Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete
Abstract
:1. Introduction
- To investigate the combined effect of ensemble and individual ML algorithms for predicting the strength property of concrete.
- To evaluate the more precious ML algorithm for predicting the compressive strength of concrete.
- To minimize the experimental efforts, cost, and time with the application of employed ML approaches.
2. Research Strategy
3. Machine Learning Algorithms
3.1. Multilayer Perceptron Regressor (MLP)
3.2. Support Vector Machine (SVM)
3.3. XGBoost Algorithm
4. Result and Discussions
4.1. MLP Model Outcome
4.2. SVM Model Outcome
4.3. XGBoost Model Outcome
5. K-Fold Cross-Validation (CV) Approach
6. Sensitivity Analysis
7. Discussion
8. Conclusions
- The XGB model performs effectively and preciously towards the prediction of C-S of GP concrete;
- The R2 result of XGB equals 0.98 and is a reflection of its high-level performance as opposed to the R2 value of SVM (0.92) and MLP (0.88);
- The statistical analysis and K-fold CV approach also confirm the accurate prediction of the XGB model;
- The lower values of the statistical results in the form of errors, such as MAE, MSE, and RMSE, also give a reflection of the high precision of the XGB model for anticipating the C-S of GP concrete;
- The sensitivity analysis shows that the maximum contributing input parameter was fly ash crossing 35% towards the prediction of C-S of GP concrete.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Provis, J.L.; Bernal, S.A. Geopolymers and related alkali-activated materials. Annu. Rev. Mater. Res. 2014, 44, 299–327. [Google Scholar]
- Yang, H.; Liu, L.; Yang, W.; Liu, H.; Ahmad, W.; Ahmad, A.; Aslam, F.; Joyklad, P. A comprehensive overview of geopolymer composites: A bibliometric analysis and literature review. Case Stud. Constr. Mater. 2022, 16, e00830. [Google Scholar]
- Ren, B.; Zhao, Y.; Bai, H.; Kang, S.; Zhang, T.; Song, S. Eco-friendly geopolymer prepared from solid wastes: A critical review. Chemosphere 2020, 267, 128900. [Google Scholar] [PubMed]
- Podolsky, Z.; Liu, J.; Dinh, H.; Doh, J.H.; Guerrieri, M.; Fragomeni, S. State of the Art on the Application of Waste Materials in Geopolymer Concrete. Case Stud. Constr. Mater. 2021, 15, e00637. [Google Scholar]
- Mohajerani, A.; Suter, D.; Jeffrey-Bailey, T.; Song, T.; Arulrajah, A.; Horpibulsuk, S.; Law, D. Recycling waste materials in geopolymer concrete. Clean Technol. Environ. Policy 2019, 21, 493–515. [Google Scholar]
- Farooq, F.; Jin, X.; Javed, M.F.; Akbar, A.; Shah, M.I.; Aslam, F.; Alyousef, R. Geopolymer concrete as sustainable material: A state of the art review. Constr. Build. Mater. 2021, 306, 124762. [Google Scholar]
- Li, L.; Khan, M.; Bai, C.; Shi, K. Uniaxial Tensile Behavior, Flexural Properties, Empirical Calculation and Microstructure of Multi-Scale Fiber Reinforced Cement-Based Material at Elevated Temperature. Materials 2021, 14, 1827. [Google Scholar]
- Singh, B.; Ishwarya, G.; Gupta, M.; Bhattacharyya, S.J.C. Geopolymer concrete: A review of some recent developments. Constr. Build. Mater. 2015, 85, 78–90. [Google Scholar]
