硅酸盐学报

基于生成式优化算法的多流态场景修补砂浆配合比优化设计

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李琴飞 ¹^,² , 张波 ¹ , 郭翔宇 ¹^,² , 王琳 ³^,⁴ , 李长姣 ³ , 侯鹏坤 ¹^,² , 陈衡 ¹^,² , 程新 ¹^,² , 杨波 ³^,⁴

硅酸盐学报 | 水泥基材料基因与智能设计专题—研究论文 2026,54(3): 857-867

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硅酸盐学报 |水泥基材料基因与智能设计专题—研究论文 2026 , 54 (3) : 857 -867

基于生成式优化算法的多流态场景修补砂浆配合比优化设计

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李琴飞¹^,², 张波¹, 郭翔宇¹^,², 王琳³^,⁴, 李长姣³, 侯鹏坤¹^,², 陈衡¹^,², 程新¹^,², 杨波³^,⁴

作者信息

^1．济南大学，山东省绿色与智能建筑材料重点实验室，济南 250022

^2．济南大学，材料科学与工程学院，济南 250022

^3．济南大学，山东省泛在智能计算重点实验室，济南 250022

^4．泉城实验室，济南 250014

通讯作者:

王琳（1983—），男，博士，教授。

作者简介:

李琴飞（1984—），男，博士，副教授

张波（1999—），男，博士研究生。

Generative Optimization Algorithm-Based Mixture Optimization Design of Repair Mortar Under Multi-Flow State Scenarios

Qinfei LI¹^,², Bo ZHANG¹, Xiangyu GUO¹^,², Lin WANG³^,⁴, Changjiao LI³, Pengkun HOU¹^,², Heng CHEN¹^,², Xin CHENG¹^,², Bo YANG³^,⁴

Affiliations

^1.Shandong Provincial Key Laboratory of Green and Intelligent Building Materials, University of Jinan, Jinan 250022, China

^2.School of Materials Science and Engineering, University of Jinan, Jinan 250022, China

^3.Shandong Key Laboratory of Ubiquitous Intelligent Computing, University of Jinan, Jinan 250022, China

^4.Quan Cheng Laboratory, Jinan 250014, China

Corresponding Author:

WANG Lin (1983-), male, Ph.D., Professor. E-mail: ise_wanglin@ujn.edu.cn

LI Qinfei (1984-), male, Ph.D., Associate Professor. E-mail: mse_liqf@ujn.edu.cn

ZHANG Bo (1999-), male, Doctoral candidate. E-mail: boll_zhang@qq.com

出版时间: 2026-03-20 doi: 10.14062/j.issn.0454-5648.20250798

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摘要

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混凝土结构在服役过程中易受环境与荷载作用而产生损伤，亟须开发与应用高性能修补材料。然而，不同施工场景对修补材料的流变性、工作性和力学性能等存在差异化需求，传统配合比优化方法难以兼顾不同流态场景的实际性能需求并实现高效优化。围绕有机改性贝利特硫铝酸盐水泥砂浆体系，提出一种基于变分自编码器的生成式优化算法（VAE-GOA）来解决该挑战。以流动度作为场景约束，以7 d抗压、7 d抗折与7 d黏结强度构建加权综合性能作为评估指标，VAE-GOA利用“多场景锦标赛选择—自适应全局探索—高低流态协同迭代”的优化流程，直接在全局配合比空间中估计并聚焦高性能解的概率分布，同时兼顾探索有潜力的区域以避免陷入局部最优区域。结果表明：VAE-GOA结合全局探索策略和多轮迭代技术可有效提高发现高潜力配合比的能力，使高流态砂浆综合性能提升约20%，低流态提升则超过25%；同时识别出不同流态下的最佳配合比范围，为不同流态场景提供多样化的配合比选择。

