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A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

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Abstract

Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the compressive strength of this concrete material is considered as a crucial parameter and an important concern for construction engineers regarding its application. In the present work, the 28-days compressive strength of recycled aggregate concrete is investigated through four artificial intelligence techniques based on a meta-heuristic search of sociopolitical algorithm (i.e. ICA) and XGBoost, called the ICA-XGBoost model. Based on performance indices, the optimum among these developed models proved to be ICA-XGBoost model. Namely, findings demonstrated that the proposed ICA-XGBoost model performed better than the other models (i.e. ICA-ANN, ICA-SVR, and ICA-ANFIS models) in estimating compressive strength of recycled aggregate concrete. The suggested model can be used in construction engineering in order to ensure adequate mechanical performance of the recycled aggregate concrete and allow its safe use for building purposes.

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References

  1. Asteris PG, Mokos VG (2019) Concrete compressive strength using artificial neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04663-2

    Article  Google Scholar 

  2. Apostolopoulou M, Douvika MG, Kanellopoulos IN, Moropoulou A, Asteris PG (2018) Prediction of compressive strength of mortars using artificial neural networks. In: Proceedings of the 1st international conference TMM_CH, transdisciplinary multispectral modelling and cooperation for the preservation of cultural heritage, Athens, Greece, 10–13

  3. Silva R, De Brito J, Dhir R (2015) The influence of the use of recycled aggregates on the compressive strength of concrete: a review. Eur J Environ Civ Eng 19(7):825–849

    Google Scholar 

  4. Tu T-Y, Chen Y-Y, Hwang C-L (2006) Properties of HPC with recycled aggregates. Cem Concr Res 36(5):943–950

    Google Scholar 

  5. Shang Y, Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019) A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09503-7

    Article  Google Scholar 

  6. Nguyen H, Moayedi H, Foong LK, Al Najjar HAH, Jusoh WAW, Rashid ASA, Jamali J (2019) Optimizing ANN models with PSO for predicting short building seismic response. Eng Comput. https://doi.org/10.1007/s00366-019-00733-0

    Article  Google Scholar 

  7. Nguyen H, Drebenstedt C, Bui X-N, Bui DT (2019) Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09470-z

    Article  Google Scholar 

  8. Zhang X, Nguyen H, Bui X-N, Tran Q-H, Nguyen D-A, Bui DT, Moayedi H (2019) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res. https://doi.org/10.1007/s11053-019-09492-7

    Article  Google Scholar 

  9. Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Sp Technol 81:632–659

    Google Scholar 

  10. Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Google Scholar 

  11. Zhang S, Bui X-N, Trung N-T, Nguyen H, Bui H-B (2019) Prediction of rock size distribution in mine bench blasting using a novel ant colony optimization-based boosted regression tree technique. Nat Resour Res. https://doi.org/10.1007/s11053-019-09603-4

    Article  Google Scholar 

  12. Zhang H, Nguyen H, Bui X-N, Nguyen-Thoi T, Bui T-T, Nguyen N, Vu D-A, Mahesh V, Moayedi H (2020) Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm. Resour Policy 66:101604

    Google Scholar 

  13. Rao A, Jha KN, Misra S (2007) Use of aggregates from recycled construction and demolition waste in concrete. Resour Conserv Recycl 50(1):71–81

    Google Scholar 

  14. Asteris PG, Moropoulou A, Skentou AD, Apostolopoulou M, Mohebkhah A, Cavaleri L, Rodrigues H, Varum H (2019) Stochastic vulnerability assessment of masonry structures: concepts, modeling and restoration aspects. Appl Sci 9(2):243

    Google Scholar 

  15. Asteris PG, Apostolopoulou M, Skentou AD, Moropoulou A (2019) Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars. Comput Concr 24(4):329–345

    Google Scholar 

  16. Limbachiya M, Leelawat T, Dhir R (2000) Use of recycled concrete aggregate in high-strength concrete. Mater Struct 33(9):574

    Google Scholar 

  17. Poon C-S, Chan D (2007) The use of recycled aggregate in concrete in Hong Kong. Resour Conserv Recycl 50(3):293–305

    Google Scholar 

  18. Poon C, Kou S, Lam L (2002) Use of recycled aggregates in molded concrete bricks and blocks. Constr Build Mater 16(5):281–289

    Google Scholar 

  19. Oikonomou ND (2005) Recycled concrete aggregates. Cem Concr Compos 27(2):315–318

    Google Scholar 

  20. Shi X, Wang Q, Zhao X, Collins F (2011) Strength and ductility of recycled aggregate concrete filled composite tubular stub columns. In: Incorporating sustainable practice in mechanics of structures and materials, London, UK, pp 83–89

  21. Wang Y, Chen J, Geng Y (2015) Testing and analysis of axially loaded normal-strength recycled aggregate concrete filled steel tubular stub columns. Eng Struct 86:192–212

