Numerical Simulation of Fatigue Behavior for Cable-stayed Orthotropic Steel Deck Bridges using Mixed-dimensional Coupling Method

Fei Yan,Zhibin Lin,Ying Huang 대한토목학회 2017 KSCE Journal of Civil Engineering Vol.21 No.6

Effective methods and technologies in fatigue behavior and assessment for cable-stayed orthotropic steel deck bridges are critical to ensure their safety and serviceability. In this study, a mixed-dimensional finite element coupling method is used for structural fatigue assessment. A general framework of the Mix-dimensional Coupling (MDC) method is constructed on the basis of a compromise between simplicity and efficiency as compared to conventional sub-modeling or substructure method. Fatigue details and performance at welded joints have been investigated through the MDC method. Efficiency of the MDC method is demonstrated by a comparison with the simplified Bridge-deck-system (BDS) method. Besides the benefits of the saving time, the numerical simulation also indicated that the MDC method can effectively capture the global behavior for better fatigue prediction, that be ignored in the conventional BDS method as expected. Findings suggest that the MDC method is a cost-effective alternative for fatigue behavior and fatigue assessment of large-span orthotropic steel deck bridges.

Shear Behavior of Single Cast-in Anchors in Plastic Hinge Zones

Derek Petersen,Zhibin Lin,Jian Zhao 한국콘크리트학회 2018 International Journal of Concrete Structures and M Vol.12 No.3

This paper presents two shear tests of 3/4-in. diameter cast-in anchors embedded in the plastic hinge zone of reinforced concrete columns. Design codes, such as ACI 318-14, require special reinforcement for concrete anchors in concrete that could be substantially damaged during an earthquake. The test anchors in this study were equipped with the anchor reinforcement recommended and verified in the literature. The column specimens were subjected to quasi-static cyclic loading before the test anchors were loaded in shear. Steel fracture was achieved in both test anchors despite cracks and concrete spalling occurred to the concrete within the plastic hinge zones. Meanwhile, the measured anchor capacities were smaller than the code-specified capacity, especially for the anchors subjected to cyclic shear. Concrete cover spalling was found critical to the observed capacity reduction, which caused combined bending and shear action in the anchor bolts. Measures should be developed to mitigate such adverse impact. In addition, further studies are needed for post-installed anchors before practical applications.

Data-Driven Support Vector Machine with Optimization Techniques for Structural Health Monitoring and Damage Detection

Guoqing Gui,Hong Pan,Zhibin Lin,Yonghua Li,Zhijun Yuan 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.2

Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.

Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties

Hong Pan,Guoqing Gui,Zhibin Lin,Changhui Yan 대한토목학회 2018 KSCE Journal of Civil Engineering Vol.22 No.3

Structural systems are often exposed to harsh environment, while these environmental factors in turn could degrade the system over time. Their health state and structural conditions are key for structural safety control and decision-making management. Although great efforts have been paid on this field, the high level of variability due to noise and other interferences, and the uncertainties associated with data collection, structural performance and in-service operational environments post great challenges in finding information to assist decision making. The machine learning techniques in recent years have been gaining increasing attentions due to their merits capturing information from statistical representation of events and thus enabling making decision. In this study, the deep Bayesian Belief Network Learning (DBBN) was used to extract structural information and probabilistically determine structural conditions. Different to conventional shallow learning that highly relies on the quality of the hand-crafted features, the deep learning is an end-to-end method to encode the information and interpret vast amount of data with minimizing or no features. A case study was conducted to address the methods for structure under viabilities and uncertainties due to operation, damage and noise interferences. Numerical results revealed that the deep learning exhibits considerably enhanced accuracy for structural diagnostics, as compared to the supervised shallow learning. With predetermined training set, the DBBN could accurately determine the structural health state in terms of damage level, which could dramatically help decision making for further structural retrofit or not. Note that the noise interference could contaminate the data representation and in turn increase the difficulty of the data mining, though the deep learning could reduce the impacts, as compared to conventional shallow learning techniques.

