Abstract
A novel multi-mode eddy current tuned mass damper (TMD) is proposed and designed to effectively control windinduced vibrations of substation lightning rod structures. The TMD eliminates the need for springs and utilizes the resonant characteristics of a cantilever beam and a single pendulum, enhancing spatial efficiency. Rigorous validation through on-site measurements confirms the vibration reduction performance of the TMD. The modal damping ratios of the top ten modes for steel pipe structure of the substation lightning rod generally remain below 3‰. The multi-mode TMD significantly reduces the root-mean-square (RMS) acceleration response of the original lightning rod structure by 50%, and increases the modal damping ratio of each target mode by 5‰ on average. The additional modal damping of the fourth mode reaches 1%. The state space equation of the TMD-structure model is established, and the TMD design steps considering the additional mass of the device are proposed based on the complex mode analysis. The optimization of the TMD parameters (mass ratio, frequency ratio and damping ratio) is performed to minimize the response of the structure under wind excitation. With the increase of mass ratio, mode order and structure damping ratio, the optimal frequency ratio decreases, but the optimal damping ratio increases. It is necessary to set the corresponding optimal frequency ratio and optimal damping ratio by global optimization to ensure the optimal damping effect of TMD.
Key Words
field measurement; lightning rod; modal damping ratio; TMD; vibration control
Address
(1) Junchen Ye, Huawei Niu, Jinlin Chen, Zhengqing Chen:
State Key Laboratory of Bridge Engineering Safety and Resilience, Changsha, Hunan, 410082, China;
(2) Junchen Ye, Huawei Niu, Jinlin Chen, Zhengqing Chen:
Key Laboratory for Bridge and Wind Engineering of Hunan Province, Hunan University, Changsha 410082, China;
(3) Fengli Yang, Guo Huang:
China Electric Power Research Institute, Beijing, 100055, China;
(4) Guowen Ran, Linwei Ding, Hong Tang, Xi Zhang:
Central China Branch of Guangdong Energy Group Guizhou Co., Ltd, Changsha, Hunan, 410000, China.
Abstract
The iterative hybrid testing (IHT) method, as one novel kinds of real-time hybrid testing (RTHT), provides a new technical support for disclosing the seismic performance of large-scale complex engineering structures. However, the IHT method adopts the methodology of direct whole time-history data exchange between the physical substructure (PS) and the numerical substructure (NS) based on the measured reaction forces of the PS, which results in the problems of slow iteration convergence speed and poor accuracy and stability. For solving these problems, one novel offline iterative hybrid testing method based on model identification and correction (IHT-MIC) is proposed in this paper. In the proposed IHT-MIC, the equivalent Maxwell model is used for precisely modelling the PS by parameter identification, and based on which the reaction force of the PS is corrected to improve the iteration convergence speed, accuracy, and stability. Firstly, the principle of the IHT-MIC is proposed. Furthermore, the numerical simulations and experimental tests are presented for validating the effectiveness and accuracy of the proposed method. It is shown from the numerical and experimental results that the least square method can accurately identify the parameters of Maxwell model, and the Maxwell model can effectively correct the reaction forces of the PS, which indicates that the accuracy of the IHT-MIC is greatly improved. Furthermore, compared with the traditional IHT, the IHT-MIC significantly improves the iteration convergence speed, reduces the oscillation amplitude during the iteration process. The proposed method may have broad application prospects in the fields of engineering structures with velocity-dependent energy dissipators.
Key Words
force correction; hybrid testing; maxwell model; model identification; offline iterative; viscous damper
Address
(1) Tao Wang, Huan Zheng, Liyan Meng:
School of Architecture and Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China;
(2) Guoshan Xu:
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China;
(3) Tao Wang, Guoshan Xu:
Key Lab of Structures Dynamic Behavior and Control, Ministry of Education, Harbin Institute of Technology, Harbin 150090, China;
(4) Guoshan Xu:
Key Lab of Intelligent Disaster Mitigation, Ministry of Industry and Information Technology, Harbin 150090, China;
(5) Zhen Wang:
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China;
(6) Zhen Wang:
Hainan Institute of Wuhan University of Technology, Sanya 572000, China.
