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- 저자 : Yanliang Du (검색결과 3 건)
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Vibration analysis of mountain tunnel lining built with forepoling method
Gao, Yang,Jiang, Yujing,Du, Yanliang,Zhang, Qian,Xu, Fei Techno-Press 2018 Smart Structures and Systems, An International Jou Vol.21 No.5
Nowadays, many tunnels have been commissioned for several decades, which require effective inspection methods to assess their health conditions. The ambient vibration test has been widely adopted for the damage identification of concrete structures. In this study, the vibration characters of tunnel lining shells built with forepoling method was analyzed based on the analytical solutions of the Donnell-Mushtari shell theory. The broken rock, foreploing, rock-concrete contacts between rock mass and concrete lining, was represented by elastic boundaries with normal and shear stiffness. The stiffness of weak contacts has significant effects on the natural frequency of tunnel lining. Numerical simulations were also carried out to compare with the results of the analytical methods, showing that even though the low nature frequency is difficult to distinguish, the presented approach is convenient, effective and accurate to estimate the natural frequency of tunnel linings. Influences of the void, the lining thickness and the concrete type on natural frequencies were evaluated.
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Aerodynamic performance evaluation of different cable-stayed bridges with composite decks
Rui Zhou,Yaojun Ge,Yongxin Yang,Yanliang Du,Lihai Zhang 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.34 No.5
The aerodynamic performance of long-span cable-stayed bridges is much dependent on its geometrical configuration and countermeasure strategies. In present study, the aerodynamic performance of three composite cable-stayed bridges with different tower configurations and passive aerodynamic countermeasure strategies is systematically investigated by conducting a series of wind tunnel tests in conjunction with theoretical analysis. The structural characteristics of three composite bridges were firstly introduced, and then their stationary aerodynamic performance and wind-vibration performance (i.e., flutter performance, VIV performance and buffeting responses) were analyzed, respectively. The results show that the bridge with three symmetric towers (i.e., Bridge I) has the lowest natural frequencies among the three bridges, while the bridge with two symmetric towers (i.e., Bridge II) has the highest natural frequencies. Furthermore, the Bridge II has better stationary aerodynamic performance compared to two other bridges due to its relatively large drag force and lift moment coefficients, and the improvement in stationary aerodynamic performance resulting from the application of different countermeasures is limited. In contrast, it demonstrates that the application of both downward vertical central stabilizers (UDVCS) and horizontal guide plates (HGP) could potentially significantly improve the flutter and vortex-induced vibration (VIV) performance of the bridge with two asymmetric towers (i.e., Bridge III), while the combination of vertical interquartile stabilizers (VIS) and airflowdepressing boards (ADB) has the capacity of improving the VIV performance of Bridge II.
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Rui Zhou,Hanlin Liu,Wenhao Yuan,Yanliang Du,Jingmang Xu,Rong Chen 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.2
The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.
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