Techno Press
Tp_Editing System.E (TES.E)
Login Search
You logged in as

sss
 
CONTENTS
Volume 33, Number 6, June 2024
 

Abstract
To improve the accuracy of time-frequency analysis (TFA) and instantaneous frequency (IF) extraction of structural dynamic response signals, this paper improves the spline-kernelled chirplet transform, and a new form of modified spline-kernelled chirplet transform (MSCT) based on revised Gaussian window function and energy concentration principle is put forward. The effectiveness of the proposed method is verified by numerical examples of single-component signal, multi-component signal, single-degree-of-freedom Duffing nonlinear system and two-layer shear frame structure model. Then, a time-varying cable test is designed to collect the acceleration response signals under linear changing tension, and the IF extraction of these signals is performed by using MSCT, which further verifies the effectiveness and accuracy of this method. Through numerical simulation and experimental verification, it is proved that the proposed method can effectively extract the IF of nonlinear structure and time-varying structure.

Key Words
instantaneous frequency (IF); modified spline-kernelled chirplet transform (MSCT); parameter optimization; time-frequency analysis (TFA); time-varying signal

Address
(1) Dong-Yan Xue, Ping-Ping Yuan, Zhou-Jie Zhao:
School of Civil Engineering and Architecture, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China;
(2) Wei-Xin Ren:
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, Guangdong, China.

Abstract
This study discusses the long-term deformation monitoring and shape sensing of bridge girder surfaces with an automated extraction scheme for point clouds in the Region Of Interest (ROI), invariant to the position of a Light Detection And Ranging system (LiDAR). Advanced smart construction necessitates continuous monitoring of the deformation and shape of bridge girders during the construction phase. An automated scheme is proposed for reconstructing geometric model of ROI in the presence of noisy non-stationary background. The proposed scheme involves (i) denoising irrelevant background point clouds using dimensions from the design model, (ii) extracting the outer boundaries of the bridge girder by transforming and processing the point cloud data in a two-dimensional image space, (iii) extracting topology of pre-defined targets using the modified Otsu method, (iv) registering the point clouds to a common reference frame or design coordinate using extracted predefined targets placed outside ROI, and (v) defining the bounding box in the point clouds using corresponding dimensional information of the bridge girder and abutments from the design model. The surface-fitted reconstructed geometric model in the ROI is superposed consistently over a long period to monitor bridge shape and derive deflection during the construction phase, which is highly correlated. The proposed scheme of combining 2D-3D with the design model overcomes the sensitivity of 3D point cloud registration to initial match, which often leads to a local extremum.

Key Words
automated ROI extraction; displacement monitoring; full-scale bridge girder; hough transform and edge extraction; LiDAR-based point clouds; long-term shape sensing

Address
(1) Ganesh Kolappan Geetha:
Department of Mechanical Engineering, Indian Institute of Technology Bhilai, India;
(2) Sahyeon Lee:
Digital Convergence Research Division, Korea Expressway Corporation Research Institute, Republic of Korea;
(3) Junhwa Lee:
Department of Civil Engineering, Pukyong National University, Republic of Korea;
(4) Sung-Han Sim:
Department of Global Smart City, Sungkyunkwan University, Republic of Korea.

Abstract
To control vertical and lateral compound vibration simultaneously using an integrated smart controller, passive tuned mass damper (TMD) and tuned liquid damper (TLD) are updated and combined to an adaptive-passive TMD-TLD (APTMD-TLD) system. As for the vertical AP-TMD part on top of the vertical spring, it can retune itself through varying the level of liquid in the tank to adjust its mass, while the lateral AP-TLD part at the bottom of the vertical spring can retune itself by changing the level of liquid. Further, for multimodal response control, the multiple AP-TMD-TLD (MAP-TMD-TLD) system is proposed as well. Each AP-TMD-TLD in the system can identify the structural vertical and lateral modal frequencies through the wavelet-transform (WT) based algorithm and retune its vertical and lateral natural frequencies both through adjusting the level of liquid in the AP-TMD and AP-TLD parts respectively. A cantilever cable-stayed landscape bridge which is sensitive to both human-induced and wind-induced vibrations is presented as a case study. For comparison, initial parameters of MAPTMD-TLD are mistuned. Results show that the presented system can retune its vertical and lateral frequencies precisely, while the retuned system has a better bi-directional compound control effect than the mistuned system before the retuning operation and can improve the serviceability significantly.

Key Words
adaptive tuned liquid damper; cantilever cable-stayed landscape bridge; human-induced vibration; multiple tuned mass damper; serviceability problem; wind-induced vibration

Address
(1) State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, 200092, P.R. China;
(2) Department of Disaster Mitigation for Structures, Tongji University, Shanghai, 200092, P.R. China.

