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Prediction of cement-based mortars compressive strength using machine learning techniques

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Abstract

The application of artificial neural networks in mapping the mechanical characteristics of the cement-based materials is underlined in previous investigations. However, this machine learning technique includes several major deficiencies highlighted in the literature, such as the overfitting problem and the inability to explain the decisions. Hence, the present study investigates the applicability of other common machine learning techniques, i.e., support vector machine, random forest (RF), decision tree, AdaBoost and k-nearest neighbors in mapping the behavior of the compressive strength (CS) of cement-based mortars. To this end, a big experimental database has been compiled based on experimental data available in the literature considering, namely the cement grade, which is an important parameter for the modeling of mortar’s CS. Other important parameters are namely the age, the water-to-binder ratio, the particle size distribution of the sand and the amount of plasticizer. Many models based on the influential factors affecting machine learning techniques have been developed, and their prediction capacities have been assessed using performance indexes. The present research highlights the potential of AdaBoost and RF models as useful tools which can assist in mortar design and/or optimization. In addition, mapping the development of mortar characteristics can assist in revealing the influence of the different mortar mix parameters on the compressive strength.

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Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

SA:

Sensitivity analysis

B/S:

Binder-to-sand ratio

DT:

Decision tree

RF:

Random forest

CG:

Cement grade

CS:

Compressive strength

kNN:

K-nearest neighbors

MAPE:

Mean absolute percentage error

MK/B:

Metakaolin percentage in relation to total binder

R 2 :

Coefficient of determination

RMSE:

Root-mean-square error

SP:

Superplasticizer

SVM:

Support vector machine

CAM:

Cosine amplitude method

W/B:

Water-to-binder ratio

PL:

Polynomial kernel

LN:

Linear kernel

OOB:

Out-of-bag

RBF:

Radial basis function kernel

SIG:

Sigmoid kernel

C:

Regularization parameter

γ:

Kernel width

AS:

Age of specimen

d:

Polynomial kernel degree

STD:

Standard deviation

R ij :

Strength of the relation

SC:

Soft computing

References

  1. Vu DD, Stroeven P, Bui VB (2001) Strength and durability aspects of calcined kaolin-blended Portland cement mortar and concrete. Cement Concr Compos 23(6):471–478

    Google Scholar 

  2. Batis G, Pantazopoulou P, Tsivilis S, Badogiannis E (2005) The effect of metakaolin on the corrosion behavior of cement mortars. Cement Concr Compos 27(1):125–130

    Google Scholar 

  3. Khatib JM, Negim EM, Gjonbalaj E (2012) High volume metakaolin as cement replacement in mortar. World J Chem 7(1):7–10

    Google Scholar 

  4. Ameri F, Shoaei P, Zareei SA, Behforouz B (2019) Geopolymers vs. alkali-activated materials (AAMs): a comparative study on durability, microstructure, and resistance to elevated temperatures of lightweight mortars. Constr Build Mater 222:49–63

    Google Scholar 

  5. Pavlikova M, Brtnik T, Keppert M, Cerny R (2009) Effect of metakaolin as partial Portland-cement replacement on properties of high performance mortars. Cement Wapno Beton 29(3):113–122

    Google Scholar 

  6. Kadri EH, Kenai S, Ezziane K, Siddique R, De Schutter G (2011) Influence of metakaolin and silica fume on the heat of hydration and compressive strength development of mortar. Appl Clay Sci 53(4):704–708

    Google Scholar 

  7. Wianglor K, Sinthupinyo S, Piyaworapaiboon M, Chaipanich A (2017) Effect of alkali-activated metakaolin cement on compressive strength of mortars. Appl Clay Sci 141:272–279

    Google Scholar 

  8. Khatib JM, Wild S (1998) Sulphate resistance of metakaolin mortar. Cem Concr Res 28(1):83–92

    Google Scholar 

  9. Saridemir M (2009) Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks. Adv Eng Softw 40(5):350–355

    MATH  Google Scholar 

  10. Gandomi AH, Alavi AH, Ting TO, Yang X-S (2013) Intelligent modeling and prediction of elastic modulus of concrete strength via gene expression programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7928:564–571

    Google Scholar 

  11. Sun L, Koopialipoor M, Jahed Armaghani D, Tarinejad R, Tahir MM (2019) Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples. Eng Comput. https://doi.org/10.1007/s00366-019-00875-1

