Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques
Abstract
:1. Introduction
2. Literature Review
3. Datasets
4. Description of Used ML Approaches
- Pre-processing of collected dataset;
- Running and fitting the training set in the RF algorithm;
- Running the model and prediction of test results;
- Accuracy and efficiency of test results.
5. Model Development and Performance Evaluation
6. Results and Discussion
6.1. Decision Tree Technique Outcome
6.2. Random Forest Technique Outcome
7. Performance Evaluation of Developed Models
8. Statistical and K-Fold Analysis
9. A Comparative Study with Existing Models
10. Conclusions
- The ensemble ML technique RFT reveals better performance with fewer variations between the experimental datasets and the projected outcomes. Additionally, the efficiency level of the random forest technique was found higher with an R2 value of 0.96 than the individual ML DTT having an R2 of 0.94.
- The smaller values of the statistical performance indicators MAE (3.253), RMSE (4.387), RSE (0.895), and RRMSE (0.0803) for RFT confirm the better accuracy than the 3.863, 4.865, 0.912, and 0.0891 values for the DTT algorithm. Additionally, a better performance index factor (PiF) for RFT was found at 0.0061 than the DTT approach with a PiF of 0.0086.
- K-fold cross-validation findings confirmed the effectiveness and better performance of the RFT-developed model.
- The performance of the established RFT (R2 of 0.96)and DTT (R2 of 0.94) models was found much better than the GEP model (R2 of 0.864) developed in previous studies.
11. Future Recommendation
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Ababneh, A.; Alhassan, M.; Abu-Haifa, M. Predicting the contribution of recycled aggregate concrete to the shear capacity of beams without transverse reinforcement using artificial neural networks. Case Stud. Constr. Mater. 2020, 13, e00414. [Google Scholar] [CrossRef]
- Abdalla, J.A.; Thomas, B.S.; Hawileh, R.A.; Yang, J.; Jindal, B.B.; Ariyachandra, E. Influence of nano-TiO2, nano-Fe2O3, nanoclay and nano-CaCO3 on the properties of cement/geopolymer concrete. Clean. Mater. 2022, 4, 100061. [Google Scholar] [CrossRef]
- 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. 2022, 16, e00840. [Google Scholar] [CrossRef]
- 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] [CrossRef]
- Ahmad, A.; Farooq, F.; Niewiadomski, P.; Ostrowski, K.; Akbar, A.; Aslam, F.; Alyousef, R. Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm. Materials 2021, 14, 794. [Google Scholar] [CrossRef] [PubMed]
- Ahmad, M.; Chen, B.; Dai, J.-G.; Kazmi, S.; Munir, M. Evolutionary artificial intelligence approach for performance prediction of bio-composites. Constr. Build. Mater. 2021, 290, 123254. [Google Scholar] [CrossRef]
- Amin, M.N.; Ahmad, A.; Khan, K.; Ahmad, W.; Nazar, S.; Faraz, M.I.; Alabdullah, A.A. Split Tensile Strength Prediction of Recycled Aggregate-Based Sustainable Concrete Using Artificial Intelligence Methods. Materials 2022, 15, 4296. [Google Scholar] [CrossRef]
- Atiq Orakzai, M. Hybrid effect of nano-alumina and nano-titanium dioxide on Mechanical properties of concrete. Case Stud. Constr. Mater. 2021, 14, e00483. [Google Scholar] [CrossRef]
- Auret, L.; Aldrich, C. Interpretation of nonlinear relationships between process variables by use of random forests. Miner. Eng. 2012, 35, 27–42. [Google Scholar] [CrossRef]
- Azim, I.; Yang, J.; Farjad Iqbal, M.; Faisal Javed, M.; Nazar, S.; Wang, F.; Liu, Q.-F. Semi-analytical model for compressive arch action capacity of RC frame structures. Structures 2020, 27, 1231–1245. [Google Scholar] [CrossRef]
