Abstract
3D concrete printing (3DCP) is crucial in the construction because of the low labor cost, eco-friendly behavior; however, getting a proper mixture is always a challenge. This study focuses on predicting the compressive strength (CS) of fiber-reinforced concrete produced with 3DCP using eight machine learning (ML) algorithms to get optimized mixture. The ML models were trained and tested using a comprehensive database on CS collected from literature considering the various fiber-reinforced cementitious composites, comprising over 299 mixtures with 11 features. The results show that the trained ML models could predict CS with R2 ranging from 0.927 to 0.990 and 0.914 to 0.988 for the training and testing dataset, respectively. Furthermore, supplementary experiments were conducted to create a new dataset to validate the predictive model's accuracy, with the extreme gradient boosting (XGB) and gene expression programming (GEP). Based on the GEP, a novel empirical equation was proposed and rigorously validated using experiments. The equation exhibits a high accuracy with the GEP algorithm (R2 = 0.89), providing real-world field applications that might be improve decision-making and mixture optimization, which contributes to advancements, efficiency, and innovative solutions in 3D printing practical domains.
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Abbreviations
- XGBoost:
-
Extreme gradient boosting
- LGBM:
-
Light gradient boosting machine
- ANN:
-
Artificial neural network
- CNN:
-
Convolutional neural network
- SVM:
-
Support vector machine
- KNN:
-
K-nearest neighbors algorithm
- GEP:
-
Gene expression programming
- RF:
-
Random forest
- W/C:
-
Water–cement ratio
- S/B:
-
Sand–binder ratio
- HPMC/B:
-
Hydroxypropyl methylcellulose–binder ratio
- SP/B:
-
Superplasticizer–binder ratio
- W/S:
-
Water–sand ratio
- W/B:
-
Water–binder ratio
- F volf :
-
Fiber volume
- CA:
-
Curing age
- Ld:
-
Loading direction
- L f/D f :
-
Aspect ratio
- F type :
-
Fiber type
- CS:
-
Compressive strength
- PVA:
-
Polyvinyl alcohol
- PE:
-
Polyethylene
- GBDT:
-
Gradient boosted decision trees
- GBR:
-
Gradient boosted tree
- R 2 :
-
Coefficient of determination
- R :
-
Correlation coefficient
- MAE:
-
Mean absolute error
- MSE:
-
Mean squared error.
- RMSE:
-
Root means square error
- MAPE:
-
Mean absolute percentage error
- SMAPE:
-
Symmetric mean absolute percentage error
References
Abd Elmoaty AEM, Morsy AM, Harraz AB (2022) Effect of fiber type and volume fraction on fiber reinforced concrete and engineered cementitious composite mechanical properties. Buildings. https://doi.org/10.3390/buildings12122108
Abdulalim Alabdullah A, Iqbal M, Zahid M, Khan K, Nasir Amin M, Jalal FE (2022) Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2022.128296
Ahmed A, Nasir Uddin M, Akbar M, Salih R, Khan MA, Bisheh H et al (2023) Prediction of shear behavior of glass FRP bars-reinforced ultra-highperformance concrete I-shaped beams using machine learning. Int J Mech Mater Des. https://doi.org/10.1007/s10999-023-09675-4
Alhakeem ZM, Jebur YM, Henedy SN, Imran H, Bernardo LFA, Hussein HM (2022) Prediction of ecofriendly concrete compressive strength using gradient boosting regression tree combined with GridSearchCV hyperparameter-optimization techniques. Materials (Basel). https://doi.org/10.3390/ma15217432
Al-Hashem MN, Amin MN, Ahmad W, Khan K, Ahmad A, Ehsan S et al (2022) Data-driven techniques for evaluating the mechanical strength and raw material effects of steel fiber-reinforced concrete. Materials (Basel). https://doi.org/10.3390/ma15196928
Ali A, Riaz RD, Malik UJ, Abbas SB, Usman M, Shah MU et al (2023) Machine learning-based predictive model for tensile and flexural strength of 3D-printed concrete. Materials (Basel). https://doi.org/10.3390/ma16114149
Anjum M, Khan K, Ahmad W, Ahmad A, Amin MN, Nafees A (2022) New SHapley Additive ExPlanations (SHAP) approach to evaluate the raw materials interactions of steel-fiber-reinforced concrete. Materials (Basel). https://doi.org/10.3390/ma15186261