    Google Scholar 

  22. Tam VW, Soomro M, Evangelista ACJ (2018) A review of recycled aggregate in concrete applications (2000–2017). Constr Build Mater 172:272–292

    Google Scholar 

  23. Asteris PG, Ashrafian A, Rezaie-Balf M (2019) Prediction of the compressive strength of self-compacting concrete using surrogate models. Comput Concr 24:137–150

    Google Scholar 

  24. Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30(5):04016003

    Google Scholar 

  25. Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50(4):629–644

    Google Scholar 

  26. Liu T, Zhang C, Cao P, Zhou K (2020) Freeze-thaw damage evolution of fractured rock mass using nuclear magnetic resonance technology. Cold Reg Sci Technol 170:102951

    Google Scholar 

  27. Duan Z-H, Kou S-C, Poon C-S (2013) Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Constr Build Mater 40:1200–1206

    Google Scholar 

  28. Younis KH, Pilakoutas K (2013) Strength prediction model and methods for improving recycled aggregate concrete. Constr Build Mater 49:688–701

    Google Scholar 

  29. Deshpande N, Londhe S, Kulkarni S (2014) Modeling compressive strength of recycled aggregate concrete by artificial neural network, model tree and non-linear regression. Int J Sustain Built Environ 3(2):187–198

    Google Scholar 

  30. Khademi F, Jamal SM, Deshpande N, Londhe S (2016) Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. Int J Sustain Built Environ 5(2):355–369

    Google Scholar 

  31. Khosravi K, Mao L, Kisi O, Yaseen ZM, Shahid S (2018) Quantifying hourly suspended sediment load using data mining models: case study of a glacierized Andean catchment in Chile. J Hydrol 567:165–179

    Google Scholar 

  32. Khosravi K, Panahi M, Bui DT (2018) Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrol Earth Syst Sci 22(9):4771–4792. https://doi.org/10.5194/hess-22-4771-2018

    Article  Google Scholar 

  33. Zhang J-P, Li Z-W, Yang J (2005) A parallel SVM training algorithm on large-scale classification problems. In: 2005 International conference on machine learning and cybernetics, IEEE, pp 1637–1641

  34. Zhang R, Ma J (2008) An improved SVM method P-SVM for classification of remotely sensed data. Int J Remote Sens 29(20):6029–6036

    Google Scholar 

  35. Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122

    Google Scholar 

  36. Abdollahzadeh G, Jahani E, Kashir Z (2016) Predicting of compressive strength of recycled aggregate concrete by genetic programming. Comput Concr 18(2):155–163

    Google Scholar 

  37. Deng F, He Y, Zhou S, Yu Y, Cheng H, Wu X (2018) Compressive strength prediction of recycled concrete based on deep learning. Constr Build Mater 175:562–569

    Google Scholar 

  38. Dantas ATA, Leite MB, de Jesus Nagahama K (2013) Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Constr Build Mater 38:717–722

    Google Scholar 

  39. Omran BA, Chen Q, Jin R (2016) Comparison of data mining techniques for predicting compressive strength of environmentally friendly concrete. J Comput Civ Eng 30(6):04016029

    Google Scholar 

  40. Khademi F, Jamal SM (2016) Predicting the 28 days compressive strength of concrete using artificial neural network. I-manager’s J Civ Eng 6(2):1–6. https://doi.org/10.26634/jce.6.2.5936

    Article  Google Scholar 

  41. Naderpour H, Rafiean AH, Fakharian P (2018) Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 16:213–219

    Google Scholar 

  42. Asteris PG, Nikoo M (2019) Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput Appl 31(9):4837–4847. https://doi.org/10.1007/s00521-018-03965-1

    Article  Google Scholar 

  43. Asteris PG, Kolovos KG (2019) Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 31(1):409–424

    Google Scholar 

  44. Asteris PG, Nozhati S, Nikoo M, Cavaleri L, Nikoo M (2019) Krill herd algorithm-based neural network in structural seismic reliability evaluation. Mech Adv Mater Struct 26(13):1146–1153

    Google Scholar 

  45. Asteris P, Roussis P, Douvika M (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors 17(6):1344

    Google Scholar 

  46. Sarir P, Chen J, Asteris PG, Armaghani DJ, Tahir MM (2019) Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng Comput. https://doi.org/10.1007/s00366-019-00808-y

    Article  Google Scholar 

  47. Liu T, Zhang C, Zhou K, Tian Y (2019) Freeze-thaw cycling damage evolution of additive cement mortar. Eur J Environ Civ Eng. https://doi.org/10.1080/19648189.2019.1615992

    Article  Google Scholar 

  48. Ni H-G, Wang J-Z (2000) Prediction of compressive strength of concrete by neural networks. Cem Concr Res 30(8):1245–1250