Ultimate strength behavior of steel plate concrete composite slabs:An experimental and theoretical study

Lili Wu,Hui Wang,Zhibin Lin 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.37 No.6

Steel plate-concrete composite slabs provide attractive features, such as more effective loading transfer, and more cost-effective stay-in-place forms, thereby enabling engineers to design more high-performance light structures. Although significant studies in the literatures have been directed toward designing and implementing the steel plate-concrete composite beams, there are limited data available for understanding of the composite slabs. To fill this gap, nine the composite slabs with different variables in this study were tested to unveil the impacts of the critical factors on the ultimate strength behavior. The key information of the findings included sample failure modes, crack pattern, and ultimate strength behavior of the composite slabs under either four-point or three-point loading. Test results showed that the failure modes varied from delamination to shear failures under different design factors. Particularly, the shear stud spacing and thicknesses of the concrete slabs significantly affected their ultimate load-carrying capacities. Moreover, an analytical model of the composite slabs was derived for determining their ultimate load-carrying capacity and was well verified by the experimental data. Further extensive parametric study using the proposed analytical methods was conducted for a more comprehensive investigation of those critical factors in their performance. These findings are expected to help engineers to better understand the structural behavior of the steel plate-concrete composite slabs and to ensure reliability of design and performance throughout their service life.

Characterization of phytochemical profile of rhizome of artificial cultured Polygonatum sibiricum with multiple rhizome buds

Cheng Weiqing,Pan Zhibin,Zheng Hanjing,Luo Gelian,Liu Zhibin,Xu Suli,Lin Junhan 한국응용생명화학회 2023 Applied Biological Chemistry (Appl Biol Chem) Vol.66 No.-

Rhizome of Polygonatum sibiricum is both a renowned traditional Chinese remedy and a commonly consumed delicacy. Due to the escalating demand and excessive overexploitation, there has been a growing interest in the artificial cultivation of this plant in recent years. To assess the therapeutic benefits of artificially cultivated P. sibiricum, it is crucial to identify and classify its phytochemical components, which are the primary bioactive compounds found in its rhizome. In this study, the phytochemical profile of an artificially cultivated P. sibiricum rhizomes with multiple rhizome buds (ACM) was characterized by using untargeted UHPLC-Q-Orbitrap-MS based approach. In addition, two-wild-types P. sibiricum rhizomes, namely the wild-type with multiple rhizome buds (WTM) and the wild-type with single rhizome bud (WTS), were used for comparison. A total of 183 phytochemicals, including 20 alkaloids, 48 flavonoids, 33 phenolic acids, and 82 terpenoids, were tentatively identified. Generally, the phytochemical profile of ACM was comparable to that of WTM and WTS. In specific, most of the identified alkaloids and phenolic acids, and approximately half of the identified terpenoids, were not significantly different. Notably, several phytochemicals with potent therapeutic properties, such as epiberberine, laetanine, sinapic acid, curcumenol, were present in ACM. Additionally, artificial cultivation increased the abundance of geniposide and naringenin, which have been linked to cardioprotective effects. These findings provide valuable insights for the future utilization of artificially cultivated P. sibiricum.

Analytical and Numerical Investigation of Overstrength Factors for Very Short Shear Links in EBFs

Shujun Hu,Jingang Xiong,Qiang Zhou,Zhibin Lin 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.11

Shear links are key components in the Eccentrically Braced Frames (EBFs) that act as a structural fuse by dissipating seismic energy during severe earthquakes. Specification AISC 341, which is frequently used in the seismic design of steel structures, prescribes a constant overstrength factor of 1.50 for shear links. However, a few existing experimental results indicated that the overstrength of very short shear links with length ratio lower than 1.0 are much greater than required. In this paper, five basic factors influencing the overstrength of very short shear links are summarized as follows: web-ultimate-to-yield-shear-strength ratio, lengthto- stiffener-spacing ratio, flange-to-web-area ratio, flange-to-web-strength ratio, length-to-depth ratio. A numerical investigation with a detailed Finite Element (FE) model, verified by a comparison with existing experimental results, is conducted to investigate the combined effects of these five basic factors on the overstrength of very short shear links. Then, a new numerical model for predicting the overstrength value is proposed based on the FE analysis results and existing available experimental data by using the numerical fitting method, and it shows good agreement.

