Structural Material Condition Assessment through Human-in-the-Loop Incremental Semisupervised Learning from Hyperspectral Images-Reference-Cited by-同舟云学术

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Structural Material Condition Assessment through Human-in-the-Loop Incremental Semisupervised Learning from Hyperspectral Images

Published:2024-11 Issue:6 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Chen ZhiQiang¹^ORCID,Tang Shimin²

Affiliation:

1. Professor, Division of Natural and Built Environment, Univ. of Missouri-Kansas City, 5100 Rockhill Rd., Kansas City, MO 66221 (corresponding author). ORCID: .

2. Postdoctoral Research Associate, Oak Ridge National Laboratory, 200, 1 Bethel Valley Rd., Oak Ridge, TN 37830.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5939

Reference69 articles.

1. A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection

2. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions;Alzubaidi L.;J. Big Data,2021

3. Mobile Hyperspectral Imaging for Material Surface Damage Detection

4. Semi-Supervised Learning on Riemannian Manifolds

5. Bennett, K., and A. Demiriz. 1999. “Semisupervised support vector machines.” In Advances in neural information processing systems, 368–374. La Jolla, CA: Neural Information Processing Systems Foundation.

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