    Google Scholar 

  49. Topcu IB, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 41(3):305–311

    Google Scholar 

  50. Kewalramani MA, Gupta R (2006) Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Autom Constr 15(3):374–379

    Google Scholar 

  51. Alshihri MM, Azmy AM, El-Bisy MS (2009) Neural networks for predicting compressive strength of structural light weight concrete. Constr Build Mater 23(6):2214–2219

    Google Scholar 

  52. Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T (2010) Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Constr Build Mater 24(5):709–718

    Google Scholar 

  53. Khademi F, Akbari M, Jamal SM, Nikoo M (2017) Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front Struct Civ Eng 11(1):90–99

    Google Scholar 

  54. Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S, Nehdi ML (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125

    Google Scholar 

  55. Chopra P, Sharma RK, Kumar M (2016) Prediction of compressive strength of concrete using artificial neural network and genetic programming. Adv Mater Sci Eng 2016:1–10. https://doi.org/10.1155/2016/7648467

    Article  Google Scholar 

  56. Nikoo M, Torabian Moghadam F, Sadowski Ł (2015) Prediction of concrete compressive strength by evolutionary artificial neural networks. Adv Mater Sci Eng 2015:1–8. https://doi.org/10.1155/2015/849126

    Article  Google Scholar 

  57. Ling H, Qian C, Kang W, Liang C, Chen H (2019) Combination of support vector machine and k-fold cross validation to predict compressive strength of concrete in marine environment. Constr Build Mater 206:355–363

    Google Scholar 

  58. Behnood A, Golafshani EM (2018) Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. J Clean Prod 202:54–64

    Google Scholar 

  59. Behnood A, Olek J, Glinicki MA (2015) Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm. Constr Build Mater 94:137–147

    Google Scholar 

  60. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 4661–4667

  61. Hosseini S, Al Khaled A (2014) A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research. Appl Soft Comput 24:1078–1094

    Google Scholar 

  62. Elsisi M (2019) Design of neural network predictive controller based on imperialist competitive algorithm for automatic voltage regulator. Neural Comput Appl 31(9):5017–5027. https://doi.org/10.1007/s00521-018-03995-9

    Article  Google Scholar 

  63. Shirazi AZ, Mohammadi Z (2017) A hybrid intelligent model combining ANN and imperialist competitive algorithm for prediction of corrosion rate in 3C steel under seawater environment. Neural Comput Appl 28(11):3455–3464

    Google Scholar 

  64. Le LT, Nguyen H, Dou J, Zhou J (2019) A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl Sci 9(13):2630

    Google Scholar 

  65. Chen T, He T (2015) Xgboost: extreme gradient boosting. R package version 04-2

  66. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378

    MathSciNet  MATH  Google Scholar 

  67. Zhou J, Li E, Wang M, Chen X, Shi X, Jiang L (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. J Perform Constr Facil 33(3):04019024

    Google Scholar 

  68. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  69. Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process-Lett Rev 11(10):203–224

    Google Scholar 

  70. Nguyen H (2019) Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam. SN Appl Sci 1(4):283. https://doi.org/10.1007/s42452-019-0295-9

    Article  Google Scholar 

  71. Bui X-N, Nguyen H, Le HA, Bui HB, Do NH (2019) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat Resour Res. https://doi.org/10.1007/s11053-019-09461-0

    Article  Google Scholar 

  72. Liu L, Moayedi H, Rashid ASA, Rahman SSA, Nguyen H (2019) Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system. Eng Comput 36(1):421–433. https://doi.org/10.1007/s00366-019-00767-4

    Article  Google Scholar 

  73. Wang B, Moayedi H, Nguyen H, Foong LK, Rashid ASA (2019) Feasibility of a novel predictive technique based on artificial neural network optimized with particle swarm optimization estimating pullout bearing capacity of helical piles. Eng Comput. https://doi.org/10.1007/s00366-019-00764-7

    Article  Google Scholar 

  74. Bui X, Muazu MA, Nguyen H (2019) Optimizing Levenberg–Marquardt backpropagation technique in predicting factor of safety of slopes after two-dimensional OptumG2 analysis. Eng Comput. https://doi.org/10.1007/s00366-019-00741-0

    Article  Google Scholar 

  75. Nguyen H, Moayedi H, Foong LK, Al Najjar HAH, Jusoh WAW, Rashid ASA, Jamali J (2019) Optimizing ANN models with PSO for predicting short building seismic response. Eng Comput. https://doi.org/10.1007/s00366-019-00733-0

    Article  Google Scholar 

  76. Moayedi H, Moatamediyan A, Nguyen H, Bui X-N, Bui DT, Rashid ASA (2019) Prediction of ultimate bearing capacity through various novel evolutionary and neural network models. Eng Comput. https://doi.org/10.1007/s00366-019-00723-2

