基于机器学习预测普通和高强混凝土弹性模量
Predicting the Elastic Modulus of Normal and High-Strength Concrete Based on Machine Learning
摘要: 高性能混凝土(High Performance Concrete, HPC)在增强建筑物及基础设施的可持续性与可靠性方面发挥着重要作用。机器学习技术已经广泛应用于预测混凝土的多项性能指标。本研究提出了应用高斯过程(Gaussian Process, GP)模型，预测普通和高强混凝土基于抗压强度的弹性模量。为优化GP模型的预测准确性，本研究采用Kalman滤波和平滑((Kalman Filtering and Smoothing, KF/KS)技术以降低数据离散性的影响。研究结果显示，GP模型能够有效利用物理模型，预测和泛化能力良好。通过应用KF/KS技术处理数据，模型的性能得到进一步提升。该模型具有较高的准确性和稳定性，有望成为弹性模量估算的快速、稳健和低成本的工具。
Abstract: High Performance Concrete (HPC) plays a crucial role in enhancing the sustainability and reliability of buildings and infrastructure. Machine learning techniques have been widely applied to predict various performance indices of concrete. This study introduces the use of Gaussian Process (GP) models to predict the elastic modulus of normal and high-strength concrete based on compressive strength. To optimize the predictive accuracy of the GP model, this research employs Kalman Filtering and Smoothing (KF/KS) techniques to reduce the impact of data dispersion. The results demonstrate that the GP model can effectively utilize physical models, showing good prediction and generalization capabilities. The performance of the model is further improved by processing data through KF/KS techniques. With high accuracy and stability, the model promises to be a fast, robust, and low-cost tool for estimating the elastic modulus.
文章引用：邹志鹏. 基于机器学习预测普通和高强混凝土弹性模量[J]. 建模与仿真, 2024, 13(3): 2591-2601. https://doi.org/10.12677/mos.2024.133236