- Azim, I.; Yang, J.; Iqbal, M.F.; Mahmood, Z.; Javed, M.F.; Wang, F.; Liu, Q.-F. Prediction of catenary action capacity of RC beam-column substructures under a missing column scenario using evolutionary algorithm. KSCE J. Civ. Eng. 2021, 25, 891–905. [Google Scholar] [CrossRef]
- Behnood, A.; Golafshani, E.M. Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. J. Clean. Prod. 2018, 202, 54–64. [Google Scholar] [CrossRef]
- Boulesteix, A.L.; Janitza, S.; Kruppa, J.; König, I.R. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics: Wiley Interdisciplinary Reviews. Data Min. Knowl. Discov. 2012, 2, 493–507. [Google Scholar] [CrossRef] [Green Version]
- Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Chou, J.-S.; Tsai, C.-F.; Pham, A.-D.; Lu, Y.-H. Machine learning in concrete strength simulations: Multi-nation data analytics. Constr. Build. Mater. 2014, 73, 771–780. [Google Scholar] [CrossRef]
- Czarnecki, S.; Shariq, M.; Nikoo, M.; Sadowski, Ł. An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements. Measurement 2021, 172, 108951. [Google Scholar] [CrossRef]
- Dao, D.V.; Ly, H.-B.; Vu, H.-L.T.; Le, T.-T.; Pham, B.T. Investigation and optimization of the C-ANN structure in predicting the compressive strength of foamed concrete. Materials 2020, 13, 1072. [Google Scholar] [CrossRef] [Green Version]
- Dao, D.V.; Trinh, S.H.; Ly, H.-B.; Pham, B.T. Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches. Appl. Sci. 2019, 9, 1113. [Google Scholar] [CrossRef] [Green Version]
- De Domenico, D.; Ricciardi, G. Shear strength of RC beams with stirrups using an improved Eurocode 2 truss model with two variable-inclination compression struts. Eng. Struct. 2019, 198, 109359. [Google Scholar] [CrossRef]
- Du, Y.; Yang, J.; Skariah Thomas, B.; Li, L.; Li, H.; Nazar, S. Hybrid graphene oxide/carbon nanotubes reinforced cement paste: An investigation on hybrid ratio. Constr. Build. Mater. 2020, 261, 119815. [Google Scholar] [CrossRef]
- Dubeau, P.; King, D.J.; Unbushe, D.G.; Rebelo, L.-M. Mapping the Dabus wetlands, Ethiopia, using random forest classification of Landsat, PALSAR and topographic data. Remote Sens. 2017, 9, 1056. [Google Scholar] [CrossRef] [Green Version]
- Dutta, S.; Murthy, A.R.; Kim, D.; Samui, P. Prediction of compressive strength of self-compacting concrete using intelligent computational modeling. Comput. Mater. Contin 2017, 53, 167–185. [Google Scholar]
- Erdal, H.I. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Eng. Appl. Artif. Intell. 2013, 26, 1689–1697. [Google Scholar] [CrossRef]
- Farooq, F.; Nasir Amin, M.; Khan, K.; Rehan Sadiq, M.; Faisal Javed, M.; Aslam, F.; Alyousef, R. A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC). Appl. Sci. 2020, 10, 7330. [Google Scholar] [CrossRef]
- Ferreira, C. Gene expression programming: A new adaptive algorithm for solving problems. arXiv 2001, arXiv:cs/0102027. [Google Scholar]
- Feys, D.; De Schutter, G.; Khayat, K.H.; Verhoeven, R. Changes in rheology of self-consolidating concrete induced by pumping. Mater. Struct. 2016, 49, 4657–4677. [Google Scholar] [CrossRef] [Green Version]
- Firoozi, A.A.; Naji, M.; Dithinde, M.; Firoozi, A.A. A Review: Influence of Potential Nanomaterials for Civil Engineering Projects. Iran. J. Sci. Technol. Trans. Civ. Eng. 2021, 45, 2057–2068. [Google Scholar] [CrossRef]
- Frank, I.E.; Todeschini, R. The Data Analysis Handbook; Elsevier: Amsterdam, The Netherlands, 1994. [Google Scholar]
- Fu, B.; Wang, Y.; Campbell, A.; Li, Y.; Zhang, B.; Yin, S.; Xing, Z.; Jin, X. Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data. Ecol. Indic. 2017, 73, 105–117. [Google Scholar] [CrossRef]
