Skip to main content
SpringerLink
Log in

Interpretable XGBoost–SHAP machine learning technique to predict the compressive strength of environment-friendly rice husk ash concrete

  • Technical Paper
  • Published:
Innovative Infrastructure Solutions Aims and scope Submit manuscript

Abstract

Global greenhouse gas emissions from the construction concrete industry are 50% higher than those from all other industries combined. Concrete incorporating waste and recycled materials could help lessen the negative effects of environmental problems. Agricultural waste is increasingly being used to substitute cement in environmentally friendly concrete production. Rice husk ash (RHA) is a workable alternative that merits further investigation. Since evaluating the properties of concrete containing RHA requires extensive and time-consuming experimentation, machine learning (ML) can accurately predict its properties. Consequently, this study aims to anticipate and develop an empirical formula for RHA concrete’s compressive strength (CS) using ML algorithms. This study employs several ML methods such as random forest, support vector machine, light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and SHAP. A total of 192 data points are used in this study to assess the CS of RHA-blended concrete. The input parameters are age, amount of cement, rice husk ash, superplasticizer, water, and aggregates. Across all ML models, the XGBoost method is used to build a highly accurate predictive model. Predicting RHA concrete's CS using an existing XGBoost model is consistently accurate. R2 demonstrates a CS of 0.99 during training and 0.94 during testing. Model characteristics and complex correlations are explained using the SHAP algorithm. The proposed model’s prediction outcomes are compared to prior research, and the best ML algorithm is selected.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Syahida Adnan Z, Ariffin NF, Syed Mohsin SM, Shukor A, Lim NH (2021) Review paper: Performance of rice husk ash as a material for partial cement replacement in concrete. Mater Today Proc 48:842–848. https://doi.org/10.1016/j.matpr.2021.02.400

    Article  Google Scholar 

  2. Pheng LS, Hou LS (2019) The economy and the construction industry, pp21–54. https://doi.org/10.1007/978-981-13-5847-0_2

  3. Eijgelaar E, Thaper C, Peeters P (2010) Antarctic cruise tourism: the paradoxes of ambassadorship, “last chance tourism” and greenhouse gas emissions. J Sustain Tour 18:337–354. https://doi.org/10.1080/09669581003653534

    Article  Google Scholar 

  4. Arrigoni A, Panesar DK, Duhamel M, Opher T, Saxe S, Posen ID et al. (2020) Life cycle greenhouse gas emissions of concrete containing supplementary cementitious materials: cut-off vs. substitution. J Clean Prod 263:121456. https://doi.org/10.1016/j.jclepro.2020.121465.

  5. Chen C, Habert G, Bouzidi Y, Jullien A (2010) Environmental impact of cement production: detail of the different processes and cement plant variability evaluation. J Clean Prod 18:478–485. https://doi.org/10.1016/j.jclepro.2009.12.014

    Article  Google Scholar 

  6. Lamb WF, Wiedmann T, Pongratz J, Andrew R, Crippa M, Olivier JGJ et al (2021) A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environ Res Lett 16:93. https://doi.org/10.1088/1748-9326/abee4e

    Article  Google Scholar 

  7. Farooq F, Ahmed W, Akbar A, Aslam F, Alyousef R (2021) Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners. J Clean Prod 292:126032. https://doi.org/10.1016/j.jclepro.2021.126032

    Article  Google Scholar 

  8. Mahlia TMI (2002) Emissions from electricity generation in Malaysia. Renew Energy 27:293–300. https://doi.org/10.1016/S0960-1481(01)00177-X

    Article  Google Scholar 

  9. Akbar A, Farooq F, Shafique M, Aslam F, Alyousef R, Alabduljabbar H. Sugarcane bagasse ash-based engineered geopolymer mortar incorporating propylene fibers. Journal of Building Engineering 2021;33. https://doi.org/10.1016/j.jobe.2020.101492.

  10. Crossin E (2015) The greenhouse gas implications of using ground granulated blast furnace slag as a cement substitute. J Clean Prod 95:101–108. https://doi.org/10.1016/j.jclepro.2015.02.082

    Article  Google Scholar 

  11. Rathnarajan S, Dhanya BS, Pillai RG, Gettu R, Santhanam M (2022) Carbonation model for concretes with fly ash, slag, and limestone calcined clay - using accelerated and five - year natural exposure data. Cement Concrete Comp 126:104329. https://doi.org/10.1016/j.cemconcomp.2021.104329

    Article  Google Scholar 

  12. Ameri F, Shoaei P, Bahrami N, Vaezi M, Ozbakkaloglu T (2019) Optimum rice husk ash content and bacterial concentration in self-compacting concrete. Constr Build Mater 222:796–813. https://doi.org/10.1016/j.conbuildmat.2019.06.190

