Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

doi:10.1007/s11709-022-0837-x

Front. Struct. Civ. Eng.

2022, Vol. 16

Issue (7) : 928-945 https://doi.org/10.1007/s11709-022-0837-x

RESEARCH ARTICLE

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

Van Quan TRAN(

), Hai-Van Thi MAI, Thuy-Anh NGUYEN, Hai-Bang LY

Faculty of Civil Engineering, University of Transport Technology, Hanoi 100000, Vietnam

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Abstract

The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R² = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.

Keywords compressive strength self-compacting concrete machine learning techniques particle swarm optimization extreme gradient boosting

Corresponding Author(s): Van Quan TRAN

Just Accepted Date: 26 July 2022 Online First Date: 11 October 2022 Issue Date: 17 November 2022

Cite this article:

Van Quan TRAN,Hai-Van Thi MAI,Thuy-Anh NGUYEN, et al. Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete[J]. Front. Struct. Civ. Eng., 2022, 16(7): 928-945.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0837-x
https://academic.hep.com.cn/fsce/EN/Y2022/V16/I7/928

Tab.1 Summary of the inputs and output

Fig.1 Histograms of the considered variables of ML models. (a) X₁; (b) X₂; (c) X₃; (d) X₄; (e) X₅; (f) X₆; (g) X₇; (h) X₈; (i) X₉; (j) X₁₀; (k) X₁₁; (l) X₁₂; (m) X₁₃; (n) CS.

Fig.2 Correlation matrix of the inputs and CS of SCC.

Fig.3 The present study’s methodology flowchart.

Tab.2 Summary of performance value for XGB on the training and testing datasets

Fig.4 Performance of different ML models over 500 simulations ML models. (a) R² value; (b) RMSE value (MPa); (c) MAE value (MPa).

Tab.3 Computational time in second of hybrid algorithms after 300 iterations

Fig.5 Mean value of R² in function of iterations using hybrid models and 5-Fold CV.

Tab.4 Summary of performance value for two hybrid models GB-PSO and XGB-PSO

Fig.6 Histograms showing the results of 500 MCS for GB-PSO and XGB-PSO. (a) R² value for training dataset; (b) R² value for testing dataset; (c) RMSE for training dataset; (d) RMSE for testing dataset; (e) MAE for training dataset; (f) MAE for testing dataset.

Fig.7 Correlation graphs between experimental and predicted CS: (a) training dataset; (b) testing dataset; (c) all data.

Fig.8 Comparison between predicted and experimental CS for (a) training process and (b) testing process.

reference	ML algorithm	input variable	sample size	statistical measures
Siddique et al. [29]	ANN	6 inputs: cement, FA, water to powder ratio, superplasticizer, sand, coarse aggregate	80	R² = 0.9187
Asteris and Kolovos [32]	ANN	11 inputs: cement, coarse aggregate, fine aggregate, water, limestone powder, FA, ground granulated BFS, silica fume, rice husk, new generation superplasticizers, viscosity modifying admixtures	205	R² = 0.9658
Asteris et al. [38]	ANNs,BPNN	11 inputs: cement, limestone powder, FA, ground granulated BFS GGBFS, silica fume, rice husk ash, coarse aggregate, fine aggregate, water, superplasticizer, viscosity modifying agent	169	R² = 0.9655
Akkurt et al. [39]	ANN, FL	11 inputs: cement, limestone powder, FA, ground granulated BFS, silica fume, rice husk ash, coarse aggregate, fine aggregate, water, superplasticizer, viscosity modifying agent	169	R² = 0.9604
Kovačević et al. [40]	RF regression	7 inputs: water to binder ratio, macro-synthetic polypropylene fibers, steel fiber, scoria, crumb rubber, natural fine aggregate, natural coarse aggregate	131	R² = 0.9477
Azimi-Pour et al. [41]	Linear and nonlinear SVM	10 inputs: cement, water to cement, water to powder, water to binder, fine aggregate to powder, coarse aggregate to powder, high water range reducer to powder, viscosity modify admixture to powder, micro silica to binder.	340	R² = 0.9388
Saha et al. [42]	Support vector regression approach	6 inputs: binder content, FA percentage, water to powder ratio, fine aggregate (f), coarse aggregate, superplasticizer dosage	115	SVR: R² = 0.955ANN: R² = 0.882
this study	LR, KNN, SVM, DTR, RF, GB, XGB, ANN_LBFGS and ANN_SGD, and the Hyperparameters tuning with PSO	13 inputs: cement, FA, water, sand or fine, coarse aggregate, superplasticizer, limestone powder, ground granulated BFS, silica fume, metakaolin, rice husk ash, viscosity modifying admixtures, curing time	1280	XGB_PSO:R² = 0.9644XGB: R² = 0.9536

