• Structural Engineering and Mechanics
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• 제86권6호
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• pp.715-730
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• 2023

Broad studies have addressed the issue of structural element damage identification, however, rubber bearing, as a key component of load transmission between the superstructure and substructure, is essential to the operational safety of a bridge, which should be paid more attention to its health condition. However, regarding the limitations of the traditional bearing damage detection methods as well as few studies have been conducted on this topic, in this paper, inspired by the model updating-based structural damage identification, a two-stage bearing damage identification method has been proposed. In the first stage, we deduce a novel bearing damage localization indicator, called element relative MSE, to accurately determine the bearing damage location. In the second one, the prior knowledge of bearing damage localization is combined with sailfish optimization (SFO) to perform the bearing damage estimation. In order to validate the feasibility, a numerical example of a 5-span continuous beam is introduced, also the noise robustness has been investigated. Meanwhile, the effectiveness and engineering applicability are further verified based on an experimental simply supported beam and actual engineering of the I-40 Bridge. The obtained results are good, which indicate that the proposed method is not only suitable for simple structures but also can accurately locate the bearing damage site and identify its severity for complex structure. To summarize, the proposed method provides a good guideline for the issue of bridge bearing detection, which could be used to reduce the difficulty of the traditional bearing failure detection approach, further saving labor costs and economic expenses.

• International Journal of Oral Biology
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• 제31권1호
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• pp.11-14
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• 2006

This study was undertaken to develop PCR primers for the identification and detection of Streptococcus anginosus using species-specific forward and reverse primers. These primers targeted the variable regions of the 16S ribosomal RNA coding gene(rDNA). The primer specificity was tested against 12 S. anginosus strains and 6 different species(10 strains) of oral bacteria. The primer sensitivity was determined by testing serial dilutions of the purified genomic DNA of S. anginosus ATCC $33397^T$. The data showed that species-specific amplicons were obtained from all the S. anginosus strains tested, but not in the six other species. The PCR could detect as little as 0.4pg of the chromosomal DNA from S. anginosus. This suggests that the PCR primers are highly sensitive and applicable to the detection and identification of S. anginosus.

• Smart Structures and Systems
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• 제23권4호
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• Smart Structures and Systems
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• 제23권5호
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• 대한임베디드공학회논문지
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• 제5권3호
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• pp.152-156
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• 2010

Non-line-of-sight (NLOS) propagation is one of the challenges in radio positioning. Distinguishing the transmission status of the communication as line-of-sight (LOS) or NLOS is of great importance for the wireless communication systems. This paper focuses on the identification of NLOS based on time-of-arrival (TOA) distance estimates and the received signal strength (RSS) measurements. We set a path loss threshold based on the joint TOA and RSS based NLOS detection method to determine LOS or NLOS. Simulation results show that the proposed method ensures the correct of detection for the LOS condition and can improve the NLOS identification for the weak noise and long distance.

• Structural Engineering and Mechanics
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• 제60권4호
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• pp.729-747
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• 2016

This study aims to present a novel optimization algorithm known as gravitational search algorithm (GSA) for structural damage detection. An objective function for damage detection is established based on structural vibration data in frequency domain, i.e., natural frequencies and mode shapes. The feasibility and efficiency of the GSA are testified on three different structures, i.e., a beam, a truss and a plate. Results show that the proposed strategy is efficient for determining the locations and the extents of structural damages using the first several modal data of the structure. Multiple damages cases in different types of structures are studied and good identification results can be obtained. The effect of measurement noise on the identification results is investigated.

• Structural Monitoring and Maintenance
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• 제4권4호
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• pp.365-379
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• 2017

This paper presents a comparison of the black-box and the physics based derived gray-box models for subspace identification for structures subjected to support-excitation. The study compares the damage detection capabilities of both these methods for linear time invariant (LTI) systems as well as linear time-varying (LTV) systems by extending the gray-box model for time-varying systems using short-time windows. The numerically simulated IASC-ASCE Phase-I benchmark building has been used to compare the two methods for different damage scenarios. The efficacy of the two methods for the identification of stiffness parameters has been studied in the presence of different levels of sensor noise to simulate on-field conditions. The proposed extension of the gray-box model for LTV systems has been shown to outperform the black-box model in capturing the variation in stiffness parameters for the benchmark building.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1310-1312
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• 1996

In this study, the algorithm for detection of evoked potentials is proposed. The observed evoked potentials are first preprocessed by blind identification so as to eliminate the ongoing EEG Bile noise. Then, statistic characteristics of the peak components i.e latency and amplitude are detected from prefiltered responses by latency-corrected averaging method. The performance of blind identification is compared with those of adaptive fillers as to deterministic and stochastic EPs, is assessed in terms of NMSE, distortion index, correlation coefficient with original EPs. The estimated deterministic and stochastic EPs restored with peak components are compared and assessed. The results show the superiority of this proposed algorithm using blind identification in detecting deterministic and stochastic EPs.

• Computers and Concrete
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• 제20권5호
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• pp.563-572
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• 2017

Ordinary reinforced concrete (RC) and prestressed concrete bridges are two popular and typical types of short- and medium-span bridges that accounts for the vast majority of all existing bridges. The cost of maintaining, repairing or replacing degraded existing RC bridges is immense. Detecting the abnormality of RC bridges at an early stage and taking the protective measures in advance are effective ways to improve maintenance practices and reduce the maintenance cost. This study proposes a systematic method from influence line (IL) identification to damage detection with applications to RC bridges. An IL identification method which integrates the cubic B-spline function with Tikhonov regularization is first proposed based on the vehicle information and the corresponding moving vehicle induced bridge response time history. Subsequently, IL change is defined as a damage index for bridge damage detection, and information fusion technique that synthesizes ILs of multiple locations/sensors is used to improve the efficiency and accuracy of damage localization. Finally, the feasibility of the proposed systematic method is verified through experimental tests on a three-span continuous RC beam. The comparison suggests that the identified ILs can well match with the baseline ILs, and it demonstrates that the proposed IL identification method has a high accuracy and a great potential in engineering applications. Results in this case indicate that deflection ILs are superior than strain ILs for damage detection of RC beams, and the performance of damage localization can be significantly improved with the information fusion of multiple ILs.

• Mass Spectrometry Letters
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• 제8권4호
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• pp.90-97
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• 2017

Androgenic anabolic steroids (AASs) are synthetic derivatives of testosterone with a common structure containing cyclopentanoperhydrophenanthrene nucleus. Their use enhances the muscle building capacity and is beneficial during performance. The AASs are one of the most abused group of substances in horse doping. Liquid chromatography tandem mass spectrometry ($LC/MS^n$) has been successfully applied to the detection of anabolic steroids in biological samples. However, the saturated hydroxysteroids viz: nandrolone, $5{\alpha}-estrane-3{\beta}$, $17{\alpha}-diol$ exhibit lower detection responses in electrospray ionisation (ESI) because of their poor ionisation efficiency. To overcome this limitation pre-column chemical derivatization has been introduced to enhance their detection responses in $LC-ESI-MS^n$ analysis. The aim of present study was to develop a sensitive method for identification and confirmation of nandrolone and its metabolite in horse urine incorporating pre-column derivatization using picolinic acid. The method consists of extraction of targeted steroid conjugates by solid phase extraction (SPE). The eluted steroid conjugates were hydrolysed by methanolysis and free steroids were recovered with liquid-liquid extraction. The resulting steroids were derivatized to form picolinoyl esters and identification was done using LC-ESI-MS/MS in positive ionization mode. The picolinated steroid adduct enhanced the detection levels in comparison to underivatized steroids.