Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras-Reference-Cited by-同舟云学术

Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

Published:2020-08-03 Issue:2 Volume:36 Page:213-226
ISSN:1093-9687
Container-title:Computer-Aided Civil and Infrastructure Engineering
language:en
Short-container-title:Computer‐Aided Civil and Infrastructure Engineering

Author:

Valikhani Alireza¹,Jaberi Jahromi Azadeh²,Pouyanfar Samira³,Mantawy Islam M.¹^ORCID,Azizinamini Atorod¹

Affiliation:

1. Department of Civil and Environmental Engineering Florida International University Miami FL USA

2. Washington State Department of Transportation Olympia WA USA

3. Microsoft Redmond WA USA

Funder

Accelerated Bridge Construction University Transportation Center

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12605

Reference59 articles.

1. AASHTO‐LRFD. (2017).Bridge design specifications. American Association of State Highway and Transportation Officials 5–138.

2. A new method for evaluating the surface roughness of concrete cut for repair or strengthening

3. American Concrete Institute. (2008).Building code requirements for structural concrete (ACI 318‐08) and commentary.Farmington Hills MI:ACI. ISBN: 978‐0‐87031‐264‐9.

Cited by 60 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A review of compaction mechanisms, influencing factors, and advanced methods in concrete vibration technology;Journal of Building Engineering;2024-09

2. Classification of surface roughness for milled A6061 aluminum alloy based on depth map models with convolutional neural networks;Forschung im Ingenieurwesen;2024-08-12

3. Shotcrete flatness evaluation of initial linings based on vehicular LiDAR scanning;Automation in Construction;2024-08

4. Quantitative surface quality evaluation for 3D-printed concrete with coarse aggregate through 3D scanning;Case Studies in Construction Materials;2024-07

5. Kriging-based surrogate data-enriching artificial neural network prediction of strength and permeability of permeable cement-stabilized base;Nature Communications;2024-06-07