Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: a feasibility study

Yi-Qing Ni,Junfang Wang,Tommy H.T. Chan 국제구조공학회 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2

This paper presents a feasibility study on structural damage alarming and localization of longspancable-supported bridges using multi-novelty indices formulated by monitoring-derived modalparameters. The proposed method which requires neither structural model nor damage model is applicable tostructures of arbitrary complexity. With the intention to enhance the tolerance to measurementnoise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in termsof auto-associative neural networks (ANNs) where the output vector is designated to differ from the inputvector while the training of the ANNs needs only the measured modal properties of the intact structure underin-service conditions. After validating the enhanced capability of the improved novelty index for structuraldamage alarming over the commonly configured novelty index, the performance of the improved noveltyindex for damage occurrence detection of large-scale bridges is examined through numerical simulationstudies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurredwith different types of structural damage. Then the improved novelty index is extended to formulate multinoveltyindices in terms of the measured modal frequencies and incomplete modeshape components fordamage region identification. The capability of the formulated multi-novelty indices for damage regionidentification is also examined through numerical simulations of the TMB and TKB.

Multiple damage detection of maglev rail joints using time-frequency spectrogram and convolutional neural network

Yi-Qing Ni,Su-Mei Wang,Gao-Feng Jiang,Yang Lu,Guobin Lin,Hong-Liang Pan,Junqi Xu,Shuo Hao 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.4

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-loosenesscaused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFSCNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

Online railway wheel defect detection under varying running-speed conditions by multi-kernel relevance vector machine

Yi-Qing Ni,Yuan-Hao Wei,You-Wu Wang 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.3

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

A vision-based system for long-distance remote monitoring of dynamic displacement: experimental verification on a supertall structure

Yi-Qing Ni,You-Wu Wang,Wei-Yang Liao,Wei-Huan Chen 국제구조공학회 2019 Smart Structures and Systems, An International Jou Vol.24 No.6

Dynamic displacement response of civil structures is an important index for in-construction and in-service structural condition assessment. However, accurately measuring the displacement of large-scale civil structures such as high-rise buildings still remains as a challenging task. In order to cope with this problem, a vision-based system with the use of industrial digital camera and image processing has been developed for long-distance, remote, and real-time monitoring of dynamic displacement of supertall structures. Instead of acquiring image signals, the proposed system traces only the coordinates of the target points, therefore enabling real-time monitoring and display of displacement responses in a relatively high sampling rate. This study addresses the in-situ experimental verification of the developed vision-based system on the Canton Tower of 600 m high. To facilitate the verification, a GPS system is used to calibrate/verify the structural displacement responses measured by the vision-based system. Meanwhile, an accelerometer deployed in the vicinity of the target point also provides frequency-domain information for comparison. Special attention has been given on understanding the influence of the surrounding light on the monitoring results. For this purpose, the experimental tests are conducted in daytime and nighttime through placing the vision-based system outside the tower (in a brilliant environment) and inside the tower (in a dark environment), respectively. The results indicate that the displacement response time histories monitored by the vision-based system not only match well with those acquired by the GPS receiver, but also have higher fidelity and are less noise-corrupted. In addition, the low-order modal frequencies of the building identified with use of the data obtained from the vision-based system are all in good agreement with those obtained from the accelerometer, the GPS receiver and an elaborate finite element model. Especially, the vision-based system placed at the bottom of the enclosed elevator shaft offers better monitoring data compared with the system placed outside the tower. Based on a wavelet filtering technique, the displacement response time histories obtained by the vision-based system are easily decomposed into two parts: a quasi-static ingredient primarily resulting from temperature variation and a dynamic component mainly caused by fluctuating wind load.

A novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges

Yi-Qing Ni,Wen-Qiang Liu,En-Ze Rui,Lei Yuan,Si-Yi Chen,You-Liang Zheng 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.4

To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BISMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage.

Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: a feasibility study

Ni, Yi-Qing,Wang, Junfang,Chan, Tommy H.T. Techno-Press 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

FUNCTIONALLY GRADED EPOXY COMPOSITES USING SILANE COUPLING AGENT FUNCTIONALIZED MULTIWALLED CARBON NANOTUBES

YI WANG,TOSHIAKI NATSUKI,QING-QING NI,YAOFENG ZHU 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2014 NANO Vol.9 No.1

Functionally graded multiwalled carbon nanotube (MWCNT) reinforced epoxy matrix compo-sites are fabricated using a centrifugal method. Aggregation of the MWCNTs during the epoxycuring process is prevented using a two-step aminosilane modifcation. Chemical interaction ofthe silane with the oxidized nanotube surface is con¯rmed using Fourier transform infraredspectroscopy and X-ray photoelectron spectroscopy. Raman spectroscopy of acid-treatedMWCNTs corroborates the formation of surface defects owing to the introduction of carboxylgroups. The mechanical and microwave absorption property gradients of the composites correfspond with those produced via silane modifcation indicating potential application to microwaveabsorbing materials. The MWCNTs are better dispersed in the epoxy resin after the modi¯cation,making it possible for them to become e±ciently graded in the epoxy matrix. We therefore show that it is possible to fabricate functionally graded nano¯ller-reinforced materials using thecentrifugal method by modifying the surface of the nanofller.

