Predicting the concrete compressive strength through MLP network hybridized with three evolutionary algorithms |
Geng, Xin
(School of Computer and Communication Engineering, Zhengzhou University of Light Industry)
Moayedi, Hossein (Department for Management of Science and Technology Development, Ton Duc Thang University) Pan, Feifei (Zhengzhou Electromechanical Engineering Research Institute) Foong, Loke Kok (Institute of Research and Development, Duy Tan University) |
1 | Xia, J., Chen, H., Li, Q., Zhou, M., Chen, L., Cai, Z., Fang, Y. and Zhou, H. (2017), "Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach", Comput. Methods Programs Biomed., 147, 37-49. https://doi.org/10.1016/j.cmpb.2017.06.005 DOI |
2 | Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S. and Zhang, X. (2019), "Enhanced Moth-flame optimizer with mutation strategy for global optimization", Inform. Sci., 492, 181-203. https://doi.org/10.1016/j.ins.2019.04.022 DOI |
3 | Yeh, I.C. (2007), "Modeling slump flow of concrete using second-order regressions and artificial neural networks", Cement Concrete Compos., 29(6), 474-480. https://doi.org/10.1016/j.cemconcomp.2007.02.001 DOI |
4 | Yu, H., Li, W., Chen, C., Liang, J., Gui, W., Wang, M. and Chen, H. (2020), "Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis", Eng. Comput., 1-29. https://doi.org/10.1007/s00366-020-01174-w DOI |
5 | Salari, N., Shohaimi, S., Najafi, F., Nallappan, M. and Karishnarajah, I. (2014), "A novel hybrid classification model of genetic algorithms, modified k-nearest neighbor and developed backpropagation neural network", PLoS One, 9(11). https://doi.org/10.1371/journal.pone.0112987 DOI |
6 | Zuo, C., Sun, J., Li, J., Zhang, J., Asundi, A. and Chen, Q. (2017), "High-resolution transport-of-intensity quantitative phase microscopy with annular illumination", Scientif. Reports, 7(1), 7654. https://doi.org/10.1038/s41598-017-06837-1 DOI |
7 | Zhang, W., Hu, Y., Liu, J., Wang, H., Wei, J., Sun, P., Wu, L. and Zheng, H. (2020c), "Progress of ethylene action mechanism and its application on plant type formation in crops", Saudi J. Biol. Sci., 27(6), 1667-1673. https://doi.org/10.1016/j.sjbs.2019.12.038 DOI |
8 | Zhao, C. and Li, J. (2020), "Equilibrium selection under the Bayes-based strategy updating rules", Symmetry, 12(5), 739. https://doi.org/10.3390/sym12050739 DOI |
9 | Zhao, D., Liu, L., Yu, F., Heidari, A.A., Wang, M., Liang, G., Muhammad, K. and Chen, H. (2020a), "Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy", Knowled.-Based Syst., 216, 106510. https://doi.org/10.1016/j.knosys.2020.106510 DOI |
10 | Zuo, C., Chen, Q., Tian, L., Waller, L. and Asundi, A. (2015), "Transport of intensity phase retrieval and computational imaging for partially coherent fields: The phase space perspective", Optics Lasers in Eng., 71, 20-32. https://doi.org/10.1016/j.optlaseng.2015.03.006 DOI |
11 | Zuo, X., Dong, M., Gao, F. and Tian, S. (2020), "The modeling of the electric heating and cooling system of the integrated energy system in the coastal area", J. Coastal Res., 103(SI), 1022-1029. https://doi.org/10.2112/SI103-213.1 DOI |
12 | Seyedashraf, O., Mehrabi, M. and Akhtari, A.A. (2018), "Novel approach for dam break flow modeling using computational intelligence", J. Hydrol., 559, 1028-1038. https://doi.org/10.1016/j.jhydrol.2018.03.001 DOI |