    Article  Google Scholar 

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

    Google Scholar 

  13. Asteris PG, Ashrafian A, Rezaie-Balf M (2019) Prediction of the compressive strength of self-compacting concrete using surrogate models. Comput Concr 24(2):137–150

    Google Scholar 

  14. 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 

  15. 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 

  16. Duan J, Asteris PG, Nguyen H, Bui X-N, Moayedi H (2020) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput. https://doi.org/10.1007/s00366-020-01003-0

    Article  Google Scholar 

  17. Kandiri A, Mohammadi Golafshani E, Behnood A (2020) Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm. Constr Build Mater 248:118676

    Google Scholar 

  18. Apostolopoulou M, Asteris PG, Armaghani DJ, Douvika MG, Lourenço PB, Cavaleri L, Bakolas A, Moropoulou A (2020) Mapping and holistic design of natural hydraulic lime mortars. Cem Concr Res 136:106167. https://doi.org/10.1016/j.cemconres.2020.106167

    Article  Google Scholar 

  19. Lu S, Koopialipoor M, Asteris PG, Bahri M, Armaghani DJ (2020) A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs. Materials 2020(13):3902

    Google Scholar 

  20. Asteris PG, Douvika MG, Karamani CA, Skentou AD, Chlichlia K, Cavaleri L, Daras T, Armaghani DJ, Zaoutis TE (2020) A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon. Comput Model Eng Sci. https://doi.org/10.32604/cmes.2020.013280

    Article  Google Scholar 

  21. Banan A et al (2020) Deep learning-based appearance features extraction for automated carp species identification. Aquacult Eng 89:102053

    Google Scholar 

  22. Fan YJ et al (2020) Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network. IEEE Access 8:25111–25121

    Google Scholar 

  23. Shamshirband S et al (2019) A survey of deep learning techniques: application in wind and solar energy resources. IEEE Access 7(1):164650–164666

    Google Scholar 

  24. Ardabili SF et al (2018) Computational intelligence approach for modeling hydrogen production: a review. Eng Appl Comput Fluid Mech 12(1):438–458

    MathSciNet  Google Scholar 

  25. Taormina R et al (2015) ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS". Eng Appl Artif Intell 45:429–440

    Google Scholar 

  26. Wu CL et al (2013) Prediction of rainfall time series using modular soft computing methods. Eng Appl Artif Intell 26(3):997–1007

    Google Scholar 

  27. Armaghani DJ, Momeni E, Asteris PG (2020) Application of group method of data handling technique in assessing deformation of rock mass. Metaheur Comput Appl 1(1):1–18

    Google Scholar 

  28. Dias WPS, Pooliyadda SP (2001) Neural networks for predicting properties of concretes with admixtures. Constr Build Mater 15:371–379

    Google Scholar 

  29. Lee SC (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25:849–857

    Google Scholar 

  30. Yeh I-C (2007) Modeling slump flow of concrete using second-order regressions and artificial neural networks. Cement Concr Compos 29(6):474–480

    Google Scholar 

  31. 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 

  32. Trtnik G, Kavčič F, Turk G (2009) Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 49:53–60

    Google Scholar 

  33. Sonebi M, Cevik A (2009) Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash. Constr Build Mater 23:2614–2622. https://doi.org/10.1016/j.conbuildmat.2009.02.012

    Article  Google Scholar 

  34. Atici U (2011) Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Syst Appl 38:9609–9618. https://doi.org/10.1016/j.eswa.2011.01.156

    Article  Google Scholar 

  35. Kostić S, Vasović D (2015) Prediction model for compressive strength of basic concrete mixture using artificial neural networks. Neural Comput Appl 26:1005–1024. https://doi.org/10.1007/s00521-014-1763-1

    Article  Google Scholar 

  36. González-Taboada I, González-Fonteboa B, Martínez-Abella F, Pérez-Ordóñez JL (2016) Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming. Constr Build Mater 106:480–499. https://doi.org/10.1016/j.conbuildmat.2015.12.136

    Article  Google Scholar 

  37. 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 

  38. Asteris PG, Apostolopoulou M, Skentou AD, Antonia 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 

  39. Ben Chaabene W, Flah M, Nehdi ML (2020) Machine learning prediction of mechanical properties of concrete: critical review. Constr Build Mater 260:119889