关键词

配合比优化设计 / 多流态场景修补砂浆 / 生成式优化算法 / 生成式模型

Abstract

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Introduction

As a predominant construction material in modern civil infrastructure (i.e., roads, bridges, ports, and airports), concrete is susceptible to adversing actions such as external loading, freeze-thaw cycling, and salt ingress. These factors readily induce surface spalling and scaling, accelerating service-life deterioration and posing significant safety risks. The existing polymer-modified mortars and epoxy-based systems are commonly employed for the repair and strengthening of concrete structures. An organically modified belite-calcium sulfoaluminate (HB-CSA) cement mortar is adopted in concrete repair projects due to its rapid setting and early strength. However, the performance requirements differ markedly across construction scenarios in low-flow applications (e.g., vertical or overhead placement), the mortar must maintain a low flowability to prevent sagging/run-off during placement, and mechanical performance depends primarily on bond strength, with relatively relaxed demands on compressive and flexural strengths. By contrast, high-flow applications (e.g., pavement repair or large-area casting) require a high flowability to ensure adequate spreading and filling workability. The compressive strength is a principal mechanical target, while the flexural and bond performance must be also satisfied. These divergent demands make it difficult for a single HB-CSA mixture to meet the performance needs of multiple repair scenarios, underscoring the practical importance of multi-scenario mixture-design optimization for HB-CSA repair mortars.

Methods

This study was to investigate an organically modified belite-calcium sulfoaluminate (HB-CSA) cement mortar system. Flow spread was employed as a constraint to distinguish high- and low-flow-state scenarios, while 7-d compressive, 7-d flexural, and 7-d bond strengths were taken as the optimization objectives. To address mixture optimization for both flow states, we introduced (i.e., pioneering its use in the cement materials field) a variational-autoencoder-based generative optimization algorithm (VAE-GOA). The method could leverage a generative model to progressively estimate the probability distribution of optimal mixtures over the global design space. In parallel, an adaptive global-exploration strategy prioritized high-potential regions to mitigate premature convergence to local optima, and an iterative optimization scheme further drived the search toward superior solutions. The approach delineated the optimal composition windows and corresponding performance of HB-CSA repair mortars under different flow-state scenarios via visualizing the VAE-GOA-estimated distribution of optimal mixtures. In addition, backscattered electron (BSE) imaging of the interfacial transition zone (ITZ) was also employed to elucidate the mechanisms underlying the improvement in bond performance.

Results and discussion

At the outset, the VAE-GOA conducts broad, globally exploratory sampling, while unavoidably covering some low-performing regions, effectively uncovering previously unexplored high-potential areas. As iterations proceed, probability mass progressively concentrates in high-performance regions. The search distribution transitions smoothly from global exploration to local exploitation, reduces attention to low-performing zones, and converges toward a compact subspace containing the best-performing mixtures. These dynamics substantiate the algorithm's intended explore-then-exploit behavior in a multi-constraint design space and effectively mitigate premature convergence to local optima.

Based on two optimization generations and despite strict flowability constraints and limited sampling, the VAE-GOA delivers both macro-level performance gains and a marked expansion of feasible design space. The optimum weighted comprehensive performance is improved by about 20% in the high-flow state group and by >25% in the low-flow state group. The candidates with compressive strength >50 MPa increase from 2 to 6, and those with bond strength >6 MPa increase from 4 to 12, substantially broadening mixture options that meet key targets. For low-flow state scenario, increasing the USCMs is accompanied by a nonlinear decrease in the HPMC, and higher USCMs generally require a lower water-binder ratio (w/b). A practical window of 15%-30%USCMs, 2%-6% Wacker 328, and about 0.1% HPMC at a w/b ratio of 0.22 achieves a low flowability witha high interfacial performance (i.e., mixture L8 attains 48.6 MPa (compressive), 9.2 MPa (flexural), and 6.5 MPa (bond)). For high-flow state scenario, the strength-oriented optimum occurs near about 5% USCMs + 1% Wacker 328 at a w/b ratio of 0.25 (i.e., H7 reaches 63.4 MPa (compressive), 8.3 MPa (flexural), and 4.5 MPa (bond)). For cost-oriented deployment, increasing the USCMs to ～30% with 5%Wacker 328 at a w/b ratio of 0.20 yields a balanced, economical option (i.e., ≈50.7/7.4/6.6 MPa for compressive/flexural/bond). Overall, substituting 15%-30% the USCMs for HB-CSA regulates a flowability and enhances a bond strength as well as reduces production costs by 13%-27%.

