硅酸盐学报

基于生成对抗的钢纤维混凝土强度预测及沙普利加性解释分析

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杨哲 ¹ , 张悦 ¹ , 徐衍 ² , 侯东帅 ¹ , 王攀 ¹ , 王慕涵 ¹ , 王鑫鹏 ¹

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): 982-992

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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) : 982 -992

基于生成对抗的钢纤维混凝土强度预测及沙普利加性解释分析

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杨哲¹, 张悦¹, 徐衍², 侯东帅¹, 王攀¹, 王慕涵¹, 王鑫鹏¹

作者信息

^1．青岛理工大学土木工程学院，山东青岛 266520

^2．中国水利水电第四工程局有限公司，西宁 810000

通讯作者:

侯东帅（1986—），男，博士，教授。

作者简介:

杨哲（1993—），男，博士研究生。

Strength Prediction and SHapley Additive Explanations Interpretability Analysis of Steel Fiber Reinforced Concrete Based on Generative Adversarial Networks

Zhe YANG¹, Yue ZHANG¹, Yan XU², Dongshuai HOU¹, Pan WANG¹, Muhan WANG¹, Xinpeng WANG¹

Affiliations

^1.School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, Shandong, China

^2.China Water Resources and Hydropower Fourth Engineering Bureau Co., Ltd., Xining 810000, China

Corresponding Author:

HOU Dongshuai (1986-), male, Ph.D., Professor. E-mail: dshou@outlook.com

YANG Zhe (1993-), male, Doctoral candidate. E-mail: yangzhe_x@163.com

出版时间: 2026-03-20 doi: 10.14062/j.issn.0454-5648.20250734

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摘要

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为研究多因素耦合作用下钢纤维混凝土（SFRC）抗压性能的影响机制，本工作采用机器学习方法对其抗压强度进行预测。研究基于141组样本和11个关键参数，构建并评估了多种ML模型，结果表明，这些模型在强度预测中具有较高精度。为解决试验数据有限的问题，引入生成对抗网络（GAN）进行数据增强，并显著提升了模型的泛化性能。进一步的SHAP分析表明，水泥与硅灰含量对强度影响最大，其配比直接作用于最终强度的发展。通过将ML建模、GAN增强与SHAP机理分析相结合，为SFRC的设计与工程应用提供了有效的预测工具和理论依据。

关键词

钢纤维混凝土 / 机器学习 / 随机森林 / 生成对抗网络 / 沙普利加性解释

Abstract

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Introduction

Predicting the compressive strength of Steel Fiber Reinforced Concrete (SFRC) is challenging due to the complex, coupled influences of numerous mix design parameters. Traditional experimental methods are often costly and time-consuming for exploring this high-dimensional factor space. While machine learning (ML) models offer a powerful alternative, their practical application is hindered by two major limitations: the scarcity of comprehensive, high-quality experimental data which restricts model performance and generalizability, and the inherent "black-box" nature of advanced ML algorithms which obscures the underlying mechanisms linking mix proportions to mechanical properties. This study aims to address these critical gaps by developing a hybrid framework that integrates ML-based prediction, data augmentation via Generative Adversarial Networks (GAN), and post-hoc model interpretability using SHapley Additive exPlanations (SHAP). The objective is not only to achieve high-precision strength prediction but also to enhance model robustness with limited data and, crucially, to uncover the influential mechanisms of key components on SFRC compressive strength.

Methods

A database of 141 mix designs with 11 input features—Cement (C), Fly Ash (FA), Silica Fume (SF), Water (W), Coarse Sand (CS), Water Reducer (WR), Recycled Aggregate (RA), Natural Aggregate (NA), Fiber Length (FL), Fiber Diameter (FD), and Fiber Content Ratio (FCR)—and the corresponding compressive strength was compiled from peer-reviewed literature. Four tree-based ML algorithms (Random Forest, XGBoost, Stacking, and AdaBoost) were employed for regression modeling. To mitigate the data scarcity issue, a GAN architecture was implemented to generate 282 synthetic but physically plausible samples, expanding the training set. The quality of the GAN-generated data was validated using t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization and statistical moment analysis. The model trained on the augmented dataset was evaluated using the coefficient of determination (R²) and Mean Squared Error (MSE). Finally, the optimal model (Random Forest) was interpreted using the SHAP framework to quantify and visualize the contribution and interaction effects of each input feature on the model's predictions.

