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

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.

Graphical abstract

Keywords

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

References

[1] EFNARC. Specification and Guidelines for Self-Compacting Concrete. Farnham: European Federation of Specialist Construction Chemicals and Concrete System, 2002

[2] Fernandez-Gomez J, Landsberger G A. Evaluation of shrinkage prediction models for self-consolidating concrete. ACI Materials Journal, 2007, 104(5): 464

[3] SkarendahlÅPeterssonÖ. Self-Compacting Concrete––State-of-the-Art report of RILEM Technical Committee 174-SCC. Cachan: RILEM Publications, 2000

[4] Ramadan K Z, Haddad R H. Self-healing of overloaded self-compacting concrete of rigid pavement. European Journal of Environmental and Civil Engineering, 2017, 21(1): 63–77

[5] Busari A A, Akinmusuru J O, Dahunsi B I O, Ogbiye A S, Okeniyi J O. Self-compacting concrete in pavement construction: Strength grouping of some selected brands of cements. Energy Procedia, 2017, 119: 863–869

[6] Pasko Jr T J. Concrete pavements––Past, present, and future. Public Roads, 1998, 62(1): 7–15

[7] Bouzoubaâ N, Lachemi M. Self-compacting concrete incorporating high volumes of class F fly ash: Preliminary results. Cement and Concrete Research, 2001, 31(3): 413–420

[8] Busari A, Akinmusuru J, Dahunsi B. Mechanical properties of dehydroxylated kaolinitic clay in self-compacting concrete for pavement construction. Silicon, 2019, 11(5): 2429–2437

[9] Rajah Surya T, Prakash M, Satyanarayanan K S, Keneth Celestine A, Parthasarathi N. Compressive strength of self compacting concrete under elevated temperature. Materials Today: Proceedings, 2021, 40: S83–S87

[10] Guo H, Zhuang X, Chen P, Alajlan N, Rabczuk T. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with Computers, 2022, 1–26

[11] Nguyen-Thanh V M, Anitescu C, Alajlan N, Rabczuk T, Zhuang X. Parametric deep energy approach for elasticity accounting for strain gradient effects. Computer Methods in Applied Mechanics and Engineering, 2021, 386: 114096

[12] Ly H B, Pham B T, Le L M, Le T T, Le V M, Asteris P G. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Computing & Applications, 2021, 33(8): 3437–3458

[13] Ly H B, Nguyen M H, Pham B T. 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

[14] Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. 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

[15] Mortazavi B, Silani M, Podryabinkin E V, Rabczuk T, Zhuang X, Shapeev A V. First-principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine-learning interatomic potentials. Advanced Materials, 2021, 33(35): 2102807

[16] Ly H B, Nguyen T A, Thi Mai H V, Tran V Q. Development of deep neural network model to predict the compressive strength of rubber concrete. Construction & Building Materials, 2021, 301: 124081

[17] Goswami S, Anitescu C, Chakraborty S, Rabczuk T. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture. Theoretical and Applied Fracture Mechanics, 2020, 106: 102447

[18] RabczukTZi GBordasSNguyen-XuanH. 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] Rabczuk T, Belytschko T. Cracking particles: A simplified meshfree method for arbitrary evolving cracks. International Journal for Numerical Methods in Engineering, 2004, 61(13): 2316–2343

[20] RabczukTBelytschko T. 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] Ren H L, Zhuang X Y, Anitescu C, Rabczuk T. An explicit phase field method for brittle dynamic fracture. Computers & Structures, 2019, 217: 45–56

[22] Ly H B, Le T T, Vu H L T, Tran V Q, Le L M, Pham B T. Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability (Basel), 2020, 12(7): 2709

[23] Nguyen T A, Ly H B, Mai H V T, Tran V Q. Prediction of later-age concrete compressive strength using feedforward neural network. Advances in Materials Science and Engineering, 2020, 2020

[24] Quan Tran V, Quoc Dang V, Si Ho L. Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach. Construction & Building Materials, 2022, 323: 126578

