가 도출되었다. 또한 주어진 최적의 변수 조건에서의 충격위치탐지 과정과 결과를 상세히 분석하였다. 본 연구에서 제시된 다중화된 FBG 센서와 error-outlier 기반의 충격탐지 알고리즘은 복합재 구조물에 대한 충격탐지에 적합한 것으로 판단되며, 향후 다양한 구조 건전성 감시에 활용될 것으로 기대된다.

• Composites Research
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• 제36권6호
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• pp.429-434
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• 2023

본 연구에서는 기존 배관 가스 누출 감지에 사용되던 음향방출 센서가 실시간 모니터링에 적용될 때 발생할 수 있는 문제들을 개선하기 위해, Macro-fiber composite (MFC) 트랜스듀서를 음향방출 센서로 사용하여 가스 누출 감지 시스템에 적용하였다. 적용 전 MFC의 구조를 최적화하기 위해 구조해석을 진행하여 제작하였고, 그 결과 MFC가 가지는 유연성으로 굴곡진 배관에 잘 밀착되어 AE 신호를 문제없이 수신할 수 있었다. AE 신호 분석 결과 고압 누출, 저압 누출 모두 파라미터 값 변화에 유의미한 결과를 보였으며, 특히, FFT 그래프의 파라미터에서 고압 누출의 경우 누출이 없는 경우 대비 120~626%의 변화량, 저압 누출의 경우 9~22%의 변화량을 보였다. 또한, 누출 발생 부위에서의 거리에 따라, 거리가 멀수록 이러한 파라미터 변화량이 줄어드는 경향을 보여, 추후 파라미터 변화량 감지를 통해 누출 감지가 가능할 뿐만 아니라, 변화량으로부터 누출 발생 위치를 파악할 수 있을 것으로 보인다.

• Smart Structures and Systems
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• 제20권5호
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• pp.549-562
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• 2017

The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.