Loading...

Media is loading
 

Description

A typical human eye will respond to wavelengths from approximately 400 to 700 nm. A hyperspectral camera can extend the wavelength to as high as 2500 nm. This extension will allow engineers to find objects, identify materials, and detect processes on structural surface, which cannot be done with visual inspection.

This project aims to develop an open-source catalogue of concrete and steel surfaces and their spectral/spatial features (discoloration, characteristic wavelength, roughness, texture, shape, etc.), extract spatial/spectral features of hyperspectral images, and develop/train a multi-class classification or regression classifier through machine learnings (supervised and/or semi-supervised), and validate the classifier as a decision-making tool for the assessment of concrete crack and degradation processes, in-situ concrete properties, and corrosion process in steel bridges.

Presentation Date

03 Aug 2020, 9:30 am - 11:30 am

Meeting Name

INSPIRE-UTC 2020 Annual Meeting

Department(s)

Civil, Architectural and Environmental Engineering

Comments

SN-5

Document Type

Presentation

Document Version

Final Version

File Type

text

Language(s)

English

portrait of presenter

Share

COinS
 
Aug 3rd, 9:30 AM Aug 3rd, 11:30 AM

Hyperspectral Image Analysis for Mechanical and Chemical Properties of Concrete and Steel Surfaces

A typical human eye will respond to wavelengths from approximately 400 to 700 nm. A hyperspectral camera can extend the wavelength to as high as 2500 nm. This extension will allow engineers to find objects, identify materials, and detect processes on structural surface, which cannot be done with visual inspection.

This project aims to develop an open-source catalogue of concrete and steel surfaces and their spectral/spatial features (discoloration, characteristic wavelength, roughness, texture, shape, etc.), extract spatial/spectral features of hyperspectral images, and develop/train a multi-class classification or regression classifier through machine learnings (supervised and/or semi-supervised), and validate the classifier as a decision-making tool for the assessment of concrete crack and degradation processes, in-situ concrete properties, and corrosion process in steel bridges.