Prediction Model-based Multi-objective Optimization for Mix-ratio Design of Recycled Aggregate Concrete

Abstract

The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression (BO-GPR) method, where the input variables in BO-GPR model depend on the mix ratio of concrete. Then the compressive strength prediction model, the material cost, and environmental factors were simultaneously considered as objectives, while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio. A total of 730 RAC datasets were used for training and testing the predication model, while the optimal design method for mix ratio was verified through RAC experiments. The experimental results show that the predicted, testing, and expected compressive strengths are nearly consistent, illustrating the effectiveness of the proposed method.

Keywords

recycled coarse aggregate / mix ratio / multi-objective optimization / prediction model / compressive strength

References

[1] Behera M, Bhattacharyya SK, Minocha AK, et al. Recycled Aggregate from C&D Waste & Its Use in Concrete-A Breakthrough Towards Sustainability in Construction Sector: A Review[J]. Construct. Build. Mater., 2014, 68: 501-516

[2] Ding Y, Wu J, Zhang X, et al. Quality Improvement of Recycled Concrete Aggregate by Accelerated Carbonation under Different Pressure[J]. J. Wuhan Univ. Technol.-Mater. Sci. Ed., 2023, 38: 623-631

[3] Ding Y, Wu J, Xu P, et al. Treatment Methods for the Quality Improvement of Recycled Concrete Aggregate (RCA) - A Review[J]. J. Wuhan Univ. Technol.-Mater. Sci. Ed., 2021, 36: 77-92

[4] Wang YG, Hughes P, Niu HC, et al. A New Method to Improve the Properties of Recycled Aggregate Concrete: Composite Addition of Basalt Fiber and Nano-Silica[J]. J. Clean. Prod., 2019, 236: 117 602

[5] Rodríguez C, Parra C, Casado G, et al. The Incorporation of Construction and Demolition Wastes as Recycled Mixed Aggregates in Non-Structural Concrete Precast Pieces[J]. J. Clean. Prod., 2016, 127: 152-161

[6] Aslani F, Ma GW, Wan DLYW, et al. Development of High-Performance Self-Compacting Concrete Using Waste Recycled Concrete Aggregates and Rubber Granules[J]. J. Clean. Prod., 2018, 182(1): 553-566

[7] Han TH, Siddique A, Khayat K, et al. An Ensemble Machine Learning Approach for Prediction and Optimization of Modulus of Elasticity of Recycled Aggregate Concrete[J]. Construct. Build. Mater., 2020, 244: 118 271

[8] Duan ZH, Kou SC, Poon CS. Using Artificial Neural Networks for Predicting the Elastic Modulus of Recycled Aggregate Concrete[J]. Construct. Build. Mater., 2013, 44: 524-532

[9] Behnood A, Olek J, Glinicki MA. Predicting Modulus Elasticity of Recycled Aggregate Concrete Using M5’ Model Tree Algorithm[J]. Construct. Build. Mater., 2015, 94: 137-147

[10] Gholampour A, Gandomi AH, Ozbakkaloglu T. New Formulations for Mechanical Properties of Recycled Aggregate Concrete Using Gene Expression Programming[J]. Construct. Build. Mater., 2017, 130: 122-145

[11] Golafshani EM, Behnood A. Automatic Regression Methods for Formulation of Elastic Modulus of Recycled Aggregate Concrete[J]. Appl. Soft Comput., 2018, 64: 377-400

[12] Golafshani EM, Behnood A. Application of Soft Computing Methods for Predicting the Elastic Modulus of Recycled Aggregate Concrete[J]. J. Clean. Prod., 2018, 176: 1 163-1 176

[13] Sadati S, Brito da Silva LE, Wunsch DC, et al. Artificial Intelligence to Investigate Modulus of Elasticity of Recycled Aggregate Concrete[J]. ACI Mater. J., 2019, 116(1): 51-62

[14] Xie TY, Yang GS, Zhao XY, et al. A Unified Model for Predicting the Compressive Strength of Recycled Aggregate Concrete Containing Supplementary Cementitious Materials[J]. J. Clean. Prod., 2020, 251: 119 752

[15] Zhang JF, Huang YM, Aslani F, et al. A Hybrid Intelligent System for Designing Optimal Proportions of Recycled Aggregate Concrete[J]. J. Clean. Prod., 2020, 273: 122 922

[16] Nunez I, Marani A, Nehdi ML. Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model[J]. Materials, 2020, 13(19): 4 331

[17] Rasmussen CE, Williams CKI. Gaussian Processes for Machine Learning, 2006, Cambridge: MIT Press

[18] Eric S, Maarten S, Andreas K. A Tutorial on Gaussian Process Regression: Modelling, Exploring, and Exploiting Functions[J]. J Math. Psychol., 2018, 85: 1-16

[19] Arthur CK, Temeng VA, Ziggah YY. Novel Approach to Predicting Blast-Induced Ground Vibration Using Gaussian Process Regression[J]. Eng. Comput-Germany, 2020, 36(1): 29-42

[20] Xia YK, Tang WZ. Study on the Estimation of Harmonic Impedance based on Bayesian Optimized Gaussian Process Regression[J]. Int. J. Elec. Power, 2022, 142: 108 294

[21] Chen YF, Li F, Zhou SQ, et al. Bayesian Optimization based Random Forest and Extreme Gradient Boosting for the Pavement Density Prediction in GPR Detection[J]. Construct. Build. Mater., 2023, 387: 131 564

[22] Ceylan Z. Estimation of Municipal Waste Generation of Turkey Using Socio-Economic Indicators by Bayesian Optimization Tuned Gaussian Process Regression[J]. Waste Manage. Res., 2020, 38(8): 840-850

[23] Adams MP, Fu TF, Cabrera AG, et al. Cracking Susceptibility of Concrete Made with Coarse Recycled Concrete Aggregates[J]. Construct. Build. Mater., 2016, 102: 802-810

[24] Purnell P, Black L. Embodied Carbon Dioxide in Concrete: Variation with Common Mix Design Parameters[J]. Cement Concr. Res., 2012, 42(6): 874-877

[25] Seyedali M, Seyed MM, Andrew L. Grey Wolf Optimizer[J]. Adv. Eng. Softw., 2014, 69: 46-61

[26] Jangir P, Jangir N. A New Non-Dominated Sorting Grey Wolf Optimizer (NS-GWO) Algorithm: Development and Application to Solve Engineering Designs and Economic Constrained Emission Dispatch Problem with Integration of Wind Power[J]. Eng. Appl. Artif. Intel., 2018, 72: 449-467

[27] Golafshani EM, Arashpour M, Kashani A. Green Mix Design of Rubbercrete Using Machine Learning-Based Ensemble Model and Constrained Multi-Objective Optimization[J]. J. Clean. Prod., 2021, 327: 129 518

[28] Yoon KP, Hwang CL. Multiple Attribute Decision Making: An Introduction, 1995, Thousand Oaks: Sage