Abstract
A potential environmentally friendly building material is concrete containing manufactured sand. The utilization of manufactured sand in the production of high-performance concrete (HPC) has been gaining popularity. A commonly used mechanical parameter for the design of HPC structures is the splitting tensile strength. Due to the complexity, expense, and time-consuming nature of conducting tensile testing, numerous researchers are interested in creating a straightforward yet precise way to forecast the value of this feature. This study presents an effective application of machine learning models for predicting the tensile strength of HPC. Five advanced predictive algorithms, namely K-nearest neighbors, Adaptive Boosting (AdaBoost), Random Forest Regressor, Extra Tree Regressor (ETR), and Voting Regressor, were utilized. The performance of the developed ML models was then evaluated using distinct performance indexes. The evaluation shows that the ETR model’s estimated results are closer to the experimental results than the other four models. This suggests that the ETR model accurately estimates split tensile strength. Conversely, Shapley additive explanations (SHAP) offer comprehensive metrics for evaluating both the significance of features and the influence of a variable on a given prediction. It is noteworthy that the SHAP interpretations aligned with the typical tensile behaviour of the concrete, thus reinforcing the causal relationship between the machine learning predictions and the actual outcomes.
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References
Abdeljaber O, Avci O, Inman DJ (2016) Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks. J Sound Vib 363:33–53. https://doi.org/10.1016/j.jsv.2015.10.029
Ahmad M, Al-Mansob RA, Kashyzadeh KR, Keawsawasvong S, Sabri Sabri MM, Jamil I et al (2022) Extreme gradient boosting algorithm for predicting shear strengths of rockfill materials. Complexity 2022:9415863. https://doi.org/10.1155/2022/9415863
Ahmad MA, Eckert C, Teredesai A (2018) Interpretable machine learning in healthcare. In: Proc. 2018 ACM Int. Conf. Bioinformatics, Comput. Biol. Heal. Informatics, New York, NY, USA: Association for Computing Machinery. pp 559–560. https://doi.org/10.1145/3233547.3233667.
Alexander M, Mindess S (2005) Aggregates in concrete. CRC Press, Boca Raton
Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:175–185. https://doi.org/10.1080/00031305.1992.10475879
Armaghani DJ, Skentou AD, Izadpanah M, Karoglou M, Khandelwal M, Konstantakatos G et al (2024) Chapter 4 - deep neural networks for the estimation of granite materials’ compressive strength using non-destructive indices. In: Nguyen H, Bui X-N, Topal E, Zhou J, Choi Y, Zhang W (eds) Applications of artificial intelligence in mining. Geotechnical and Geoengineering. Elsevier, The Netherlands, pp 45–74. https://doi.org/10.1016/B978-0-443-18764-3.00024-2
ASTM C33 A (2004) Standard specification for concrete aggregates. Am Soc Test Mater. pp 1–11
Barkhordari MS, Jawdhari A (2023) Machine learning based prediction model for plastic hinge length calculation of reinforced concrete structural walls. Adv Struct Eng. https://doi.org/10.1177/1369433223117425
Behnood A, Olek J, Glinicki MA (2015) Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm. Constr Build Mater 94:137–47. https://doi.org/10.1016/j.conbuildmat.2015.06.055
Behnood A, Verian KP, Modiri GM (2015) Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength. Constr Build Mater 98:519–29. https://doi.org/10.1016/j.conbuildmat.2015.08.124
Bin Ahmed F, Abid Ahsan K, Shariff T, Rahman MS (2021) Formulation of polynomial equation predicting the splitting tensile strength of concrete. Mater Today Proc 38:3269–78. https://doi.org/10.1016/j.matpr.2020.10.017
