Guided wave field calculation in anisotropic layered structures using normal mode expansion method

Lingfang Li,Hanfei Mei,Mohammad Faisal Haider,Dimitris Rizos,Yong Xia,Victor Giurgiutiu 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.2

The guided wave technique is commonly used in structural health monitoring as the guided waves can propagate far in the structures without much energy loss. The guided waves are conventionally generated by the surface-mounted piezoelectric wafer active sensor (PWAS). However, there is still lack of understanding of the wave propagation in layered structures, especially in structures made of anisotropic materials such as carbon fiber reinforced polymer (CFRP) composites. In this paper, the Rayleigh-Lamb wave strain tuning curves in a PWAS-mounted unidirectional CFRP plate are analytically derived using the normal mode expansion (NME) method. The excitation frequency spectrum is then multiplied by the tuning curves to calculate the frequency response spectrum. The corresponding time domain responses are obtained through the inverse Fourier transform. The theoretical calculations are validated through finite element analysis and an experimental study. The PWAS responses under the free, debonded and bonded CFRP conditions are investigated and compared. The results demonstrate that the amplitude and travelling time of wave packet can be used to evaluate the CFRP bonding conditions. The method can work on a baseline-free manner.

Unsupervised one-class classification for condition assessment of bridge cables using Bayesian factor analysis

Lingfang Li,Xiaoyou Wang,Wei Tian,Yao Du,Rong-rong Hou,Yong Xia 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The singlepeaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

Convolutional neural network-based data anomaly detection considering class imbalance with limited data

Lingfang Li,Yao Du,Rong-rong Hou,Xiaoyou Wang,Wei Tian,Yong Xia 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

Influence of Methylcellulose on Properties of Wheat Gliadin Film Cast from Aqueous Ethanol

Yihu Song,Lingfang Li,Qiang Zheng 한국식품과학회 2009 Food Science and Biotechnology Vol.18 No.4

Present work was focused on the influence of methylcellulose (MC) on steady rheology of wheat gliadin solution and the properties of glycerol plasticized gliadin films. The presence of MC below 0.99 wt% improved viscosity and flow activation energy of the 10 wt% gliadin solution significantly. In the casting films containing 0.2 g glycerol/g dry protein, the MC component aggregated in the gliadin matrix. The blend films containing less than 7.7 wt% MC exhibited higher Young’s modulus (E) and tensile strength (σb) and lower elongation at break (εb) in comparison with the pure gliadin film, which was related to the intermolecular interaction between MC and gliadins, the brittle fracture of the aggregated MC component, and the increase in glass transition temperature (Tg) of the gliadin phase. Increasing MC content led to a slight increase in water vapor permeability (WVP) without significant influence on the moisture absorption (MA).

A novel CHD7 variant in a chinese family with CHARGE syndrome

Shan Yanhong,Yao LingFang,Li Linli,Gao Xueping,Jiang Jinghan 한국유전학회 2024 Genes & Genomics Vol.46 No.3

Objective CHARGE syndrome is a rare autosomal dominant (AD) multi-system disorder with a broad and variable clinical manifestation and occurs in approximately 1/10,000 newborns in the world. Mutations in the CHD7 gene are the genetic cause of over 90% of patients with typical CHARGE syndrome. The present study reported a novel variant in the CHD7 gene in a Chinese family with an abnormal fetus. Methods Routine prenatal ultrasound screening showed fetal heart abnormality and left foot varus. Chromosomal microarray analysis (CMA) and fetus-parent whole-exome sequencing (trio-WES) were performed to determine the genetic cause of the fetus. The candidate variant was further verified using Sanger sequencing. Results CMA analysis revealed normal results. However, WES analysis identified a de novo heterozygous variant of c.2919_2922del (NM_017780.4) on exon 11 of CHD7 gene, resulting in a premature truncation of the CHD7 protein (p.Gly975*). The variant was classified as Pathogenic (PVS1 + PS2_Moderate + PM2_Supporting) based on the ACMG guidelines. Combined with the clinical phenotype of fetal heart abnormalities, it was confirmed CHARGE syndrome. Conclusion We identified a novel heterozygous variant c.2919_2922del in CHD7 of a Chinese fetus with CHARGE syndrome, enriching the genotype-phenotype spectrum of CHD7. These results suggest that genetic testing could help facilitate prenatal diagnosis of CHARGE syndrome, thus promoting the appropriate genetic counseling. Objective CHARGE syndrome is a rare autosomal dominant (AD) multi-system disorder with a broad and variable clinical manifestation and occurs in approximately 1/10,000 newborns in the world. Mutations in the CHD7 gene are the genetic cause of over 90% of patients with typical CHARGE syndrome. The present study reported a novel variant in the CHD7 gene in a Chinese family with an abnormal fetus. Methods Routine prenatal ultrasound screening showed fetal heart abnormality and left foot varus. Chromosomal microarray analysis (CMA) and fetus-parent whole-exome sequencing (trio-WES) were performed to determine the genetic cause of the fetus. The candidate variant was further verified using Sanger sequencing. Results CMA analysis revealed normal results. However, WES analysis identified a de novo heterozygous variant of c.2919_2922del (NM_017780.4) on exon 11 of CHD7 gene, resulting in a premature truncation of the CHD7 protein (p.Gly975*). The variant was classified as Pathogenic (PVS1 + PS2_Moderate + PM2_Supporting) based on the ACMG guidelines. Combined with the clinical phenotype of fetal heart abnormalities, it was confirmed CHARGE syndrome. Conclusion We identified a novel heterozygous variant c.2919_2922del in CHD7 of a Chinese fetus with CHARGE syndrome, enriching the genotype-phenotype spectrum of CHD7. These results suggest that genetic testing could help facilitate prenatal diagnosis of CHARGE syndrome, thus promoting the appropriate genetic counseling.

Solving point burnup equations by Magnus method

Yun Cai,Xingjie Peng,Qing Li,Lin Du,Lingfang Yang 한국원자력학회 2019 Nuclear Engineering and Technology Vol.51 No.4

The burnup equation of nuclides is one of the most equations in nuclear reactor physics, which isgenerally coupled with transport calculations. The burnup equation describes the variation of the nuclideswith time. Because of its very stiffness and the need for large time step, this equation is solved byspecial methods, for example transmutation trajectory analysis (TTA) or the matrix exponential methodswhere the matrix exponential is approximated by CRAM. However, TTA or CRAM functions well whenthe flux is constant. In this work, a new method is proposed when the flux changes. It's an improvedmethod compared to TTA or CRAM. Furtherly, this new method is based on TTA or CRAM, and it is moreaccurate than them. The accuracy and efficiency of this method are investigated. Several cases are usedand the results show the accuracy and efficiency of this method are great.