    Article  Google Scholar 

  13. Antiohos SK, Papadakis VG, Tsimas S (2014) Rice husk ash (RHA) effectiveness in cement and concrete as a function of reactive silica and fineness. Cem Concr Res 61–62:20–27. https://doi.org/10.1016/j.cemconres.2014.04.001

    Article  Google Scholar 

  14. Zhang Z, Yang F, Liu JC, Wang S (2020) Eco-friendly high strength, high ductility engineered cementitious composites (ECC) with substitution of fly ash by rice husk ash. Cement Concrete Res 137:106200. https://doi.org/10.1016/j.cemconres.2020.106200

    Article  Google Scholar 

  15. Ramasamy V (2012) Compressive strength and durability properties of Rice Husk Ash concrete. KSCE J Civ Eng 16:93–102. https://doi.org/10.1007/s12205-012-0779-2

    Article  Google Scholar 

  16. Khassaf SI, Jasim AT, Mahdi FK (2014) Investigation the properties of concrete containing rice husk ash to reduction the seepage in canals. Int J Sci Technol Res 3:348–354

    Google Scholar 

  17. Chao-Lung H, Le A-T, Chun-Tsun C (2011) Effect of rice husk ash on the strength and durability characteristics of concrete. Constr Build Mater 25:3768–3772. https://doi.org/10.1016/j.conbuildmat.2011.04.009

    Article  Google Scholar 

  18. Ozturk E, Ince C, Derogar S, Ball R (2022) Factors affecting the CO2 emissions, cost efficiency and eco-strength efficiency of concrete containing rice husk ash: a database study. Const Build Mater 326:126905. https://doi.org/10.1016/j.conbuildmat.2022.126905

    Article  Google Scholar 

  19. Kusbiantoro A, Nuruddin MF, Shafiq N, Qazi SA (2012) The effect of microwave incinerated rice husk ash on the compressive and bond strength of fly ash based geopolymer concrete. Constr Build Mater 36:695–703. https://doi.org/10.1016/j.conbuildmat.2012.06.064

    Article  Google Scholar 

  20. Saraswathy V, Song HW (2007) Corrosion performance of rice husk ash blended concrete. Constr Build Mater 21:1779–1784. https://doi.org/10.1016/j.conbuildmat.2006.05.037

    Article  Google Scholar 

  21. Basri MSM, Mustapha F, Mazlan N, Ishak MR (2020) Optimization of adhesion strength and microstructure properties by using response surface methodology in enhancing the rice husk ash-based geopolymer composite coating. Polymers 12:1–15. https://doi.org/10.3390/polym12112709

    Article  Google Scholar 

  22. Kishore R, Bhikshma V, Jeevana PP (2011) Study on strength characteristics of high strength Rice Husk Ash concrete. Proc Eng 14:2666–2672. https://doi.org/10.1016/j.proeng.2011.07.335

    Article  Google Scholar 

  23. Ganesan K, Rajagopal K, Thangavel K (2008) Rice husk ash blended cement: assessment of optimal level of replacement for strength and permeability properties of concrete. Constr Build Mater 22:1675–1683. https://doi.org/10.1016/j.conbuildmat.2007.06.011

    Article  Google Scholar 

  24. De Souza RC, Ghavami K, Stroeven P (2006) Porosity and water permeability of rice husk ash-blended cement composites reinforced with bamboo pulp. J Mater Sci 41:6925–6937. https://doi.org/10.1007/s10853-006-0217-2

    Article  Google Scholar 

  25. Habeeb GA, Fayyadh MM (2009) Rice husk ash concrete: the effect of RHA average particle size on mechanical properties and drying shrinkage. Aust J Basic Appl Sci 3:1616–1622

    Google Scholar 

  26. Muhammad A, Thienel KC, Sposito R (2021) Suitability of blending rice husk ash and calcined clay for the production of self-compacting concrete: a review. Materials 14:52. https://doi.org/10.3390/ma14216252

    Article  Google Scholar 

  27. Rattanachu P, Toolkasikorn P, Tangchirapat W, Chindaprasirt P, Jaturapitakkul C (2020) Performance of recycled aggregate concrete with rice husk ash as cement binder. Cement Concrete Comp 108:103533. https://doi.org/10.1016/j.cemconcomp.2020.103533

    Article  Google Scholar 

  28. Madadi A, Eskandari-Naddaf H, Gharouni-Nik M (2017) Lightweight ferrocement matrix compressive behavior: experiments versus finite element analysis. Arab J Sci Eng 42:4001–4013. https://doi.org/10.1007/s13369-017-2557-4

    Article  Google Scholar 

  29. Korouzhdeh T, Eskandari-Naddaf H, Gharouni-Nik M (2017) An improved ant colony model for cost optimization of composite beams. Appl Artif Intell 31:44–63. https://doi.org/10.1080/08839514.2017.1296681