Results and discussion

Using the original dataset, all ML models demonstrated competent predictive capability. The Random Forest model achieved the best performance with an R² of 0.93 and an MSE of 15.6 MPa², followed closely by XGBoost (R²=0.92, MSE=16.6 MPa²). The introduction of GAN-generated data led to a remarkable and consistent improvement in all models' performance. For Random Forest, the R² increased to 0.98 and the MSE decreased by approximately 56% to 6.9 MPa². The Stacking model achieved an R² of 0.99. This significant enhancement confirms that GAN-based data augmentation effectively enriches the feature space and improves the models' ability to generalize from limited experimental data.

The SHAP analysis provided profound insights into the governing factors of compressive strength. The summary plot identified cement, water, and silica fume as the three most influential features. Cement and silica fume exhibited strong positive SHAP values, highlighting their fundamental role in forming a dense cementitious matrix. The impact of water displayed a distinct non-linear relationship: optimal content promoted hydration and strength, while excess water reduced strength, likely by increasing porosity. The interaction SHAP dependence plots revealed nuanced coupling effects. For instance, the positive contribution of a high WR dosage was more pronounced and stable under conditions of lower water content, emphasizing the critical coupling between water and chemical admixtures in determining the water-to-cementitious materials ratio. In contrast, parameters related to fiber geometry (FL, FD) and content (FCR) showed relatively low and concentrated SHAP values near zero, indicating their secondary role in influencing compressive strength, which aligns with their primary function of improving toughness rather than pure compressive capacity. The SHAP force plots and Sankey diagram further illustrated how specific combinations of high cement/silica fume and optimal water content drive predictions toward high strength, whereas high coarse sand content or inappropriate water levels contribute to lower strength outcomes.

Conclusions

This work successfully developed an integrated framework combining ML, GAN, and SHAP for predicting and interpreting the compressive strength of SFRC. The main conclusions are as follows:1) Among the evaluated ML models trained on the original dataset, Random Forest provided the most accurate predictions for SFRC compressive strength.2) GAN-based data augmentation proved highly effective, substantially improving the predictive accuracy and generalization ability of all models, with performance metrics (R² and MSE) showing dramatic improvement.3) SHAP interpretability analysis quantitatively identified cement, silica fume, and water as the most critical factors affecting compressive strength, with water exhibiting a non-linear effect. The analysis also elucidated important feature interactions, such as that between water and water reducer. Fiber parameters were found to have a minor influence on compressive strength prediction.4) The proposed framework offers a powerful, data-driven tool that moves beyond mere "black-box" prediction. It enhances prediction robustness from small datasets and provides actionable, mechanism-based insights for optimizing SFRC mix design, thereby bridging the gap between empirical data mining and fundamental materials science understanding.

Key words

steel fiber reinforced concrete / machine learning / random forest / generative adversarial network / shapley additive explanations

引用本文

杨哲, 张悦, 徐衍, 侯东帅, 王攀, 王慕涵, 王鑫鹏. 基于生成对抗的钢纤维混凝土强度预测及沙普利加性解释分析. 硅酸盐学报, 2026 , 54 (3) : 982 -992 . DOI: 10.14062/j.issn.0454-5648.20250734

Zhe YANG, Yue ZHANG, Yan XU, Dongshuai HOU, Pan WANG, Muhan WANG, Xinpeng WANG. Strength Prediction and SHapley Additive Explanations Interpretability Analysis of Steel Fiber Reinforced Concrete Based on Generative Adversarial Networks[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (3) : 982 -992 . DOI: 10.14062/j.issn.0454-5648.20250734

基金

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国家自然科学基金(U24A20166)

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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.0454-5648.20250734

接收时间：2025-09-30
首发时间：2026-10-27
出版时间：2026-03-20

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出版历史

收稿日期：2025-09-30
修回日期：2025-11-13

基金

国家自然科学基金(U24A20166)

作者信息

^1．青岛理工大学土木工程学院，山东青岛 266520

^2．中国水利水电第四工程局有限公司，西宁 810000

通讯作者:

侯东帅（1986—），男，博士，教授。

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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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