Arunothayan AR, Nematollahi B, Ranade R, Bong SH, Sanjayan JG, Khayat KH (2021) Fiber orientation effects on ultra-high performance concrete formed by 3D printing. Cem Concr Res. https://doi.org/10.1016/j.cemconres.2021.106384
Asadi Shamsabadi E, Roshan N, Hadigheh SA, Nehdi ML, Khodabakhshian A, Ghalehnovi M (2022) Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2022.126592
Ay Ş, Ekinci E, Garip Z (2023) A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases. J Supercomput. https://doi.org/10.1007/s11227-023-05132-3
Bagheri A, Cremona C (2020) Formulation of mix design for 3D printing of geopolymers: a machine learning approach. Mater Adv 1:720–727. https://doi.org/10.1039/d0ma00036a
Behnood A, Behnood V, Modiri Gharehveran M, Alyamac KE (2017) Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm. Constr Build Mater 142:199–207. https://doi.org/10.1016/j.conbuildmat.2017.03.061
Bos F, Wolfs R, Ahmed Z, Salet T (2016) Additive manufacturing of concrete in construction: potentials and challenges of 3D concrete printing. Virtual Phys Prototyp 11:209–225. https://doi.org/10.1080/17452759.2016.1209867
Breiman L (2001) Random forests. Random for 2001:5–32
Cakiroglu C, Aydın Y, Bekdaş G, Geem ZW (2023) Interpretable predictive modelling of basalt fiber reinforced concrete splitting tensile strength using ensemble machine learning methods and SHAP approach. Materials (Basel) 16:4578. https://doi.org/10.3390/ma16134578
Chen H, Xu B, Mo YL, Zhou T (2018) Behavior of meso-scale heterogeneous concrete under uniaxial tensile and compressive loadings. Constr Build Mater 178:418–431. https://doi.org/10.1016/j.conbuildmat.2018.05.052
Chen H, Zeng J, Wang J, Xu B, Mo YL (2023) Multiscale homogenization numerical study on the mechanism of interface debonding detection for steel–concrete composite structures with multichannel surface wave measurements. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2023.130386
de Carvalho RF, de Pasolini VH, Fraga Breciani JG, Silva Costa AB, de Sousa RC (2024) Poultry manure combustion parameters to produce bioenergy: a thermogravimetric analysis by isoconventional models and machine learning. Case Stud Therm Eng. https://doi.org/10.1016/j.csite.2023.103757
Dehestani M, Nikbin IM, Asadollahi S (2014) Effects of specimen shape and size on the compressive strength of self-consolidating concrete (SCC). Constr Build Mater 66:685–691. https://doi.org/10.1016/j.conbuildmat.2014.06.008
de-Prado-Gil J, Palencia C, Silva-Monteiro N, Martínez-García R (2022) To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models. Case Stud Constr Mater. https://doi.org/10.1016/j.cscm.2022.e01046
Ding T, Xiao J, Zou S, Zhou X (2020) Anisotropic behavior in bending of 3D printed concrete reinforced with fibers. Compos Struct. https://doi.org/10.1016/j.compstruct.2020.112808
Dong Z, Quan W, Ma X, Li X, Zhou J (2023) Asymptotic homogenization of effective thermal-elastic properties of concrete considering its three-dimensional mesostructure. Comput Struct. https://doi.org/10.1016/j.compstruc.2022.106970
Duan J, Asteris PG, Nguyen H, Bui XN, Moayedi H (2021) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput 37:3329–3346. https://doi.org/10.1007/s00366-020-01003-0
Freidman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232
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
Ghunimat D, Alzoubi AE, Alzboon A, Hanandeh S (2023) Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression. Asian J Civ Eng 24:169–177. https://doi.org/10.1007/s42107-022-00495-z
Goldberger J, Roweis S, Hinton G, Salakhutdinov R (2005) Neighbourhood components analysis. Adv Neural Inf Process Syst 17:513–552
Gong X, Zheng B, Xu G, Chen H, Chen C (2021) Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer. J Thorac Dis 13:6240–6251. https://doi.org/10.21037/jtd-21-1107
Guo P, Meng W, Xu M, Li VC, Bao Y (2021) Predicting mechanical properties of high-performance fiber-reinforced cementitious composites by integrating micromechanics and machine learning. Materials (Basel). https://doi.org/10.3390/ma14123143