13 | Shan, W., Qiao, Z., Heidari, A.A., Chen, H., Turabieh, H. and Teng, Y. (2020), "Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis", Knowled.-Based Syst., 214, 106728. https://doi.org/10.1016/j.knosys.2020.106728 DOI |
14 | Yang, Y., Li, Y., Yao, J., Iglauer, S., Luquot, L., Zhang, K., Sun, H., Zhang, L., Song, W. and Wang, Z. (2020), "Dynamic pore-scale dissolution by CO2-saturated brine in carbonates: Impact of homogeneous versus fractured versus vuggy pore structure", Water Resour. Res., 56(4), e2019WR026112. https://doi.org/10.1029/2019WR026112 DOI |
15 | Chen, H., Heidari, A.A., Chen, H., Wang, M., Pan, Z. and Gandomi, A.H. (2020), "Multi-population differential evolutionassisted Harris hawks optimization: Framework and case studies", Future Gener. Comput. Syst., 111, 175-198. https://doi.org/10.1016/j.future.2020.04.008 DOI |
16 | Chithra, S., Kumar, S.S., Chinnaraju, K. and Ashmita, F.A. (2016), "A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks", Constr. Build. Mater., 114, 528-535. https://doi.org/10.1016/j.conbuildmat.2016.03.214 DOI |
17 | Cigizoglu, H.K. and Kisi, O. (2005), "Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data", Hydrol. Res., 36(1), 49-64. https://doi.org/10.2166/nh.2005.0005 DOI |
18 | Henigal, A., Elbeltgai, E., Eldwiny, M. and Serry, M. (2016), "Artificial neural network model for forecasting concrete compressive strength and slump in Egypt", J. Al Azhar Univ. Eng. Sector, 11(39), 435-446. https://doi.org/10.21608/AUEJ.2016.19445 DOI |
19 | Hashemian, A.H., Manochehri, S., Afshari, D., Manochehri, Z., Salari, N. and Shahsavari, S. (2019), "Prognosis of multiple sclerosis disease using data mining approaches random forest and support vector machine based on genetic algorithm", Tehran Univ. Medical J., 77(1), 33-40. |
20 | Hecht-Nielsen, R. (1992), Neural Networks for Perception, Elsevier, pp. 65-93. |
21 | Holland, J.H. (1992), "Genetic algorithms", Scientif. Am., 267(1), 66-73. DOI |
22 | Hong, X.C., Wang, G.Y., Liu, J., Song, L. and Wu, E.T. (2021), "Modeling the impact of soundscape drivers on perceived birdsongs in urban forests", J. Cleaner Product., 292, 125315. https://doi.org/10.1016/j.jclepro.2020.125315 DOI |
23 | Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neural Networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8 DOI |
24 | Ines, A.V. and Droogers, P. (2002), "Inverse modelling in estimating soil hydraulic functions: A genetic algorithm approach", Hydrol. Earth Syst. Sci. Discuss., 6(1), 49-66. https://doi.org/10.5194/hess-6-49-2002 DOI |
25 | Khabbazi, A., Atashpaz-Gargari, E. and Lucas, C. (2009), "Imperialist competitive algorithm for minimum bit error rate beamforming", Int. J. Bio-Inspired Computat., 1(1-2), 125-133. https://doi.org/10.1504/IJBIC.2009.022781 DOI |
26 | Li, A., Fang, Q., Zhang, D., Luo, J. and Hong, X. (2018a), "Blast vibration of a large-span high-speed railway tunnel based on microseismic monitoring", Smart Struct. Syst., Int. J., 21(5), 561-569. https://doi.org/10.12989/sss.2018.21.5.561 DOI |
27 | Sun, M., Hou, B., Wang, S., Zhao, Q., Zhang, L., Song, L. and Zhang, H. (2021), "Effects of NaClO shock on MBR performance under continuous operating conditions", Environ. Sci.: Water Res. Technol., 7(2), 396-404. DOI https://doi.org/10.1039/D0EW00760A DOI |
28 | Thirumalai, C., Chandhini, S.A. and Vaishnavi, M. (2017), "Analysing the concrete compressive strength using Pearson and Spearman", Proceedings of 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, April, pp. 215-218. https://doi.org/10.1109/ICECA.2017.8212799 DOI |