[25] Topçu İ B, Sarıdemir M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 2008, 41(3): 305–311

[26] Mai H V T, Nguyen T A, Ly H B, Tran V Q. Prediction compressive strength of concrete containing GGBFS using random forest model. Advances in Civil Engineering, 2021, 2021: e6671448

[27] Mai H V T, Nguyen T A, Ly H B, Tran V Q. 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

[28] Ly H B, Nguyen T A, Pham B T. Investigation on factors affecting early strength of high-performance concrete by Gaussian Process Regression. PLoS One, 2022, 17(1): e0262930

[29] Siddique R, Aggarwal P, Aggarwal Y. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, 2011, 42(10): 780–786

[30] Abu Yaman M, Abd Elaty M, Taman M. Predicting the ingredients of self compacting concrete using artificial neural network. Alexandria Engineering Journal, 2017, 56(4): 523–532

[31] Asteris P G, Kolovos K G, Douvika M G, Roinos K. Prediction of self-compacting concrete strength using artificial neural networks. European Journal of Environmental and Civil Engineering, 2016, 20(sup1): s102–s122

[32] Asteris P G, Kolovos K G. Self-compacting concrete strength prediction using surrogate models. Neural Computing & Applications, 2019, 31(S1): 409–424

[33] Malagavell V, Manalel P A. 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] Zhang J, Ma G, Huang Y, sun J, Aslani F, Nener B. Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Construction & Building Materials, 2019, 210: 713–719

[35] Azimi-Pour M, Eskandari-Naddaf H, Pakzad A. 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

[36] Pedregosa F, Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011, 12: 2825–2830

[37] Chen T, Guestrin C. 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

[38] Asteris P, Kolovos K, Douvika M, Roinos K. Prediction of self-compacting concrete strength using artificial neural networks. European Journal of Environmental and Civil Engineering, 2016, 20(sup1): s102–s122

[39] Akkurt S, Tayfur G, Can S. Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research, 2004, 34: 1429–1433

[40] Kovačević M, Lozančić S, Nyarko E K. Application of artificial intelligence methods for predicting the compressive strength of self-compacting concrete with class F fly ash. Materials (Basel), 2022, 15: 4191

[41] Azimi-Pour M, Eskandari-Naddaf H, Pakzad A. 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

[42] Saha P, Debnath P, Thomas P. Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach. Neural Computing & Applications, 2020, 32(12): 7995–8010

[43] Siddique R. Properties of self-compacting concrete containing class F fly ash. Materials & Design, 2011, 32(3): 1501–1507

[44] Sukumar B, Nagamani K, Srinivasa Raghavan R. Evaluation of strength at early ages of self-compacting concrete with high volume fly ash. Construction & Building Materials, 2008, 22(7): 1394–1401

[45] Gesoğlu M, Güneyisi E, Özbay E. 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

[46] Güneyisi E, Gesoğlu M, Özbay E. Strength and drying shrinkage properties of self-compacting concretes incorporating multi-system blended mineral admixtures. Construction & Building Materials, 2010, 24(10): 1878–1887

[47] Dinakar P. Design of self-compacting concrete with fly ash. Magazine of Concrete Research, 2012, 64(5): 401–409

[48] Guru Jawahar J, Sashidhar C, Ramana Reddy I V, Annie Peter J. Micro and macrolevel properties of fly ash blended self compacting concrete. Materials & Design, 2013, 46: 696–705

[49] Boel V, Audenaert K, De Schutter G, Heirman G, Vandewalle L, Desmet B, Vantomme J. Transport properties of self compacting concrete with limestone filler or fly ash. Materials and Structures, 2007, 40(5): 507–516

[50] Jalal M, Mansouri E. 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

[51] Dinakar P, Babu K G, Santhanam M. Mechanical properties of high-volume fly ash self-compacting concrete mixtures. Structural Concrete, 2008, 9(2): 109–116

[52] Nehdi M, Pardhan M, Koshowski S. Durability of self-consolidating concrete incorporating high-volume replacement composite cements. Cement and Concrete Research, 2004, 34(11): 2103–2112