Gupta S, Sihag P (2022) Prediction of the compressive strength of concrete using various predictive modeling techniques. Neural Comput Appl 34:6535–6545. https://doi.org/10.1007/s00521-021-06820-y
Hossain MAS, Nasir Uddin M, Hossain MM (2023) Prediction of compressive strength fiber-reinforced geopolymer concrete (FRGC) using gene expression programming (GEP). Mater Today Proc. https://doi.org/10.1016/j.matpr.2023.02.458
Izadgoshasb H, Kandiri A, Shakor P, Laghi V, Gasparini G (2021) Predicting compressive strength of 3D printed mortar in structural members using machine learning. Appl Sci. https://doi.org/10.3390/app112210826
Jung J, Yoon JI, Park HK, Kim JY, Kim HS (2019) An efficient machine learning approach to establish structure-property linkages. Comput Mater Sci 156:17–25. https://doi.org/10.1016/j.commatsci.2018.09.034
Kang MC, Yoo DY, Gupta R (2021) Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.121117
Kaushik V, Singh D, Kumar M (2023) Effects of fibers on compressive strength of concrete. Mater Today Proc 80:3281–3285. https://doi.org/10.1016/j.matpr.2021.07.229
Khan MA, Memon SA, Farooq F, Javed MF, Aslam F, Alyousef R (2021) Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Adv Civ Eng. https://doi.org/10.1155/2021/6618407
Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 3rd Int Conf Learn Represent ICLR 2015—Conf Track Proc 2015
Leevy JL, Hancock J, Zuech R, Khoshgoftaar TM (2020) Detecting cybersecurity attacks using different network features with LightGBM and XGBoost learners. Proc 2020 IEEE 2nd Int Conf Cogn Mach Intell CogMI 2020 2020:190–197. https://doi.org/10.1109/CogMI50398.2020.00032
Liang M, Chang Z, Wan Z, Gan Y, Schlangen E, Šavija B (2022) Interpretable ensemble-machine-learning models for predicting creep behavior of concrete. Cem Concr Compos. https://doi.org/10.1016/j.cemconcomp.2021.104295
Liu M, Liang L, Sun W (2019) Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach. Comput Methods Appl Mech Eng 347:201–217. https://doi.org/10.1016/j.cma.2018.12.030
Liu JC, Huang L, Chen Z, Ye H (2022) A comparative study of artificial intelligent methods for explosive spalling diagnosis of hybrid fiber-reinforced ultra-high-performance concrete. Int J Civ Eng 20:639–660. https://doi.org/10.1007/s40999-021-00689-7
Liu K, Zhang L, Wang W, Zhang G, Xu L, Fan D et al (2023) Development of compressive strength prediction platform for concrete materials based on machine learning techniques. J Build Eng. https://doi.org/10.1016/j.jobe.2023.107977
Ma G, Li Z, Wang L, Wang F, Sanjayan J (2019) Mechanical anisotropy of aligned fiber reinforced composite for extrusion-based 3D printing. Constr Build Mater 202:770–783. https://doi.org/10.1016/j.conbuildmat.2019.01.008
Mahdinia S, Eskandari-Naddaf H, Shadnia R (2019) Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method. Constr Build Mater 198:27–41. https://doi.org/10.1016/j.conbuildmat.2018.11.265
Mahjoubi S, Barhemat R, Guo P, Meng W, Bao Y (2021) Prediction and multi-objective optimization of mechanical, economical, and environmental properties for strain-hardening cementitious composites (SHCC) based on automated machine learning and metaheuristic algorithms. J Clean Prod. https://doi.org/10.1016/j.jclepro.2021.129665
Mai HVT, Nguyen MH, Ly HB (2023) Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2023.130339
Mechtcherine V, Buswell R, Kloft H, Bos FP, Hack N, Wolfs R et al (2021) Integrating reinforcement in digital fabrication with concrete: a review and classification framework. Cem Concr Compos. https://doi.org/10.1016/j.cemconcomp.2021.103964
Minaz Hossain M, Nasir Uddin M, Abu Sayed Hossain M (2023) Prediction of compressive strength ultra-high steel fiber reinforced concrete (UHSFRC) using artificial neural networks (ANNs). Mater Today Proc. https://doi.org/10.1016/j.matpr.2023.02.409
Mohamed O, Zuaiter H (2024) Fresh properties, strength, and durability of fiber-reinforced geopolymer and conventional concrete: a review. Polymers (Basel). https://doi.org/10.3390/polym16010141