- Abdeldjalil, M.; Yousfi, S. Identification of sands of dune and concretes using a granular model—Case of arid region. Case Stud. Constr. Mater. 2020, 13, e00458. [Google Scholar]
- Bouron, S.; Hammoum, F.; Ruat, H.; Métais, P.; Lesueur, D. Improving the durability of asphalt mixtures with hydrated lime: Field results from highway A84. Case Stud. Constr. Mater. 2021, 14, e00551. [Google Scholar]
- Amaral, L.F.; Delaqua, G.C.G.; Nicolite, M.; Marvila, M.T.; de Azevedo, A.R.G.; Alexandre, J.; Vieira, C.M.F.; Monteiro, S.N. Eco-friendly mortars with addition of ornamental stone waste-A mathematical model approach for granulometric optimization. J. Clean. Prod. 2020, 248, 119283. [Google Scholar]
- Marvila, M.T.; Azevedo, A.R.G.; Alexandre, J.; Vieira, C.M.F.; Zanelato, E.B.; Delaqua, G.C.G.; Xavier, G.C.; Monteiro, S.N. Study of the compressive strength of mortars as a function of material composition, workability, and specimen geometry. Model. Simul. Eng. 2020, 2020, 1676190. [Google Scholar]
- Macedo, A.; Zanelato, E.; Manhães, A.; Azevedo, A.; Marvila, M.; Alexandre, J.; Monteiro, S.; Petrucci, L. Influence of construction and demolition waste incorporation in concrete. In Advances in Powder and Ceramic Materials Science; Springer: Cham, Switzerland, 2020; pp. 109–117. [Google Scholar]
- Duxson, P.; Fernández-Jiménez, A.; Provis, J.L.; Lukey, G.C.; Palomo, A.; van Deventer, J.S. Geopolymer technology: The current state of the art. J. Mater. Sci. 2007, 42, 2917–2933. [Google Scholar]
- Refaie, F.A.-Z.; Abbas, R.; Fouad, F.H. Sustainable construction system with Egyptian metakaolin based geopolymer concrete sandwich panels. Case Stud. Constr. Mater. 2020, 13, e00436. [Google Scholar]
- Saini, G.; Vattipalli, U. Assessing properties of alkali activated GGBS based self-compacting geopolymer concrete using nano-silica. Case Stud. Constr. Mater. 2020, 12, e00352. [Google Scholar]
- Upshaw, M.; Cai, C.S. Feasibility study of MK-based geopolymer binder for RAC applications: Effects of silica fume and added CaO on compressive strength of mortar samples. Case Stud. Constr. Mater. 2021, 14, e00500. [Google Scholar]
- Bakharev, T. Geopolymeric materials prepared using Class F fly ash and elevated temperature curing. Cem. Concr. Res. 2005, 35, 1224–1232. [Google Scholar]
- Xiao, R.; Jiang, X.; Wang, Y.; He, Q.; Huang, B. Experimental and Thermodynamic Study of Alkali-Activated Waste Glass and Calcium Sulfoaluminate Cement Blends: Shrinkage, Efflorescence Potential, and Phase Assemblages. J. Mater. Civ. Eng. 2021, 33, 04021312. [Google Scholar]
- Palomo, A.; Grutzeck, M.; Blanco, M.J.C. Alkali-activated fly ashes: A cement for the future. Cem. Concr. Res. 1999, 29, 1323–1329. [Google Scholar]
- Sultan, M.E.; Abo-El-Enein, S.A.; Sayed, A.Z.; El-Sokkary, T.M.; Hammad, H.A. Incorporation of cement bypass flue dust in fly ash and blast furnace slag-based geopolymer. Case Stud. Constr. Mater. 2018, 8, 315–322. [Google Scholar]
- Chindaprasirt, P.; Chareerat, T.; Sirivivatnanon, V. Workability and strength of coarse high calcium fly ash geopolymer. Cem. Concr. Compos. 2007, 29, 224–229. [Google Scholar]