[53] Uysal M, Sumer M. Performance of self-compacting concrete containing different mineral admixtures. Construction & Building Materials, 2011, 25(11): 4112–4120

[54] Venkatakrishnaiah R, Sakthivel G. Bulk utilization of flyash in self compacting concrete. KSCE Journal of Civil Engineering, 2015, 19(7): 2116–2120

[55] Hemalatha T, Ramaswamy A, Chandra Kishen J M. Micromechanical analysis of self compacting concrete. Materials and Structures, 2015, 48(11): 3719–3734

[56] Liu M. Self-compacting concrete with different levels of pulverized fuel ash. Construction & Building Materials, 2010, 24(7): 1245–1252

[57] Bingöl A F, Tohumcuİ. 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

[58] Barbhuiya S. Effects of fly ash and dolomite powder on the properties of self-compacting concrete. Construction & Building Materials, 2011, 25(8): 3301–3305

[59] SunZ JDuan W WTianM LFangY F. Experimental research on self-compacting concrete with different mixture ratio of fly ash. Advanced Materials Research, 2011, 236–238: 490–495

[60] Pathak N, Siddique R. Properties of self-compacting-concrete containing fly ash subjected to elevated temperatures. Construction & Building Materials, 2012, 30: 274–280

[61] Patel R, Hossain K, Shehata M, Bouzoubaâ N, Lachemi M. Development of statistical models for mixture design of high-volume fly ash self-consolidating concrete. ACI Materials Journal, 2004, 101: 294–302

[62] Sonebi M. Medium strength self-compacting concrete containing fly ash: Modelling using factorial experimental plans. Cement and Concrete Research, 2004, 34(7): 1199–1208

[63] Bui V K, Akkaya Y, Shah S P. Rheological model for self-consolidating concrete. Materials Journal, 2002, 99(6): 549–559

[64] Ghezal A, Khayat K. Optimizing self-consolidating concrete with limestone filler by using statistical factorial design methods. ACI Materials Journal, 2002, 99: 264–272

[65] Dinakar P, Sethy K P, Sahoo U C. Design of self-compacting concrete with ground granulated blast furnace slag. Materials & Design, 2013, 43: 161–169

[66] Felekoğlu B, Türkel S, Baradan B. Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete. Building and Environment, 2007, 42(4): 1795–1802

[67] Gesoğlu M, Özbay E. 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

[68] Grdic Z, Despotovic I, Toplicic-Curcic G. Properties of self-compacting concrete with different types of additives. Facta Universitatis––Series: Architecture and Civil Engineering, 2008, 6(2): 173–177

[69] Güneyisi E, Gesoglu M, Azez O A, Öz H Ö. 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

[70] Memon S A, Shaikh M A, Akbar H. Utilization of rice husk ash as viscosity modifying agent in self compacting concrete. Construction and building materials, 2011, 25(2): 1044–1048

[71] Rahman M E, Muntohar A S, Pakrashi V, Nagaratnam B H, Sujan D. Self compacting concrete from uncontrolled burning of rice husk and blended fine aggregate. Materials & Design, 2014, 55: 410–415

[72] Sfikas I P, Trezos K G. Effect of composition variations on bond properties of self-compacting concrete specimens. Construction & Building Materials, 2013, 41: 252–262

[73] Valcuende M, Marco E, Parra C, Serna P. Influence of limestone filler and viscosity-modifying admixture on the shrinkage of self-compacting concrete. Cement and Concrete Research, 2012, 42(4): 583–592

[74] Şahmaran M, Yamanİ Ö, Tokyay M. Transport and mechanical properties of self consolidating concrete with high volume fly ash. Cement and Concrete Composites, 2009, 31(2): 99–106

[75] PatelR. 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] Nepomuceno M C S, Pereira-de-Oliveira L A, Lopes S M R. 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

[77] Krishnapal P, Yadav R K, Rajeev C. Strength characteristics of self compacting concrete containing fly ash. Research Journal of Engineering Sciences, 2013, 2278: 9472