- Gandomi, A.H.; Babanajad, S.K.; Alavi, A.H.; Farnam, Y. Novel approach to strength modeling of concrete under triaxial compression. J. Mater. Civ. Eng. 2012, 24, 1132–1143. [Google Scholar] [CrossRef]
- Gandomi, A.H.; Roke, D.A. Assessment of artificial neural network and genetic programming as predictive tools. Adv. Eng. Softw. 2015, 88, 63–72. [Google Scholar] [CrossRef]
- Gandomi, A.H.; Yun, G.J.; Alavi, A.H. An evolutionary approach for modeling of shear strength of RC deep beams. Mater. Struct. 2013, 46, 2109–2119. [Google Scholar] [CrossRef]
- Gholampour, A.; Mansouri, I.; Kisi, O.; Ozbakkaloglu, T. Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models. Neural Comput. Appl. 2020, 32, 295–308. [Google Scholar] [CrossRef]
- Glenn, J. Nanotechnology in Concrete: Critical Review and Statistical Analysis. Master’s Thesis, The College of Engineering and Computer Science Florida Atlantic University, Boca Raton, FL, USA, 2013. [Google Scholar]
- Gomaa, E.; Han, T.; ElGawady, M.; Huang, J.; Kumar, A. Machine learning to predict properties of fresh and hardened alkali-activated concrete. Cem. Concr. Compos. 2021, 115, 103863. [Google Scholar] [CrossRef]
- Han, Q.; Gui, C.; Xu, J.; Lacidogna, G. A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm. Constr. Build. Mater. 2019, 226, 734–742. [Google Scholar] [CrossRef]
- Hanselmann, M.; Kothe, U.; Kirchner, M.; Renard, B.Y.; Amstalden, E.R.; Glunde, K.; Heeren, R.M.; Hamprecht, F.A. Toward digital staining using imaging mass spectrometry and random forests. J. Proteome Res. 2009, 8, 3558–3567. [Google Scholar] [CrossRef] [Green Version]
- Hawreen, A.; Bogas, J. Creep, shrinkage and mechanical properties of concrete reinforced with different types of carbon nanotubes. Constr. Build. Mater. 2019, 198, 70–81. [Google Scholar] [CrossRef]
- Iqbal, M.F.; Javed, M.F.; Rauf, M.; Azim, I.; Ashraf, M.; Yang, J.; Liu, Q.-F. Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming. Sci. Total Environ. 2021, 780, 146524. [Google Scholar] [CrossRef]
- Javed, M.F.; Farooq, F.; Memon, S.A.; Akbar, A.; Khan, M.A.; Aslam, F.; Alyousef, R.; Alabduljabbar, H.; Rehman, S.K.U. New prediction model for the ultimate axial capacity of concrete-filled steel tubes: An evolutionary approach. Crystals 2020, 10, 741. [Google Scholar] [CrossRef]
- Jhatial, A.; Sohu, S.; Bhatti, N.; Lakhiar, M.; Oad, R. Effect of steel fibres on the compressive and flexural strength of concrete. Int. J. Adv. Appl. Sci. 2018, 5, 16–21. [Google Scholar] [CrossRef]
- Kang, F.; Li, J.; Dai, J. Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms. Adv. Eng. Softw. 2019, 131, 60–76. [Google Scholar] [CrossRef]
- Karbassi, A.; Mohebi, B.; Rezaee, S.; Lestuzzi, P. Damage prediction for regular reinforced concrete buildings using the decision tree algorithm. Comput. Struct. 2014, 130, 46–56. [Google Scholar] [CrossRef]
- Khan, K.; Ahmad, W.; Amin, M.N.; Ahmad, A.; Nazar, S.; Alabdullah, A.A. Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms. Polymers 2022, 14, 3065. [Google Scholar] [CrossRef]
- Khan, K.; Ahmad, W.; Amin, M.N.; Ahmad, A.; Nazar, S.; Alabdullah, A.A.; Arab, A.M.A. Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms. Materials 2022, 15, 4108. [Google Scholar] [CrossRef] [PubMed]
- Khan, M.; Aslam, F.; Faisal, A.; Alabduljabbar, H.; Deifalla, A. New prediction models for the compressive strength and dry-thermal conductivity of bio-composites using novel machine learning algorithms. J. Clean. Prod. 2022, 350, 131364. [Google Scholar] [CrossRef]
