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Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation

Ning Chen, Shibo Zhao, Zhiwei Gao Orcid Logo, Dawei Wang, Pengfei Liu, Markus Oeser, Yue Hou Orcid Logo, Linbing Wang

Construction and Building Materials, Volume: 323, Start page: 126580

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1016/j.conbuildmat.2022.126580

Published in: Construction and Building Materials
ISSN: 0950-0618
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa61797
first_indexed 2022-11-28T15:56:08Z
last_indexed 2023-01-13T19:22:47Z
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recordtype SURis
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spelling 2022-11-28T15:56:10.1642291 v2 61797 2022-11-07 Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2022-11-07 ACEM Journal Article Construction and Building Materials 323 126580 Elsevier BV 0950-0618 Virtual material design; Compressive strength prediction; Data augmentation; Deep learning; Lightweight model 14 3 2022 2022-03-14 10.1016/j.conbuildmat.2022.126580 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University This work was supported by the International Research Cooperation Seed Fund of Beijing University of Technology (No. 2021A05), Opening project fund of Materials Service Safety Assessment Facilities (MSAF-2021-109), Talent Promotion Program by Beijing Association for Science and Technology, and the Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds (Scientific Research Categories) of Beijing City. 2022-11-28T15:56:10.1642291 2022-11-07T19:20:04.2931240 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Ning Chen 1 Shibo Zhao 2 Zhiwei Gao 0000-0002-5501-9855 3 Dawei Wang 4 Pengfei Liu 5 Markus Oeser 6 Yue Hou 0000-0002-4334-2620 7 Linbing Wang 8
title Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation
spellingShingle Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation
Yue Hou
title_short Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation
title_full Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation
title_fullStr Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation
title_full_unstemmed Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation
title_sort Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Ning Chen
Shibo Zhao
Zhiwei Gao
Dawei Wang
Pengfei Liu
Markus Oeser
Yue Hou
Linbing Wang
format Journal article
container_title Construction and Building Materials
container_volume 323
container_start_page 126580
publishDate 2022
institution Swansea University
issn 0950-0618
doi_str_mv 10.1016/j.conbuildmat.2022.126580
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 0
active_str 0
published_date 2022-03-14T20:29:34Z
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score 11.047609

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