Biswas RK, Iwanami M, Chijiwa N, Uno K (2020) Effect of non-uniform rebar corrosion on structural performance of RC structures: a numerical and experimental investigation. Constr Build Mater 230:116908. https://doi.org/10.1016/j.conbuildmat.2019.116908
Bonavetti VL, Irassar EF (1994) The effect of stone dust content in sand. Cem Concr Res 24:580–90. https://doi.org/10.1016/0008-8846(94)90147-3
Breiman L (2001) Random forests. Mach Learn 45:5–32
Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth Int. Group. Vol 37, pp 237–51
Bui D-K, Nguyen T, Chou J-S, Nguyen-Xuan H, Ngo TD (2018) A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Constr Build Mater 180:320–33. https://doi.org/10.1016/j.conbuildmat.2018.05.201
Byrd RH, Chin GM, Nocedal J, Wu Y (2012) Sample size selection in optimization methods for machine learning. Math Program 134:127–155. https://doi.org/10.1007/s10107-012-0572-5
Cavaleri L, Barkhordari MS, Repapis CC, Armaghani DJ, Ulrikh DV, Asteris PG (2022) Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete. Constr Build Mater 359:129504
Ding X, Li C, Xu Y, Li F, Zhao S (2016) Experimental study on long-term compressive strength of concrete with manufactured sand. Constr Build Mater 108:67–73
Duan ZH, Kou SC, Poon CS (2013) Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete. Constr Build Mater 44:524–32. https://doi.org/10.1016/j.conbuildmat.2013.02.064
Dutta D, Barai SV (2019) Prediction of compressive strength of concrete: machine learning approaches. In: Rao ARM, Ramanjaneyulu K (eds) Recent advances in structural engineering. Springer Singapore, Singapore, pp 503–513
Géron A (2022) Hands-on machine learning with scikit-learn, keras, and tensorflow. O’Reilly Media, Inc., California
Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3–42. https://doi.org/10.1007/s10994-006-6226-1
Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269–276
Grömping U (2009) Variable importance assessment in regression: linear regression versus random forest. Am Stat 63:308–319. https://doi.org/10.1198/tast.2009.08199
Isleem HF, Chukka NDKR, Bahrami A, Oyebisi S, Kumar R, Qiong T (2023) Nonlinear finite element and analytical modelling of reinforced concrete filled steel tube columns under axial compression loading. Res Eng 19:101341. https://doi.org/10.1016/j.rineng.2023.101341
Isleem HF, Zewudie BB, Bahrami A, Kumar R, Xingchong W, Samui P (2023b) Parametric investigation of rectangular CFRP confined concrete columns reinforced by inner elliptical steel tubes using finite element and machine learning models. Heliyon 10:e23666
Jiang X, Mahadevan S, Adeli H (2007) Bayesian wavelet packet denoising for structural system identification. Struct Control Heal Monit 14:333–56. https://doi.org/10.1002/stc.161
Jiang W, Xie Y, Li W, Wu J, Long G (2021) Prediction of the splitting tensile strength of the bonding interface by combining the support vector machine with the particle swarm optimization algorithm. Eng Struct 230:111696. https://doi.org/10.1016/j.engstruct.2020.111696
Jis A (2009) 5005; crushed stone and manufactured sand for concrete. Japanese Stand Assoc Tokyo, Japan
John V, Liu Z, Guo C, Mita S, Kidono K (2016) Real-time lane estimation using deep features and extra trees regression. image video technol. In: 7th Pacific-Rim Symp. PSIVT 2015, Auckland, New Zealand, Novemb. 25–27, 2015, Revis. Sel. Pap. 7, Springer. pp 721–733
Kadleček V, Modrý S, Kadleček V (2002) Size effect of test specimens on tensile splitting strength of concrete: general relation. Mater Struct 35:28–34. https://doi.org/10.1007/BF02482087
