Generation of cement paste microstructure using machine learning models

IF 6.2 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Minfei Liang , Kun Feng , Shan He , Yidong Gan , Yu Zhang , Erik Schlangen , Branko Šavija
{"title":"Generation of cement paste microstructure using machine learning models","authors":"Minfei Liang ,&nbsp;Kun Feng ,&nbsp;Shan He ,&nbsp;Yidong Gan ,&nbsp;Yu Zhang ,&nbsp;Erik Schlangen ,&nbsp;Branko Šavija","doi":"10.1016/j.dibe.2025.100624","DOIUrl":null,"url":null,"abstract":"<div><div>The microstructure of cement paste determines the overall performance of concrete and therefore obtaining the microstructure is an essential step in concrete studies. Traditional methods to obtain the microstructure, such as scanning electron microscopy (SEM) and X-ray computed tomography (XCT), are time-consuming and expensive. Herein we propose using Denoising Diffusion Probabilistic Models (DDPM) to synthesize realistic microstructures of cement paste. A DDPM with a U-Net architecture is employed to generate high-fidelity microstructure images that closely resemble those derived from SEM. The synthesized images are subjected to comprehensive image analysis, phase segmentation, and micromechanical analysis to validate their accuracy. Findings demonstrate that DDPM-generated microstructures not only visually match the original microstructures but also exhibit similar greyscale statistics, phase assemblage, phase connectivity, and micromechanical properties. This approach offers a cost-effective and efficient alternative for generating microstructure data, facilitating advanced multiscale computational studies of cement paste properties.</div></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":"21 ","pages":"Article 100624"},"PeriodicalIF":6.2000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Developments in the Built Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666165925000249","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The microstructure of cement paste determines the overall performance of concrete and therefore obtaining the microstructure is an essential step in concrete studies. Traditional methods to obtain the microstructure, such as scanning electron microscopy (SEM) and X-ray computed tomography (XCT), are time-consuming and expensive. Herein we propose using Denoising Diffusion Probabilistic Models (DDPM) to synthesize realistic microstructures of cement paste. A DDPM with a U-Net architecture is employed to generate high-fidelity microstructure images that closely resemble those derived from SEM. The synthesized images are subjected to comprehensive image analysis, phase segmentation, and micromechanical analysis to validate their accuracy. Findings demonstrate that DDPM-generated microstructures not only visually match the original microstructures but also exhibit similar greyscale statistics, phase assemblage, phase connectivity, and micromechanical properties. This approach offers a cost-effective and efficient alternative for generating microstructure data, facilitating advanced multiscale computational studies of cement paste properties.
利用机器学习模型生成水泥浆体微观结构
水泥浆体的微观结构决定了混凝土的整体性能,因此微观结构的获取是混凝土研究中必不可少的一步。传统的获取微观结构的方法,如扫描电子显微镜(SEM)和x射线计算机断层扫描(XCT),既耗时又昂贵。在此,我们提出用去噪扩散概率模型(DDPM)来合成水泥浆体的真实微观结构。采用U-Net结构的DDPM生成的高保真微观结构图像与SEM的图像非常相似。对合成图像进行综合图像分析、相位分割和微力学分析,以验证其精度。研究结果表明,ddpm生成的微观结构不仅在视觉上与原始微观结构相匹配,而且具有相似的灰度统计、相组合、相连通性和微观力学性能。该方法为生成微观结构数据提供了一种经济高效的替代方法,促进了水泥浆体性能的先进多尺度计算研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.40
自引率
1.20%
发文量
31
审稿时长
22 days
期刊介绍: Developments in the Built Environment (DIBE) is a recently established peer-reviewed gold open access journal, ensuring that all accepted articles are permanently and freely accessible. Focused on civil engineering and the built environment, DIBE publishes original papers and short communications. Encompassing topics such as construction materials and building sustainability, the journal adopts a holistic approach with the aim of benefiting the community.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信