硅酸盐学报

超高性能混凝土早期收缩性能及神经网络预测模型

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蒋志鹏 ¹ , 高畅 ¹ , 汤金辉 ² , 胡张莉 ² , 王磊 ¹

硅酸盐学报 | 研究论文 2025,53(5): 1098-1109

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硅酸盐学报 |研究论文 2025 , 53 (5) : 1098 -1109

超高性能混凝土早期收缩性能及神经网络预测模型

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蒋志鹏¹, 高畅¹, 汤金辉², 胡张莉², 王磊¹

作者信息

^1．长沙理工大学土木工程学院，长沙 410114

^2．东南大学材料科学与工程学院，南京 211189

通讯作者:

高畅（1991—），女，博士，讲师。

作者简介:

蒋志鹏（2003—），男，本科生。

Early Shrinkage Performance and Neural Network Prediction Model of Ultra-High Performance Concrete

Zhipeng JIANG¹, Chang GAO¹, Jinhui TANG², Zhangli HU², Lei WANG¹

Affiliations

^1.School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China

^2.School of Materials Science and Engineering, Southeast University, Nanjing 211189, China

Corresponding Author:

GAO Chang (1991-), female, Ph.D., Lecturer. E-mail: gc812@csust.edu.cn

JIANG Zhipeng (2003-), male, Undergraduate. E-mail: jiangzhipeng@stu.csust.edu.cn

出版时间: 2025-05-20 doi: 10.14062/j.issn.0454-5648.20240562

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摘要

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超高性能混凝土（UHPC）是一种具有高强、高耐久性的先进水泥基材料，但早期收缩大是影响其结构体积稳定性，并带来潜在开裂风险的重要影响因素。本工作探究了胶砂比、水胶比、不同纤维种类与掺量及养护环境对UHPC早期收缩性能的影响，并结合BPNN和WOA-BPNN神经网络建立了UHPC早期收缩模型。结果表明：细集料对UHPC早期干燥收缩和自收缩有抑制作用，当胶砂比从1.2降低至0.8时，其早期干燥收缩及自收缩分布增加了108%和60%；UHPC的早期干燥收缩和自收缩随着水胶比的降低而增加，水胶比的降低导致UHPC自干燥现象提前；聚丙烯纤维的掺入对其早期干燥收缩和自收缩具有抑制作用，呈先增后减的趋势，聚丙烯纤维体积掺量为0.10%时效果最好；养护方式对其早期干燥收缩和自收缩的影响较大，干燥养护条件下的干燥收缩率是标养条件下的2.5倍，干燥养护条件下的自收缩率是标养条件下的1.2倍；WOA-BPNN神经网络构建的UHPC早期干燥收缩和自收缩预测模型相比于BPNN体现出更好的精准性和鲁棒性。

关键词

超高性能混凝土 / 早期干燥收缩 / 早期自收缩 / 机器学习算法

Abstract

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Introduction

Ultra-high performance concrete is an advanced cement-based material with high strength and high durability. Due to the low water-binder ratio and high binder consumption of UHPC, the early shrinkage of UHPC during the setting and hardening process is larger than that of conventional concrete and high-strength concrete, which easily leads to the cracking of engineering structures and affects the performance of structures. Therefore, it is of great significance to study the early shrinkage characteristics of UHPC for the optimization of the material and the prediction of early cracking. In this paper, the effects of binder-sand ratio, water-binder ratio, different fiber types and contents and curing environment on the early shrinkage performance of UHPC were investigated, and the early shrinkage model of UHPC was established by combining BPNN and WOA-BPNN neural network.

Methods

In this study, a total of seven groups of specimens were set up, considering four control factors, i.e., the cement-sand ratio, water-binder ratio, curing environment and polypropylene fiber volume content. Each group consisted of four specimens with size of 25 mm×25 mm×280 mm (including three specimens for drying-shrinkage tests and one specimen for autogenous-shrinkage tests). The specimens were cured in a curing box with a temperature of (25±2) ℃ and a relative humidity of (98%±2%). At the same time, a control group was set up, which was cured in a natural environment with a temperature of (28±5) ℃ and a relative humidity of (60%±15%). The shrinkage deformation of the specimens was measured by a specific length meter at 0, 1, 2, 3, 4, 6, 8, 10, 12 h and 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 11.0, 14.0, 18.0, 21.0, 25.0, 28.0 d. Combined with BPNN and WOA-BPNN machine learning models, the measured data were trained with small samples, and finally a neural network model that can be used to predict the early drying and autogenous shrinkage performance of UHPC was obtained.