Tab.5 Performance comparison with published works in predicting the CS of SCC

Tab.6 Performance of the XGB model over the number of inputs

Fig.9 Feature importance analysis presented by SHAP value.

1	EFNARC. Specification and Guidelines for Self-Compacting Concrete. Farnham: European Federation of Specialist Construction Chemicals and Concrete System, 2002
2	J Fernandez-Gomez, G A Landsberger. Evaluation of shrinkage prediction models for self-consolidating concrete. ACI Materials Journal, 2007, 104(5): 464 https://doi.org/10.14359/18902
3	Å SkarendahlÖ Petersson. Self-Compacting Concrete––State-of-the-Art report of RILEM Technical Committee 174-SCC. Cachan: RILEM Publications, 2000
4	K Z Ramadan, R H Haddad. Self-healing of overloaded self-compacting concrete of rigid pavement. European Journal of Environmental and Civil Engineering, 2017, 21(1): 63–77
5	A A Busari, J O Akinmusuru, B I O Dahunsi, A S Ogbiye, J O Okeniyi. Self-compacting concrete in pavement construction: Strength grouping of some selected brands of cements. Energy Procedia, 2017, 119: 863–869 https://doi.org/10.1016/j.egypro.2017.07.139
6	T J Pasko Jr. Concrete pavements––Past, present, and future. Public Roads, 1998, 62(1): 7–15
7	N Bouzoubaâ, M Lachemi. Self-compacting concrete incorporating high volumes of class F fly ash: Preliminary results. Cement and Concrete Research, 2001, 31(3): 413–420 https://doi.org/10.1016/S0008-8846(00)00504-4
8	A Busari, J Akinmusuru, B Dahunsi. Mechanical properties of dehydroxylated kaolinitic clay in self-compacting concrete for pavement construction. Silicon, 2019, 11(5): 2429–2437 https://doi.org/10.1007/s12633-017-9654-6
9	T Rajah Surya, M Prakash, K S Satyanarayanan, A Keneth Celestine, N Parthasarathi. Compressive strength of self compacting concrete under elevated temperature. Materials Today: Proceedings, 2021, 40: S83–S87 https://doi.org/10.1016/j.matpr.2020.03.746
10	H Guo, X Zhuang, P Chen, N Alajlan, T Rabczuk. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with Computers, 2022, 1–26 https://doi.org/10.1007/s00366-021-01586-2
11	V M Nguyen-Thanh, C Anitescu, N Alajlan, T Rabczuk, X Zhuang. Parametric deep energy approach for elasticity accounting for strain gradient effects. Computer Methods in Applied Mechanics and Engineering, 2021, 386: 114096 https://doi.org/10.1016/j.cma.2021.114096
12	H B Ly, B T Pham, L M Le, T T Le, V M Le, P G Asteris. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Computing & Applications, 2021, 33(8): 3437–3458 https://doi.org/10.1007/s00521-020-05214-w
13	H B Ly, M H Nguyen, B T Pham. Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Computing & Applications, 2021, 33(24): 17331 https://doi.org/10.1007/s00521-021-06321-y
14	E Samaniego, C Anitescu, S Goswami, V M Nguyen-Thanh, H Guo, K Hamdia, X Zhuang, T Rabczuk. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790 https://doi.org/10.1016/j.cma.2019.112790
15	B Mortazavi, M Silani, E V Podryabinkin, T Rabczuk, X Zhuang, A V Shapeev. First-principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine-learning interatomic potentials. Advanced Materials, 2021, 33(35): 2102807 https://doi.org/10.1002/adma.202102807
16	H B Ly, T A Nguyen, H V Thi Mai, V Q Tran. Development of deep neural network model to predict the compressive strength of rubber concrete. Construction & Building Materials, 2021, 301: 124081 https://doi.org/10.1016/j.conbuildmat.2021.124081
17	S Goswami, C Anitescu, S Chakraborty, T Rabczuk. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture. Theoretical and Applied Fracture Mechanics, 2020, 106: 102447 https://doi.org/10.1016/j.tafmec.2019.102447