Kamran M, Wattimena RK, Armaghani DJ, Asteris PG, Jiskani IM, Mohamad ET (2023) Intelligent based decision-making strategy to predict fire intensity in subsurface engineering environments. Process Saf Environ Prot 171:374–84. https://doi.org/10.1016/j.psep.2022.12.096
Khan MI (2012) Predicting properties of high performance concrete containing composite cementitious materials using artificial neural networks. Autom Constr 22:516–24. https://doi.org/10.1016/j.autcon.2011.11.011
Kumar R, Rai B, Samui P (2023a) Machine learning techniques for prediction of failure loads and fracture characteristics of high and ultra-high strength concrete beams. Innov Infrastruct Solut 8:219
Kumar R, Rai B, Samui P (2023) A comparative study of prediction of compressive strength of ultra-high performance concrete using soft computing technique. Struct Concr. https://doi.org/10.1002/suco.202200850
Kumar DR, Samui P, Wipulanusat W, Keawsawasvong S, Sangjinda K, Jitchaijaroen W (2023) Soft Computing techniques for predicting penetration and uplift resistances of dual pipelines in cohesive soils. Eng Sci. https://doi.org/10.30919/es897
Kumar DR, Wipulanusat W, Kumar M, Keawsawasvong S, Samui P (2024) Optimized neural network-based state-of-the-art soft computing models for the bearing capacity of strip footings subjected to inclined loading. Intell Syst with Appl 21:200314. https://doi.org/10.1016/j.iswa.2023.200314
Ley C, Bordas SPA (2018) What makes data science different? A discussion involving statistics2.0 and computational sciences. Int J Data Sci Anal 6:167–75. https://doi.org/10.1007/s41060-017-0090-x
Li FL, Liu CJ, Pan LY, Li CY (2014) Machine-made sand concrete. China Water Power Press, Beijing
Li Y, Hishamuddin FNS, Mohammed AS, Armaghani DJ, Ulrikh DV, Dehghanbanadaki A et al (2021) The effects of rock index tests on prediction of tensile strength of granitic samples: a neuro-fuzzy intelligent system. Sustainability. https://doi.org/10.3390/su131910541
Lim C-H, Yoon Y-S, Kim J-H (2004) Genetic algorithm in mix proportioning of high-performance concrete. Cem Concr Res 34:409–20. https://doi.org/10.1016/j.cemconres.2003.08.018
Liu Q, Sun P, Fu X, Zhang J, Yang H, Gao H et al (2020) Comparative analysis of BP neural network and RBF neural network in seismic performance evaluation of pier columns. Mech Syst Signal Process 141:106707. https://doi.org/10.1016/j.ymssp.2020.106707
Lu P, Chen S, Zheng Y (2012) Artificial intelligence in civil engineering. Math Probl Eng 2012:145974. https://doi.org/10.1155/2012/145974
Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Proc. 31st Int. Conf. Neural Inf. Process. Syst., Red Hook, NY, USA: Curran Associates Inc. pp 4768–4777.
Maddodi B, Bhandary RP, Sharma V, Yadav JS, Mohapatra S et al (2022) Experimental and statistical evaluation of mechanical properties of green cement concretes—taguchi integrated supervised learning approach. Eng Sci 18:148–58. https://doi.org/10.30919/es8e689
Neville A, Aïtcin P-C (1998) High performance concrete—An overview. Mater Struct 31:111–7. https://doi.org/10.1007/BF02486473
Nguyen H, Vu T, Vo TP, Thai H-T (2021) Efficient machine learning models for prediction of concrete strengths. Constr Build Mater 266:120950. https://doi.org/10.1016/j.conbuildmat.2020.120950
Nikoo M, Torabian Moghadam F, Sadowski Ł (2015) Prediction of concrete compressive strength by evolutionary artificial neural networks. Adv Mater Sci Eng 2015:849126. https://doi.org/10.1155/2015/849126
O’Hegarty R, Kinnane O, Newell J, West R (2021) High performance, low carbon concrete for building cladding applications. J Build Eng 43:102566. https://doi.org/10.1016/j.jobe.2021.102566
Oluokun FA, Harold J, Deatherage EGB (1991) Splitting tensile strength and compressive strength relationships at early ages. ACI Mater J. https://doi.org/10.14359/1859