Morsy AM, Abd Elmoaty AEM, Harraz AB (2022) Predicting mechanical properties of engineering cementitious composite reinforced with PVA using artificial neural network. Case Stud Constr Mater. https://doi.org/10.1016/j.cscm.2022.e00998
Mousavi SM, Aminian P, Gandomi AH, Alavi AH, Bolandi H (2012) A new predictive model for compressive strength of HPC using gene expression programming. Adv Eng Softw 45:105–114. https://doi.org/10.1016/j.advengsoft.2011.09.014
Munir MJ, Kazmi SMS, Wu YF, Lin X, Ahmad MR (2022) Development of novel design strength model for sustainable concrete columns: a new machine learning-based approach. J Clean Prod. https://doi.org/10.1016/j.jclepro.2022.131988
Nakkeeran G, Krishnaraj L, Bahrami A, Almujibah H, Panchal H, Zahra MMA (2023) Machine learning application to predict the mechanical properties of glass fiber mortar. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2023.103454
Uddin MN, Li L, Ahmed A, Yahya Mohammed Almajhali K (2022) Prediction of PVA fiber effect in engineered composite cement (ECC) by artificial neural network (ANN). Mater Today Proc 65:537–542. https://doi.org/10.1016/j.matpr.2022.03.088
Uddin MN, Mahamoudou F, Deng BY, Elobaid Musa MM, Tim Sob LW (2023a) Prediction of rheological parameters of 3D printed polypropylene fiber-reinforced concrete (3DP-PPRC) by machine learning. Mater Today Proc. https://doi.org/10.1016/j.matpr.2023.03.191
Uddin MN, Ye J, Deng B, Li L, Yu K (2023b) Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC). J Build Eng. https://doi.org/10.1016/j.jobe.2023.106648
Uddin MN, Li L, Deng B-Y, Ye J (2023c) Interpretable XGBoost-SHAP machine learning technique to predict the compressive strength of environment-friendly rice husk ash concrete. Innov Infrastruct Solut 8:147
Uddin MN, Shanmugasundaram N, Praveenkumar S, Li L (2024) Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning. Int J Mech Mater Des. https://doi.org/10.1007/s10999-023-09695-0
Nazar S, Yang J, Faisal Javed M, Khan K, Li L, Liu Q (2023) An evolutionary machine learning-based model to estimate the rheological parameters of fresh concrete. Structures 48:1670–1683. https://doi.org/10.1016/j.istruc.2023.01.019
Neville AM (1995) Properties of concrete, vol 4. Longman, London
Özcan F (2012) Gene expression programming based formulations for splitting tensile strength of concrete. Constr Build Mater 26:404–410. https://doi.org/10.1016/j.conbuildmat.2011.06.039
Pakzad SS, Roshan N, Ghalehnovi M (2023) Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. Sci Rep. https://doi.org/10.1038/s41598-023-30606-y
Pham L, Tran P, Sanjayan J (2020) Steel fibres reinforced 3D printed concrete: influence of fibre sizes on mechanical performance. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.118785
Pham L, Lin X, Gravina RJ, Tran P (2021) Influence of pva and pp fibres at different volume fractions on mechanical properties of 3d printed concrete. Lect Notes Civ Eng 101:2013–2024. https://doi.org/10.1007/978-981-15-8079-6_185
Platt J (1999) Probabilistic outputs for SVMs and comparisons to regularized likelihood methods. Adv Large Margin Classif 10:61–74
Probst P, Wright MN, Boulesteix AL (2019) Hyperparameters and tuning strategies for random forest. Wiley Interdiscip Rev Data Min Knowl Discov. https://doi.org/10.1002/widm.1301
Shen Z, Deifalla AF, Kamiński P, Dyczko A (2022) Compressive strength evaluation of ultra-high-strength concrete by machine learning. Materials (Basel). https://doi.org/10.3390/ma15103523
Shoji D, He Z, Zhang D, Li VC (2022) The greening of engineered cementitious composites (ECC): A review. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2022.126701
Sim JI, Yang KH, Jeon JK (2013) Influence of aggregate size on the compressive size effect according to different concrete types. Constr Build Mater 44:716–725. https://doi.org/10.1016/j.conbuildmat.2013.03.066
Soleimani S, Jiao P, Rajaei S, Forsati R (2018a) A new approach for prediction of collapse settlement of sandy gravel soils. Eng Comput 34:15–24. https://doi.org/10.1007/s00366-017-0517-y
Soleimani S, Rajaei S, Jiao P, Sabz A, Soheilinia S (2018b) New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming. Meas J Int Meas Confed 113:99–107. https://doi.org/10.1016/j.measurement.2017.08.043