18	T RabczukG ZiS BordasH Nguyen-Xuan. A simple and robust three-dimensional cracking-particle method without enrichment. Computer Methods in Applied Mechanics and Engineering, 2010, 199(37−40): 2437−2455
19	T Rabczuk, T Belytschko. Cracking particles: A simplified meshfree method for arbitrary evolving cracks. International Journal for Numerical Methods in Engineering, 2004, 61(13): 2316–2343 https://doi.org/10.1002/nme.1151
20	T RabczukT Belytschko. A three-dimensional large deformation meshfree method for arbitrary evolving cracks. Computer Methods in Applied Mechanics and Engineering, 2007, 196(29−30): 2777−2799
21	H L Ren, X Y Zhuang, C Anitescu, T Rabczuk. An explicit phase field method for brittle dynamic fracture. Computers & Structures, 2019, 217: 45–56 https://doi.org/10.1016/j.compstruc.2019.03.005
22	H B Ly, T T Le, H L T Vu, V Q Tran, L M Le, B T Pham. Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability (Basel), 2020, 12(7): 2709 https://doi.org/10.3390/su12072709
23	T A Nguyen, H B Ly, H V T Mai, V Q Tran. Prediction of later-age concrete compressive strength using feedforward neural network. Advances in Materials Science and Engineering, 2020, 2020
24	V Quan Tran, V Quoc Dang, L Si Ho. Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach. Construction & Building Materials, 2022, 323: 126578 https://doi.org/10.1016/j.conbuildmat.2022.126578
25	İ B Topçu, M Sarıdemir. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 2008, 41(3): 305–311 https://doi.org/10.1016/j.commatsci.2007.04.009
26	H V T Mai, T A Nguyen, H B Ly, V Q Tran. Prediction compressive strength of concrete containing GGBFS using random forest model. Advances in Civil Engineering, 2021, 2021: e6671448 https://doi.org/10.1155/2021/6671448
27	H V T Mai, T A Nguyen, H B Ly, V Q Tran. Investigation of ANN model containing one hidden layer for predicting compressive strength of concrete with blast-furnace slag and fly ash. Advances in Materials Science and Engineering, 2021, 2021: e5540853 https://doi.org/10.1155/2021/5540853
28	H B Ly, T A Nguyen, B T Pham. Investigation on factors affecting early strength of high-performance concrete by Gaussian Process Regression. PLoS One, 2022, 17(1): e0262930 https://doi.org/10.1371/journal.pone.0262930
29	R Siddique, P Aggarwal, Y Aggarwal. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, 2011, 42(10): 780–786 https://doi.org/10.1016/j.advengsoft.2011.05.016
30	M Abu Yaman, M Abd Elaty, M Taman. Predicting the ingredients of self compacting concrete using artificial neural network. Alexandria Engineering Journal, 2017, 56(4): 523–532 https://doi.org/10.1016/j.aej.2017.04.007
31	P G Asteris, K G Kolovos, M G Douvika, K Roinos. Prediction of self-compacting concrete strength using artificial neural networks. European Journal of Environmental and Civil Engineering, 2016, 20(sup1): s102–s122 https://doi.org/10.1080/19648189.2016.1246693
32	P G Asteris, K G Kolovos. Self-compacting concrete strength prediction using surrogate models. Neural Computing & Applications, 2019, 31(S1): 409–424
33	V Malagavell, P A Manalel. Modeling of compressive strength of admixture-based self compacting concrete using fuzzy logic and artificial neural networks. Asian Journal of Applied Sciences, 2014, 7(7): 536–551
34	J Zhang, G Ma, Y Huang, J sun, F Aslani, B Nener. Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Construction & Building Materials, 2019, 210: 713–719 https://doi.org/10.1016/j.conbuildmat.2019.03.189
35	M Azimi-Pour, H Eskandari-Naddaf, A Pakzad. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Construction & Building Materials, 2020, 230: 117021 https://doi.org/10.1016/j.conbuildmat.2019.117021