- Kim, J.S.; Kwon, S.H.; Jang, K.P.; Choi, M.S. Concrete pumping prediction considering different measurement of the rheological properties. Constr. Build. Mater. 2018, 171, 493–503. [Google Scholar] [CrossRef]
- Krkač, M.; Špoljarić, D.; Bernat, S.; Arbanas, S.M. Method for prediction of landslide movements based on random forests. Landslides 2017, 14, 947–960. [Google Scholar] [CrossRef]
- Li, H.; Lin, J.; Lei, X.; Wei, T. Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm. Mater. Today Commun. 2022, 30, 103117. [Google Scholar] [CrossRef]
- Ling, H.; Qian, C.; Kang, W.; Liang, C.; Chen, H. Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment. Constr. Build. Mater. 2019, 206, 355–363. [Google Scholar] [CrossRef]
- Maghrebi, M.; Waller, T.; Sammut, C. Matching experts’ decisions in concrete delivery dispatching centers by ensemble learning algorithms: Tactical level. Autom. Constr. 2016, 68, 146–155. [Google Scholar] [CrossRef]
- Makariou, D.; Barrieu, P.; Chen, Y. A random forest based approach for predicting spreads in the primary catastrophe bond market: Insurance. Math. Econ. 2021, 101, 140–162. [Google Scholar] [CrossRef]
- Mandeville, A.N.; O’Connell, P.E.; Sutcliffe, J.V.; Nash, J.E. River flow forecasting through conceptual models part III—The Ray catchment at Grendon Underwood. J. Hydrol. 1970, 11, 109–128. [Google Scholar] [CrossRef]
- Mohamed, O.A.; Ati, M.; Najm, O.F. Predicting Compressive Strength of Sustainable Self-Consolidating Concrete Using Random Forest. In Key Engineering Materials; Trans Tech Publications Ltd.: Wollerau, Switzerland, 2017; Volume 744, pp. 141–145. [Google Scholar]
- Mohammadzadeh, S.D.; Kazemi, S.-F.; Mosavi, A.; Nasseralshariati, E.; Tah, J.H.M. Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model. Infrastructures 2019, 4, 26. [Google Scholar] [CrossRef]
- Mohsen, M.O.; Al Ansari, M.S.; Taha, R.; Al Nuaimi, N.; Taqa, A.A. Carbon nanotube effect on the ductility, flexural strength, and permeability of concrete. J. Nanomater. 2019, 2019, 6490984. [Google Scholar] [CrossRef]
- Murad, Y. Compressive strength prediction for concrete modified with nanomaterials. Case Stud. Constr. Mater. 2021, 15, e00660. [Google Scholar] [CrossRef]
- Nazar, S.; Yang, J.; Ahmad, A.; Shah, S.F.A. Comparative study of evolutionary artificial intelligence approaches to predict the rheological properties of fresh concrete. Mater. Today Commun. 2022, 32, 103964. [Google Scholar] [CrossRef]
- Nelson, J.A.; Young, J. Additions of colloidal silicas and silicates to portland cement pastes. Cem. Concr. Res. 1977, 7, 277–282. [Google Scholar] [CrossRef]
- Norhasri, M.S.M.; Hamidah, M.S.; Fadzil, A.M. Applications of using nano material in concrete: A review. Constr. Build. Mater. 2017, 133, 91–97. [Google Scholar] [CrossRef]
- Nour, A.I.; Güneyisi, E.M. Prediction model on compressive strength of recycled aggregate concrete filled steel tube columns. Compos. Part B Eng. 2019, 173, 106938. [Google Scholar] [CrossRef]
- Onaizi, A.M.; Huseien, G.F.; Lim, N.H.A.S.; Amran, M.; Samadi, M. Effect of nanomaterials inclusion on sustainability of cement-based concretes: A comprehensive review. Constr. Build. Mater. 2021, 306, 124850. [Google Scholar] [CrossRef]
- Ozcan, G.; Kocak, Y.; Gulbandilar, E. Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models. Comput. Concr. 2017, 19, 275–282. [Google Scholar] [CrossRef]
- Qian, Y.; De Schutter, G. Enhancing thixotropy of fresh cement pastes with nanoclay in presence of polycarboxylate ether superplasticizer (PCE). Cem. Concr. Res. 2018, 111, 15–22. [Google Scholar] [CrossRef]
- Rao, W.; Zhang, L.; Zhang, Z.; Wu, Z. Noise-suppressing chaos generator to improve BER for DCSK systems. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–6. [Google Scholar]
