Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

Abstract

Concrete is the most commonly used construction material. However, its production leads to high carbon dioxide (CO₂) emissions and energy consumption. Therefore, developing waste-substitutable concrete components is necessary. Improving the sustainability and greenness of concrete is the focus of this research. In this regard, 899 data points were collected from existing studies where cement, slag, fly ash, superplasticizer, coarse aggregate, and fine aggregate were considered potential influential factors. The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult. Instead of the traditional compressive strength test, this study combines five novel metaheuristic algorithms with extreme gradient boosting (XGB) to predict the compressive strength of green concrete based on fly ash and blast furnace slag. The intelligent prediction models were assessed using the root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), and variance accounted for (VAF). The results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded the best overall prediction performance with R² values of 0.9930 and 0.9576, VAF values of 99.30 and 95.79, MAE values of 0.52 and 2.50, RMSE of 1.34 and 3.31 for the training and testing sets, respectively. The remaining five prediction methods yield promising results. Therefore, the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete. Finally, the developed SSA-XGB considered the effects of all the input factors on the compressive strength. The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

Graphical abstract

Keywords

sustainable concrete / fly ash / slay / extreme gradient boosting technique / squirrel search algorithm / parametric analysis

References

[1] Amran M, Murali G, Khalid N H A, Fediuk R, Ozbakkaloglu T, Lee Y H, Haruna S, Lee Y Y. Slag uses in making an ecofriendly and sustainable concrete: A review. Construction & Building Materials, 2021, 272: 121942

[2] Li J, Xu G. Circular economy towards zero waste and decarbonization. Circular Economy, 2022, 1(1): 100002

[3] Zhang N, Xi B, Li J, Liu L, Song G. Utilization of CO₂ into recycled construction materials: A systematic literature review. Journal of Material Cycles and Waste Management, 2022, 24(6): 2108–2125

[4] Sellami A, Merzoud M, Amziane S. Improvement of mechanical properties of green concrete by treatment of the vegetals fibers. Construction & Building Materials, 2013, 47: 1117–1124

[5] Xi B, Zhou Y, Yu K, Hu B, Huang X, Sui L, Xing F. Use of nano-SiO₂ to develop a high performance green lightweight engineered cementitious composites containing fly ash cenospheres. Journal of Cleaner Production, 2020, 262: 121274

[6] Shi C, Li Y, Zhang J, Li W, Chong L, Xie Z. Performance enhancement of recycled concrete aggregate—A review. Journal of Cleaner Production, 2016, 112: 466–472

[7] Zhou Y, Xi B, Sui L, Zheng S, Xing F, Li L. Development of high strain-hardening lightweight engineered cementitious composites: Design and performance. Cement and Concrete Composites, 2019, 104: 103370

[8] Malhotra V M. Durability of concrete incorporating high-volume of low-calcium (ASTM Class F) fly ash. Cement and Concrete Composites, 1990, 12(4): 271–277

[9] Sun J, Shen X, Tan G, Tanner J E. Compressive strength and hydration characteristics of high-volume fly ash concrete prepared from fly ash. Journal of Thermal Analysis and Calorimetry, 2019, 136(2): 565–580

[10] Kara de Maeijer P, Craeye B, Snellings R, Kazemi-Kamyab H, Loots M, Janssens K, Nuyts G. Effect of ultra-fine fly ash on concrete performance and durability. Construction & Building Materials, 2020, 263: 120493

[11] Samad S, Shah A. Role of binary cement including Supplementary Cementitious Material (SCM), in production of environmentally sustainable concrete: A critical review. International Journal of Sustainable Built Environment, 2017, 6(2): 663–674

[12] Guo L P, Sun W, Zheng K R, Chen H J, Liu B. Study on the flexural fatigue performance and fractal mechanism of concrete with high proportions of ground granulated blast-furnace slag. Cement and Concrete Research, 2007, 37(2): 242–250

[13] Afroughsabet V, Biolzi L, Ozbakkaloglu T. Influence of double hooked-end steel fibers and slag on mechanical and durability properties of high performance recycled aggregate concrete. Composite Structures, 2017, 181: 273–284

[14] Feng D C, Liu Z T, Wang X D, Chen Y, Chang J Q, Wei D F, Jiang Z M. Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Construction & Building Materials, 2020, 230: 117000

[15] Song H, Ahmad A, Farooq F, Ostrowski K A, Maślak M, Czarnecki S, Aslam F. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Construction & Building Materials, 2021, 308: 125021

[16] Kang M C, Yoo D Y, Gupta R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Construction & Building Materials, 2021, 266: 121117