36	F Pedregosa, F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg, J Vanderplas, A Passos, D Cournapeau, M Brucher, M Perrot, E Duchesnay. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011, 12: 2825–2830
37	T Chen, C Guestrin. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York: Association for Computing Machinery, 2016, 785–794 https://doi.org/10.1145/2939672.2939785
38	P Asteris, K Kolovos, M Douvika, K Roinos. Prediction of self-compacting concrete strength using artificial neural networks. European Journal of Environmental and Civil Engineering, 2016, 20(sup1): s102–s122 https://doi.org/10.1080/19648189.2016.1246693
39	S Akkurt, G Tayfur, S Can. Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research, 2004, 34: 1429–1433 https://doi.org/10.1016/j.cemconres.2004.01.020
40	M Kovačević, S Lozančić, E K Nyarko. Application of artificial intelligence methods for predicting the compressive strength of self-compacting concrete with class F fly ash. Materials (Basel), 2022, 15: 4191 https://doi.org/10.3390/ma15124191
41	M Azimi-Pour, H Eskandari-Naddaf, A Pakzad. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Construction & Building Materials, 2020, 230: 117021 https://doi.org/10.1016/j.conbuildmat.2019.117021
42	P Saha, P Debnath, P Thomas. Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach. Neural Computing & Applications, 2020, 32(12): 7995–8010 https://doi.org/10.1007/s00521-019-04267-w
43	R Siddique. Properties of self-compacting concrete containing class F fly ash. Materials & Design, 2011, 32(3): 1501–1507 https://doi.org/10.1016/j.matdes.2010.08.043
44	B Sukumar, K Nagamani, R Srinivasa Raghavan. Evaluation of strength at early ages of self-compacting concrete with high volume fly ash. Construction & Building Materials, 2008, 22(7): 1394–1401 https://doi.org/10.1016/j.conbuildmat.2007.04.005
45	M Gesoğlu, E Güneyisi, E Özbay. Properties of self-compacting concretes made with binary, ternary, and quaternary cementitious blends of fly ash, blast furnace slag, and silica fume. Construction & Building Materials, 2009, 23(5): 1847–1854 https://doi.org/10.1016/j.conbuildmat.2008.09.015
46	E Güneyisi, M Gesoğlu, E Özbay. Strength and drying shrinkage properties of self-compacting concretes incorporating multi-system blended mineral admixtures. Construction & Building Materials, 2010, 24(10): 1878–1887 https://doi.org/10.1016/j.conbuildmat.2010.04.015
47	P Dinakar. Design of self-compacting concrete with fly ash. Magazine of Concrete Research, 2012, 64(5): 401–409 https://doi.org/10.1680/macr.10.00167
48	J Guru Jawahar, C Sashidhar, I V Ramana Reddy, J Annie Peter. Micro and macrolevel properties of fly ash blended self compacting concrete. Materials & Design, 2013, 46: 696–705 https://doi.org/10.1016/j.matdes.2012.11.027
49	V Boel, K Audenaert, G De Schutter, G Heirman, L Vandewalle, B Desmet, J Vantomme. Transport properties of self compacting concrete with limestone filler or fly ash. Materials and Structures, 2007, 40(5): 507–516 https://doi.org/10.1617/s11527-006-9159-z
50	M Jalal, E Mansouri. Effects of fly ash and cement content on rheological, mechanical, and transport properties of high-performance self-compacting concrete. Science and Engineering of Composite Materials, 2012, 19(4): 393–405 https://doi.org/10.1515/secm-2012-0052
51	P Dinakar, K G Babu, M Santhanam. Mechanical properties of high-volume fly ash self-compacting concrete mixtures. Structural Concrete, 2008, 9(2): 109–116 https://doi.org/10.1680/stco.2008.9.2.109