- Yip, C.K.; Lukey, G.C.; van Deventer, J.S.J. The coexistence of geopolymeric gel and calcium silicate hydrate at the early stage of alkaline activation. Cem. Concr. Res. 2005, 35, 1688–1697. [Google Scholar]
- Jiang, X.; Xiao, R.; Bai, Y.; Huang, B.; Ma, Y. Influence of waste glass powder as a supplementary cementitious material (SCM) on physical and mechanical properties of cement paste under high temperatures. J. Clean. Prod. 2022, 340, 130778. [Google Scholar]
- Wang, Y.; Xiao, R.; Hu, W.; Jiang, X.; Zhang, X.; Huang, B. Effect of granulated phosphorus slag on physical, mechanical and microstructural characteristics of Class F fly ash based geopolymer. Constr. Build. Mater. 2021, 291, 123287. [Google Scholar]
- Xiao, R.; Zhang, Y.; Jiang, X.; Polaczyk, P.; Ma, Y.; Huang, B. Alkali-activated slag supplemented with waste glass powder: Laboratory characterization, thermodynamic modelling and sustainability analysis. J. Clean. Prod. 2021, 286, 125554. [Google Scholar]
- Davidovits, J. Geopolymers: Inorganic polymeric new materials. J. Therm. Anal. Calorim. 1991, 37, 1633–1656. [Google Scholar]
- Gunasekara, C.; Law, D.W.; Setunge, S. Long term permeation properties of different fly ash geopolymer concretes. Constr. Build. Mater. 2016, 124, 352–362. [Google Scholar]
- Jindal, B.B. Investigations on the properties of geopolymer mortar and concrete with mineral admixtures: A review. Constr. Build. Mater. 2019, 227, 116644. [Google Scholar]
- Nguyen, K.T.; Nguyen, Q.D.; Le, T.A.; Shin, J.; Lee, K. Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches. Constr. Build. Mater. 2020, 247, 118581. [Google Scholar]
- Da Rocha, T.S.; Dias, D.P.; França, F.C.C.; de Guerra, R.R.S.; de Oliveira, L.R.D.C. Metakaolin-based geopolymer mortars with different alkaline activators (Na+ and K+). Constr. Build. Mater. 2018, 178, 453–461. [Google Scholar]
- Tchakouté, H.K.; Rüscher, C.H.; Kong, S.; Kamseu, E.; Leonelli, C. Geopolymer binders from metakaolin using sodium waterglass from waste glass and rice husk ash as alternative activators: A comparative study. Constr. Build. Mater. 2016, 114, 276–289. [Google Scholar]
- Almutairi, A.L.; Tayeh, B.A.; Adesina, A.; Isleem, H.F.; Zeyad, A.M. Potential applications of geopolymer concrete in construction: A review. Case Stud. Constr. Mater. 2021, 15, e00733. [Google Scholar]
- De Azevedo, A.R.G.; Teixeira Marvila, M.; de Oliveira, L.B.; Macario Ferreira, W.; Colorado, H.; Rainho Teixeira, S.; Mauricio Fontes Vieira, C. Circular economy and durability in geopolymers ceramics pieces obtained from glass polishing waste. Int. J. Appl. Ceram. Technol. 2021, 18, 1891–1900. [Google Scholar]
- Cao, M.; Xie, C.; Li, L.; Khan, M. Effect of different PVA and steel fiber length and content on mechanical properties of CaCO3 whisker reinforced cementitious composites. Mater. Constr. 2019, 69, e200. [Google Scholar]
- Pasupathy, K.; Berndt, M.; Castel, A.; Sanjayan, J.; Pathmanathan, R. Carbonation of a blended slag-fly ash geopolymer concrete in field conditions after 8 years. Constr. Build. Mater. 2016, 125, 661–669. [Google Scholar]
- Shill, S.K.; Al-Deen, S.; Ashraf, M.; Hutchison, W. Resistance of fly ash based geopolymer mortar to both chemicals and high thermal cycles simultaneously. Constr. Build. Mater. 2020, 239, 117886. [Google Scholar]