    Article  Google Scholar 

  77. Yegnanarayana B (2009) Artificial neural networks. PHI Learning Pvt. Ltd, New Delhi

    Google Scholar 

  78. Buscema PM, Massini G, Breda M, Lodwick WA, Newman F, Asadi-Zeydabadi M (2018) Artificial adaptive systems using auto contractive maps: theory, applications and extensions, vol 131. Springer, Berlin

    MATH  Google Scholar 

  79. Gao W, Guirao JLG, Abdel-Aty M, Xi W (2018) An independent set degree condition for fractional critical deleted graphs. Discrete Contin Dyn Syst-S 12(4&5):877–886. https://doi.org/10.3934/dcdss.2019058

    Article  MathSciNet  MATH  Google Scholar 

  80. Asteris PG, Armaghani DJ, Hatzigeorgiou GD, Karayannis CG, Pilakoutas K (2019) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Comput Concr 24(5):469–488

    Google Scholar 

  81. Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378

    Google Scholar 

  82. Nguyen H, Bui X-N, Bui H-B, Mai N-L (2018) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3717-5

    Article  Google Scholar 

  83. Apostolopoulou M, Armaghani DJ, Bakolas A, Douvika MG, Moropoulou A, Asteris PG (2019) Compressive strength of natural hydraulic lime mortars using soft computing techniques. Proc Struct Integr 17:914–923

    Google Scholar 

  84. Choubin B, Khalighi-Sigaroodi S, Malekian A, Kişi Ö (2016) Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrol Sci J 61(6):1001–1009

    Google Scholar 

  85. Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Google Scholar 

  86. Chang F-J, Chang Y-T (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29(1):1–10

    Google Scholar 

  87. Manogaran G, Varatharajan R, Priyan M (2018) Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimed Tools Appl 77(4):4379–4399

    Google Scholar 

  88. Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu A-X, Chen W, Kougias I, Kazakis N (2018) Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ 621:1124–1141

    Google Scholar 

  89. Aghdam IN, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75(7):553

    Google Scholar 

  90. Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310–324

    Google Scholar 

  91. Shabri A (2014) A hybrid wavelet analysis and adaptive neuro-fuzzy inference system for drought forecasting. Appl Math Sci 8(139):6909–6918

    Google Scholar 

  92. Psyllaki P, Stamatiou K, Iliadis I, Mourlas A, Asteris P, Vaxevanidis N (2018) Surface treatment of tool steels against galling failure. In: MATEC web of conferences. EDP Sciences, p 04024

  93. Cavaleri L, Chatzarakis GE, Trapani FD, Douvika MG, Roinos K, Vaxevanidis NM, Asteris PG (2017) Modeling of surface roughness in electro-discharge machining using artificial neural networks. Adv Mater Res 6(2):169–184

    Google Scholar 

  94. Cavaleri L, Asteris PG, Psyllaki PP, Douvika MG, Skentou AD, Vaxevanidis NM (2019) Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks. Appl Sci 9(14):2788

    Google Scholar 

  95. Armaghani DJ, Hatzigeorgiou GD, Karamani C, Skentou A, Zoumpoulaki I, Asteris PG (2019) Soft computing-based techniques for concrete beams shear strength. Proc Struct Integr 17:924–933

    Google Scholar 

  96. Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly H-B, Gróf G, Ho HL (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323

    Google Scholar 

  97. Khozani ZS, Khosravi K, Pham BT, Kløve B, Mohtar W, Melini WH, Yaseen ZM (2019) Determination of compound channel apparent shear stress: application of novel data mining models. J Hydroinform 21(5):798–811. https://doi.org/10.2166/hydro.2019.037

    Article  Google Scholar 

  98. Dhaliwal S, Nahid A-A, Abbas R (2018) Effective intrusion detection system using XGBoost. Information 9(7):149

    Google Scholar 

  99. Nguyen H, Bui X-N, Bui H-B, Cuong DT (2019) Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophys 67(2):477–490. https://doi.org/10.1007/s11600-019-00268-4

    Article  Google Scholar 

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Acknowledgments

 This research was supported by Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam and the Center for Mining, Electro-Mechanical Research of HUMG.

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Authors and Affiliations

  1. School of Civil Engineering, Central South University, Changsha, 410083, Hunan, China

    Jin Duan

  2. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121, Heraklion, Athens, Greece

    Panagiotis G. Asteris

  3. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Hoang Nguyen

  4. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

    Xuan-Nam Bui

  5. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

    Xuan-Nam Bui

  6. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  7. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

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  1. Jin Duan
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Correspondence to Hoang Nguyen.

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Duan, J., Asteris, P.G., Nguyen, H. et al. A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Engineering with Computers 37, 3329–3346 (2021). https://doi.org/10.1007/s00366-020-01003-0

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