Yixiong Jing- Master of Engineering
- Cambridge at University of Cambridge
Yixiong Jing
- Master of Engineering
- Cambridge at University of Cambridge
I am looking for potential collaboration in structural health monitoring.
About
6
Publications
756
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59
Citations
Introduction
I am interested in implementing 3D deep learning to resolve geometric-related problems in infrastructures. You can find updated codes and data related to my recent research on my GitHub website: https://github.com/Jingyixiong
Current institution
Additional affiliations
January 2021 - present
Position
- PhD Student
Description
- I am doing my PhD in structural health monitoring using point cloud and deep learning.
September 2017 - April 2020
Position
- Master's Student
Description
- I am mainly working on the pounding of skew bridges under the earthquake.
Publications
Publications (6)
Ageing structures require periodic inspections to identify structural defects.
Previous work has used geometric distortions to locate cracks in synthetic
masonry bridge point clouds but has struggled to detect small cracks. To
address this limitation, this study proposes a novel 3D multimodal feature,
3DMulti-FPFHI, which combines a customized Fast...
Management of ageing masonry arch bridges entails periodic site inspections to identify signs of potential structural degradation. Previous research has focused on detecting surface cracks from images. This paper develops an alternative approach where cracks are identified from point clouds via geometric distortions. An image-based anomaly detectio...
Transformer architecture based on the attention mechanism achieves impressive results in natural language processing (NLP) tasks. This paper transfers the successful experience to a 3D point cloud segmentation task. Inspired by newly proposed 3D Transformer neural networks, this paper introduces a new Transformer-based module, which is called Local...
Masonry bridges form an important component of the transport infrastructure in the UK and Europe. Structural issues in these bridges are often identified by the presence of visual surface defects, such as cracks. To automate this process, recent research efforts have focused on identifying defects from images, neglecting the potential information t...
Masonry arch bridges constitute the majority of the European bridge stock and feature a wide range of geometric characteristics. Due to a general lack of construction drawings, their geometry is difficult to parameterize. Laser scanning devices are commonly used to capture bridge geometry. However, this requires time-consuming segmentation of point...
Masonry arch bridges constitute the majority of the European bridge stock. Most of these bridges were constructed in the 19th century and feature a wide range of geometric characteristics. Since construction drawings rarely exist, the first step in the assessment of these bridges is the characterisation of their in-situ geometry, which may involve...