52	M Nehdi, M Pardhan, S Koshowski. Durability of self-consolidating concrete incorporating high-volume replacement composite cements. Cement and Concrete Research, 2004, 34(11): 2103–2112 https://doi.org/10.1016/j.cemconres.2004.03.018
53	M Uysal, M Sumer. Performance of self-compacting concrete containing different mineral admixtures. Construction & Building Materials, 2011, 25(11): 4112–4120 https://doi.org/10.1016/j.conbuildmat.2011.04.032
54	R Venkatakrishnaiah, G Sakthivel. Bulk utilization of flyash in self compacting concrete. KSCE Journal of Civil Engineering, 2015, 19(7): 2116–2120 https://doi.org/10.1007/s12205-015-0706-4
55	T Hemalatha, A Ramaswamy, J M Chandra Kishen. Micromechanical analysis of self compacting concrete. Materials and Structures, 2015, 48(11): 3719–3734 https://doi.org/10.1617/s11527-014-0435-z
56	M Liu. Self-compacting concrete with different levels of pulverized fuel ash. Construction & Building Materials, 2010, 24(7): 1245–1252 https://doi.org/10.1016/j.conbuildmat.2009.12.012
57	A F Bingöl, İ Tohumcu. Effects of different curing regimes on the compressive strength properties of self compacting concrete incorporating fly ash and silica fume. Materials & Design, 2013, 51: 12–18 https://doi.org/10.1016/j.matdes.2013.03.106
58	S Barbhuiya. Effects of fly ash and dolomite powder on the properties of self-compacting concrete. Construction & Building Materials, 2011, 25(8): 3301–3305 https://doi.org/10.1016/j.conbuildmat.2011.03.018
59	Z J SunW W DuanM L TianY F Fang. Experimental research on self-compacting concrete with different mixture ratio of fly ash. Advanced Materials Research, 2011, 236–238: 490–495
60	N Pathak, R Siddique. Properties of self-compacting-concrete containing fly ash subjected to elevated temperatures. Construction & Building Materials, 2012, 30: 274–280 https://doi.org/10.1016/j.conbuildmat.2011.11.010
61	R Patel, K Hossain, M Shehata, N Bouzoubaâ, M Lachemi. Development of statistical models for mixture design of high-volume fly ash self-consolidating concrete. ACI Materials Journal, 2004, 101: 294–302
62	M Sonebi. Medium strength self-compacting concrete containing fly ash: Modelling using factorial experimental plans. Cement and Concrete Research, 2004, 34(7): 1199–1208 https://doi.org/10.1016/j.cemconres.2003.12.022
63	V K Bui, Y Akkaya, S P Shah. Rheological model for self-consolidating concrete. Materials Journal, 2002, 99(6): 549–559 https://doi.org/10.14359/12364
64	A Ghezal, K Khayat. Optimizing self-consolidating concrete with limestone filler by using statistical factorial design methods. ACI Materials Journal, 2002, 99: 264–272
65	P Dinakar, K P Sethy, U C Sahoo. Design of self-compacting concrete with ground granulated blast furnace slag. Materials & Design, 2013, 43: 161–169 https://doi.org/10.1016/j.matdes.2012.06.049
66	B Felekoğlu, S Türkel, B Baradan. Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete. Building and Environment, 2007, 42(4): 1795–1802 https://doi.org/10.1016/j.buildenv.2006.01.012
67	M Gesoğlu, E Özbay. Effects of mineral admixtures on fresh and hardened properties of self-compacting concretes: Binary, ternary and quaternary systems. Materials and Structures, 2007, 40(9): 923–937 https://doi.org/10.1617/s11527-007-9242-0
68	Z Grdic, I Despotovic, G Toplicic-Curcic. Properties of self-compacting concrete with different types of additives. Facta Universitatis––Series: Architecture and Civil Engineering, 2008, 6(2): 173–177 https://doi.org/10.2298/FUACE0802173G
69	E Güneyisi, M Gesoglu, O A Azez, H Ö Öz. Effect of nano silica on the workability of self-compacting concretes having untreated and surface treated lightweight aggregates. Construction & Building Materials, 2016, 115: 371–380 https://doi.org/10.1016/j.conbuildmat.2016.04.055