29 | Tien Bui, D., Abdullahi, M.A.M., Ghareh, S., Moayedi, H. and Nguyen, H. (2019), "Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete", Eng. Comput., 37(1), 701-712. https://doi.org/10.1007/s00366-019-00850-w DOI |
30 | Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Computat. Mater. Sci., 41(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009 DOI |
31 | Wang, M., Chen, H., Yang, B., Zhao, X., Hu, L., Cai, Z., Huang, H. and Tong, C. (2017), "Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses", Neurocomputing, 267, 69-84. https://doi.org/10.1016/j.neucom.2017.04.060 DOI |
32 | Xu, X. and Chen, H.L. (2014), "Adaptive computational chemotaxis based on field in bacterial foraging optimization", Soft Comput., 18(4), 797-807. https://doi.org/10.1007/s00500-013-1089-4 DOI |
33 | Galan, A. (1967), "Estimate of concrete strength by ultrasonic pulse velocity and damping constant", Journal Proceedings, 64(10), 678-684. |
34 | Clerc, M. and Kennedy, J. (2002), "The particle swarm-explosion, stability, and convergence in a multidimensional complex space", IEEE Transact. Evolut. Computat., 6(1), 58-73. https://doi.org/10.1109/4235.985692 DOI |
35 | Cybenko, G. (1989), "Approximation by superpositions of a sigmoidal function", Mathe. Control Signals Syst., 2(4), 303-314. https://doi.org/10.1007/BF02551274 DOI |
36 | Fallahian, M., Khoshnoudian, F. and Talaei, S. (2018), "Application of couple sparse coding ensemble on structural damage detection", Smart Struct. Syst., Int. J., 21(1), 1-14. https://doi.org/10.12989/sss.2018.21.1.001 DOI |
37 | Hu, L., Hong, G., Ma, J., Wang, X. and Chen, H. (2015), "An efficient machine learning approach for diagnosis of paraquat-poisoned patients", Comput. Biol. Medicine, 59, 116-124. https://doi.org/10.1016/j.compbiomed.2015.02.003 DOI |
38 | Nourani, V., Pradhan, B., Ghaffari, H. and Sharifi, S.S. (2014), "Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models", Natural Hazards, 71(1), 523-547. https://doi.org/10.1007/s11069-013-0932-3 DOI |
39 | Chen, H.L., Wang, G., Ma, C., Cai, Z.N., Liu, W.B. and Wang, S.J. (2016), "An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease", Neurocomputing, 184, 131-144. https://doi.org/10.1016/j.neucom.2015.07.138 DOI |
40 | Chahnasir, E.S., Zandi, Y., Shariati, M., Dehghani, E., Toghroli, A., Mohamad, E.T., Shariati, A., Safa, M., Wakil, K. and Khorami, M. (2018), "Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors", Smart Struct. Syst., Int. J., 22(4), 413-424. https://doi.org/10.12989/sss.2018.22.4.413 DOI |
41 | Liu, J., Liu, Y. and Wang, X. (2020a), "An environmental assessment model of construction and demolition waste based on system dynamics: a case study in Guangzhou", Environ. Sci. Pollut. Res., 27(30), 37237-37259. https://doi.org/10.1007/s11356-019-07107-5 DOI |
42 | Poli, R., Kennedy, J. and Blackwell, T. (2007), "Particle swarm optimization", Swarm Intell., 1(1), 33-57. https://doi.org/10.1007/s11721-007-0002-0 DOI |
43 | Qi, C., Fourie, A. and Chen, Q. (2018), "Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill", Constr. Build. Mater., 159, 473-478. https://doi.org/10.1016/j.conbuildmat.2017.11.006 DOI |
44 | Qiao, W., Wang, Y., Zhang, J., Tian, W., Tian, Y. and Yang, Q. (2021), "An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration", J. Environ. Manag., 289, 112438. https://doi.org/10.1016/j.jenvman.2021.112438 DOI |
