
Xiao PanUniversity of British Columbia - Vancouver | UBC · Department of Civil Engineering
Xiao Pan
Doctor of Philosophy
Structural Damage Detection, Computer Vision, Deep Learning, Machine Learning, Point Cloud Analysis
About
13
Publications
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71
Citations
Citations since 2017
Introduction
Xiao Pan is a Ph.D. candidate in Structural and Earthquake Engineering at the University of British Columbia (UBC). His own research is directed at structural health monitoring, structural dynamic testing using AI-based computer vision and 3D scanning techniques, as well as performance-based assessment of civil structures.
Publications
Publications (13)
Shake table testing is a common technique used to examine the responses of structures under dynamic loads. Shake table is often regulated using linear controller, such as proportional-integral-derivative (PID) controller. However, traditional PID control cannot consider inherent nonlinearities in the structural and control systems. In this paper, a...
In this paper, a novel accurate and economical 3D computer vision‐based framework is proposed to quantify out‐of‐plane displacements of steel plate structures. First, a sequence of image frames of the steel plate structures of interest is collected. Second, using image association, structure‐from‐motion, and multi‐view stereo algorithms, a 3D point...
Structural bolts are critical components used in different structural elements, such as beam‐column connections and friction damping devices. The clamping force in structural bolts is highly influenced by the bolt rotation. Much of the existing vision‐based research about bolt rotation estimation relies on traditional computer vision algorithms suc...
Dynamic analysis of structures subjected to earthquake excitation is a time-consuming process, particularly in the case of extremely small time step required, or in the presence of high geometric and material nonlinearity. Performing parametric studies in such cases is even more tedious. The advancement of computer graphics hardware in recent years...
Vision-based methods for structural vibration measurements are proposed and validated through shake table tests.
Damage detection of steel panels using unmanned aerial vehicles and 3D vision techniques
Structural bolts are critical components used in different structural elements, such as beam-column connections and friction damping devices. The clamping force in structural bolts is highly influenced by the bolt rotation. Much of the existing vision-based research about bolt rotation estimation relies on traditional computer vision algorithms suc...
Tall rigid blocks are prevalent in ancient historical constructions. Such structures are prone to rocking behaviour under strong ground motion, which is recognisably challenging to predict and mitigate. Our study is motivated by the need to provide innovative non-intrusive solutions to attenuate the rocking response of historical buildings and monu...
Reinforced concrete (RC) buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based...
This paper presents a novel metallic damper, named Honeycomb Structural Fuse (HSF), for seismic applications. The HSF utilizes commonly available welded wide flange sections with honeycomb-shape perforations on web. The HSF is designed to dissipate earthquake energy through plastic deformation of the web in shear, while the flanges remain elastic....
This paper proposes a novel type of metallic damper, named Honeycomb Structural Fuse (HSF), a modified version of the previously developed Welded Wide Flange Fuse (WWFF). Based on the commonly available welded wide flange section, honeycomb‐shape perforations were laser cut on web. The HSF is designed to dissipate earthquake energy through plastic...
Projects
Projects (3)
The goal of the project is to explore and validate recent advances in computer vision and machine learning for solving structural health monitoring problems
Seismic Vibration Control using Smart Materials and Devices; Structural Health Monitoring and Damage Detection through Machine Learning; Vision and Vibration-based SHM using Deep Learning Models
Energy absorbing potential of vibrating barriers for the neighbouring structures under earthquake loading