Abstract
Cement-based sensors are exposed to continuous dynamic loading and/or damage, degrading their sensing stability, nevertheless, predictions of long-term sensing stability have rarely been reported. Therefore, this study presents a deep-learning analysis combining experimental data and a LSTM model to predict the stability of long-term piezoresistivity. Related experiments are conducted, and the test results are used as training data. The simulations indicate that the parameters of the LSTM model have a notable effect on the predicted long-term piezoresistive sensing performances of the composites. By comparing the predictions with the experimental results, the validity of the proposed deep-learning approach is evaluated, and the following conclusions can be drawn from this study.
Key Words
AI & carbon nanotube; deep-learning; long-term cyclic loading; piezoresistive sensors; prediction and long short-term memory; recurrent neural network
Address
(1) ZY Chen, Yahui Meng:
School of Science, Guangdong University of Petrochem Technol, Maoming, Guangdong, China;
(2) Huakun Wu:
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China;
(3) Timothy Chen:
Division of Engineering and Applied Science, Caltech, CA 91125, USA.
Abstract
This research introduces an innovative method for targetless displacement measurement of reinforced soil retaining walls, employing an optimal AI deep learning network in conjunction with advanced smart monitoring technologies. Conventional displacement measurement techniques often rely on physical targets, which can introduce inaccuracies and complicate real-time internet big data collection. Our approach eliminates the need for these targets by utilizing a AI deep learning framework that processes high-dimensional sensor data to accurately detect and quantify displacements by digital platform. By optimizing the AI deep learning network architecture, we enhance the model's ability to learn complex patterns associated with soil-structure interactions with AI knowledge management. Field experiments validate the efficacy of our method, demonstrating significant improvements in measurement precision and responsiveness. The findings indicate that this targetless technique not only streamlines the monitoring process but also provides critical insights into the dynamic behavior of AI based field surveys under varying environmental and load conditions. This advancement has substantial implications for the design, safety, and maintenance based on geotechnical infrastructures.
Key Words
AI knowledge management; computer-aided internet big data simulation; convolutional neural networks; deep learning neural network; digital image processing; image matching; remote sensing and monitoring; vision technology
Address
(1) Ying-Chiang Cho:
School of Physics and Information Engineering, Minnan Normal University, Fujan, China;
(2) C.C. Hung:
School of Big Data, Fuzhou University of International Studies and Trade, Fujan, China.
Abstract
It is crucial to detect the local damages on steel strands and cables to ensure the safety of a cable-supported structure. A magnetostrictive (MS) guided wave-based method for local wire breakage condition assessment is presented in this study. Traditional guided wave-based structure health monitoring methods, which rely on damage-reflected wave packets, often struggle with the challenge of significant wave energy attenuation over long propagation distances. To overcome this problem, a damage detector comprising a pair of MS transducers was developed. The detector is designed to move along the steel strand, obtaining the transmitted wave energy at various locations. An outlier analysis was conducted by taking the transmitted wave energy as a feature sensitive to damage. The threshold value for the transmitted wave energy was determined for wire breakage detection and localization, and the wave energy transmission coefficient was employed for local damage severity estimation. Numerical and experimental studies were carried out on a seven-wire steel strand with a single wire breakage case. The results demonstrate that the damage location and local damage severity can be estimated accurately using the proposed method.
Key Words
outlier analysis; roving magnetostrictive guided wave detector; steel strand; wave energy method; wire breakage
Address
(1) Xiaodong Sui, Yuanfeng Duan, Yaozhi Luo, Chungbang Yun:
College of Civil Engineering and Architecture, Zhejiang University, China;
(2) Xiaodong Sui, Ru Zhang:
Department of Civil Engineering, Hangzhou City University, China;
(3) Xiaodong Sui, Ru Zhang:
Key Laboratory of Safe Construction and Intelligent Maintenance for Urban Shield Tunnels of Zhejiang Province, Hangzhou City University, China.