    Article  Google Scholar 

  30. Gkountakou F, Papadopoulos B (2020) The use of fuzzy linear regression and ANFIS methods to predict the compressive strength of cement. Symmetry 12:208. https://doi.org/10.3390/SYM12081295

    Article  Google Scholar 

  31. Tayfur G, Erdem TK, Kırca Ö (2014) Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks. J Mater Civ Eng 26:04014079. https://doi.org/10.1061/(asce)mt.1943-5533.0000985

    Article  Google Scholar 

  32. Khademi F, Akbari M, Mohammadmehdi S, Nikoo M (2017) Multiple linear regression, arti fi cial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front Struct Civ Eng. https://doi.org/10.1007/s11709-016-0363-9

    Article  Google Scholar 

  33. Nazari A, Sanjayan JG (2015) Modeling of compressive strength of geopolymers by a hybrid ANFIS-ICA approach. J Mater Civ Eng 27:04014167. https://doi.org/10.1061/(asce)mt.1943-5533.0001126

    Article  Google Scholar 

  34. Vakhshouri B, Nejadi S (2018) Prediction of compressive strength of self-compacting concrete by ANFIS models. Neurocomputing 280:13–22. https://doi.org/10.1016/j.neucom.2017.09.099

    Article  Google Scholar 

  35. Hodhod OA, Khalafalla MS, Osman MSM (2019) ANN models for nano silica/ silica fume concrete strength prediction. Water Science 33:118–127. https://doi.org/10.1080/11104929.2019.1669005

    Article  Google Scholar 

  36. Bui DK, Nguyen T, Chou JS, 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 180:320–333. https://doi.org/10.1016/j.conbuildmat.2018.05.201

    Article  Google Scholar 

  37. Farooq F, Amin MN, Khan K, Sadiq MR, Javed MF, Aslam F et al (2020) A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC). Applied Sciences (Switzerland) 10:1–18. https://doi.org/10.3390/app10207330

    Article  Google Scholar 

  38. Nasir Uddin M, Li LZ, Ahmed A, Yahya MAK (2022) Prediction of PVA fiber effect in engineered composite cement (ECC) by artificial neural network (ANN). Mater Today Proc. https://doi.org/10.1016/j.matpr.2022.03.088.

  39. Nguyen MST, Kim SE (2021) A hybrid machine learning approach in prediction and uncertainty quantification of ultimate compressive strength of RCFST columns. Const Build Mater 302:124208. https://doi.org/10.1016/j.conbuildmat.2021.124208

    Article  Google Scholar 

  40. Leong HY, Ong DEL, Sanjayan JG, Nazari A, Kueh SM (2018) Effects of significant variables on compressive strength of soil-fly ash geopolymer: variable analytical approach based on neural networks and genetic programming. J Mater Civ Eng 30:04018129. https://doi.org/10.1061/(asce)mt.1943-5533.0002246

    Article  Google Scholar 

  41. Xu J, Zhou L, He G, Ji X, Dai Y, Dang Y (2021) Comprehensive machine learning-based model for predicting compressive strength of ready-mix concrete. Materials 14:1–18. https://doi.org/10.3390/ma14051068

    Article  Google Scholar 

  42. Aslam F, Farooq F, Amin MN, Khan K, Waheed A, Akbar A et al. (2020) Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete. Adv Civil Eng. https://doi.org/10.1155/2020/8850535.

  43. Shahmansouri AA, Akbarzadeh Bengar H, Ghanbari S (2020) Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method. J Build Eng 31:101326. https://doi.org/10.1016/j.jobe.2020.101326

    Article  Google Scholar 

  44. Algaifi HA, Alqarni AS, Alyousef R, Bakar SA, Ibrahim MHW, Shahidan S et al (2021) Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2021.04.008

    Article  Google Scholar 

  45. Cook R, Lapeyre J, Ma H, Kumar A (2019) Prediction of compressive strength of concrete: critical comparison of performance of a hybrid machine learning model with standalone models. J Mater Civ Eng 31:04019255. https://doi.org/10.1061/(asce)mt.1943-5533.0002902

    Article  Google Scholar 

  46. Zhang J, Ma G, Huang Y, Sun J, Aslani F, Nener B (2019) Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Construct Build Mater 210:713–719. https://doi.org/10.1016/j.conbuildmat.2019.03.189

    Article  Google Scholar 

  47. Shariati M, Mafipour MS, Mehrabi P, Bahadori A, Zandi Y, Salih MNA et al (2019) Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete. Appl Sci (Switzerland) 9:55. https://doi.org/10.3390/app9245534

    Article  Google Scholar 

  48. Mohamad ET, Jahed Armaghani D, Momeni E, Nezhad A, KhalilAbad SV (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 74:745–757. https://doi.org/10.1007/s10064-014-0638-0