- Arshad, S.; Sharif, M.B.; Irfan-ul-Hassan, M.; Khan, M.; Zhang, J.-L. Efficiency of supplementary cementitious materials and natural fiber on mechanical performance of concrete. Arab. J. Sci. Eng. 2020, 45, 8577–8589. [Google Scholar]
- Hanjitsuwan, S.; Hunpratub, S.; Thongbai, P.; Maensiri, S.; Sata, V.; Chindaprasirt, P.J.C.; Composites, C. Effects of NaOH concentrations on physical and electrical properties of high calcium fly ash geopolymer paste. Cem. Concr. Compos. 2014, 45, 9–14. [Google Scholar]
- Elsheikh, A.H.; Panchal, H.; Shanmugan, S.; Muthuramalingam, T.; El-Kassas, A.M.; Ramesh, B. Recent progresses in wood-plastic composites: Pre-processing treatments, manufacturing techniques, recyclability and eco-friendly assessment. Clean. Eng. Technol. 2022, 8, 100450. [Google Scholar]
- Moustafa, E.B.; Hammad, A.H.; Elsheikh, A.H. A new optimized artificial neural network model to predict thermal efficiency and water yield of tubular solar still. Case Stud. Therm. Eng. 2022, 30, 101750. [Google Scholar]
- Elsheikh, A.H.; Shanmugan, S.; Sathyamurthy, R.; Thakur, A.K.; Issa, M.; Panchal, H.; Muthuramalingam, T.; Kumar, R.; Sharifpur, M. Low-cost bilayered structure for improving the performance of solar stills: Performance/cost analysis and water yield prediction using machine learning. Sustain. Energy Technol. Assess. 2022, 49, 101783. [Google Scholar]
- Somna, K.; Jaturapitakkul, C.; Kajitvichyanukul, P.; Chindaprasirt, P.J.F. NaOH-activated ground fly ash geopolymer cured at ambient temperature. Fuel 2011, 90, 2118–2124. [Google Scholar]
- Kumar, S.; Kumar, R.; Mehrotra, S.P. Influence of granulated blast furnace slag on the reaction, structure and properties of fly ash based geopolymer. J. Mater. Sci. 2010, 45, 607–615. [Google Scholar]
- Khan, M.; Cao, M.; Hussain, A.; Chu, S.H. Effect of silica-fume content on performance of CaCO3 whisker and basalt fiber at matrix interface in cement-based composites. Constr. Build. Mater. 2021, 300, 124046. [Google Scholar]
- Ahmad, W.; Farooq, S.H.; Usman, M.; Khan, M.; Ahmad, A.; Aslam, F.; Yousef, R.A.; Abduljabbar, H.A.; Sufian, M. Effect of coconut fiber length and content on properties of high strength concrete. Materials 2020, 13, 1075. [Google Scholar]
- Chindaprasirt, P.; de Silva, P.; Sagoe-Crentsil, K.; Hanjitsuwan, S. Effect of SiO2 and Al2O3 on the setting and hardening of high calcium fly ash-based geopolymer systems. J. Mater. Sci. 2012, 47, 4876–4883. [Google Scholar]
- Guo, X.; Shi, H.; Dick, W.A.J.C.; Composites, C. Compressive strength and microstructural characteristics of class C fly ash geopolymer. Cem. Concr. Compos. 2010, 32, 142–147. [Google Scholar]
- Samsudin, M.H.; Ban, C.C. Optimization on the hybridization ratio of ground granulated blast furnace slag and high calcium wood ash (GGBS–HCWA) for the fabrication of geopolymer mortar. Adv. Environ. Biol. 2015, 9, 22–25. [Google Scholar]
- Ban, C.C.; Ken, P.W.; Ramli, M. Mechanical and durability performance of novel self-activating geopolymer mortars. Procedia Eng. 2017, 171, 564–571. [Google Scholar]