45 | Zhang, L., Zheng, J., Tian, S., Zhang, H., Guan, X., Zhu, S., Zhang, X., Bai, Y., Xu, P., Zhang, J. and Li, Z. (2020a), "Effects of Al3+ on the microstructure and bioflocculation of anoxic sludge", J. Environ. Sci., 91, 212-221. https://doi.org/10.1016/j.jes.2020.02.010 DOI |
46 | Han, S.H., Kim, J.K. and Park, Y.D. (2003), "Prediction of compressive strength of fly ash concrete by new apparent activation energy function", Cement Concrete Res., 33(7), 965-971. https://doi.org/10.1016/S0008-8846(03)00007-3 DOI |
47 | Gao, N., Wang, B., Lu, K. and Hou, H. (2021), "Complex band structure and evanescent Bloch wave propagation of periodic nested acoustic black hole phononic structure", Appl. Acoust., 177, 107906. https://doi.org/10.1016/j.apacoust.2020.107906 DOI |
48 | Gargari, E.A., Hashemzadeh, F., Rajabioun, R. and Lucas, C. (2008), "Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process", Int. J. Intell. Comput. Cybernet., 1(3), 337-355. https://doi.org/10.1108/17563780810893446 DOI |
49 | Liu, J., Yi, Y. and Wang, X. (2020b), "Exploring factors influencing construction waste reduction: A structural equation modeling approach", J. Cleaner Product., 276, 123185. https://doi.org/10.1016/j.jclepro.2020.123185 DOI |
50 | Liu, M., Xue, Z., Zhang, H. and Li, Y. (2021), "Dual-channel membrane capacitive deionization based on asymmetric ion adsorption for continuous water desalination", Electrochem. Commun., 125, 106974. https://doi.org/10.1016/j.elecom.2021.106974 DOI |
51 | Marquardt, D.W. (1963), "An algorithm for least-squares estimation of nonlinear parameters", J. Soc. Indust. Appl. Mathe., 11(2), 431-441. https://doi.org/10.1137/0111030 DOI |
52 | McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Mathe. Biophys., 5(4), 115-133. https://doi.org/10.1007/BF02478259 DOI |
53 | Moayedi, H. and Armaghani, D.J. (2018), "Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil", Eng. Comput., 34(2), 347-356. https://doi.org/10.1007/s00366-017-0545-7 DOI |
54 | Mahzan, S., Staszewski, W.J. and Worden, K. (2010), "Experimental studies on impact damage location in composite aerospace structures using genetic algorithms and neural networks", Smart Struct. Syst., Int. J., 6(2), 147-165. https://doi.org/10.12989/sss.2010.6.2.147 DOI |
55 | Moayedi, H., Abdullahi, M.A.M., Nguyen, H. and Rashid, A.S.A. (2019b), "Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils", Eng. Comput., 37(1), 437-447. https://doi.org/10.1007/s00366-019-00834-w DOI |
56 | Onat, O. and Gul, M. (2018), "Application of Artificial Neural Networks to the prediction of out-of-plane response of infill walls subjected to shake table", Smart Struct. Syst., Int. J., 21(4), 521-535. https://doi.org/10.12989/sss.2018.21.4.521 DOI |
57 | Salari, N., Shohaimi, S., Najafi, F., Nallappan, M. and Karishnarajah, I. (2012), "An improved artificial neural network based model for prediction of late onset heart failure", Life Sci. J., 9(4), 3684-3689. |
58 | Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B. and Liu, D. (2016), "Evolving support vector machines using fruit fly optimization for medical data classification", Knowled.-Based Syst., 96, 61-75. https://doi.org/10.1016/j.knosys.2016.01.002 DOI |
59 | Hu, J., Chen, H., Heidari, A.A., Wang, M., Zhang, X., Chen, Y. and Pan, Z. (2020), "Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection", Knowledge-Based Systems, 213, 106684. https://doi.org/10.1016/j.knosys.2020.106684 DOI |
