Deep learning enabled particle analysis for quality assurance of construction materials-Reference-Cited by-同舟云学术

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Deep learning enabled particle analysis for quality assurance of construction materials

Published:2022-08 Issue: Volume:140 Page:104374
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Zeng Ziyue,Wei Yongqi,Wei Zhenhua,Yao Wu,Wang Changying,Huang Bin,Gong Mingzi,Yang Jiansen

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference65 articles.

1. Realization of high-percentage addition of fly ash in the materials for the preparation of geopolymer derived from acid-activated metakaolin;Guo;J. Clean. Prod.,2021

2. Mechanical strength and permeation properties of high calcium fly ash-based geopolymer containing recycled brick powder;Wong;J. Build. Eng.,2020

3. Different mineral admixtures in concrete: a review;Kanamarlapudi;Sn Appl. Sci.,2020

4. Interfacial design of nano-TiO2 modified fly ash-cement based low carbon composites;Meng;Constr. Build. Mater.,2021

5. Strength behavior analysis of fiber reinforced fly ash concrete;Poojari;Mater. Today Proc.,2021

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1. Weakly supervised deep learning-based concrete aggregates automatic segmentation for assessing separation degree;Journal of Building Engineering;2024-04

2. Compressive strength evaluation of cement-based materials in sulphate environment using optimized deep learning technology;Developments in the Built Environment;2023-12

3. Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network;Construction and Building Materials;2023-12

4. Cementron: Machine learning the alite and belite phases in cement clinker from optical images;Construction and Building Materials;2023-09

5. Material recognition for construction quality monitoring using deep learning methods;Construction Innovation;2023-07-12

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