基于可解释数据驱动模型的三维混凝土曲率和倾角打印过程随机分析

IF 3.9 3区 工程技术 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
Baixi Chen, Lei Yang, Sheng Jiang
{"title":"基于可解释数据驱动模型的三维混凝土曲率和倾角打印过程随机分析","authors":"Baixi Chen,&nbsp;Lei Yang,&nbsp;Sheng Jiang","doi":"10.1617/s11527-025-02785-9","DOIUrl":null,"url":null,"abstract":"<div><p>The increasing adoption of 3D concrete printing (3DCP) in construction highlights the importance of understanding the stochastic behavior of the printing process to ensure quality control. This study proposes an explainable data-driven stochastic analysis framework, incorporating SHapley Additive exPlanation (SHAP), to evaluate and explain the impact of material uncertainty on the printing process for walls with curvature and inclination. Among seven machine learning algorithms examined, the Sparse Gaussian Process Regression model demonstrated superior predictive performance and was selected for data-driven modeling. SHAP-based analysis identified the degree of inclination and initial cohesion as the most critical factors influencing the printing process, surpassing other material, geometric, and printing features in importance. Stochastic analysis revealed that increasing the degree of inclination reduces both the buildability of the 3DCP process and associated uncertainty, while geometric curvature enhances buildability but increases its variation. Printing configurations, such as print speed and layer height, had negligible effects on buildability and uncertainty within small-scale geometries. Regardless of printing geometry and configurations, initial cohesion was identified as the most influential contributor to process uncertainty, making it a key focus for optimization to reduce variability and enhance reliability in 3DCP processes.</p></div>","PeriodicalId":691,"journal":{"name":"Materials and Structures","volume":"58 8","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stochastic analysis of 3D concrete printing process with curvature and inclination by explainable data-driven modelling\",\"authors\":\"Baixi Chen,&nbsp;Lei Yang,&nbsp;Sheng Jiang\",\"doi\":\"10.1617/s11527-025-02785-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The increasing adoption of 3D concrete printing (3DCP) in construction highlights the importance of understanding the stochastic behavior of the printing process to ensure quality control. This study proposes an explainable data-driven stochastic analysis framework, incorporating SHapley Additive exPlanation (SHAP), to evaluate and explain the impact of material uncertainty on the printing process for walls with curvature and inclination. Among seven machine learning algorithms examined, the Sparse Gaussian Process Regression model demonstrated superior predictive performance and was selected for data-driven modeling. SHAP-based analysis identified the degree of inclination and initial cohesion as the most critical factors influencing the printing process, surpassing other material, geometric, and printing features in importance. Stochastic analysis revealed that increasing the degree of inclination reduces both the buildability of the 3DCP process and associated uncertainty, while geometric curvature enhances buildability but increases its variation. Printing configurations, such as print speed and layer height, had negligible effects on buildability and uncertainty within small-scale geometries. Regardless of printing geometry and configurations, initial cohesion was identified as the most influential contributor to process uncertainty, making it a key focus for optimization to reduce variability and enhance reliability in 3DCP processes.</p></div>\",\"PeriodicalId\":691,\"journal\":{\"name\":\"Materials and Structures\",\"volume\":\"58 8\",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials and Structures\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1617/s11527-025-02785-9\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials and Structures","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1617/s11527-025-02785-9","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

3D混凝土打印(3DCP)在建筑中的应用越来越多,这凸显了了解打印过程的随机行为以确保质量控制的重要性。本研究提出了一个可解释的数据驱动的随机分析框架,结合SHapley加性解释(SHAP),来评估和解释材料不确定性对具有曲率和倾斜度的墙壁打印过程的影响。在研究的7种机器学习算法中,稀疏高斯过程回归模型表现出优越的预测性能,并被选中用于数据驱动建模。基于shap的分析发现,倾斜程度和初始凝聚力是影响打印过程的最关键因素,其重要性超过了其他材料、几何和打印特征。随机分析表明,倾角的增加降低了三维cp工艺的可建性和不确定性,而几何曲率提高了可建性,但增加了其变化。打印配置,例如打印速度和层高度,对小规模几何形状的可构建性和不确定性的影响可以忽略不计。无论打印几何形状和配置如何,初始内聚性都被认为是影响工艺不确定性的最重要因素,因此,在3d打印工艺中,降低可变性和提高可靠性是优化的重点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stochastic analysis of 3D concrete printing process with curvature and inclination by explainable data-driven modelling

The increasing adoption of 3D concrete printing (3DCP) in construction highlights the importance of understanding the stochastic behavior of the printing process to ensure quality control. This study proposes an explainable data-driven stochastic analysis framework, incorporating SHapley Additive exPlanation (SHAP), to evaluate and explain the impact of material uncertainty on the printing process for walls with curvature and inclination. Among seven machine learning algorithms examined, the Sparse Gaussian Process Regression model demonstrated superior predictive performance and was selected for data-driven modeling. SHAP-based analysis identified the degree of inclination and initial cohesion as the most critical factors influencing the printing process, surpassing other material, geometric, and printing features in importance. Stochastic analysis revealed that increasing the degree of inclination reduces both the buildability of the 3DCP process and associated uncertainty, while geometric curvature enhances buildability but increases its variation. Printing configurations, such as print speed and layer height, had negligible effects on buildability and uncertainty within small-scale geometries. Regardless of printing geometry and configurations, initial cohesion was identified as the most influential contributor to process uncertainty, making it a key focus for optimization to reduce variability and enhance reliability in 3DCP processes.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Materials and Structures
Materials and Structures 工程技术-材料科学:综合
CiteScore
6.40
自引率
7.90%
发文量
222
审稿时长
5.9 months
期刊介绍: Materials and Structures, the flagship publication of the International Union of Laboratories and Experts in Construction Materials, Systems and Structures (RILEM), provides a unique international and interdisciplinary forum for new research findings on the performance of construction materials. A leader in cutting-edge research, the journal is dedicated to the publication of high quality papers examining the fundamental properties of building materials, their characterization and processing techniques, modeling, standardization of test methods, and the application of research results in building and civil engineering. Materials and Structures also publishes comprehensive reports prepared by the RILEM’s technical committees.
×
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