A vibration-based approach for detecting arch dam damage using RBF neural networks and Jaya algorithms

Zahoor Hussain,Ali Zar,Muhammad Akbar,Bassam A. Tayeh,Zhibin Lin 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.5

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBFNN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

Increased Expression of miR-146a in Children With Allergic Rhinitis After Allergen-Specific Immunotherapy

Xi Luo,Haiyu Hong,Jun Tang,Xingmei Wu,Zhibin Lin,Renqiang Ma,Yunping Fan,Geng Xu,Dabo Liu,Huabin Li 대한천식알레르기학회 2016 Allergy, Asthma & Immunology Research Vol.8 No.2

Purpose: MicroRNAs (miRs) were recently recognized to be important for immune cell differentiation and immune regulation. However, whether miRs were involved in allergen-specific immunotherapy (SIT) remains largely unknown. This study sought to examine changes in miR-146a and T regulatory cells in children with persistent allergic rhinitis (AR) after 3 months of subcutaneous immunotherapy (SCIT) and sublingual immunotherapy (SLIT). Methods: Twenty-four HDM-sensitized children with persistent AR were enrolled and treated with SCIT (n=13) or SLIT (n=11) for 3 months. Relative miR-146a and Foxp3 mRNA expression, the TRAF6 protein level, and the ratio of post-treatment to baseline IL-10+CD4+ T cells between the SCIT and SLIT groups were examined in the peripheral blood mononuclear cells (PBMCs) of AR patients using quantitative reverse transcription polymerase chain reaction (qRT-PCR), flow cytometry, and Western blot analysis, respectively. Serum levels of IL-5 and IL-10 were determined using ELISA. Results: After 3 months of SIT, both the TNSS and INSS scores were significantly decreased compared to the baseline value (P<0.01). The relative expression of miR-146a and Foxp3 mRNA was significantly increased after both SCIT and SLIT (P<0.01). The ratio of post-treatment to baseline IL-10+CD4+ T cells and the serum IL-10 level were significantly increased in both the SCIT and SLIT groups (P<0.01), whereas the TRAF6 protein level and serum IL-5 level were significantly decreased (P<0.01). No significant differences in these biomarkers were observed between the SCIT and SLIT groups. Conclusions: Our findings suggest that miR-146a and its related biomarkers may be comparably modulated after both SCIT and SLIT, highlighting miR-146a as a potential therapeutic target for the improved management of AR.

Yellow pigment from gardenia fruit: structural identification and evaluation of cytotoxic activity in HepG2 cells by induction of apoptosis

Liqin Tang,Haocheng Liu,Manqin Fu,Yujuan Xu,Jing Wen,Jijun Wu,Yuanshan Yu,Xian Lin,Lu Li,Zhibin Bu,Wanyuan Yang 한국식품과학회 2022 Food Science and Biotechnology Vol.31 No.11

The preparation process of yellow pigment (YP) from gardenia (Gardenia jasminoides) fruit was investigated, and the main components of YP were characterized by liquid chromatography-time of flight-mass spectrometer/mass spectrometer (LC-TOF–MS/MS). Furthermore, cytotoxic activity in HepG2 cells by induction of apoptosis was also evaluated. The preparation results indicated that the color value of YP was 498.34, which was 8.6 times higher than crude YP. Fifteen compounds in YP were identified, and crocins were the predominant compounds. The cell experiment results showed that YP inhibited the proliferation of HepG2 cells in a time- and dose-dependent manner. Moreover, YP also inhibited HepG2 cells in G2/M stage, increased the level of intracellular reactive oxygen species (ROS), and enhanced cell apoptosis. Real-time quantitative polymerase chain reaction (RT-PCR) analysis revealed the up-regulation of caspase-3, 8, 9, and bax and down-regulation of bcl-2 in HepG2 cells. Overall, these findings suggested that YP had potential cytotoxic activity in HepG2 cells by induction of apoptosis, which might be beneficial to human health.