    Google Scholar 

  40. Jalal M, Grasley Z, Gurganus C, Bullard JW (2020) Experimental investigation and comparative machine-learning prediction of strength behavior of optimized recycled rubber concrete. Constr Build Mater 256:119478

    Google Scholar 

  41. Ghorbani B, Arulrajah A, Narsilio G, Horpibulsuk S (2020) Experimental and ANN analysis of temperature effects on the permanent deformation properties of demolition wastes. Transp Geotech 24:100365

    Google Scholar 

  42. Akkurt S, Tayfur G, Can S (2004) Fuzzy logic model for the prediction of cement compressive strength. Cem Concr Res 34:1429–1433

    Google Scholar 

  43. Baykasoğlu A, Dereli TU, Tanış S (2004) Prediction of cement strength using soft computing techniques. Cem Concr Res 34:2083–2090

    Google Scholar 

  44. Özcan F, Atiş CD, Karahan O, Uncuoǧlu E, Tanyildizi H (2009) Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv Eng Softw 40:856–863

    MATH  Google Scholar 

  45. Pérez JL, Cladera A, Rabuñal JR, Martínez-Abella F (2012) Optimization of existing equations using a new genetic programming algorithm: application to the shear strength of reinforced concrete beams. Adv Eng Softw 50:82–96. https://doi.org/10.1016/j.advengsoft.2012.02.008

    Article  Google Scholar 

  46. Mansouri I, Kisi O (2015) Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Compos B Eng 70:247–255

    Google Scholar 

  47. Nasrollahzadeh K, Nouhi E (2018) Fuzzy inference system to formulate compressive strength and ultimate strain of square concrete columns wrapped with fiber-reinforced polymer. Neural Comput Appl 30(1):69–86

    Google Scholar 

  48. Falcone R, Lima C, Martinelli E (2020) Soft computing techniques in structural and earthquake engineering: a literature review. Eng Struct 207:110269

    Google Scholar 

  49. Lan G, Wang Y, Zeng G, Zhang J (2020) Compressive strength of earth block masonry: estimation based on neural networks and adaptive network-based fuzzy inference system. Compos Struct 235:111731

    Google Scholar 

  50. Vapnik V (2013) The nature of statistical learning theory. Springer, London

    MATH  Google Scholar 

  51. Abe S (2010) Feature selection and extraction. Support vector machines for pattern classification. Springer, London, pp 331–341

    MATH  Google Scholar 

  52. Damaševičius R (2010) Optimization of SVM parameters for recognition of regulatory DNA sequences. TOP 18:339–353

    MathSciNet  MATH  Google Scholar 

  53. Song S, Zhan Z, Long Z et al (2011) Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data. PLoS ONE 6(2):e17191. https://doi.org/10.1371/journal.pone.0017191

    Article  Google Scholar 

  54. Keerthi SS, Lin C-J (2003) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15(7):1667–1689

    MATH  Google Scholar 

  55. Lin H-T, Lin C-J (2003) A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Neural Comput 3:1–32

    Google Scholar 

  56. Zhu X, Zhang S, Jin Z et al (2010) Missing value estimation for mixed-attribute data sets. IEEE Trans Knowl Data Eng 23(1):110–121

    Google Scholar 

  57. Damaševičius R (2010) Structural analysis of regulatory DNA sequences using grammar inference and support vector machine. Neurocomputing 73:633–638

    Google Scholar 

  58. Ali S, Smith KA (2003) Automatic parameter selection for polynomial kernel. In Proceedings fifth IEEE workshop on mobile computing systems and applications, pp. 243–249. IEEE

  59. Myles AJ, Feudale RN, Liu Y et al (2004) An introduction to decision tree modeling. J Chemom A J Chemom Soc 18:275–285

    Google Scholar 

  60. Debeljak M, Džeroski S (2011) Decision trees in ecological modelling. Modelling complex ecological dynamics. Springer, London, pp 197–209

    Google Scholar 

  61. Murthy SK (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Discov 2:345–389

    Google Scholar 

  62. Kheir RB, Greve MH, Abdallah C, Dalgaard T (2010) Spatial soil zinc content distribution from terrain parameters: a GIS-based decision-tree model in Lebanon. Environ Pollut 158:520–528