70	S A Memon, M A Shaikh, H Akbar. Utilization of rice husk ash as viscosity modifying agent in self compacting concrete. Construction and building materials, 2011, 25(2): 1044–1048
71	M E Rahman, A S Muntohar, V Pakrashi, B H Nagaratnam, D Sujan. Self compacting concrete from uncontrolled burning of rice husk and blended fine aggregate. Materials & Design, 2014, 55: 410–415 https://doi.org/10.1016/j.matdes.2013.10.007
72	I P Sfikas, K G Trezos. Effect of composition variations on bond properties of self-compacting concrete specimens. Construction & Building Materials, 2013, 41: 252–262 https://doi.org/10.1016/j.conbuildmat.2012.11.094
73	M Valcuende, E Marco, C Parra, P Serna. Influence of limestone filler and viscosity-modifying admixture on the shrinkage of self-compacting concrete. Cement and Concrete Research, 2012, 42(4): 583–592 https://doi.org/10.1016/j.cemconres.2012.01.001
74	M Şahmaran, İ Ö Yaman, M Tokyay. Transport and mechanical properties of self consolidating concrete with high volume fly ash. Cement and Concrete Composites, 2009, 31(2): 99–106 https://doi.org/10.1016/j.cemconcomp.2008.12.003
75	R Patel. Development of statistical models to simulate and optimize self-consolidating concrete mixes incorporating high volumes of fly ash. Thesis for the Master’s Degree. Toronto: Ryerson University, 2004
76	M C S Nepomuceno, L A Pereira-de-Oliveira, S M R Lopes. Methodology for the mix design of self-compacting concrete using different mineral additions in binary blends of powders. Construction & Building Materials, 2014, 64: 82–94 https://doi.org/10.1016/j.conbuildmat.2014.04.021
77	P Krishnapal, R K Yadav, C Rajeev. Strength characteristics of self compacting concrete containing fly ash. Research Journal of Engineering Sciences, 2013, 2278: 9472
78	S Dhiyaneshwaran, P Ramanathan, B Bose, R Venkatasubramani. Study on durability characteristics of self-compacting concrete with fly ash. Jordan Journal of Civil Engineering, 2013, 7: 342–353
79	B Mahalingam, K Nagamani. Effect of processed fly ash on fresh and hardened properties of self compacting concrete. International Journal of Earth Sciences, 2011, 4(5): 930–940
80	S Mahesh. Self compacting concrete and its properties. International Journal of Engineering Research and Applications, 2014, 4(8): 72–80
81	M M Al-Rubaye. Self-compacting concrete: Design, properties and simulation of the flow characteristics in the L-box. Dissertation for the Doctoral Degree. Cardiff: Cardiff University, 2016
82	A MahatoG GambhirA KumarA DuttaK Kisley. Self compacting concrete. Thesis for the Bachelor’s Degree. Bhubaneswar: KIIT University, 2016
83	J SeoE Torres W Schaffer. Self-Consolidating Concrete for Prestressed Bridge Girders. WisDOT ID NO. 0092-15-03. 2017
84	R P Douglas, V K Bui, Y Akkaya, S P Shah. Properties of Self-consolidating concrete containing class F fly ash: With a Verification of the minimum paste volume method. Aci Material Journal, 2006, 233: 45–64
85	T M Mitchell. Machine Learning. New York: McGraw-Hill Education, 1997
86	C Cortes, V Vapnik. Support-vector networks. Machine Learning, 1995, 20(3): 273–297 https://doi.org/10.1007/BF00994018
87	J R Quinlan. Induction of decision trees. Machine Learning, 1986, 1(1): 81–106 https://doi.org/10.1007/BF00116251
88	L Breiman. Random forests. Machine Learning, 2001, 45(1): 5–32 https://doi.org/10.1023/A:1010933404324
89	V K Ayyadevara. Pro Machine Learning Algorithms. Berkeley: Apress, 2018, 117–134
90	J H Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 2001, 29(5): 1189–1232 https://doi.org/10.1214/aos/1013203451
