Abstract
The purpose of this study is to demonstrate and verify the application of phase-control absolute-acceleration-feedback active tuned mass dampers (PCA-ATMD) to multiple-degree-of-freedom (MDOF) building structures. In addition, servo speed control technique has been developed as a replacement for force control in order to mitigate the negative effects caused by friction and inertia. The essence of the proposed PCA-ATMD is to achieve a 90° phase lag for a structure by implementing the desired control force so that the PCA-ATMD can receive the maximum power flow with which to effectively mitigate the structural vibration. An MDOF building structure with a PCA-ATMD and a real-time filter forming a complete system is modeled using a state-space representation and is presented in detail. The feedback measurement for the phase control algorithm of the MDOF structure is compact, with only the absolute acceleration of one structural floor and ATMD
Key Words
active tuned mass damper; discrete-time direct output feedback; multi-degree-of-freedom building structure; phase control; seismic excitation; servo-velocity control; shaking table experiment
Address
Department of Civil Engineering, National Central University, No. 300, Zhongda Rd., Zhongli District, Taoyuan City 320317, Taiwan (R.O.C.).
Abstract
Dynamic deflection is important for evaluating the performance of a long-span cable-stayed bridge, and its continuous measurement is still cumbersome. This study proposes a dynamic deflection monitoring method for cable-stayed bridge based on Bi-directional Long Short-term Memory (BiLSTM) neural network taking advantages of the characteristics of spatial variation of cable acceleration response (CAR) and main girder deflection response (MGDR). Firstly, the relationship between the spatial and temporal variation of the CAR and the MGDR is described based on the geometric deformation of the bridge. Then a data-driven relational model based on BiLSTM neural network is established using CAR and MGDR data, and it is further used to monitor the MGDR via measuring the CAR. Finally, numerical simulations and field test are conducted to verify the proposed method. The root mean squared error (RMSE) of the numerical simulations are less than 4 while the RMSE of the field test is 1.5782, which indicate that it provides a cost-effective and convenient method for real-time deflection monitoring of cable-stayed bridges.
Key Words
BiLSTM; cable acceleration; cable-stayed bridge; dynamic deflection
Address
(1) Yi-Fan Li, Wen-Yu He:
Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui 230009, China;
(2) Wei-Xin Ren:
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, Guangdong 518061, China;
(3) Gang Liu, Hai-Peng Sun:
China Design Group, Nanjing, Jiangsu 210000, China.
Abstract
The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.
Key Words
concrete pavement; deep learning; expansion joint; image processing; road surface damage; vision-based system
Address
(1) Jung Hee Lee:
Department of Artificial Intelligence, Ajou University, 206 World cup-ro Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea;
(2) Ibragimov Eldor:
Research and Development Division, SISTech Co. Ltd, 329 Daeyang AI Center, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747, Republic of Korea;
(3) Heungbae Gil:
ICT Convergence Research Division, Korea Expressway Corporation Research Institute, Dongbusunhwan-daero, 17-gil, Hwaseong-si, Gyeonggi-do, Republic of Korea;
(4) Jong-Jae Lee:
Department of Civil and Environmental Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747, Re-public of Korea.
Abstract
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.
Key Words
arch dam; damage detection; jaya algorithm; neural network; radial basis function
Address
(1) Ali Zar:
Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China;
(2) Zahoor Hussain, Zhibin Lin:
Department of Civil and Environmental Engineering, North Dakota State University, Fargo, ND 58018, USA;
(3) Zahoor Hussain:
Department of Civil Engineering, Sir Syed University of Engineering & Technology, Karchi 75300, Pakistan;
(4) Muhammad Akbar:
Department of Engineering Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China;
(5) Bassam A. Tayeh:
Civil Engineering Department, Faculty of Engineering, Islamic University of Gaza, P.O Box 108, Gaza Strip, Palestine.
Abstract
Both the first author and the company of the second author were involved, directly or indirectly, in the design stage of a permanent link between the bottom of the Italian peninsula and the nearby Sicily island. This ambitious project was left in stand-by from 2013 to 2023. The current political revival originates some thoughts on the updated desired performance of suspension bridges, without any immediate reference to that specific crossing. It is simply regarded as a starting point. After an update on recent worldwide realizations, the authors focus their attention on four basic aspects: the span length, the girder scheme, the foundation technology and the bridge runability. Eventually, structural control and monitoring aspects are discussed as potentially innovative options in designing suspension bridges with railway crossing.
Address
(1) F. Casciati:
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China;
(2) S. Casciati:
SIART srl. Via dei Mille 73, Pavia, Italy.