Holding the HPMC and w/b ratio roughly fixed while pushing the USCMs beyond 30% (H8 vs. H1) reduces the compressive strength by 14.7 MPa but elevates the bond strength to 6.9 MPa, highlighting a deliberate bond-first Pareto choice. The backscattered electron imaging corroborates this pheoneman. The interfacial transition zone (ITZ) in H8 exhibits a significantly lower porosity and a denser microstructure at the old-new interface, explaining the observed bond enhancement.

Conclusions

In this study, the VAE-GOA could expedite the discovery of high-potential HB-CSA mixtures under strict flow constraints and limited sampling. The comprehensive performance of high-flow state mortars was increased by 20%, and that of low-flow state mortars by >25%. Beyond single best points, the method could yield scenario-specific composition windows and a diverse portfolio choices that practitioners could select from according to the strength, bond, and cost priorities. In particular, low-flow applications were supported by a stable window (e.g., 15%-30% USCMs with 0.1% HPMC and w/b ≈ 0.22), while high-flow placement admited both a strength-oriented option (～5% USCMs + 1% Wacker 328 at w/b = 0.25) and a cost-oriented alternative (30% USCMs + 5% Wacker 328 at w/b = 0.20). The VAE-GOA could be sample-efficient, resilient to local optima, and readily extensible to multi-objective constraints. Looking ahead, coupling the framework with durability targets (e.g., freeze-thaw and chloride ingress), long-term field validation, and uncertainty-aware priors could broaden its applicability to scenario-aware specification and lifecycle-optimized repair design.

Key words

mixture optimization / multi-flow state scenario repair mortar / generative optimization algorithms / generative models

引用本文

李琴飞, 张波, 郭翔宇, 王琳, 李长姣, 侯鹏坤, 陈衡, 程新, 杨波. 基于生成式优化算法的多流态场景修补砂浆配合比优化设计. 硅酸盐学报, 2026 , 54 (3) : 857 -867 . DOI: 10.14062/j.issn.0454-5648.20250798

Qinfei LI, Bo ZHANG, Xiangyu GUO, Lin WANG, Changjiao LI, Pengkun HOU, Heng CHEN, Xin CHENG, Bo YANG. Generative Optimization Algorithm-Based Mixture Optimization Design of Repair Mortar Under Multi-Flow State Scenarios[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (3) : 857 -867 . DOI: 10.14062/j.issn.0454-5648.20250798

基金

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国家自然科学基金面上项目(61872419; 62072213; 52572029)
全国建材行业重大科技攻关“揭榜挂帅”项目(202401JBGS1101; 2023JBGS11-03)
国家自然科学基金区域创新发展联合基金(U22A20126)
山东省自然科学基金重大基础研究项目(ZR2022ZD01)
山东省自然科学基金杰出青年基金项目(ZR2022JQ30)

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2026年第54卷第3期

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文章信息

doi: 10.14062/j.issn.0454-5648.20250798

接收时间：2025-11-01
首发时间：2026-10-27
出版时间：2026-03-20

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收稿日期：2025-11-01
修回日期：2025-11-13

基金

国家自然科学基金面上项目(61872419; 62072213; 52572029)

全国建材行业重大科技攻关“揭榜挂帅”项目(202401JBGS1101; 2023JBGS11-03)

国家自然科学基金区域创新发展联合基金(U22A20126)

山东省自然科学基金重大基础研究项目(ZR2022ZD01)

山东省自然科学基金杰出青年基金项目(ZR2022JQ30)

作者信息

^1．济南大学，山东省绿色与智能建筑材料重点实验室，济南 250022

^2．济南大学，材料科学与工程学院，济南 250022

^3．济南大学，山东省泛在智能计算重点实验室，济南 250022

^4．泉城实验室，济南 250014

通讯作者:

王琳（1983—），男，博士，教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250798

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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