- Khan, M.; Cao, M.; Xie, C.; Ali, M. Efficiency of basalt fiber length and content on mechanical and microstructural properties of hybrid fiber concrete. Fatigue Fract. Eng. Mater. Struct. 2021, 44, 2135–2152. [Google Scholar]
- Xie, C.; Cao, M.; Khan, M.; Yin, H.; Guan, J. Review on different testing methods and factors affecting fracture properties of fiber reinforced cementitious composites. Constr. Build. Mater. 2021, 273, 121766. [Google Scholar]
- Cao, M.; Mao, Y.; Khan, M.; Si, W.; Shen, S. Different testing methods for assessing the synthetic fiber distribution in cement-based composites. Constr. Build. Mater. 2018, 184, 128–142. [Google Scholar]
- Khan, M.; Cao, M.; Xie, C.; Ali, M. Hybrid fiber concrete with different basalt fiber length and content. Struct. Concr. 2021, 23, 346–364. [Google Scholar]
- Cao, M.; Khan, M.; Ahmed, S. Effectiveness of Calcium Carbonate Whisker in Cementitious Composites. Period. Polytechnica. Civ. Eng. 2020, 64, 265. [Google Scholar]
- Ahmad, W.; Ahmad, A.; Ostrowski, K.A.; Aslam, F.; Joyklad, P.; Zajdel, P. Sustainable approach of using sugarcane bagasse ash in cement-based composites: A systematic review. Case Stud. Constr. Mater. 2021, 15, e00698. [Google Scholar]
- Shah, M.I.; Memon, S.A.; Niazi, M.S.K.; Amin, M.N.; Aslam, F.; Javed, M.F. Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete. Adv. Civ. Eng. 2021, 2021, 6682283. [Google Scholar]
- Dutta, S.; Samui, P.; Kim, D. Comparison of machine learning techniques to predict compressive strength of concrete. Comput. Concr. 2018, 21, 463–470. [Google Scholar]
- Chaabene, W.B.; Flah, M.; Nehdi, M.L. Machine learning prediction of mechanical properties of concrete: Critical review. Constr. Build. Mater. 2020, 260, 119889. [Google Scholar]
- Ahmad, A.; Farooq, F.; Ostrowski, K.A.; Śliwa-Wieczorek, K.; Czarnecki, S. Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material. Materials 2021, 14, 2297. [Google Scholar]
- Ahmad, W.; Ahmad, A.; Ostrowski, K.A.; Aslam, F.; Joyklad, P.; Zajdel, P. Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials. Materials 2021, 14, 5762. [Google Scholar]
- Ahmad, A.; Ostrowski, K.A.; Maślak, M.; Farooq, F.; Mehmood, I.; Nafees, A. Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature. Materials 2021, 14, 4222. [Google Scholar] [PubMed]
- Song, H.; Ahmad, A.; Farooq, F.; Ostrowski, K.A.; Maślak, M.; Czarnecki, S.; Aslam, F. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Constr. Build. Mater. 2021, 308, 125021. [Google Scholar]
- Song, Y.; Zhao, J.; Ostrowski, K.A.; Javed, M.F.; Ahmad, A.; Khan, M.I.; Aslam, F.; Kinasz, R. Prediction of Compressive Strength of Fly-Ash-Based Concrete Using Ensemble and Non-Ensemble Supervised Machine-Learning Approaches. Appl. Sci. 2022, 12, 361. [Google Scholar]
- Ahmad, A.; Ahmad, W.; Aslam, F.; Joyklad, P. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Stud. Constr. Mater. 2021, 16, e00840. [Google Scholar]
- Ahmad, A.; Chaiyasarn, K.; Farooq, F.; Ahmad, W.; Suparp, S.; Aslam, F. Compressive strength prediction via gene expression programming (GEP) and artificial neural network (ANN) for concrete containing RCA. Buildings 2021, 11, 324. [Google Scholar]
