基于多重响应的钢渣超高性能混凝土组成优化设计研究

硅酸盐通报 ›› 2021, Vol. 40 ›› Issue (9): 3029-3038.

基于多重响应的钢渣超高性能混凝土组成优化设计研究

冯元^1,2, 余睿¹, 范定强^1,3, 曾敏^4,5, 胡方杰^4,5, 水中和¹, 王思雨^1,2, 刘康宁^1,2, 谭珺辉², 王武峰⁶

1.武汉理工大学,硅酸盐建筑材料国家重点实验室,武汉 430070;
2.武汉理工大学材料科学与工程国际化示范学院,武汉 430070;
3.武汉理工大学材料科学与工程学院,武汉 430070;
4.中铁建大桥设计研究院,武汉 430063;
5.中国铁建股份有限公司桥梁工程实验室,武汉 430063;
6.中山市武汉理工大学先进工程技术研究院,中山 528400

收稿日期:2021-03-17 修回日期:2021-04-07 出版日期:2021-09-15 发布日期:2021-10-08
通讯作者: 余睿,博士,研究员。E-mail:r.yu@whut.edu.cn
作者简介:冯元(2000—),女。主要从事超高性能混凝土的研究。E-mail:2235571847@qq.com
基金资助:
国家级大学生创新创业训练计划(S202010497007);中山市武汉理工大学先进工程技术研究院开放课题(WUT202004)

Optimized Design of Ultra-High Performance Concrete Incorporating Steel Slag Based on Multiple Response Approach

FENG Yuan^1,2, YU Rui¹, FAN Dingqiang^1,3, ZENG Min^4,5, HU Fangjie^4,5, SHUI Zhonghe¹, WANG Siyu^1,2, LIU Kangning^1,2, TAN Junhui², WANG Wufeng⁶

1. State Key Laboratory of Silicate Materials for Architectures, Wuhan 430070, China;
2. International School of Materials Science and Engineering, Wuhan University of Technology, Wuhan University of Technology, Wuhan 430070, China;
3. School of Materials Science and Engineering, Wuhan University of Technology, Wuhan 430070, China;
4. China Railway Construction Bridge Design and Research Branch, Wuhan 430063, China;
5. China Railway Construction Laboratory of Bridge Engineering, Wuhan 430063, China;
6. Wuhan University of Technology Advanced Engineering Technology Research Institute of Zhongshan City, Zhongshan 528400, China

Received:2021-03-17 Revised:2021-04-07 Online:2021-09-15 Published:2021-10-08

摘要/Abstract

摘要： 为实现生态型超高性能混凝土(UHPC)的组成优化和钢渣利用率的提高,将钢渣粉作为辅助胶凝材料,利用D-最优化设计方法制备生态型UHPC,并建立了UHPC的工作性能与抗压强度预测模型,以进行多重响应分析。最后借助预测模型,以低水泥用量、高钢渣粉利用率设计出了符合要求的生态型UHPC。结果表明:预测模型构建合理且准确度高,在预测模型中减水剂和硅灰对于工作性能影响程度较大,水泥、减水剂以及钢渣的交互作用对工作性能影响明显,钢渣粉的加入可增强工作性能;硅灰和钢渣粉对抗压强度影响显著,水泥和钢渣粉的交互作用对抗压强度的影响较小,随钢渣粉掺量的增加抗压强度存在最优值;优化的生态型UHPC的配比可以实现钢渣粉替代30%(质量分数)水泥,同时保证抗压强度130 MPa以上,工作性能260 mm以上。

关键词: 超高性能混凝土, D-最优化设计方法, 钢渣粉, 多重响应分析, 生态型, 性能预测

Abstract: In order to achieve the composition optimization of eco-type UHPC and the improvement of the utilization rate of steel slag, using steel slag as auxiliary gelled material, the eco-type UHPC was prepared by D-optimal design method and establish the workability prediction model and the compressive strength prediction model to carry out multiple response analysis. With the help of the prediction model, the eco-type UHPC with low cement consumption and high steel slag utilization rate is designed to meet the requirements. The results show that the prediction model is reasonable and accurate. In the prediction model, superplasticizer and silica fume have a great influence on the workability, the interaction of cement, superplasticizer and steel slag have obvious influence on the workability and the addition of steel slag powder enhances the workability. The influences of silica fume and steel slag powder om compressive strength are significant, the interaction of cement and steel slag has little effect on compressive strength and the optimal value of compressive strength exists with the increase of steel slag powder. The optimal ratio of the eco-type UHPC realizes the substitution of steel slag for 30% (mass fraction) cement, and ensure the compressive strength above 130 MPa and the working performance above 260 mm.

Key words: ultra-high performance concrete, D-optimal design method, steel slag powder, multiple response analysis, eco-type, performance prediction

中图分类号:

TU528

冯元, 余睿, 范定强, 曾敏, 胡方杰, 水中和, 王思雨, 刘康宁, 谭珺辉, 王武峰. 基于多重响应的钢渣超高性能混凝土组成优化设计研究[J]. 硅酸盐通报, 2021, 40(9): 3029-3038.

FENG Yuan, YU Rui, FAN Dingqiang, ZENG Min, HU Fangjie, SHUI Zhonghe, WANG Siyu, LIU Kangning, TAN Junhui, WANG Wufeng. Optimized Design of Ultra-High Performance Concrete Incorporating Steel Slag Based on Multiple Response Approach[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2021, 40(9): 3029-3038.