Pant A, Ramana GV (2022) Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model. Geotext Geomembr 50:1188–98. https://doi.org/10.1016/j.geotexmem.2022.08.003
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in {P}ython. J Mach Learn Res 12:2825–2830
Phyo PP, Byun YC, Park N (2022) Short-term energy forecasting using machine-learning-based ensemble voting regression. Symmetry (basel) 14:1–13. https://doi.org/10.3390/sym14010160
Pyo S, Kim H-K, Lee BY (2017) Effects of coarser fine aggregate on tensile properties of ultra high performance concrete. Cem Concr Compos 84:28–35. https://doi.org/10.1016/j.cemconcomp.2017.08.014
Qiu Y, Huang S, Armaghani DJ, Pradhan B, Zhou A, Zhou J (2023) An optimized system of random forest model by global harmony search with generalized opposition-based learning for forecasting TBM advance rate. Comput Model Eng Sci 138:2873–2897
Ray S, Rahman MM, Haque M, Hasan MW, Alam MM (2021) Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber. J King Saud Univ - Eng Sci. https://doi.org/10.1016/j.jksues.2021.02.009
Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27:302–313
Sagi O, Rokach L (2020) Explainable decision forest: transforming a decision forest into an interpretable tree. Inf Fusion 61:124–138. https://doi.org/10.1016/j.inffus.2020.03.013
Schapire RE (1990) The strength of weak learnability. Mach Learn 5:197–227
Schapire RE (2013) Explaining adaboost. Empir inference Festschrift Honor Vladimir N Vapnik. Springer, Berlin, Heidelberg, pp 37–52
Topçu İB, Sarıdemir M (2008) Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic. Comput Mater Sci 42:74–82. https://doi.org/10.1016/j.commatsci.2007.06.011
Wang J, Yang Z, Liu Y (2014) Effects of the lithologic character of manufactured sand on properties of concrete. J Wuhan Univ Technol Sci Ed 29:1213–1218. https://doi.org/10.1007/s11595-014-1070-9
Wang C, Xu S, Yang J (2021) Adaboost algorithm in artificial intelligence for optimizing the IRI prediction accuracy of asphalt concrete pavement. Sensors. https://doi.org/10.3390/s21175682
Wiegrink K, Marikunte S, Shah SP (1996) Shrinkage cracking of high-strength concrete. Mater J 93:409–415
Yang YH (2007) Study on preparation and properties of the c80 manufactured sand concrete (Thesis for Master Degree). Wuhan Univ Sci Technol Wuhan, China
Yari M, Armaghani DJ, Maraveas C, Ejlali AN, Mohamad ET, Asteris PG (2023) Several tree-based solutions for predicting flyrock distance due to mine blasting. Appl Sci. https://doi.org/10.3390/app13031345
Zhao SB, Ding XX, Li CY (2012) Bond-Slip relation of plain steel bar in concrete with machine-made sand. Innov Civ Eng Archit Sustain Infrastruct 238:142–146. https://doi.org/10.4028/www.scientific.net/AMM.238.142
Zhao SB, Ding XX, Li CM, Li CY (2013) Experimental study of bond properties between deformed steel bar and concrete with machine-made sand. J Build Mater 16:191–196
Zhao S, Hu F, Ding X, Zhao M, Li C, Pei S (2017) Dataset of tensile strength development of concrete with manufactured sand. Data Br 11:469–72. https://doi.org/10.1016/j.dib.2017.02.043
Zhu H, Wang Z, Xu J, Han Q. 2019;Microporous structures and compressive strength of high-performance rubber concrete with internal curing agent. Constr Build Mater 215:128–34. https://doi.org/10.1016/j.conbuildmat.2019.04.184.
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Kumar, R., Samui, P. & Rai, B. Prediction of the Splitting Tensile Strength of Manufactured Sand Based High-Performance Concrete Using Explainable Machine Learning. Iran J Sci Technol Trans Civ Eng (2024). https://doi.org/10.1007/s40996-024-01401-0
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DOI: https://doi.org/10.1007/s40996-024-01401-0