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- 저자 : Lingfang Xia (검색결과 4 건)
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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.
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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.
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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.
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Zheng Feng,Hao Wen,Zhaoxia Jiang,Shuai Liu,Xingzhu Ju,Xiaojun Chen,Lingfang Xia,Junyan Xu,Rui Bi,Xiaohua Wu 대한부인종양학회 2018 Journal of Gynecologic Oncology Vol.29 No.5
Objective: To present the surgical outcomes of advanced epithelial ovarian cancer (AEOC) since the implementation of a personalized approach and to validate multiple predictive models for R0 resection. Methods: Personalized strategies included: 1) Non-invasive model: preoperative clinico-radiological assessment according to Suidan criteria with a predictive score for all individuals. Patients with a score 0–2 were recommended for primary debulking surgery (PDS, group A), or otherwise were counseled on the choices of PDS, neoadjuvant chemotherapy (NAC, group B) or staging laparoscopy (S-LPS). 2) Minimally invasive model: S-LPS with a predictive index value (PIV) according to Fagotti. Individuals with a PIV <8 underwent PDS (group C) or otherwise received NAC (group D). Intraoperative assessment (with Eisenkop, peritoneal cancer index [PCI], and Aletti scores) and surgical results were prospectively collected. Results: Between September 2015 and August 2017, 161 pathologically confirmed epithelial ovarian cancer patients were included. A total of 52 (32.3%) patients had a predictive score of 0–2, and 109 (67.7%) patients had a score ≥3. Among these individuals, 41 (25.5%) patients received S-LPS. Finally, 110 (68.3%) patients underwent PDS (A+C), and 51 (31.7%) patients received NAC (B+D). The R0 resection rates in PDS and NAC patients were 56.4% and 60.8%, respectively. The area under the curve (AUC) of Suidan criteria was 0.548 for group (A+C). The AUC of Fagotti score was 0.702 for group C. The AUC of Eisenkop, PCI, and Aletti scores were 0.808, 0.797, and 0.524, respectively. Conclusion: The Suidan criteria were not effective in these AEOC patients. S-LPS was helpful in decision-making for PDS and should be endorsed in the future.
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