    Article  Google Scholar 

  49. Chakali Y, Sadok AH, Tahlaiti M, Nacer T (2021) A PSO-ANN intelligent hybrid model to predict the compressive strength of limestone fillers roller compacted concrete (RCC) to build dams. KSCE J Civ Eng 25:3008–3018. https://doi.org/10.1007/s12205-021-1531-6

    Article  Google Scholar 

  50. Garg H (2016) A hybrid PSO-GA algorithm for constrained optimization problems. Appl Math Comput 274:292–305. https://doi.org/10.1016/j.amc.2015.11.001

    Article  Google Scholar 

  51. Sevim UK, Bilgic HH, Cansiz OF, Ozturk M, Atis CD (2021) Compressive strength prediction models for cementitious composites with fly ash using machine learning techniques. Construct Build Mater 271:121584. https://doi.org/10.1016/j.conbuildmat.2020.121584

    Article  Google Scholar 

  52. Khashman A, Akpinar P (2017) Non-destructive prediction of concrete compressive strength using neural networks. Proc Comput Sci 108:2358–2362. https://doi.org/10.1016/j.procs.2017.05.039

    Article  Google Scholar 

  53. Gholampour A, Gandomi AH, Ozbakkaloglu T (2017) New formulations for mechanical properties of recycled aggregate concrete using gene expression programming. Constr Build Mater 130:122–145. https://doi.org/10.1016/j.conbuildmat.2016.10.114

    Article  Google Scholar 

  54. Jiao D, Shi C, Yuan Q, An X, Liu Y (2018) Mixture design of concrete using simplex centroid design method. Cement Concr Compos 89:76–88. https://doi.org/10.1016/j.cemconcomp.2018.03.001

    Article  Google Scholar 

  55. Praveenkumar S, Sankarasubramanian G (2021) Optimization of mix proportions for high performance concrete using TOPSIS method. J Build Pathol Rehabilit 6:135. https://doi.org/10.1007/s41024-021-00135-0

    Article  Google Scholar 

  56. Xiaoyong L, Wendi M (2011) Optimization for mix design of high-performance concrete using orthogonal test. Commun Comput Inf Sci 232:364–372. https://doi.org/10.1007/978-3-642-23998-4_51

    Article  Google Scholar 

  57. Muthukumar M, Mohan D (2004) Optimization of mechanical properties of polymer concrete and mix design recommendation based on design of experiments. J Appl Polym Sci 94:1107–1116. https://doi.org/10.1002/app.21008

    Article  Google Scholar 

  58. Nguyen TD, Tran TH, Hoang ND (2020) Prediction of interface yield stress and plastic viscosity of fresh concrete using a hybrid machine learning approach. Adv Eng Inf 44:101057. https://doi.org/10.1016/j.aei.2020.101057

    Article  Google Scholar 

  59. Cu YTH, Tran MV, Ho CH, Nguyen PH (2020) Relationship between workability and rheological parameters of self-compacting concrete used for vertical pump up to supertall buildings. J Build Eng 32:101786. https://doi.org/10.1016/j.jobe.2020.101786

    Article  Google Scholar 

  60. Kang MC, Yoo DY, Gupta R (2021) Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Construct Build Mater 266:121117. https://doi.org/10.1016/j.conbuildmat.2020.121117

    Article  Google Scholar 

  61. Nasiri H, Homafar A, Chelgani SC (2021) Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using an explainable artificial intelligence. Res Geophys Sci 8:100034. https://doi.org/10.1016/j.ringps.2021.100034

    Article  Google Scholar 

  62. Ren Q, Li M, Zhang M, Shen Y, Si W (2019) Prediction of ultimate axial capacity of square concrete-filled steel tubular short columns using a hybrid intelligent algorithm. Appl Sci (Switzerland) 9:2802. https://doi.org/10.3390/app9142802

    Article  Google Scholar 

  63. Uddin MN, Li LZ, Khan RKM, Shahriar F, Sob LWT (2021) Axial capacity prediction of concrete-filled steel tubular short members using multiple linear regression and artificial neural network. Mater Sci Forum 1047:220–226. https://doi.org/10.4028/www.scientific.net/msf.1047.220

    Article  Google Scholar 

  64. Uddin MN, Yu K, Li L, Ye J, Tafsirojjaman T, Alhaddad W (2022) Machine learning model to estimate the shear capacity for rc beams with stirrups using standard building codes. Innov Infrastruct Solut, pp 1–20. https://doi.org/10.1007/s41062-022-00826-8

  65. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data Mining, vol. 13–17- Augu, San Francisco, CA, USA: ACM, p 785–94. https://doi.org/10.1145/2939672.2939785

  66. Liang M, Chang Z, Wan Z, Gan Y, Schlangen E, Šavija B (2022) Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete. Cement Concrete Comp 125:104295. https://doi.org/10.1016/j.cemconcomp.2021.104295