60 | Zhang, M., Zhang, L., Tian, S., Zhang, X., Guo, J., Guan, X. and Xu, P. (2020b), "Effects of graphite particles/Fe3+ on the properties of anoxic activated sludge", Chemosphere, 253, 126638. https://doi.org/10.1016/j.chemosphere.2020.126638 DOI |
61 | Kennedy, J. (2010), "Particle swarm optimization", In: Encyclopedia of Machine Learning, Springer, pp. 760-766. |
62 | Li, C., Hou, L., Sharma, B.Y., Li, H., Chen, C., Li, Y., Zhao, X., Huang, H., Cai, Z. and Chen, H. (2018b), "Developing a new intelligent system for the diagnosis of tuberculous pleural effusion", Comput. Methods Programs Biomed., 153, 211-225. https://doi.org/10.1016/j.cmpb.2017.10.022 DOI |
63 | Tu, J., Chen, H., Liu, J., Heidari, A.A., Zhang, X., Wang, M., Ruby, R. and Pham, Q.V. (2021), "Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance", Knowled.-Based Syst., 212, 106642. https://doi.org/10.1016/j.knosys.2020.106642 DOI |
64 | Wang, M. and Chen, H. (2020), "Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis", Appl. Soft Comput., 88, 105946. https://doi.org/10.1016/j.asoc.2019.105946 DOI |
65 | Whitley, D. (1994), "A genetic algorithm tutorial", Statist. Comput., 4(2), 65-85. https://doi.org/10.1007/BF00175354 DOI |
66 | Saeidian, B., Mesgari, M.S. and Ghodousi, M. (2016), "Evaluation and comparison of Genetic Algorithm and Bees Algorithm for location-allocation of earthquake relief centers", Int. J. Disaster Risk Reduct., 15, 94-107. https://doi.org/10.1016/j.ijdrr.2016.01.002 DOI |
67 | Zhao, X., Zhang, X., Cai, Z., Tian, X., Wang, X., Huang, Y., Chen, H. and Hu, L. (2019), "Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients", Computat. Biol. Chem., 78, 481-490. https://doi.org/10.1016/j.compbiolchem.2018.11.017 DOI |
68 | Zhang, Y., Liu, R., Heidari, A.A., Wang, X., Chen, Y., Wang, M. and Chen, H. (2020d), "Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis", Neurocomputing, 430, 185-212. https://doi.org/10.1016/j.neucom.2020.1010.1038 DOI |
69 | Zhang, Y., Liu, R., Wang, X., Chen, H. and Li, C. (2020e), "Boosted binary Harris hawks optimizer and feature selection", Eng. Comput., 25, 26. https://doi.org/10.1007/s00366-020-01028-5 DOI |
70 | Zhao, X., Li, D., Yang, B., Ma, C., Zhu, Y. and Chen, H. (2014), "Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton", Appl. Soft Comput., 24, 585-596. https://doi.org/10.1016/j.asoc.2014.07.024 DOI |
71 | Zhao, X., Gu, B., Gao, F. and Chen, S. (2020b), "Matching model of energy supply and demand of the integrated energy system in coastal areas", J. Coastal Res., 103(SI), 983-989. https://doi.org/10.2112/SI103-205.1 DOI |
72 | Zheng, J., Zhang, C. and Li, A. (2020), "Experimental investigation on the mechanical properties of curved metallic plate dampers", Appl. Sci., 10(1), 269. https://doi.org/10.3390/app10010269 DOI |
73 | Atashpaz-Gargari, E. and Lucas, C. (2007), "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition", 2007 IEEE Congress on Evolutionary Computation, Singapore, September, pp. 4661-4667. https://doi.org/10.1109/CEC.2007.4425083 DOI |
74 | Abdalhmid, J.M., Ashour, A.F. and Sheehan, T. (2019), "Long-term drying shrinkage of self-compacting concrete: Experimental and analytical investigations", Constr. Build. Mater., 202, 825-837. https://doi.org/10.1016/j.conbuildmat.2018.12.152 DOI |
75 | Akin, O. and Sahin, M. (2017), "Active neuro-adaptive vibration suppression of a smart beam", Smart Struct. Syst., Int. J., 20(6), 657-668. https://doi.org/10.12989/sss.2017.20.6.657 DOI |