Steinberger D, Song H, Sandfeld S (2019) Machine learning-based classification of dislocation microstructures. Front Mater. https://doi.org/10.3389/fmats.2019.00141
Van Der Putten J, Vijayan RA, De Schutter G, Van Tittelboom K (2021) Development of 3D printable cementitious composites with the incorporation of polypropylene fibers. Materials (Basel). https://doi.org/10.3390/ma14164474
Wang ZL, Adachi Y (2019) Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach. Mater Sci Eng A 744:661–670. https://doi.org/10.1016/j.msea.2018.12.049
Wang Y, Jin H, Demartino C, Chen W, Yu Y (2022) Mechanical properties of SFRC: database construction and model prediction. Case Stud Constr Mater. https://doi.org/10.1016/j.cscm.2022.e01484
Wangler T, Roussel N, Bos FP, Salet TAM, Flatt RJ (2019) Digital concrete: a review. Cem Concr Res. https://doi.org/10.1016/j.cemconres.2019.105780
Weng Y, Li M, Ruan S, Wong TN, Tan MJ, Ow Yeong KL et al (2020) Comparative economic, environmental and productivity assessment of a concrete bathroom unit fabricated through 3D printing and a precast approach. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.121245
Weng Y, Mohamed NAN, Lee BJS, Gan NJH, Li M, Jen Tan M et al (2021) Extracting BIM information for lattice toolpath planning in digital concrete printing with developed dynamo script: a case study. J Comput Civ Eng. https://doi.org/10.1061/(asce)cp.1943-5487.0000964
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 (Basel) 14:1–18. https://doi.org/10.3390/ma14051068
Ye J, Zheng Y, Yu J, Yu K, Dong F, Xiao J (2021a) Research progress on 3D printable fiber reinforced concrete. Kuei Suan Jen Hsueh Pao/j Chin Ceram Soc 49:2538–2548. https://doi.org/10.14062/j.issn.0454-5648.20210213
Ye J, Cui C, Yu J, Yu K, Xiao J (2021b) Fresh and anisotropic-mechanical properties of 3D printable ultra-high ductile concrete with crumb rubber. Compos Part B Eng. https://doi.org/10.1016/j.compositesb.2021.108639
Yu J, Leung CKY (2019) Impact of 3D printing direction on mechanical performance of strain-hardening cementitious composite (SHCC). RILEM Bookseries 19:255–265. https://doi.org/10.1007/978-3-319-99519-9_24
Yu K, McGee W, Ng TY, Zhu H, Li VC (2021a) 3D-printable engineered cementitious composites (3DP-ECC): fresh and hardened properties. Cem Concr Res 143:106388. https://doi.org/10.1016/j.cemconres.2021.106388
Yu K, McGee W, Ng TY, Zhu H, Li VC (2021b) 3D-printable engineered cementitious composites (3DP-ECC): fresh and hardened properties. Cem Concr Res. https://doi.org/10.1016/j.cemconres.2021.106388
Zhang Z, Chen G, Yang S (2022) Ensemble support vector recurrent neural network for brain signal detection. IEEE Trans Neural Netw Learn Syst 33:6856–6866. https://doi.org/10.1109/TNNLS.2021.3083710
Zhang X, Dai C, Li W, Chen Y (2023) Prediction of compressive strength of recycled aggregate concrete using machine learning and Bayesian optimization methods. Front Earth Sci. https://doi.org/10.3389/feart.2023.1112105
Funding
This study was funded by National Natural Science Foundation of China (No. 51978504), Shanghai Municipal Key Laboratory of Intelligent Information Processing, Science and Technology Commission of Shanghai Municipality (No. 19DZ1202500).
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Md Nasir Uddin: Conceptualization, Writing – original draft, Data collection, and software Junhong Ye: Supervision, Writing – review & editing, and Data Collection. Aminul Haque: Supervision, Writing – review & editing. Kequan Yu: Supervision, Writing – review & editing. Lingzhi Li: Investigation, Supervision, Writing – review & editing
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Uddin, M.N., Ye, J., Haque, M.A. et al. A novel compressive strength estimation approach for 3D printed fiber-reinforced concrete: integrating machine learning and gene expression programming. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00439-x
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DOI: https://doi.org/10.1007/s41939-024-00439-x