[17] Shariati M, Mafipour M S, Mehrabi P, Ahmadi M, Wakil K, Trung N T, Toghroli A. Prediction of concrete strength in presence of furnace slag and fly ash using Hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm). Smart Structures and Systems, 2020, 25(2): 183–195

[18] Chopra P, Sharma R K, Kumar M. Prediction of compressive strength of concrete using artificial neural network and genetic programming. Advances in Materials Science and Engineering, 2016, 2016: 1–10

[19] Dao D V, Adeli H, Ly H B, Le L M, Le V M, Le T T, Pham B T. A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a monte carlo simulation. Sustainability, 2020, 12(3): 830

[20] Yeh I C. Modeling concrete strength with augment-neuron networks. Journal of Materials in Civil Engineering, 1998, 10(4): 263–268

[21] Yeh I C. Design of high-performance concrete mixture using neural networks and nonlinear programming. Journal of Computing in Civil Engineering, 1999, 13(1): 36–42

[22] Paninski L. Estimation of entropy and mutual information. Neural Computation, 2003, 15(6): 1191–1253

[23] Yang Y, Zhang Q. A hierarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering, 1997, 30(4): 207–222

[24] Zhou J, Qiu Y, Armaghani D J, Zhang W, Li C, Zhu S, Tarinejad R. Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques. Geoscience Frontiers, 2021, 12(3): 101091

[25] Duan J, Asteris P G, Nguyen H, Bui X N, Moayedi H. A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Engineering with Computers, 2021, 37(4): 3329–3346

[26] ChenTGuestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016, 785–794

[27] Khishe M, Mosavi M R. Chimp optimization algorithm. Expert Systems with Applications, 2020, 149: 113338

[28] Chou J S, Truong D N. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 2021, 389: 125535

[29] Chopra N, Mohsin Ansari M. Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 2022, 198: 116924

[30] SeyyedabbasiAKianiF. Sand Cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 2022, 1–25

[31] Xue J, Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering, 2020, 8(1): 22–34

[32] LiSLiD. Artificial Intelligence for Materials Science. Cham: Springer, 2021, 115–131

[33] Li E, Zhou J, Shi X, Jahed Armaghani D, Yu Z, Chen X, Huang P. Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill. Engineering with Computers, 2021, 37(4): 3519–3540

[34] Li E, Yang F, Ren M, Zhang X, Zhou J, Khandelwal M. Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms. Journal of Rock Mechanics and Geotechnical Engineering, 2021, 13(6): 1380–1397

[35] van der GaagMHoffmanTRemijsenM HijmanRde Haan Lvan MeijelBVanhartenPValmaggia LDehertMCuijpersA. The five-factor model of the Positive and Negative Syndrome Scale II: A ten-fold cross-validation of a revised model. Schizophrenia Research, 2006, 85(1−3): 280−287

[36] Bentéjac C, Csörgő A, Martínez-Muñoz G. A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 2021, 54(3): 1937–1967

[37] Chidiac S E, Panesar D K. Evolution of mechanical properties of concrete containing ground granulated blast furnace slag and effects on the scaling resistance test at 28 days. Cement and Concrete Composites, 2008, 30(2): 63–71

[38] Mardani-Aghabaglou A, Tuyan M, Yılmaz G, ArıözÖ, Ramyar K. Effect of different types of superplasticizer on fresh, rheological and strength properties of self-consolidating concrete. Construction & Building Materials, 2013, 47: 1020–1025

[39] Rangaraju P R, Olek J, Diamond S. An investigation into the influence of inter-aggregate spacing and the extent of the ITZ on properties of Portland cement concretes. Cement and Concrete Research, 2010, 40(11): 1601–1608

[40] Beshr H, Almusallam A, Maslehuddin M. Effect of coarse aggregate quality on the mechanical properties of high strength concrete. Construction & Building Materials, 2003, 17(2): 97–103

[41] Kronlöf A. Effect of very fine aggregate on concrete strength. Materials and Structures, 1994, 27(1): 15–25

Acknowledgements

Bin Xi and Enming Li wish to acknowledge the funding provided by the China Scholarship Council (Nos. 202008440524 and 202006370006). This research was partially supported by the Distinguished Youth Science Foundation of Hunan Province of China (No. 2022JJ10073), Innovation Driven Project of Central South University (No. 2020CX040), and Shenzhen Science and Technology Plan (No. JCYJ20190808123013260).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/ 10.1007/s11709-023-0997-3 and is accessible for authorized users.

Conflict of Interest

The authors declare that they have no conflict of interest.

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