[78] Dhiyaneshwaran S, Ramanathan P, Bose B, Venkatasubramani R. Study on durability characteristics of self-compacting concrete with fly ash. Jordan Journal of Civil Engineering, 2013, 7: 342–353

[79] Mahalingam B, Nagamani K. 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] Mahesh S. Self compacting concrete and its properties. International Journal of Engineering Research and Applications, 2014, 4(8): 72–80

[81] Al-RubayeM M. 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] MahatoAGambhir GKumarADuttaAKisleyK. Self compacting concrete. Thesis for the Bachelor’s Degree. Bhubaneswar: KIIT University, 2016

[83] SeoJTorresE SchafferW. Self-Consolidating Concrete for Prestressed Bridge Girders. WisDOT ID NO. 0092-15-03. 2017

[84] Douglas R P, Bui V K, Akkaya Y, Shah S P. 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] MitchellT M. Machine Learning. New York: McGraw-Hill Education, 1997

[86] Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273–297

[87] Quinlan J R. Induction of decision trees. Machine Learning, 1986, 1(1): 81–106

[88] Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32

[89] Ayyadevara V K. Pro Machine Learning Algorithms. Berkeley: Apress, 2018, 117–134

[90] Friedman J H. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 2001, 29(5): 1189–1232

[91] June L W, Hassan M A. Modifications of the limited memory BFGs algorithm for large-scale nonlinear optimization. Mathematical Journal of Okayama University, 2005, 47(1): 175–188

[92] Bottou L. Neural Networks: Tricks of the Trade. Heidelberg: Springer, 2012, 421–436

[93] Eberhart R, Kennedy J. 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] Liu B, Vu-Bac N, Rabczuk T. A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms. Composite Structures, 2021, 273: 114269

[95] BlankeS. Hyperactive: An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. 2019. Available at the website of GITHUB

[96] Stone M. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B. Methodological, 1974, 36(2): 111–133

[97] Liu B, Vu-Bac N, Zhuang X, Rabczuk T. Stochastic multiscale modeling of heat conductivity of polymeric clay nanocomposites. Mechanics of Materials, 2020, 142: 103280

[98] Shapley L S. Quota Solutions of n-Person Games. Belvoir: Defense Technical Information Center, 1953, 343

[99] Strumbelj E, Kononenko I. An efficient explanation of individual classifications using game theory. Journal of Machine Learning Research, 2010, 11: 1–18

[100] Štrumbelj E, Kononenko I. Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems, 2014, 41(3): 647–665

[101] LundbergS MLee S I. A unified approach to interpreting model predictions. Advances in neural information processing systems. 2017, 30

[102] Ly H B, Le L M, Duong H T, Nguyen T C, Pham T A, Le T T, Le V M, Nguyen-Ngoc L, Pham B T. 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

[103] Dao D V, Trinh S H, Ly H B, Pham B T. 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

[104] Jung Y, Hu J. AK-fold averaging cross-validation procedure. Journal of Nonparametric Statistics, 2015, 27(2): 167–179

[105] MarcotB GHanea A M. 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] Nguyen T A, Ly H B, Mai H V T, Tran V Q. On the training algorithms for artificial neural network in predicting the shear strength of deep beams. Complexity, 2021, 2021: e5548988

[107] Pham B T, Nguyen M D, Dao D V, Prakash I, Ly H B, Le T T, Ho L S, Nguyen K T, Ngo T Q, Hoang V, Son L H, Ngo H T T, Tran H T, Do N M, Van Le H, Ho H L, Tien Bui D. 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

[108] Oner A, Akyuz S. An experimental study on optimum usage of GGBS for the compressive strength of concrete. Cement and Concrete Composites, 2007, 29(6): 505–514

[109] Shen J, Xu Q. Effect of moisture content and porosity on compressive strength of concrete during drying at 105 °C. Construction & Building Materials, 2019, 195: 19–27

[110] Zhou J, Chen X, Wu L, Kan X. Influence of free water content on the compressive mechanical behaviour of cement mortar under high strain rate. Sadhana, 2011, 36(3): 357–369

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