参考文献

[1] Chang, T.P., Chuang, F.C. and Lin, H.C. (1996) A Mix Proportioning Methodology for High-Performance Concrete. Journal of the Chinese Institute of Engineers, 19, 645-655. [Google Scholar] [CrossRef] 
[2] Yeh, I.-C. (1998) Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks. Cement and Concrete Research, 28, 1797-1808. [Google Scholar] [CrossRef] 
[3] Bharatkumar, B.H., Narayanan, R., Raghuprasad, B.K., et al. (2001) Mix Proportioning of High Performance Concrete. Cement and Concrete Composites, 23, 71-80. [Google Scholar] [CrossRef] 
[4] Lim, C.-H., Yoon, Y.-S. and Kim, J.-H. (2004) Genetic Algorithm in Mix Proportioning of High-Performance Concrete. Cement and Concrete Research, 34, 409-420. [Google Scholar] [CrossRef] 
[5] Marvila, M.T., de Azevedo, A.R.G., de Matos, P.R., et al. (2021) Materials for Production of High and Ultra-High Performance Concrete: Review and Perspective of Possible Novel Materials. Materials, 14, Article 4304. [Google Scholar] [CrossRef] [PubMed]
[6] Neville, A. and Aitcin, P.C. (1998) High Performance Concrete—An Overview. Materials and Structures, 31, 111-117. [Google Scholar] [CrossRef] 
[7] Aıtcin, P.C. (2003) The Durability Characteristics of High Performance Concrete: A Review. Cement and Concrete Composites, 25, 409-420. [Google Scholar] [CrossRef] 
[8] Nematzadeh, M. and Naghipour, M. (2012) Compressive Strength and Modulus of Elasticity of Freshly Compressed Concrete. Construction and Building Materials, 34, 476-485. [Google Scholar] [CrossRef] 
[9] Mesbah, H.A., Lachemi, M. and Aitcin, P.C. (2002) Determination of Elastic Properties of High-Performance Concrete at Early Ages. Materials Journal, 99, 37-41. [Google Scholar] [CrossRef] 
[10] Wang, R., Hu, Z., Li, Y., et al. (2022) Review on the Deterioration and Approaches to Enhance the Durability of Concrete in the Freeze-Thaw Environment. Construction and Building Materials, 321, Article 126371. [Google Scholar] [CrossRef] 
[11] Wang, R., Zhang, Q. and Li, Y. (2022) Deterioration of Concrete under the Coupling Effects of Freeze-Thaw Cycles and Other Actions: A Review. Construction and Building Materials, 319, Article 126045. [Google Scholar] [CrossRef] 
[12] Demir, F. (2008) Prediction of Elastic Modulus of Normal and High Strength Concrete by Artificial Neural Networks. Construction and Building Materials, 22, 1428-1435. [Google Scholar] [CrossRef] 
[13] Teng, T.L., Chu, Y.A., Chang, F.A., et al. (2004) Calculating the Elastic Moduli of Steel-Fiber Reinforced Concrete Using a Dedicated Empirical Formula. Computational Materials Science, 31, 337-346. [Google Scholar] [CrossRef] 
[14] Narayanan, N. and Ramamurthy, K. (2000) Structure and Properties of Aerated Concrete: A Review. Cement and Concrete Composites, 22, 321-329. [Google Scholar] [CrossRef] 
[15] Williams, C.K. and Rasmussen, C.E. (2006) Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, 4.
[16] Peng, B., Wei, S., Zong, G., et al. (2019) Shear Resistance Estimation for Unreinforced Masonry Walls Based on Gaussian Process Models. Advances in Structural Engineering, 22, 831-845. [Google Scholar] [CrossRef] 
[17] Peng, B., Wang, D.D., Zong, G., et al. (2022) Calculation of Reliability Index for In-Plane Shear Failure of Unreinforced Masonry Walls Based on Gaussian Process Model. European Journal of Environmental and Civil Engineering, 26, 1322-1335. [Google Scholar] [CrossRef] 
[18] Dao, D.V., Adeli, H., Ly, H.B., et al. (2020) A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability, 12, Article 830. [Google Scholar] [CrossRef] 
[19] 张研, 苏国韶, 燕柳斌. 高强混凝土强度预测的高斯过程机器学习模型[J]. 混凝土, 2011(11): 18-20.
[20] Hussain, F., Ali Khan, S., Khushnood, R.A., et al. (2022) Machine Learning-Based Predictive Modeling of Sustainable Lightweight Aggregate Concrete. Sustainability, 15, Article 641. [Google Scholar] [CrossRef] 
[21] Chu, S.H., Kurumisawa, K. and Kong, Y.K. (2023) Physically Explicable Mathematical Model for Strength Prediction of UHPFRC. Engineering Structures, 275, Article 115191. [Google Scholar] [CrossRef] 
[22] Yazdi, J.S., Kalantary, F. and Yazdi, H.S. (2013) Prediction of Elastic Modulus of Concrete Using Support Vector Committee Method. Journal of Materials in Civil Engineering, 25, 9-20. [Google Scholar] [CrossRef] 
[23] 中华人民共和国住房和城乡建设部. GB 50010-2010混凝土结构设计规范[S]. 北京: 中国建筑工业出版社, 2010.
[24] ACI Committee (2014) Building Code Requirements for Structural Concrete (ACI 318-14). American Concrete Institute, Farmington Hills, MI.
[25] ACI Committee (2010) Report on High-Strength Concrete (ACI 363R-10). American Concrete Institute, Farmington Hills, MI.
[26] SETRA and AFGC (2002) Ultra High Performance Fiber-Reinforced Concretes-Interim Recommendations (BétonsFibrés à Ultra-Hautes Performances-Recommandations Provisoires).
[27] European Committee for Standardization (2004) Eurocode 2: Design of Concrete Structures-Part 1-1: General Rules and Rules for Buildings.
[28] Canadian Standards Association (2004) Design of Concrete Structures. Ontario.
[29] FIP-CEB (1990) High Strength Concrete: State-of-the-Art Report, Bulletin d’Information No. 197. Lausanne, Switzerland.
[30] NorgesStandardiseringsforbund (1992) Concrete Structures—Design Rules, NS 3473.
[31] Alsalman, A., Dang, C.N., Prinz, G.S., et al. (2017) Evaluation of Modulus of Elasticity of Ultra-High Performance Concrete. Construction and Building Materials, 153, 918-928. [Google Scholar] [CrossRef] 
[32] Ma, J., Orgass, M., Dehn, F., et al. (2004) Comparative Investigations on Ultra-High Performance Concrete with and without Coarse Aggregates. Proceedings of the International Symposium on Ultra High Performance Concrete, Kassel, 13-15 September 2004, 13-15.
[33] Sritharan, S., Bristow, B. and Perry, V. (2003) Characterizing an Ultra-High Performance Material for Bridge Applications under Extreme Loads. Proceedings of the 3rd International Symposium on High Performance Concrete, Orlando, 19-22 October 2003, 7.
[34] 郭晓宇, 亢景付, 朱劲松. 超高性能混凝土单轴受压本构关系[J]. 东南大学学报(自然科学版), 2017, 47(2): 369-376.
[35] Ahmad, S., Zubair, A. and Maslehuddin, M. (2015) Effect of Key Mixture Parameters on Flow and Mechanical Properties of Reactive Powder Concrete. Construction and Building Materials, 99, 73-81. [Google Scholar] [CrossRef] 
[36] Graybeal, B.A. (2007) Compressive Behavior of Ultra-High-Performance Fiber-Reinforced Concrete. Materials Journal, 104, 146-152. [Google Scholar] [CrossRef] 
[37] Ma, H., Yan, L., Xia, Y., et al. (2020) Kalman Filtering and Information Fusion. Springer, Singapore. [Google Scholar] [CrossRef] 
[38] Hennig, P., Osborne, M.A. and Kersting, H.P. (2022) Probabilistic Numerics: Computation as Machine Learning. Cambridge University Press, Cambridge. [Google Scholar] [CrossRef] 

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