    Article  Google Scholar 

  67. Shehadeh A, Alshboul O, Al Mamlook RE, Hamedat O (2021) Machine learning models for predicting the residual value of heavy construction equipment: an evaluation of modified decision tree, LightGBM, and XGBoost regression. Auto Constr 129:103827. https://doi.org/10.1016/j.autcon.2021.103827

    Article  Google Scholar 

  68. Mangalathu S, Jeon J-S (2019) Machine learning-based failure mode recognition of circular reinforced concrete bridge columns: comparative study. J Struct Eng 145:04019104. https://doi.org/10.1061/(asce)st.1943-541x.0002402

    Article  Google Scholar 

  69. Ekanayake IU, Meddage DPP, Rathnayake U (2022) A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Stud Construct Mater 16:e01059. https://doi.org/10.1016/j.cscm.2022.e01059

    Article  Google Scholar 

  70. Mangalathu S, Hwang SH, Jeon JS (2020) Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Eng Struct 219:110927. https://doi.org/10.1016/j.engstruct.2020.110927

    Article  Google Scholar 

  71. Hilloulin B, Tran VQ (2022) Using machine learning techniques for predicting autogenous shrinkage of concrete incorporating superabsorbent polymers and supplementary cementitious materials. J Build Eng 49:104086. https://doi.org/10.1016/j.jobe.2022.104086

    Article  Google Scholar 

  72. Asteris PG, Kolovos KG (2019) Self-compacting concrete strength prediction using surrogate models. Neural Comput Appl 31:409–424. https://doi.org/10.1007/s00521-017-3007-7

    Article  Google Scholar 

  73. Asteris PG, Kolovos KG, Douvika MG, Roinos K (2016) Prediction of self-compacting concrete strength using artificial neural networks. Eur J Environ Civ Eng 20:s102–s122. https://doi.org/10.1080/19648189.2016.1246693

    Article  Google Scholar 

  74. Gupta R, Kewalramani MA, Goel A (2006) Prediction of concrete strength using neural-expert system. J Mater Civ Eng 18:462–466. https://doi.org/10.1061/(asce)0899-1561(2006)18:3(462)

    Article  Google Scholar 

  75. Fazel Zarandi MH, Türksen IB, Sobhani J, Ramezanianpour AA (2008) Fuzzy polynomial neural networks for approximation of the compressive strength of concrete. Appl Soft Comput J 8:488–498. https://doi.org/10.1016/j.asoc.2007.02.010

    Article  Google Scholar 

  76. Naderpour H, Rafiean AH, Fakharian P (2018) Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 16:213–219. https://doi.org/10.1016/j.jobe.2018.01.007

    Article  Google Scholar 

  77. Yucel M, Namlı E. High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods 2019:118–40. https://doi.org/10.4018/978-1-7998-0301-0.ch007.

  78. Iqtidar A, Khan NB, Kashif-ur-Rehman S, Javed MF, Aslam F, Alyousef R et al (2021) Prediction of compressive strength of rice husk ash concrete through different machine learning processes. Crystals 11:352. https://doi.org/10.3390/cryst11040352

    Article  Google Scholar 

  79. Khademi F, Jamal SM, Deshpande N, Londhe S (2016) Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. Int J Sustain Built Environ 5:355–369. https://doi.org/10.1016/j.ijsbe.2016.09.003

    Article  Google Scholar 

  80. Li QF, Song ZM (2022) High-performance concrete strength prediction based on ensemble learning. Construct Building Mater 324:126694. https://doi.org/10.1016/j.conbuildmat.2022.126694

    Article  Google Scholar 

  81. Lyngdoh GA, Zaki M, Krishnan NMA, Das S (2022) Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning. Cement Concrete Comp 128:104414. https://doi.org/10.1016/j.cemconcomp.2022.104414

    Article  Google Scholar 

  82. Lv Z, Jiang A, Jin J, Lv X (2021) Multifractal analysis and compressive strength prediction for concrete through acoustic emission parameters. Adv Civil Eng 2021:78. https://doi.org/10.1155/2021/6683878

    Article  Google Scholar 

  83. Ozcan G, Kocak Y, Gulbandilar E (2017) Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models. Comput Concrete 19:275–282. https://doi.org/10.12989/cac.2017.19.3.275

  84. Zhou J, Qiu Y, Zhu S, Armaghani DJ, Khandelwal M, Mohamad ET (2021) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Underground Space (China) 6:506–515. https://doi.org/10.1016/j.undsp.2020.05.008

    Article  Google Scholar 

  85. Zhang Z, Huang Y, Qin R, Ren W, Wen G (2021) XGBoost-based on-line prediction of seam tensile strength for Al-Li alloy in laser welding: Experiment study and modelling. J Manuf Process 64:30–44. https://doi.org/10.1016/j.jmapro.2020.12.004