Abstract
The Hybrid Mass Damper (HMD) has proven effective in mitigating vibrations in high-rise structures subject to seismic and wind-induced excitations. One derivative configuration of the HMD mounts an Active Mass Damper (AMD) atop a Tuned Mass Damper (TMD). However, the control efficacy of such HMDs may be compromised when confronted with loads that exceed their design parameters. Additionally, the confined structural space within high-rise structures often limits the feasibility and economic viability of retrofitting HMD systems. This study introduces an Acceleration-based Fuzzy Power Approach Rate Sliding Mode Control (AFP-SMC) algorithm aimed at enhancing the control efficacy of HMDs while minimizing their stroke and force output requirements. Employing the Canton Tower as a research prototype, an analytical model incorporating HMDs was established, and a comparative analysis between the AFP-SMC and Linear Quadratic Gaussian (LQG) control algorithms was conducted for efficacy. The control performance of the AFP-SMC control algorithm under different control parameter variations was investigated. Furthermore, by experimentally assessing the AMD subsystem within the Canton Tower, friction and ripple force formulas were derived to bolster the analytical model, thereby validating the robustness of the AFP-SMC algorithm. The results show that the proposed AFP-SMC algorithm effectively reduces the vibration response of the structure and the stroke and control force output of HMDs, and exhibits superior overall control performance and robustness compared to the LQG algorithm.

Key Words
fuzzy control; hybrid mass damper; LQG; sliding mode control; vibration control

Address
(1) Zhenfeng Lai, Yanhui Liu, Dongfan Ye, Ping Tan, Fulin Zhou:
Earthquake Engineering Research & Test Center (EERTC), Guangzhou University, Guangzhou 510006, China;
(2) Yanhui Liu, Dongfan Ye, Ping Tan:
Key Laboratory of Earthquake Resistance, Earthquake Mitigation and Structural Safety, Ministry of Education, Guangzhou 510006, China;
(3) Zhenfeng Lai, Fulin Zhou:
Guangdong Provincial Key Laboratory of Earthquake Engineering and Applied Technology, Guangzhou 510006, China.

Abstract
Automated crack detection is crucial for structural health monitoring and post-earthquake rapid damage detection. However, realizing high precision automatic crack detection in the absence of corresponding manual labeling presents a formidable challenge. This paper presents a novel crack segmentation transfer learning method and a novel crack segmentation model called Swin-CrackFormer. The proposed method facilitates efficient crack image style transfer through a meticulously designed data preprocessing technique, followed by the utilization of a GAN model for image style transfer. Moreover, the proposed Swin-CrackFormer combines the advantages of Transformer and convolution operations to achieve effective local and global feature extraction. To verify the effectiveness of the proposed method, this study validates the proposed method on three unlabeled crack datasets and evaluates the Swin-CrackFormer model on the METU dataset. Experimental results demonstrate that the crack transfer learning method significantly improves the crack segmentation performance on unlabeled crack datasets. Moreover, the Swin-CrackFormer model achieved the best detection result on the METU dataset, surpassing existing crack segmentation models.

Key Words
crack detection; deep learning; unsupervised generative attentional networks; vision Transformer

Address
State Key Laboratory of Disaster Reduction in Civil Engineering, College of Civil Engineering, Tongji University, 1239 Siping Rd., Shanghai, 200092, China.

Abstract
In this paper, a robust wireless sensor network configuration design method is proposed to develop the optimal configuration under the consideration of sensor failure and energy consumption. A malfunctioned sensor in a wireless sensor network may lead to data transmission failure of the entire sensing cluster, inducing severe deterioration in system identification performance. The proposed method determines a wireless sensor network configuration that is robust against sensor failure. By utilizing Bayesian inference, we introduce a robust indicator to evaluate the impact on estimation accuracy of sensor configurations with various malfunctioned sensors. Moreover, a network formation strategy is proposed to optimize the energy efficiency of the wireless sensor network configuration. Therefore, the resultant robust wireless sensor network configuration can operate with the minimum energy consumption while the measurement information of the sensor network with malfunctioned sensors can be guaranteed. The proposed method is illustrated by designing the robust wireless sensor network configurations of a truss model and a bridge model.

Key Words
Bayesian inference; energy efficiency; multi-type sensing devices; parameter identification; robust wireless sensor network; sensor failure

Address
(1) Xiao-Han Hao:
School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
(2) Sin-Chi Kuok, Ka-Veng Yuen:
State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao SAR, China;
(3) Sin-Chi Kuok, Ka-Veng Yuen:
Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, University of Macau, Macao SAR, China.

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2024 Techno-Press ALL RIGHTS RESERVED.
P.O. Box 33, Yuseong, Daejeon 34186 Korea, Email: admin@techno-press.com