- Sufian, M.; Ullah, S.; Ostrowski, K.A.; Ahmad, A.; Zia, A.; Śliwa-Wieczorek, K.; Siddiq, M.; Awan, A.A. An Experimental and Empirical Study on the Use of Waste Marble Powder in Construction Material. Materials 2021, 14, 3829. [Google Scholar]
- Xu, Y.; Ahmad, W.; Ahmad, A.; Ostrowski, K.A.; Dudek, M.; Aslam, F.; Joyklad, P. Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques. Materials 2021, 14, 7034. [Google Scholar]
- Elsheikh, A.H.; Elaziz, M.A.; Vendan, A. Modeling ultrasonic welding of polymers using an optimized artificial intelligence model using a gradient-based optimizer. Weld. World 2022, 66, 27–44. [Google Scholar]
- Elsheikh, A.H.; Shehabeldeen, T.A.; Zhou, J.; Showaib, E.; Elaziz, M.A. Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer. J. Intell. Manuf. 2021, 32, 1377–1388. [Google Scholar]
- Elsheikh, A.H.; Sharshir, S.W.; Elaziz, M.A.; Kabeel, A.E.; Guilan, W.; Haiou, Z. Modeling of solar energy systems using artificial neural network: A comprehensive review. Sol. Energy 2019, 180, 622–639. [Google Scholar]
- Bayar, G.; Bilir, T. A novel study for the estimation of crack propagation in concrete using machine learning algorithms. Constr. Build. Mater. 2019, 215, 670–685. [Google Scholar]
- Lee, S.C. Prediction of concrete strength using artificial neural networks. Eng. Struct. 2003, 25, 849–857. [Google Scholar]
- Nguyen-Sy, T.; Wakim, J.; To, Q.-D.; Vu, M.-N.; Nguyen, T.-D.; Nguyen, T.-T. Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Constr. Build. Mater. 2020, 260, 119757. [Google Scholar]
- Gunasekara, C.; Atzarakis, P.; Lokuge, W.; Law, D.W.; Setunge, S.J.P. Novel analytical method for mix design and performance prediction of high calcium fly ash geopolymer concrete. Polymers 2021, 13, 900. [Google Scholar]
- Kupwade-Patil, K.; Allouche, E.N. Impact of alkali silica reaction on fly ash-based geopolymer concrete. J. Mater. Civ. Eng. 2013, 25, 131–139. [Google Scholar]
- Kusbiantoro, A.; Nuruddin, M.F.; Shafiq, N.; Qazi, S.A. The effect of microwave incinerated rice husk ash on the compressive and bond strength of fly ash based geopolymer concrete. Constr. Build. Mater. 2012, 36, 695–703. [Google Scholar]
- Pane, I.; Imran, I.; Budiono, B. Compressive strength of fly ash-based geopolymer concrete with a variable of sodium hydroxide (NaOH) solution molarity. In MATEC Web of Conferences; EDP Sciences: Les Ulis, France, 2018; p. 01004. [Google Scholar]
- Embong, R.; Kusbiantoro, A.; Shafiq, N.; Nuruddin, M.F. Strength and microstructural properties of fly ash based geopolymer concrete containing high-calcium and water-absorptive aggregate. J. Clean. Prod. 2016, 112, 816–822. [Google Scholar]
- Mehta, A.; Siddique, R. Sulfuric acid resistance of fly ash based geopolymer concrete. Constr. Build. Mater. 2017, 146, 136–143. [Google Scholar]
- Lavanya, G.; Jegan, J. Engineering, Durability study on high calcium fly ash based geopolymer concrete. Adv. Mater. Sci. Eng. 2015, 2015, 731056. [Google Scholar]
- Nazari, A.; Torgal, F.P. Predicting compressive strength of different geopolymers by artificial neural networks. Ceram. Int. 2013, 39, 2247–2257. [Google Scholar]