    Google Scholar 

  63. Tso GKF, Yau KKW (2007) Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks. Energy 32:1761–1768

    Google Scholar 

  64. Zhao Y, Zhang Y (2008) Comparison of decision tree methods for finding active objects. Adv Sp Res 41:1955–1959

    Google Scholar 

  65. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106

    Google Scholar 

  66. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC Press, Routledge

    MATH  Google Scholar 

  67. Quinlan JR (2014) C4. 5: Programs for machine learning. Elsevier, Amsterdam

    Google Scholar 

  68. Michael JA, Gordon SL (1997) Data mining technique for marketing, sales and customer support. Wiley, New York

    Google Scholar 

  69. Cho JH, Kurup PU (2011) Decision tree approach for classification and dimensionality reduction of electronic nose data. Sens Actuat, B Chem 160(1):542–548

    Google Scholar 

  70. Yang B-S, Oh M-S, Tan ACC (2009) Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Syst Appl 36:1840–1849

    Google Scholar 

  71. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. Proc Thirteenth Int Conf Mach Learn 96:148–156

    Google Scholar 

  72. Guo L, Ge P-S, Zhang M-H et al (2012) Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine. Expert Syst Appl 39:4274–4286

    Google Scholar 

  73. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    MathSciNet  MATH  Google Scholar 

  74. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Brooks/Cole Publishing, Monterey

    MATH  Google Scholar 

  75. Breiman L (2001) Random forests. Mach Learn 45:5–32

    MATH  Google Scholar 

  76. Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recognit Lett 27:294–300

    Google Scholar 

  77. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:175–185

    MathSciNet  Google Scholar 

  78. Rezaei Z, Selamat A, Taki A et al (2017) Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images. Appl Soft Comput 53:380–395

    Google Scholar 

  79. Silva-Ramírez E-L, Pino-Mejías R, López-Coello M (2015) Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns. Appl Soft Comput 29:65–74

    Google Scholar 

  80. Pu Y, Zhao X, Chi G et al (2019) Design and implementation of a parallel geographically weighted k-nearest neighbor classifier. Comput Geosci 127:111–122

    Google Scholar 

  81. Courard L, Darimont A, Schouterden M, Ferauche F, Willem X, Degeimbre R (2003) Durability of mortars modified with metakaolin. Cem Concr Res 33(9):1473–1479

    Google Scholar 

  82. Sumasree C, Sajja S (2016) Effect of metakaolin and cerafibermix on mechanical and durability properties of mortars. Int J Sci, Eng Technol 4(3):501–506

    Google Scholar 

  83. Mardani-Aghabaglou A, Sezer Gİ, Ramyar K (2014) Comparison of fly ash, silica fume and metakaolin from mechanical properties and durability performance of mortar mixtures view point. Constr Build Mater 70:17–25

    Google Scholar 

  84. Potgieter-Vermaak SS, Potgieter JH (2006) Metakaolin as an extender in South African cement. J Mater Civ Eng 18(4):619–623

    Google Scholar 

  85. Cizer O, Van Balen K, Van Gemert D, Elsen J (2008) Blended lime-cement mortars for conservation purposes: Microstructure and strength development. Structural analysis of historic construction: preserving safety and significance. In: Proceedings of the 6th international conference on structural analysis of historic construction, SAHC08, vol. 2, pp. 965–972

  86. Al-Chaar GK, Alkadi M, Asteris PG (2013) Natural pozzolan as a partial substitute for cement in concrete. Open Constr Build Technol J 7:33–42

    Google Scholar 

  87. Curcio F, DeAngelis BA, Pagliolico S (1998) Metakaolin as a pozzolanic microfiller for high-performance mortars. Cem Concr Res 28(6):803–809

    Google Scholar 

  88. Cyr M, Idir R, Escadeillas G, Julien S, Menchon N (2007) Stabilization of industrial by-products in mortars containing metakaolin. American Concrete Institute, ACI Special Publication, (242 SP), pp. 51–62

  89. Saidat F, Mouret M, Cyr M (2012) Chemical activation of metakaolin in cement-based materials. American Concrete Institute, ACI Special Publication, (288 SP), pp. 451–465

  90. Ashok M, Parande AK, Jayabalan P (2017) Strength and durability study on cement mortar containing nano materials. Adv Nano Res 5(2):99–111

