A5100361742- Structural Health Monitoring Techniques
- Ultrasonics and Acoustic Wave Propagation
- Infrastructure Maintenance and Monitoring
- Structural Engineering and Vibration Analysis
- Concrete Corrosion and Durability
- Advanced Fiber Optic Sensors
- Probabilistic and Robust Engineering Design
- Non-Destructive Testing Techniques
- Drilling and Well Engineering
- Hydraulic Fracturing and Reservoir Analysis
- Optical measurement and interference techniques
- Vibration and Dynamic Analysis
- Photonic and Optical Devices
- Machine Fault Diagnosis Techniques
- Fatigue and fracture mechanics
- Advanced Measurement and Detection Methods
- Civil and Geotechnical Engineering Research
- Seismic Performance and Analysis
- Image and Signal Denoising Methods
- Structural Load-Bearing Analysis
- Inertial Sensor and Navigation
- GNSS positioning and interference
- Power Systems and Technologies
- Advanced Algorithms and Applications
- Advanced Sensor and Control Systems
Xinjiang Industry Technical College
2025
South China Agricultural University
2011-2025
Guangxi University of Science and Technology
2024-2025
Curtin University
2015-2024
Hong Kong Polytechnic University
2009-2024
China University of Petroleum, Beijing
2013-2024
Wuhan University of Technology
2021-2024
Guangxi University
2024
Chongqing University
2020-2024
Nanjing Normal University
2019-2024
With the advancement of deep learning, data-driven structural damage identification (SDI) has shown considerable development. However, collecting vibration signals related to poses certain challenges, which can undermine accuracy results produced by SDI methods in scenarios where data is scarce. This paper introduces an innovative approach bridge a few-shot context integrating adaptive simulated annealing particle swarm optimization-convolutional neural network (ASAPSO-CNN) as foundational...
Signal transmission loss of using wireless sensors for structural health monitoring is a usual case, which undermines the reliability conditions. The measured vibration data with high ratio can hardly be used analysis, that is, modal identification, as it will lead to significant errors in results. This paper proposes novel approach based on convolutional neural networks recovering lost monitoring. network fully feed-forward bottleneck architecture and skip connection, constructs nonlinear...
This article proposes a deep sparse autoencoder framework for structural damage identification. can be employed to obtain the optimal solutions some pattern recognition problems with highly nonlinear nature, such as learning mapping between vibration characteristics and damage. Three main components are defined in proposed framework, namely, pre-processing component data whitening process, dimensionality reduction where of original input vector is reduced while preserving required necessary...
With the ever-increasing demand in urban mobility and modern logistics sector, vehicle population has been steadily growing over past several decades. One natural consequence of growth is increase traffic congestion. Almost all (metropolitan) cities including major ones, like Los Angeles, Beijing, New York, are suffering from heavy Statistics show that, 2015, 43 China a prolonged travel time more than 1.5 h every day during rush hours. In meanwhile, accidents plaguing economic development as well.
This article proposes a novel dynamic response reconstruction approach for structural health monitoring using densely connected convolutional networks. Skip connection and dense block techniques are carefully applied in the designed network architecture, which greatly facilitates information flow, increases training efficiency accuracy of feature extraction propagation with fewer parameters network. Sub-pixel shuffling dropout used to reduce computational demand improve efficiency. The is...
Vehicles equipped with various types of sensors have the great potentials to effectively evaluate health conditions a population bridges at low cost. However, existing drive-by structural monitoring (SHM) methods acquire vehicle vibration responses offline and export them computer for postprocessing. Furthermore, trajectory information on bridge is important scaling up SHM in situ applications, which not synchronously measured by systems. Therefore, single-board computer-based IoT sensing...
Vibration displacement of civil structures is crucial information for structural health monitoring (SHM). The challenges and costs associated with traditional physical sensors make measurement not always straightforward owing to difficulties such as inaccessibility. While recent computer vision based methods measurements offer simplicity, unfortunately they lag in terms accuracy robustness. This paper introduces a monocular camera system designed measure out-of-plane vibration displacement....