参考文献

[1] 梁天霄,郑元勋.超高性能混凝土的研究综述[J].科技展望,2016,26(36):19-20.
LIANG T X, ZHENG Y X. Review of research on ultra-high performance concrete[J]. Science and Technology, 2016, 26(36): 19-20 (in Chinese).
[2] 刘翼玮,张祖华,史才军,等.硅灰对高强地聚物胶凝材料性能的影响[J].硅酸盐学报,2020,48(11):1689-1699.
LIU Y W, ZHANG Z H, SHI C J, et al. Influence of silica fume on performance of high-strength geopolymer[J]. Journal of the Chinese Ceramic Society, 2020, 48(11): 1689-1699 (in Chinese).
[3] RICHARD P, CHEYREZY M. Composition of reactive powder concretes[J]. Cement and Concrete Research, 1995, 25(7): 1501-1511.
[4] WORRELL E, PRICE L, MARTIN N, et al. Carbon dioxide emissions from the global cement industry[J]. Annual Review of Energy and the Environment, 2001, 26(1): 303-329.
[5] 郑永超,周钰沦,房桂明,等.利用钢渣制备矿物掺合料对混凝土性能的影响[J].混凝土与水泥制品,2020(7):87-91.
ZHENG Y C, ZHOU Y L, FANG G M, et al. Preparation of mineral admixtures from steel slag and its effect on concrete performance[J]. China Concrete and Cement Products, 2020(7): 87-91 (in Chinese).
[6] 王毓.钢渣活性激发及其在水泥基材料中的应用研究[D].淮南:安徽理工大学,2018.
WANG Y. Study on active excitation of steel slag and application of building materials[D]. Huainan: Anhui University of Science & Technology, 2018 (in Chinese).
[7] 赵计辉.钢渣的粉磨/水化特征及其复合胶凝材料的组成与性能[D].北京:中国矿业大学(北京),2015.
ZHAO J H. Grinding and hydration characteristics of steel slag and composition and properties of composite cemtitious materials containing steel slag powder[D]. Beijing: China University of Mining & Technology, Beijing, 2015 (in Chinese).
[8] SHI C J, QIAN J S. High performance cementing materials from industrial slags: a review[J]. Resources, Conservation and Recycling, 2000, 29(3): 195-207.
[9] PENG Y Z, CHEN K, HU S G. Durability and microstructure of ultra-high performance concrete having high volume of steel slag powder and ultra-fine fly ash[J]. Advanced Materials Research, 2011, 255/256/257/258/259/260: 452-456.
[10] WANG Q, SHI M X, YANG J. Influence of classified steel slag with particle sizes smaller than 20 μm on the properties of cement and concrete[J]. Construction and Building Materials, 2016, 123: 601-610.
[11] HABEL K. Time dependent behavior of elements combining ultra-high performance fiber reinforced concretes (UHPFRC) and reinforced concrete[J]. Materials and Structures, 2005.
[12] WANG X P, YU R, SONG Q L, et al. Optimized design of ultra-high performance concrete (UHPC) with a high wet packing density[J]. Cement and Concrete Research, 2019, 126: 105921.
[13] HAMMOUDI A, MOUSSACEB K, BELEBCHOUCHE C, et al. Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates[J]. Construction and Building Materials, 2019, 209: 425-436.
[14] GHAFARI E, BANDARABADI M, COSTA H, et al. Prediction of fresh and hardened state properties of UHPC: comparative study of statistical mixture design and an artificial neural network model[J]. Journal of Materials in Civil Engineering, 2015, 27(11): 04015017.
[15] GHAFARI E, COSTA H, JÚLIO E. Statistical mixture design approach for eco-efficient UHPC[J]. Cement and Concrete Composites, 2015, 55: 17-25.
[16] VENKATESAN M, ZAIB Q, SHAH I H, et al. Optimum utilization of waste foundry sand and fly ash for geopolymer concrete synthesis using D-optimal mixture design of experiments[J]. Resources, Conservation and Recycling, 2019, 148: 114-123.
[17] OZLEM A, KADRI U A, BAHAR S. Self-consolidating high-strength concrete optimization by mixture design method[J]. ACI Materials Journal, 2010, 107(4): 357-364.
[18] 余睿,范定强,水中和,等.基于颗粒最紧密堆积理论的超高性能混凝土配合比设计[J].硅酸盐学报,2020,48(8):1145-1154.
YU R, FAN D Q, SHUI Z H, et al. Mix design of ultra-high performance concrete based on particle densely packing theory[J]. Journal of the Chinese Ceramic Society, 2020, 48(8): 1145-1154 (in Chinese).
[19] MURALIDHAR R V, CHIRUMAMILA R R, MARCHANT R, et al. A response surface approach for the comparison of lipase production by Candida cylindracea using two different carbon sources[J]. Biochemical Engineering Journal, 2001, 9(1): 17-23.
[20] 沈晓冬.海洋工程水泥与混凝土材料[M].北京:化学工业出版社,2016:48-49.
SHEN X D. Marine engineering cement and concrete materials[M]. Beijing: Chemical Industry Press, 2016: 48-49 (in Chinese).
[21] MONTGOMERY D C. Design and analysis of experiments: response surface method and designs[M]. New Jersey: Wiley Online Library, 2005.
[22] DERRINGER G, SUICH R. Simultaneous optimization of several response variables[J]. Journal of Quality Technology, 1980, 12(4): 214-219.