    Google Scholar 

  91. Geng HN, Li Q (2017) Water absorption and hydration products of metakaolin modified mortar. Key Eng Mater 26:505–509

    Google Scholar 

  92. Khater HM (2010) Influence of metakaolin on resistivity of cement mortar to magnesium chloride solution. Ceram Silik 54(4):325–333

    Google Scholar 

  93. Parande AK, Ramesh Babu B, AswinKarthik M, Deepak Kumaar KK, Palaniswamy N (2008) Study on strength and corrosion performance for steel embedded in metakaolin blended concrete/mortar. Constr Build Mater 22(3):127–134

    Google Scholar 

  94. Mansour MS, Abadlia MT, Jauberthie R, Messaoudene I (2012) Metakaolin as a pozzolan for high-performance mortar. Cement Wapno Beton 2:102–108

    Google Scholar 

  95. Lee ST, Moon HY, Hooton RD, Kim JP (2005) Effect of solution concentrations and replacement levels of metakaolin on the resistance of mortars exposed to magnesium sulfate solutions. Cem Concr Res 35(7):1314–1323

    Google Scholar 

  96. Armaghani DJ, Hajihassani M, Sohaei H, Mohamad ET, Marto A, Motaghedi H, Moghaddam MR (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 8(12):10937–10950

    Google Scholar 

  97. Momeni E, Nazir R, Armaghani DJ, Maizir H (2015) Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sci Res J 19(1):85–93

    Google Scholar 

  98. Khandelwal M, Armaghani DJ, Faradonbeh RS, Ranjith PG, Ghoraba S (2016) A new model based on gene expression programming to estimate air flow in a single rock joint. Environ Earth Sci 75(9):739

    Google Scholar 

  99. Alavi AH, Gandomi AH (2012) Energy-based numerical models for assessment of soil liquefaction. Geosci Front 3(4):541–555

    Google Scholar 

  100. Wu H-H, Wu S (2009) Various proofs of the Cauchy-Schwarz inequality. Octogon Math Magaz 17(1):221–229

    MathSciNet  Google Scholar 

  101. Cavaleri L, Chatzarakis GE, Di Trapani F, 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 

  102. Apostolopoulou M, Douvika MG, Kanellopoulos IN, Moropoulou A, Asteris PG (2018) “Prediction of Compressive Strength of Mortars using Artificial Neural Networks”. In: 1st International conference TMM_CH, transdisciplinary multispectral modelling and cooperation for the preservation of cultural heritage”, Athens, Greece, 10–13 October

  103. Chen H, Asteris PG, Armaghani DJ, Gordan B, Pham BT (2019) Assessing dynamic conditions of the retaining wall using two hybrid intelligent models. Appl Sci 2019(9):1042

    Google Scholar 

  104. Psyllaki P, Stamatiou K, Iliadis I, Mourlas A, Asteris P, Vaxevanidis N (2018) Surface treatment of tool steels against galling failure. MATEC Web Conf 188:04024

    Google Scholar 

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

    Google Scholar 

  106. 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 

  107. Zhou J, Asteris PG, Armaghani DJ, Pham BT (2020) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 139:106390. https://doi.org/10.1016/j.soildyn.2020.106390

    Article  Google Scholar 

  108. Asteris PG, Apostolopoulou M, Armaghani DJ, Cavaleri L, Chountalas AT, Guney D, Hajihassani M, Hasanipanah M, Khandelwal M, Karamani C, Koopialipoor M, Kotsonis E, Le T-T, Lourenço PB, Ly H-B, Moropoulou A, Nguyen H, Pham BT, Samui P, Zhou J (2020) On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength. Metaheur Comput Appl 1:63–99

    Google Scholar 

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Authors and Affiliations

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

    Panagiotis G. Asteris & Evgenios A. Kotsonis

  2. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Mohammadreza Koopialipoor

  3. Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Danial J. Armaghani

  4. ISISE, Department of Civil Engineering, University of Minho, Azurém, 4800-058, Guimarães, Portugal

    Paulo B. Lourenço

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  1. Panagiotis G. Asteris
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Table 6 Cement-based mortars experimental database
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Asteris, P.G., Koopialipoor, M., Armaghani, D.J. et al. Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Comput & Applic 33, 13089–13121 (2021). https://doi.org/10.1007/s00521-021-06004-8

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