- Rehman, S.K.U.; Ibrahim, Z.; Jameel, M.; Memon, S.A.; Javed, M.F.; Aslam, M.; Mehmood, K.; Nazar, S. Assessment of Rheological and Piezoresistive Properties of Graphene Based Cement Composites. Int. J. Concr. Struct. Mater. 2018, 12, 64. [Google Scholar] [CrossRef]
- Rinchon, J.P.M. Strength durability-based design mix of self-compacting concrete with cementitious blend using hybrid neural network-genetic algorithm. IPTEK J. Proc. Ser. 2017, 3. [Google Scholar] [CrossRef] [Green Version]
- Sadowski, L.; Nikoo, M.; Nikoo, M. Concrete compressive strength prediction using the imperialist competitive algorithm. Comput. Concr. 2018, 22, 355–363. [Google Scholar]
- Sadowski, Ł.; Piechówka-Mielnik, M.; Widziszowski, T.; Gardynik, A.; Mackiewicz, S. Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust. J. Clean. Prod. 2019, 212, 727–740. [Google Scholar] [CrossRef]
- Samui, P. Multivariate adaptive regression spline (Mars) for prediction of elastic modulus of jointed rock mass. Geotech. Geol. Eng. 2013, 31, 249–253. [Google Scholar] [CrossRef]
- Saruhan, V.; Keskinateş, M.; Felekoğlu, B. A comprehensive review on fresh state rheological properties of extrusion mortars designed for 3D printing applications. Constr. Build. Mater. 2022, 337, 127629. [Google Scholar] [CrossRef]
- Schwarz, D.F.; König, I.R.; Ziegler, A. On safari to Random Jungle: A fast implementation of Random Forests for high-dimensional data. Bioinformatics 2010, 26, 1752–1758. [Google Scholar] [CrossRef] [Green Version]
- Shah, M.I.; Javed, M.F.; Aslam, F.; Alabduljabbar, H. Machine learning modeling integrating experimental analysis for predicting the properties of sugarcane bagasse ash concrete. Constr. Build. Mater. 2022, 314, 125634. [Google Scholar] [CrossRef]
- Shahmansouri, A.A.; Bengar, H.A.; Ghanbari, S. Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method. J. Build. Eng. 2020, 31, 101326. [Google Scholar] [CrossRef]
- Shekari, A.; Razzaghi, M.S. Influence of nano particles on durability and mechanical properties of high performance concrete. Procedia Eng. 2011, 14, 3036–3041. [Google Scholar] [CrossRef] [Green Version]
- Sonebi, M.; Cevik, A. Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash. Constr. Build. Mater. 2009, 23, 2614–2622. [Google Scholar] [CrossRef]
- 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] [CrossRef]
- Stein, H.; Stevels, J. Influence of silica on the hydration of 3 CaO, SiO2. J. Appl. Chem. 1964, 14, 338–346. [Google Scholar] [CrossRef]
- Sun, Y.; Li, G.; Zhang, J.; Qian, D. Prediction of the strength of rubberized concrete by an evolved random forest model. Adv. Civ. Eng. 2019, 2019, 5198583. [Google Scholar] [CrossRef] [Green Version]
- Svetnik Liaw, A.; Tong, C.; Wang, T. Application of Breiman’s random forest to modeling structure-activity relationships of pharmaceutical molecules. In Proceedings International Workshop on Multiple Classifier Systems; Springer: Berlin/Heidelberg, Germany, 2004; pp. 334–343. [Google Scholar]
- Thanmanaselvi, M.; Ramasamy, V. A study on durability characteristics of nano-concrete. Mater. Today Proc. 2021, in press. [CrossRef]
- Vakhshouri, B.; Nejadi, S. Prediction of compressive strength in light-weight self-compacting concrete by ANFIS analytical model. Arch. Civ. Eng. 2015, 2, 53–72. [Google Scholar] [CrossRef]
- Vakhshouri, B.; Nejadi, S. Prediction of compressive strength of self-compacting concrete by ANFIS models. Neurocomputing 2018, 280, 13–22. [Google Scholar] [CrossRef]