91	L W June, M A Hassan. Modifications of the limited memory BFGs algorithm for large-scale nonlinear optimization. Mathematical Journal of Okayama University, 2005, 47(1): 175–188
92	L Bottou. Neural Networks: Tricks of the Trade. Heidelberg: Springer, 2012, 421–436
93	R Eberhart, J Kennedy. A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya: IEEE, 1995, 39–43
94	B Liu, N Vu-Bac, T Rabczuk. A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms. Composite Structures, 2021, 273: 114269 https://doi.org/10.1016/j.compstruct.2021.114269
95	S Blanke. Hyperactive: An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. 2019. Available at the website of GITHUB
96	M Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B. Methodological, 1974, 36(2): 111–133 https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
97	B Liu, N Vu-Bac, X Zhuang, T Rabczuk. Stochastic multiscale modeling of heat conductivity of polymeric clay nanocomposites. Mechanics of Materials, 2020, 142: 103280 https://doi.org/10.1016/j.mechmat.2019.103280
98	L S Shapley. Quota Solutions of n-Person Games. Belvoir: Defense Technical Information Center, 1953, 343
99	E Strumbelj, I Kononenko. An efficient explanation of individual classifications using game theory. Journal of Machine Learning Research, 2010, 11: 1–18
100	E Štrumbelj, I Kononenko. Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems, 2014, 41(3): 647–665 https://doi.org/10.1007/s10115-013-0679-x
101	S M LundbergS I Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems. 2017, 30
102	H B Ly, L M Le, H T Duong, T C Nguyen, T A Pham, T T Le, V M Le, L Nguyen-Ngoc, B T Pham. Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections. Applied Sciences (Basel, Switzerland), 2019, 9(11): 2258 https://doi.org/10.3390/app9112258
103	D V Dao, S H Trinh, H B Ly, B T Pham. Prediction of compressive strength of geopolymer concrete using entirely steel slag aggregates: Novel hybrid artificial intelligence approaches. Applied Sciences (Basel, Switzerland), 2019, 9(6): 1113 https://doi.org/10.3390/app9061113
104	Y Jung, J Hu. AK-fold averaging cross-validation procedure. Journal of Nonparametric Statistics, 2015, 27(2): 167–179 https://doi.org/10.1080/10485252.2015.1010532
105	B G MarcotA M Hanea. What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Computational Statistics, 2021, 36(3): 2009–2031
106	T A Nguyen, H B Ly, H V T Mai, V Q Tran. On the training algorithms for artificial neural network in predicting the shear strength of deep beams. Complexity, 2021, 2021: e5548988 https://doi.org/10.1155/2021/5548988
107	B T Pham, M D Nguyen, D V Dao, I Prakash, H B Ly, T T Le, L S Ho, K T Nguyen, T Q Ngo, V Hoang, L H Son, H T T Ngo, H T Tran, N M Do, H Van Le, H L Ho, D Tien Bui. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. Science of the Total Environment, 2019, 679: 172–184 https://doi.org/10.1016/j.scitotenv.2019.05.061
108	A Oner, S Akyuz. An experimental study on optimum usage of GGBS for the compressive strength of concrete. Cement and Concrete Composites, 2007, 29(6): 505–514 https://doi.org/10.1016/j.cemconcomp.2007.01.001
109	J Shen, Q Xu. Effect of moisture content and porosity on compressive strength of concrete during drying at 105 °C. Construction & Building Materials, 2019, 195: 19–27 https://doi.org/10.1016/j.conbuildmat.2018.11.046
110	J Zhou, X Chen, L Wu, X Kan. Influence of free water content on the compressive mechanical behaviour of cement mortar under high strain rate. Sadhana, 2011, 36(3): 357–369 https://doi.org/10.1007/s12046-011-0024-6

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