Results and discussion

The early drying-shrinkage and autogenous-shrinkage of UHPC increased with an increase of the cement-sand ratio, and the MIC values of drying shrinkage and cement-sand ratio also increased. The early drying-shrinkage and autogenous-shrinkage of specimen with a cement-sand ratio of 1.2 increased by 108% and 60%, respectively, compared with specimen with a cement-sand ratio of 0.8. The early drying-shrinkage and autogenous-shrinkage of UHPC increased with a decrease of the water-binder ratio. The appropriate amount of polypropylene fiber and steel fiber mixture would limit the early shrinkage of UHPC. With an increase of polypropylene fiber content, the inhibitory effect on both the early drying-shrinkage and autogenous-shrinkage grew firstly and then weakened. The specimens with fiber content of 0.10 % exhibit the best inhibitory effect. The drying-shrinkage expressed a great correlation with the curing method, and the MIC value was 0.56. The correlation between the autogenous-shrinkage and curing method is small, and the MIC value is 0.27. The drying-shrinkage rate and self-shrinkage rate under dry curing conditions were 2.5 times and 1.2 times that under standard curing conditions, respectively.

The two machine learning (ML) algorithms were used in the shrinkage prediction and exhibit good accuracy. The WOA-BPNN algorithm expressed delightful predicted accuracy, whose R², RMSE and MAE for drying-shrinkage model are 0.959, 0.050 and 0.040, respectively, and R², RMSE and MAE for autogenous-shrinkage model are 0.896, 0.076 and 0.053, respectively. The predicted results indicated that the whale optimization algorithm could improve the ML model effectively.

Conclusions

The main conclusions of this paper are given as follows: 1) The fine aggregate has an inhibitory effect on the early drying-shrinkage and autogenous-shrinkage of UHPC. When the cement-sand ratio decreases from 1.2 to 0.8, the early drying-shrinkage and autogenous-shrinkage increase by 108% and 60%, respectively. 2) The early drying-shrinkage and autogenous-shrinkage of UHPC increase with a decrease of water-binder ratio, and the decrease of water-binder ratio leads to the advance of UHPC self-drying phenomenon. 3) The research found that the polypropylene fiber volume content of 0.10% exhibit the best inhibitory effect on the UHPC shrinkage. 4) The curing method had a great influence on its early drying-shrinkage and autogenous-shrinkage. The drying-shrinkage rate of specimens under dry curing condition is 2.5 times that under standard curing condition, and the autogenous-shrinkage rate of specimens under dry curing condition is 1.2 times that under standard curing condition. The early drying-shrinkage and autogenous-shrinkage predicted results of UHPC based on the WOA-BPNN neural network show nice accuracy and robustness compared to that of BPNN.

Key words

ultra-high performance concrete / early drying shrinkage / early autogenous shrinkage / machine learning algorithms

引用本文

蒋志鹏, 高畅, 汤金辉, 胡张莉, 王磊. 超高性能混凝土早期收缩性能及神经网络预测模型. 硅酸盐学报, 2025 , 53 (5) : 1098 -1109 . DOI: 10.14062/j.issn.0454-5648.20240562

Zhipeng JIANG, Chang GAO, Jinhui TANG, Zhangli HU, Lei WANG. Early Shrinkage Performance and Neural Network Prediction Model of Ultra-High Performance Concrete[J]. Journal of the Chinese Ceramic Society, 2025 , 53 (5) : 1098 -1109 . DOI: 10.14062/j.issn.0454-5648.20240562

基金

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国家自然科学基金(52308233)
湖南省自然科学基金(2022JJ40062)
国家自然科学基金重大项目(52293432)
高性能土木工程材料国家重点实验室开放课题(2023CEM002)
高速铁路建造技术国家工程实验室开放课题(HSR202101)

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2025年第53卷第5期

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文章信息

doi: 10.14062/j.issn.0454-5648.20240562

接收时间：2024-08-26
首发时间：2026-10-22
出版时间：2025-05-20

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出版历史

收稿日期：2024-08-26
修回日期：2024-11-23

基金

国家自然科学基金(52308233)

湖南省自然科学基金(2022JJ40062)

国家自然科学基金重大项目(52293432)

高性能土木工程材料国家重点实验室开放课题(2023CEM002)

高速铁路建造技术国家工程实验室开放课题(HSR202101)

作者信息

^1．长沙理工大学土木工程学院，长沙 410114

^2．东南大学材料科学与工程学院，南京 211189

通讯作者:

高畅（1991—），女，博士，讲师。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20240562

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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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