- Xu, J.; Chen, Y.; Xie, T.; Zhao, X.; Xiong, B.; Chen, Z. Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques. Constr. Build. Mater. 2019, 226, 534–554. [Google Scholar] [CrossRef]
- Yousef, E.A.; Mouhcine, B.A.; Mounir, Z.; Adil, H.A. Prediction of compressive strength of self-compacting concrete using four machine learning technics. Mater. Today Proc. 2022, 57, 859–866. [Google Scholar]
- Zhang, J.; Li, D.; Wang, Y. Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models. J. Clean. Prod. 2020, 258, 120665. [Google Scholar] [CrossRef]
- Zhang, J.; Ma, G.; Huang, Y.; Aslani, F.; Nener, B. Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Constr. Build. Mater. 2019, 210, 713–719. [Google Scholar] [CrossRef]
- Zounemat-Kermani, M.; Stephan, D.; Barjenbruch, M.; Hinkelmann, R. Ensemble data mining modeling in corrosion of concrete sewer: A comparative study of network-based (MLPNN & RBFNN) and tree-based (RF, CHAID, & CART) models. Adv. Eng. Inform. 2020, 43, 101030. [Google Scholar]
Parameters | W/C | CNT | NS | NC | NA | CC | CA | FA | CS |
---|---|---|---|---|---|---|---|---|---|
mean | 0.414 | 0.023 | 0.938 | 0.27957 | 0.0019 | 442.379 | 976.656 | 1018.093 | 57.215 |
standard deviation | 0.1084 | 0.090 | 1.608 | 1.386 | 0.0065 | 88.907 | 679.422 | 918.044 | 22.418 |
standard error | 0.011 | 0.0093 | 0.166 | 0.144 | 0.0006 | 9.219 | 70.453 | 95.197 | 2.325 |
median | 0.450 | 0 | 0.050 | 0 | 0 | 430 | 920 | 757 | 51.799 |
mode | 0.250 | 0 | 0 | 0 | 0 | 430 | 920 | 0 | 80.492 |
kurtosis | −0.866 | 24.442 | 12.021 | 33.118 | 11.425 | −0.201 | 2.135 | −0.579 | −0.281 |
skewness | −0.652 | 4.979 | 2.983 | 5.615 | 3.484 | 0.685 | 1.219 | 0.849 | 0.837 |
range | 0.350 | 0.500 | 10 | 10 | 0.030 | 364.5 | 2720.343 | 2648.607 | 86.805 |
minimum | 0.200 | 0 | 0 | 0 | 0 | 325.5 | 0 | 0 | 23.5948 |
maximum | 0.550 | 0.500 | 10 | 10 | 0.030 | 690 | 2720.343 | 2648.607 | 110.4 |
sample variance | 0.012 | 0.008 | 2.587 | 1.920 | 4.19 × 10−5 | 7904.498 | 461,614.6 | 842,804.2 | 502.5577 |
Model | RSE | MAE | RMSE | RRMSE | PiF |
---|---|---|---|---|---|
DTT model | 0.9128 | 3.863 | 4.865 | 0.0891 | 0.0086 |
RFT model | 0.895 | 3.253 | 4.387 | 0.0803 | 0.0061 |
DTT | RFT | |||||||
---|---|---|---|---|---|---|---|---|
K-Fold | R2 | MAE | MSE | RMSE | R2 | MAE | MSE | RMSE |
1 | 0.623 | 3.354 | 4.295 | 2.072 | 0.791 | 4.692 | 5.863 | 2.422 |
2 | 0.790 | 5.310 | 9.138 | 3.023 | 0.690 | 4.167 | 5.291 | 2.300 |
3 | 0.421 | 1.549 | 6.760 | 2.600 | 0.797 | 4.457 | 5.412 | 2.326 |
4 | 0.468 | 7.177 | 6.105 | 2.471 | 0.660 | 5.737 | 5.152 | 2.269 |
5 | 0.738 | 4.600 | 6.614 | 2.572 | 0.932 | 3.465 | 4.148 | 2.036 |
6 | 0.568 | 6.013 | 9.209 | 3.035 | 0.971 | 5.056 | 7.121 | 2.668 |
7 | 0.453 | 5.139 | 6.183 | 2.487 | 0.435 | 4.242 | 4.952 | 2.225 |
8 | 0.249 | 6.698 | 8.648 | 2.941 | 0.831 | 4.631 | 4.329 | 2.081 |
9 | 0.906 | 3.096 | 5.754 | 2.399 | 0.980 | 1.991 | 1.497 | 1.224 |
10 | 0.369 | 6.220 | 9.430 | 3.071 | 0.933 | 5.500 | 3.844 | 1.960 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Nazar, S.; Yang, J.; Ahmad, W.; Javed, M.F.; Alabduljabbar, H.; Deifalla, A.F. Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques. Buildings 2022, 12, 2160. https://doi.org/10.3390/buildings12122160
Nazar S, Yang J, Ahmad W, Javed MF, Alabduljabbar H, Deifalla AF. Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques. Buildings. 2022; 12(12):2160. https://doi.org/10.3390/buildings12122160
Chicago/Turabian StyleNazar, Sohaib, Jian Yang, Waqas Ahmad, Muhammad Faisal Javed, Hisham Alabduljabbar, and Ahmed Farouk Deifalla. 2022. "Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques" Buildings 12, no. 12: 2160. https://doi.org/10.3390/buildings12122160