Seismic analysis of bridges based on stress-dependent damping

Li Su,Yuanfeng Wang,Pengfei Li,Shengqi Mei,Kun Guo 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.62 No.3

Damping value has considerable influence on the dynamic and seismic behaviors of bridges. However, currently the constant damping ratios that are prescribed by most bridge seismic design codes can’t truly represent the complicated damping character of actual structures. In this paper, a cyclic loading experiment was conducted to study the effect of stress amplitude on material damping of concrete to present an analyzing model of the material damping of concrete. Furthermore, based on the fundamental damping of structure measured under ambient vibration, combined with the presented stress-dependent material damping concrete, the seismic response of a bridge pier was calculated. Comparison between the calculated and experiment results verified the validity of the presented damping model. Finally, a modified design and analysis method for bridge was proposed based on stress-dependent damping theory, and a continuous rigid frame bridge was selected as the example to calculate the actual damping values and the dynamic response of the bridge under different earthquake intensities. The calculation results indicated that using the constant damping given by the Chinese seismic design code of bridges would overestimate the energy dissipation capacity of the bridge.

Experimental study and modelling of CFRP-confined damaged and undamaged square RC columns under cyclic loading

Li Su,Xiaoran Li,Yuanfeng Wang 국제구조공학회 2016 Steel and Composite Structures, An International J Vol.21 No.2

While the cyclic behaviour of fiber-reinforced polymer (FRP)-confined columns is studied rather extensively, the cyclic response especially the energy dissipation of FRP-confined damaged and undamaged square RC columns is not yet fully understood. In this paper, an experimental and numerical investigation was conducted to study the cyclic behavior of two different types of Carbon FRP (CFRP)-confined square RC columns: strengthened and repaired. The main variables investigated are initial damage, confinement of CFRP, longitudinal steel reinforcement ratio. The experimental results show that lower initial damage, added confinement with CFRP and longitudinal reinforcement enhance the ductility, energy dissipation capacity and strength of the columns, decrease the stiffness and strength degradation rates of all CFRP-confined square RC columns. Two hysteretic constitutive models were developed for confined damaged and undamaged concrete and cast into the non-linear beam-column fiber-based models in the software Open System for Earthquake Engineering Simulation (OpenSees) to analyze the cyclic behavior of CFRP-confined damaged and undamaged columns. The results of the numerical models are in good agreement with the experiments.

Influence of some key factors on material damping of steel beams

Yuanfeng Wang,Yuhua Pan,Jie Wen,Li Su,Shengqi Mei 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.49 No.3

Material damping affects the dynamic behaviors of engineering structures considerably, but up to till now little research is maintained on influence factors of material damping. Based on the damping-stress function of steel, the material damping of steel beams is obtained by calculating the stress distribution of the beams with an analytical method. Some key influence factors of the material damping, such as boundary condition, amplitude and frequency of excitation, load position as well as the cross-sectional dimension of a steel beam are analyzed respectively. The calculated results show that even in elastic scope, material damping does not remain constant but varies with these influence factors. Although boundary condition affects material damping to some extent, such influence can be neglected when the maximum stress amplitude of the beam is less than the fatigue limit of steel. Exciting frequency, load position and cross-section dimension have great effects on the material damping of the beam which maintain the similar changing trend under different boundary conditions respectively.

3D-monoclinic M–BTC MOF (M = Mn, Co, Ni) as highly efficient catalysts for chemical fixation of CO2 into cyclic carbonates

Yuanfeng Wu,Xianghai Song,Shuai Li,Jiahui Zhang,Xinghui Yang,Pengxin Shen,Lijing Gao,Ruiping Wei,Jin Zhang,Guomin Xiao 한국공업화학회 2018 Journal of Industrial and Engineering Chemistry Vol.58 No.-

[(CH3)2NH2][M3(BTC)(HCOO)4(H2O)].H2O (M–BTC, M = Mn, Ni, Co) were prepared under hydrothermal conditions and used as highly efficient catalysts for cycloaddition of CO2 with epichlorohydrin (ECH). The microstructure and physicochemical properties of the compounds were determined by PXRD, FT-IR, XPS, N2-adsorption, TG–DSC, NH3–TPD and CO2–TPD. 98.01% conversion of ECH and 96.05% selectivity to chloropropene carbonate was obtained over the Mn–BTC under the optimized reaction conditions (105 °C, 3.0 MPa, 9 h, 1.5 wt.% of ECH). Besides, the recyclability result exhibited the Mn–BTC compound can be utilized as least three times with a slight reduction in its catalytic ability. In addition, cycloaddition of CO2 with other epoxides and DFT calculation were also performed. The result exhibited the yield followed the order: ECH > 1, 2-epoxybutane > propene oxide > Allyl glycidyl ether, which was mainly determined by the energy of reaction.

Abnormal Behavior Recognition Based on Spatio-temporal Context

Yuanfeng Yang,Lin Li,Zhaobin Liu,Gang Liu 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.3

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes whereanomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects’behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial contextof local behavior and the temporal context of global behavior in two different stages. In the first stage of topicmodeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporalcorrelations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation(LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each videoclip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the secondphase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular,an abnormal behavior recognition method was developed based on the learned spatio-temporal context ofbehaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomalyrecognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performedusing the validity of spatio-temporal context learning for local behavior topics and abnormal behaviorrecognition. Furthermore, the performance of the proposed approach in abnormal behavior recognitionimproved effectively and significantly in complex surveillance scenes.

