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RISS 인기검색어
- 검색키워드
- 저자 : Chunfeng Xie (검색결과 3 건)
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Chunfeng Wan,Guangcai Zhang,Liyu Xie,Songtao Xue 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.3
The absence of excitation measurements may pose a big challenge in the application of structural damage identification owing to the fact that substantial effort is needed to reconstruct or identify unknown input force. To address this issue, in this paper, an iterative strategy, a synergy of Tikhonov regularization method for force identification and modified Jaya algorithm (M-Jaya) for stiffness parameter identification, is developed for damage identification with partial output-only responses. On the one hand, the probabilistic clustering learning technique and nonlinear updating equation are introduced to improve the performance of standard Jaya algorithm. On the other hand, to deal with the difficulty of selection the appropriate regularization parameters in traditional Tikhonov regularization, an improved L-curve method based on B-spline interpolation function is presented. The applicability and effectiveness of the iterative strategy for simultaneous identification of structural damages and unknown input excitation is validated by numerical simulation on a 21-bar truss structure subjected to ambient excitation under noise free and contaminated measurements cases, as well as a series of experimental tests on a five-floor steel frame structure excited by sinusoidal force. The results from these numerical and experimental studies demonstrate that the proposed identification strategy can accurately and effectively identify damage locations and extents without the requirement of force measurements. The proposed M-Jaya algorithm provides more satisfactory performance than genetic algorithm, Gaussian bare-bones artificial bee colony and Jaya algorithm.
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Operational performance evaluation of bridges using autoencoder neural network and clustering
Chunfeng Wan,Songtao Xue,Huachen Jiang,Liyu Xie,Da Fang,Shuai Gao,Kang Yang,YouLiang Ding 국제구조공학회 2024 Smart Structures and Systems, An International Jou Vol.33 No.3
To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicleinduced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.
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Haiyun Chen,Jialong Liang,Xin Gu,Jiawen Zhou,Chunfeng Xie,Xianhui Lv,Rong Wang,Qing Liu,Zhiyuan Mao,Haijian Sun,Guoping Zuo,Dengshun Miao,Jianliang Jin 생화학분자생물학회 2020 Experimental and molecular medicine Vol.52 No.-
To study whether TGF-β1/IL-11/MEK/ERK (TIME) signaling mediates senescence-associated pulmonary fibrosis (SAPF) in Bmi-1-deficient (Bmi-1−/−) mice and determines the major downstream mediator of Bmi-1 and crosstalk between p16INK4a and reactive oxygen species that regulates SAPF, phenotypes were compared among 7-week-old p16INK4a and Bmi-1 double-knockout, N-acetylcysteine (NAC)-treated Bmi-1−/−, Bmi-1−/−, and wild-type mice. Pulmonary fibroblasts and alveolar type II epithelial (AT2) cells were used for experiments. Human pulmonary tissues were tested for type Ι collagen, α-smooth muscle actin (α-SMA), p16INK4a, p53, p21, and TIME signaling by using enzyme-linked immunosorbent assay (ELISA). Our results demonstrated that Bmi-1 deficiency resulted in a shortened lifespan, ventilatory resistance, poor ventilatory compliance, and SAPF, including cell senescence, DNA damage, a senescence-associated secretory phenotype and collagen overdeposition that was mediated by the upregulation of TIME signaling. The signaling stimulated cell senescence, senescence-related secretion of TGF-β1 and IL-11 and production of collagen 1 by pulmonary fibroblasts and the epithelial-to-mesenchymal transition of AT2 cells. These processes were inhibited by anti-IL-11 or the MEK inhibitor PD98059. NAC treatment prolonged the lifespan and ameliorated pulmonary dysfunction and SAPF by downregulating TIME signaling more than p16INK4a deletion by inhibiting oxidative stress and DNA damage and promoting ubiquitinproteasome degradation of p16INK4a and p53. Cytoplasmic p16INK4a accumulation upregulated MEK/ERK signaling by inhibiting the translocation of pERK1/2 (Thr202/Tyr204) from the cytoplasm to the nucleus in senescent fibroblasts. The accumulation of collagen 1 and α-SMA in human lungs accompanied by cell senescence may be mediated by TIME signaling. Thus, this signaling in aging fibroblasts or AT2 cells could be a therapeutic target for preventing SAPF.
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