- Mishra, A.; Choudhary, D.; Jain, N.; Kumar, M.; Sharda, N.; Dutt, D. Effect of concentration of alkaline liquid and curing time on strength and water absorption of geopolymer concrete. ARPN J. Eng. Appl. Sci. 2008, 3, 14–18. [Google Scholar]
- Rattanasak, U.; Chindaprasirt, P. Influence of NaOH solution on the synthesis of fly ash geopolymer. Miner. Eng. 2009, 22, 1073–1078. [Google Scholar]
- Ahmad, A.; Ahmad, W.; Chaiyasarn, K.; Ostrowski, K.A.; Aslam, F.; Zajdel, P.; Joyklad, P. Prediction of geopolymer concrete compressive strength using novel machine learning algorithms. Polymers 2021, 13, 3389. [Google Scholar] [PubMed]
- Shang, M.; Li, H.; Ahmad, A.; Ahmad, W.; Ostrowski, K.A.; Aslam, F.; Joyklad, P.; Majka, T.M. Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms. Materials 2022, 15, 647. [Google Scholar]
- Song, H.; Ahmad, A.; Ostrowski, K.A.; Dudek, M. Analyzing the compressive strength of ceramic waste-based concrete using experiment and artificial neural network (ANN) approach. Materials 2021, 14, 4518. [Google Scholar]
- Pelalak, R.; Nakhjiri, A.T.; Marjani, A.; Rezakazemi, M.; Shirazian, S. Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors. Sci. Rep. 2021, 11, 1891. [Google Scholar]
- Dong, W.; Huang, Y.; Lehane, B.; Ma, G. XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Autom. Constr. 2020, 114, 103155. [Google Scholar]
- Duan, J.; Asteris, P.G.; Nguyen, H.; Bui, X.-N.; Moayedi, H. A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng. Comput. 2021, 37, 3329–3346. [Google Scholar]
- Elsheikh, A.H.; Saba, A.I.; Panchal, H.; Shanmugan, S.; Alsaleh, N.A.; Ahmadein, M. Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview. Healthcare 2021, 9, 1614. [Google Scholar]
Parameters | Fly Ash | CA | FA | NaOH | Na2SiO3 | SiO2 | Na2O | Molarity | CT |
---|---|---|---|---|---|---|---|---|---|
Mean of variables | 465.79 | 1060.99 | 598.93 | 94.26 | 167.87 | 30.12 | 13.16 | 11.65 | 28.13 |
Variable’s median | 494.00 | 1091.00 | 600.00 | 95.00 | 138.00 | 30.00 | 12.00 | 12.00 | 24.00 |
Variable’s mode | 550.00 | 838.00 | 600.00 | 95.00 | 239.00 | 30.00 | 12.00 | 8.00 | 24.00 |
Standard Deviation | 86.54 | 210.13 | 65.61 | 38.05 | 57.61 | 1.20 | 1.67 | 2.98 | 17.01 |
Standard Error | 6.97 | 16.93 | 5.29 | 3.07 | 4.64 | 0.10 | 0.13 | 0.24 | 1.37 |
Input ranges | 300.00 | 846.00 | 291.00 | 157.00 | 136.00 | 6.00 | 7.20 | 12.00 | 69.00 |
Lowest values | 300.00 | 838.00 | 459.00 | 41.00 | 103.00 | 28.70 | 9.00 | 8.00 | 3.00 |
Highest values | 600.00 | 1684.00 | 750.00 | 198.00 | 239.00 | 34.70 | 16.20 | 20.00 | 72.00 |
ML Approaches | MAE (MPa) | MSE (MPa) | RMSE (MPa) |
---|---|---|---|
MLP | 3.48 | 19.0969 | 4.37 |
XGB | 1.49 | 3.1684 | 1.78 |
SVM | 2.77 | 14.5924 | 3.82 |
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Cao, R.; Fang, Z.; Jin, M.; Shang, Y. Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete. Materials 2022, 15, 2400. https://doi.org/10.3390/ma15072400
Cao R, Fang Z, Jin M, Shang Y. Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete. Materials. 2022; 15(7):2400. https://doi.org/10.3390/ma15072400
Chicago/Turabian StyleCao, Rongchuan, Zheng Fang, Man Jin, and Yu Shang. 2022. "Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete" Materials 15, no. 7: 2400. https://doi.org/10.3390/ma15072400