    Article  Google Scholar 

  86. Efficient estimating compressive strength of ultra-high performance concrete using XGBoost model n.d.

  87. Nguyen-Sy T, Wakim J, To QD, Vu MN, Nguyen TD, Nguyen TT (2020) Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Construct Build Mater 260:119757. https://doi.org/10.1016/j.conbuildmat.2020.119757

    Article  Google Scholar 

  88. Xu JG, Chen SZ, Xu WJ, Sen SZ (2021) Concrete-to-concrete interface shear strength prediction based on explainable extreme gradient boosting approach. Construct Build Mater 308:125088. https://doi.org/10.1016/j.conbuildmat.2021.125088

    Article  Google Scholar 

  89. Gao X, Lin C. Prediction model of the failure mode of beam-column joints using machine learning methods. Engineering Failure Analysis 2021;120:105072. https://doi.org/10.1016/j.engfailanal.2020.105072.

  90. Mangalathu S, Jang H, Hwang SH, Jeon JS (2020) Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls. Eng Struct 208:110331. https://doi.org/10.1016/j.engstruct.2020.110331

    Article  Google Scholar 

  91. Wen X, Xie Y, Wu L, Jiang L (2021) Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident Anal Prevent 159:106261. https://doi.org/10.1016/j.aap.2021.106261

    Article  Google Scholar 

  92. Breiman L (2001) Random forests. Random Forests, pp 5–32

  93. Chou JS, Tsai CF, Pham AD, Lu YH (2014) Machine learning in concrete strength simulations: multi-nation data analytics. Constr Build Mater 73:771–780. https://doi.org/10.1016/j.conbuildmat.2014.09.054

    Article  Google Scholar 

  94. Gholami R, Fakhari N (2017) Support vector machine: principles, parameters, and applications. 1st edn Elsevier Inc. https://doi.org/10.1016/B978-0-12-811318-9.00027-2

  95. Bakouregui AS, Mohamed HM, Yahia A, Benmokrane B (2021) Explainable extreme gradient boosting tree-based prediction of load-carrying capacity of FRP-RC columns. Eng Struct 245:112836. https://doi.org/10.1016/j.engstruct.2021.112836

    Article  Google Scholar 

  96. Rzychoń M, Żogała A, Róg L (2021) SHAP-based interpretation of an XGBoost model in the prediction of grindability of coals and their blends. Int J Coal Prep Util 00:1–21. https://doi.org/10.1080/19392699.2021.1959324

    Article  Google Scholar 

  97. Hengl T, Leenaars JGB, Shepherd KD, Walsh MG, Heuvelink GBM, Mamo T et al (2017) Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutr Cycl Agroecosyst 109:77–102. https://doi.org/10.1007/s10705-017-9870-x

    Article  Google Scholar 

  98. Zhang D, Qian L, Mao B, Huang C, Huang B, Si Y (2018) A Data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE Access 6:21020–21031. https://doi.org/10.1109/ACCESS.2018.2818678

    Article  Google Scholar 

  99. Song K, Yan F, Ding T, Gao L, Lu S (2020) A steel property optimization model based on the XGBoost algorithm and improved PSO. Comput Mater Sci 174:109472. https://doi.org/10.1016/j.commatsci.2019.109472

    Article  Google Scholar 

  100. Guo J, Yang L, Bie R, Yu J, Gao Y, Shen Y et al (2019) An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring. Comput Netw 151:166–180. https://doi.org/10.1016/j.comnet.2019.01.026

    Article  Google Scholar 

  101. Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In; Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794. https://doi.org/10.1145/2939672.2939785

  102. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. (2017) LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 2017-Dec, pp 3147–55

  103. Feng D-C, Wang W-J, Mangalathu S, Taciroglu E (2021) Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls. J Struct Eng 147:04021173. https://doi.org/10.1061/(asce)st.1943-541x.0003115

    Article  Google Scholar 

  104. Yousuf S, Shafigh P, Ibrahim Z, Hashim H, Panjehpour M (2019) Crossover effect in cement-based materials: a review. Appl Sci (Switzerland) 9:2776. https://doi.org/10.3390/app9142776

    Article  Google Scholar 

  105. Haach VG, Vasconcelos G, Loureno PB (2011) Influence of aggregates grading and water/cement ratio in workability and hardened properties of mortars. Constr Build Mater 25:2980–2987. https://doi.org/10.1016/j.conbuildmat.2010.11.011

    Article  Google Scholar 

  106. Abdullahi M, Ojelade GO, Auta SM (2017) Modified water-cement ratio law for compressive strength of rice husk ash concrete. Niger J Technol 36:373. https://doi.org/10.4314/njt.v36i2.8

    Article  Google Scholar 

  107. Lo TY, Tang WC, Cui HZ (2007) The effects of aggregate properties on lightweight concrete. Build Environ 42:3025–3029. https://doi.org/10.1016/j.buildenv.2005.06.031