76 | Anderson, J.A. (1995), An Introduction to Neural Networks, MIT Press. |
77 | Boga, A.R., Ozturk, M. and Topcu, I.B. (2013), "Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI", Compos. Part B: Eng., 45(1), 688-696. https://doi.org/10.1016/j.compositesb.2012.05.054 DOI |
78 | Han, C., Zhang, B., Chen, H., Wei, Z. and Liu, Y. (2019), "Spatially distributed crop model based on remote sensing", Agricult. Water Manag., 218, 165-173. https://doi.org/10.1016/j.agwat.2019.03.035 DOI |
79 | Ghiasi, R. and Ghasemi, M.R. (2018), "Optimization-based method for structural damage detection with consideration of uncertainties- a comparative study", Smart Struct. Syst., Int. J., 22(5), 561-574. https://doi.org/10.12989/sss.2018.22.5.561 DOI |
80 | Hakim, S.J.S. and Razak, H.A. (2014), "Modal parameters based structural damage detection using artificial neural networks - a review", Smart Struct. Syst., Int. J., 14(2), 159-189. https://doi.org/10.12989/sss.2014.14.2.159 DOI |
81 | Moayedi, H., Mehrabi, M., Kalantar, B., Abdullahi Mu'azu, M., A. Rashid, A.S., Foong, L.K. and Nguyen, H. (2019a), "Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide", Geomat. Natural Hazards Risk, 10(1), 1879-1911. https://doi.org/10.1080/19475705.2019.1650126 DOI |
82 | Moayedi, H., Raftari, M., Sharifi, A., Jusoh, W.A.W. and Rashid, A.S.A. (2019c), "Optimization of ANFIS with GA and PSO estimating α ratio in driven piles", Eng. Comput., 1-12. 10.1007/s00366-018-00694-w DOI |
83 | Moayedi, H., Mehrabi, M., Bui, D.T., Pradhan, B. and Foong, L.K. (2020), "Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility", J. Environ. Manag., 260, 109867. https://doi.org/10.1016/j.jenvman.2019.109867 DOI |
84 | Nehdi, M., El Chabib, H. and Said, A. (2006), "Evaluation of shear capacity of FRP reinforced concrete beams using artificial neural networks", Smart Struct. Syst., Int. J., 2(1), 81-100. https://doi.org/10.12989/sss.2006.2.1.081 DOI |
85 | Popovics, S. (1990), "Analysis of concrete strength versus water-cement ratio relationship", Mater. J., 87(5), 517-529. |
86 | Nguyen, H., Mehrabi, M., Kalantar, B., Moayedi, H. and Abdullahi, M.A.M. (2019), "Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping", Geomat. Natural Hazards Risk, 10(1), 1667-1693. https://doi.org/10.1080/19475705.2019.1607782 DOI |
87 | Nikoo, M., Torabian Moghadam, F. and Sadowski, L. (2015a), "Prediction of concrete compressive strength by evolutionary artificial neural networks", Adv. Mater. Sci. Eng. https://doi.org/10.1155/2015/849126 DOI |
88 | Nikoo, M., Zarfam, P. and Sayahpour, H. (2015b), "Determination of compressive strength of concrete using Self Organization Feature Map (SOFM)", Eng. Comput., 31(1), 113-121. https://doi.org/10.1007/s00366-013-0334-x DOI |
89 | Oluokun, F.A. (1994), "Fly ash concrete mix design and the water-cement ratio law", Mater. J., 91(4), 362-371. https://doi.org/10.1007/BF02472668 DOI |
90 | Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N. and Bhatti, M.A. (2006), "Predicting the compressive strength and slump of high strength concrete using neural network", Constr. Build. Mater., 20(9), 769-775. https://doi.org/10.1016/j.conbuildmat.2005.01.054 DOI |
91 | Park, K., Kim, S. and Torbol, M. (2016), "Operational modal analysis of reinforced concrete bridges using autoregressive model", Smart Struct. Syst., Int. J., 17(6), 1017-1030. https://doi.org/10.12989/sss.2016.17.6.1017 DOI |