Influence of some key factors on material damping of steel beams

Wang, Yuanfeng,Pan, Yuhua,Wen, Jie,Su, Li,Mei, Shengqi Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.49 No.3

Material damping affects the dynamic behaviors of engineering structures considerably, but up to till now little research is maintained on influence factors of material damping. Based on the damping-stress function of steel, the material damping of steel beams is obtained by calculating the stress distribution of the beams with an analytical method. Some key influence factors of the material damping, such as boundary condition, amplitude and frequency of excitation, load position as well as the cross-sectional dimension of a steel beam are analyzed respectively. The calculated results show that even in elastic scope, material damping does not remain constant but varies with these influence factors. Although boundary condition affects material damping to some extent, such influence can be neglected when the maximum stress amplitude of the beam is less than the fatigue limit of steel. Exciting frequency, load position and cross-section dimension have great effects on the material damping of the beam which maintain the similar changing trend under different boundary conditions respectively.

Effect of Bulk-Accumulation on Switching Speed of Dual-Gate a-IGZO TFT-Based Circuits

Xiuling Li,Di Geng,Mativenga, Mallory,Yuanfeng Chen,Jin Jang IEEE 2014 IEEE electron device letters Vol.35 No.12

<P>The effect of bulk accumulation on switching speed of dual-gate amorphous indium-gallium-zinc-oxide thin-film transistor (TFT)-based circuits is investigated. Given that bulk accumulation is achieved by synchronized driving of the top gate (TG) and bottom gate, it can be modulated by varying the length of the TG (L<SUB>TG</SUB>) for fixed source-drain distance (L). It is confirmed from fabricated ring oscillators that switching speed increases with increasing L<SUB>TG</SUB> for fixed L, verifying that bulk accumulation improves switching speed. However, switching speed drops dramatically when the TG overlaps the source/drain electrodes due to additional parasitic capacitance. TFT-circuits with the longest, but nonoverlapping TGs are demonstrated to exhibit the fastest switching speed; operation frequency exceeding 2.63 MHz for input voltage V<SUB>DD</SUB> of 20 V, which is also the fastest among all inverted staggered amorphous-oxide-semiconductor TFT-based circuits.</P>

A Multi-objective Two-sided Disassembly Line Balancing Optimization Based on Artificial Bee Colony Algorithm: A Case Study of an Automotive Engine

Lei Zhang,Yuanfeng Wu,Xikun Zhao,Shiwen Pan,Ziqi Li,Hong Bao,Yongtin Tian 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.5

Disassembly is an important part of green manufacturing and remanufacturing. The disassembly line is an optimum form for mass and automatic disassembly in the industry. To optimize the disassembly system and the use of resources, the disassembly line balancing problem (DLBP) has attracted much attention. Compared with the conventional one-sided straight disassembly line, the two-sided disassembly line can use both the left and right sides of a conveyor belt for disassembly operation, thereby improving production efficiency, especially for large-sized and complicated products. On the other hand, due to the constraints and precedence among parts, it is a challenge to plan the disassembly scheme for a two-sided disassembly line. In this paper, a model is established to solve a two-sided disassembly line balancing problem (TDLBP). First, a hybrid graph is utilized to express constraints and precedence relationships, and a novel encoding and decoding method is developed for the disassembly scheme planning of a two-sided line for handling the challenge caused by constraints and precedence among parts. Then, a multi-objective TDLBP optimization model is proposed including the number of matedworkstations, idle time, smoothness index, the auxiliary indicator, and a meta-heuristic based on an artificial bee colony (ABC) algorithm is designed to solve TDLBP. Finally, the proposed model and method are applied to an automotive engine case, and the results are compared with NSGA-II, hybrid artificial bee colony algorithm (HABC), and multi-objective flower pollination algorithm (MOFPA) to verify the practicality of the proposed model in solving the TDLBP.

Energy Management Strategy for Hybrid Energy Storage System based on Model Predictive Control

Shen Yongpeng,Li Yuanfeng,Liu Dongqi,Wang Yanfeng,Sun Jianbin,Sun Songnan 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4

Electric vehicle (EV) is developed because of its environmental friendliness, energy-saving and high efficiency. For improving the performance of the energy storage system of EV, this paper proposes an energy management strategy (EMS) based model predictive control (MPC) for the battery/supercapacitor hybrid energy storage system (HESS), which takes stabilizing the DC bus voltage and improving the efficiency of the system as two major optimization goals. In addition, an enumeration algorithm is presented to solve the optimization function. The experimental results show the performance of the proposed EMS which is able to enhance the overall instantaneous power and prevent the battery from overloading. Meanwhile, compared with the results of a single battery storage system, the maximum amplitude of the battery current in the HESS is reduced by 40.81% and whole system energy loss is reduced by 24.13% with the proposed power management strategy.

An active learning method with difficulty learning mechanism for crack detection

Zhicheng Zhang,Jiangpeng Shu,Jun Li,Jiawei Zhang,Weijian Zhao,Yuanfeng Duan 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is asignificant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320 × 320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.