    Article  Google Scholar 

  108. Vijay A, Sajeeb R (2022) Effect of superplasticizer on the characteristics of stabilized earth concrete. Mater Today Proc. https://doi.org/10.1016/j.matpr.2022.02.635

    Article  Google Scholar 

  109. Alsadey S (2012) Influence of Superplasticizer on Strength of Concrete. Int J Res Eng Technol 1:164–166

    Google Scholar 

  110. Rashwan MM, Abaas KA, Ali MA (2020) Assessment of using an optimal dose of superplasticizer with the optimum replacement of rice husk ash (Rha)on concrete compressive strength. Int J Civil Eng Technol (Ijciet), 11. https://doi.org/10.34218/ijciet.11.10.2020.002

  111. Sandhu RK, Siddique R (2017) Influence of rice husk ash (RHA) on the properties of self-compacting concrete: a review. Constr Build Mater 153:751–764. https://doi.org/10.1016/j.conbuildmat.2017.07.165

    Article  Google Scholar 

  112. Ibrahim S, Meawad A. Towards green concrete: Study the role of waste glass powder on cement/superplasticizer compatibility. Journal of Building Engineering 2022;47. https://doi.org/10.1016/j.jobe.2021.103751.

  113. Nowak-Michta A (2015) Influence of superplasticizer on porosity structures in hardened concretes. Proc Eng 108:262–269. https://doi.org/10.1016/j.proeng.2015.06.146

    Article  Google Scholar 

  114. Feng QG, Lin QY, Yu QJ, Zhao SY, Yang LF, Sugita S (2004) Concrete with highly active rice husk ash. J Wuhan Univ Technol Mater Sci Edn 19:74–77. https://doi.org/10.1007/bf02835067

    Article  Google Scholar 

  115. Van Tuan N, Ye G, Van Breugel K, Fraaij ALA, Bui DD (2011) The study of using rice husk ash to produce ultra high performance concrete. Constr Build Mater 25:2030–2035. https://doi.org/10.1016/j.conbuildmat.2010.11.046

    Article  Google Scholar 

  116. Wang J, Xiao J, Zhang Z, Han K, Hu X, Jiang F (2021) Action mechanism of rice husk ash and the effect on main performances of cement-based materials: a review. Construct Build Mater 288:123068. https://doi.org/10.1016/j.conbuildmat.2021.123068

    Article  Google Scholar 

  117. Giaccio G, Zerbino R (1998) Failure mechanism of concrete: combined effects of coarse aggregates and strength level. Adv Cem Based Mater 7:41–48. https://doi.org/10.1016/S1065-7355(97)00014-X

    Article  Google Scholar 

  118. Zhou FP, Barr BIG, Lydon FD (1995) Fracture properties of high strength concrete with varying silica fume content and aggregates. Cem Concr Res 25:543–552. https://doi.org/10.1016/0008-8846(95)00043-C

    Article  Google Scholar 

  119. Behera M, Rahman MR (2021) Evaluating the combined effect of recycled aggregate and rice husk ash on concrete properties. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.10.127

    Article  Google Scholar 

  120. Zareei SA, Ameri F, Dorostkar F, Ahmadi M (2017) Rice husk ash as a partial replacement of cement in high strength concrete containing micro silica: Evaluating durability and mechanical properties. Case Stud Construct Mater 7:73–81. https://doi.org/10.1016/j.cscm.2017.05.001

    Article  Google Scholar 

  121. Rêgo JHS, Nepomuceno AA, Figueiredo EP, Hasparyk NP, Borges LD (2015) Effect of Particle Size of Residual Rice-Husk Ash in Consumption of Ca(OH)2. J Mater Civ Eng 27:04014178. https://doi.org/10.1061/(asce)mt.1943-5533.0001136

    Article  Google Scholar 

  122. Kannan V (2018) Strength and durability performance of self compacting concrete containing self-combusted rice husk ash and metakaolin. Constr Build Mater 160:169–179. https://doi.org/10.1016/j.conbuildmat.2017.11.043

    Article  Google Scholar 

  123. Luxan MP, Madruga F, Saavedra J (1989) Rapid evaluation of pozzolanic activity of natural products by conductivity measurement. Cem Concr Res 19:63–68

    Article  Google Scholar 

  124. Kang SH, Hong SG, Moon J (2019) The use of rice husk ash as reactive filler in ultra-high performance concrete. Cem Concr Res 115:389–400. https://doi.org/10.1016/j.cemconres.2018.09.004

    Article  Google Scholar 

  125. Abood Habeeb G, Bin MH (2010) Study on properties of rice husk ash and its use as cement replacement material. Mater Res 13:185–190

    Article  Google Scholar 

  126. Bui DD (2001) Rice husk ash as a mineral admixture for high performance concrete, 122

  127. Ye G, Nguyen VT (2012) Mitigation of autogenous shrinkage of ultra-high performance concrete by rice husk ash. Kuei Suan Jen Hsueh Pao/J Chinese Ceram Soc 40:212–216

    Google Scholar 

  128. Mosaberpanah MA, Umar SA (2020) utilizing rice husk ash as supplement to cementitious materials on performance of ultra high performance concrete: a review. Mater Today Sustain, 7–8. https://doi.org/10.1016/j.mtsust.2019.100030.