Lentivirus-mediated Silencing of Rhomboid Domain Containing 1 Suppresses Tumor Growth and Induces Apoptosis in Hepatoma HepG2 Cells

Liu, Xue-Ni,Tang, Zheng-Hao,Zhang, Yi,Pan, Qing-Chun,Chen, Xiao-Hua,Yu, Yong-Sheng,Zang, Guo-Qing Asian Pacific Journal of Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.1

Rhomboids were identified as the first intramembrane serine proteases about 10 years ago. Since then, the study of the rhomboid protease family has blossomed. Rhomboid domain containing 1 (RHBDD1), highly-expressed in human testis, contains a rhomboid domain with unknown function. In the present study, we tested the hypothesis that RHBDD1 was associated with proliferation and apoptosis in hepatocellular carcinoma using recombinant lentivirus-mediated silencing of RHBDD1 in HepG2 cells. Our results showed that down-regulation of RHBDD1 mRNA levels markedly suppressed proliferation and colony formation capacity of HepG2 human hepatoma cancer cells in vitro, and induced cell cycle arrest. We also found that RHBDD1 silencing could obviously trigger HepG2 cell apoptosis. In summary, it was demonstrated that RHBDD1 might be a positive regulator for proliferative and apoptotic characteristics of hepatocellular carcinoma.

Associations of hypoxia inducible factor-1a gene polymorphisms with susceptibility to digestive tract cancers: a case–control study and meta-analysis

Zhi-Hai Ni,Xian-Jun Liang,Jing-Gang Mo,Yi Zhang,Jian-Hua Liang,Yu-Sha Yang,Yong Zhou,Zhao-Hua Li,Jian-Liang Zhang,Yin-Lu Ding,Peng Zhang,Jin-Qing Wang 한국유전학회 2015 Genes & Genomics Vol.37 No.11

We aim to investigate the correlations of hypoxia inducible factor-1a (HIF-1a) C1772T (rs11549465) and G1790A (rs11549467) gene polymorphisms with digestive tract cancers. A sum of 267 digestive tract cancers patients were hospitalized in Taizhou Central Hospital of Zhejiang Province as case group between December 2012 and December 2014. Additionally, 275 healthy people who had a physical examination in our hospital at the same time were selected as control group. Polymerase chain reaction-restriction fragment length polymorphism was utilized for detecting allele and genotype frequency of different locus in case and control group. Meta-analysis was performed using Comprehensive Metaanalysis 2.0 (Biostat Inc., Englewood, New Jersey, USA). Our result showed statistical significance only exists in family history of cancer between case and control group (P\0.05). Both C1772T (rs11549465) and G1790A (rs11549467) polymorphisms showed positive correlations with an increasing risk of digestive tract cancers. The frequencies of TT genotype of C1772T (rs11549465) and GA, AA genotypes of G1790A (rs11549467) polymorphisms in case group were evidently higher compared with the controls (all P\0.05). Besides, the comparison of allele and dominant models of HIF-1a C1772T (rs11549465) and G1790A (rs11549467) between two groups showed a significant difference (all P\0.05). Meta-analysis results further confirmed that the onset risk of digestive tract cancers may be improved under allele and dominant models of HIF-1a C1772T (rs11549465) and G1790A (rs11549467) (all P\0.05). Single nucleotide polymorphisms of HIF-1a C1772T (rs11549465) and G1790A (rs11549467) may play a role in development of digestive tract cancers.

Thiazinogeldanamycin, a New Geldanamycin Derivative Produced by Streptomyces hygroscopicus 17997

( Si Yang Ni ),( Lin Zhuan Wu ),( Hong Yuan Wang ),( Mao Luo Gan ),( Yu Cheng Wang ),( Wei Qing He ),( Yi Guang Wang ) 한국미생물 · 생명공학회 2011 Journal of microbiology and biotechnology Vol.21 No.6

A new geldanamycin (GDM) derivative was discovered and isolated from the fermentation broth of Streptomyces hygroscopicus 17997. Its chemical structure was elucidated as thiazinogeldanamycin by LC-MS, sulfur analysis, and NMR. The addition of cysteine to the fermentation medium significantly stimulated the production level of thiazinogeldanamycin, suggesting cysteine as a precursor of thiazinogeldanamycin production. Although showing a decreased cytotoxicity against HepG2 cancer cells, thiazinogeldanamycin exhibited an improved water solubility and photostability. Thiazinogeldanamycin may represent the first natural GDM derivative characterized so far that uses GDM as its precursor. Its appearance also clearly indicates that an appropriate end-point of fermentation is of critical importance for the maximal production of GDM by Streptomyces hygroscopicus 17997.