  129. Xu W, Lo TY, Wang W, Ouyang D, Wang P, Xing F (2016) Pozzolanic reactivity of silica fume and ground rice husk ash as reactive silica in a cementitious system: a comparative study. Materials, 9. https://doi.org/10.3390/ma9030146

  130. Feng Q, Yamamichi H, Shoya M, Sugita S (2004) Study on the pozzolanic properties of rice husk ash by hydrochloric acid pretreatment. Cem Concr Res 34:521–526. https://doi.org/10.1016/j.cemconres.2003.09.005

    Article  Google Scholar 

  131. Ahsan MB, Hossain Z (2018) Supplemental use of rice husk ash (RHA) as a cementitious material in concrete industry. Constr Build Mater 178:1–9. https://doi.org/10.1016/j.conbuildmat.2018.05.101

    Article  Google Scholar 

  132. Rodríguez De Sensale G (2010) Effect of rice-husk ash on durability of cementitious materials. Cement Concrete Comp 32:718–725. https://doi.org/10.1016/j.cemconcomp.2010.07.008

    Article  Google Scholar 

  133. Chindaprasirt P, Rukzon S (2008) Strength, porosity and corrosion resistance of ternary blend Portland cement, rice husk ash and fly ash mortar. Constr Build Mater 22:1601–1606. https://doi.org/10.1016/j.conbuildmat.2007.06.010

    Article  Google Scholar 

  134. Gastaldini ALG, Isaia GC, Gomes NS, Sperb JEK (2007) Chloride penetration and carbonation in concrete with rice husk ash and chemical activators. Cement Concr Compos 29:176–180. https://doi.org/10.1016/j.cemconcomp.2006.11.010

    Article  Google Scholar 

  135. Monteiro PJM, Kurtis KE (2003) Time to failure for concrete exposed to severe sulfate attack. Cem Concr Res 33:987–993. https://doi.org/10.1016/S0008-8846(02)01097-9

    Article  Google Scholar 

  136. de Sensale GR, Ribeiro AB, Gonçalves A (2008) Effects of RHA on autogenous shrinkage of Portland cement pastes. Cement Concr Compos 30:892–897. https://doi.org/10.1016/j.cemconcomp.2008.06.014

    Article  Google Scholar 

  137. Bentz DP, Jensen OM (2004) Mitigation strategies for autogenous shrinkage cracking. Cement Concr Compos 26:677–685. https://doi.org/10.1016/S0958-9465(03)00045-3

    Article  Google Scholar 

  138. Tuan N Van, Ye G, Breugel K Van (2010) Effect of rice husk ash on autogenous shrinkage of ultra high performance concrete. In: Proceedings of International RILEM conference on advances in construction materials through science and engineering, Hong-Kong

  139. Ogundipe OM, Olanike AO, Nnochiri ES, Ale PO (2018) Effects of coarse aggregate size on the compressive strength of concrete. Civil Eng J 4:836–842

    Article  Google Scholar 

  140. Agar-Ozbek AS, Weerheijm J, Schlangen E, Van Breugel K (2013) Investigating porous concrete with improved strength: testing at different scales. Constr Build Mater 41:480–490. https://doi.org/10.1016/j.conbuildmat.2012.12.040

    Article  Google Scholar 

  141. Fu TC, Yeih W, Chang JJ, Huang R (2014) The influence of aggregate size and binder material on the properties of pervious concrete. Adv Mater Sci Eng 2014:971. https://doi.org/10.1155/2014/963971

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Disaster Mitigation for Structures, College of Civil Engineering, Tongji University, Siping Road 1239, Shanghai, 200092, China

    Md Nasir Uddin, Ling-Zhi Li & Bo-Yu Deng

  2. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China

    Junhong Ye

Authors
  1. Md Nasir Uddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ling-Zhi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo-Yu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junhong Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md Nasir Uddin.

Ethics declarations

Conflict of interest

All authors declare that they have no conflicts of interest.

Ethical approval

This paper neither was published nor is under review elsewhere. This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

For this type of study formal consent is not required.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Uddin, M.N., Li, LZ., Deng, BY. et al. Interpretable XGBoost–SHAP machine learning technique to predict the compressive strength of environment-friendly rice husk ash concrete. Innov. Infrastruct. Solut. 8, 147 (2023). https://doi.org/10.1007/s41062-023-01122-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41062-023-01122-9

Keywords

Search

Navigation