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Articles

Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models

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  • Murad YZ, Tarawneh BK, Ashteyat AM. Prediction model for concrete carbonation depth using gene expression programming. Comput Concr. 2020;26:497–504.
  • Ashraf M, Iqbal MF, Rauf M, et al. Developing a sustainable concrete incorporating bentonite clay and silica fume: Mechanical and durability performance. J Clean Prod. 2022;337:130315.
  • Falak ME, Benemaran RS, Seifi R. Improvement of the mechanical and durability parameters of construction concrete of the Qotursuyi spa. Concr Res. 2020;13:119–134.
  • Yuan J, Zhao M, Esmaeili‐Falak M. A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques. Struct Concr. 2022. 10.1002/suco.202100682.
  • Esmaeili-Falak M, Katebi H Javadi AA. Experimental Study of the Mechanical Behavior of Frozen Soils - A Case Study of Tabriz Subway. Periodica Polytechnica Civil Engineering. 2018;62(1):117–125.
  • Esmaeili-Falak ML, Eghlim A, Nematzadeh S. Improvement of mechanical parameters of concrete yielded from pozzolanic cement for irrigation and drainage projects. J Struct Constr Eng. 2019;6:43–58.
  • Okamura H, Ouchi M. Self-compacting concrete. ACT. 2003;1(1):5–15.
  • Liu Q, Iqbal MF, Yang J, et al. Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation. Constr Build Mater. 2021;268:121082.
  • Yazıcı H. The effect of silica fume and high-volume class C fly ash on mechanical properties, chloride penetration and freeze–thaw resistance of self-compacting concrete. Constr Build Mater. 2008;22(4):456–462.
  • Kanellopoulos A, Petrou MF, Ioannou I. Durability performance of self-compacting concrete. Constr Build Mater. 2012;37:320–325.
  • Ryan PC, O’Connor A. Comparing the durability of self-compacting concretes and conventionally vibrated concretes in chloride rich environments. Constr Build Mater. 2016;120:504–513.
  • Frazão C, Camões A, Barros J, et al. Durability of steel fiber reinforced self-compacting concrete. Constr Build Mater. 2015;80:155–166.
  • Awoyera PO, Akinwumi II, Karthika V, et al. Lightweight self-compacting concrete incorporating industrial rejects and mineral admixtures: Strength and durability assessment. Silicon. 2020;12(8):1779–1785.
  • Oh BH, Jang SY. Effects of material and environmental parameters on chloride penetration profiles in concrete structures. Cem Concr Res. 2007;37(1):47–53.
  • Xi Y, Bažant ZP. Modeling chloride penetration in saturated concrete. J Mater Civ Eng. 1999;11(1):58–65.
  • Jiang P, Jiang L, Zha J, et al. Influence of temperature history on chloride diffusion in high volume fly ash concrete. Constr Build Mater. 2017;144: 677–685.
  • Harinadha Reddy K, Govinda Raju S. Enhancing energy system security and stability–a novel approach using PSO support islanding detection in a grid connected distributed energy system and environmental effects of grid faults. Energy Sources, Part A Recover Util Environ Eff. 2020.
  • Eltamaly AM, Farh HMH, Abokhalil AG. A novel PSO strategy for improving dynamic change partial shading photovoltaic maximum power point tracker. Energy Sources, Part A Recover Util Environ Eff. 2020.
  • Liu R, Ding H, Zhang Z, et al. Study on prediction model of diesel engine with regulated two-stage turbocharging system based on hybrid genetic algorithm-particle swarm optimization method at different altitudes. Energy Sources, Part A Recover Util Environ Eff. 2020.
  • Hoang AT, Nižetić S, Ong HC, et al. A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels. Sustain Energy Technol Assess. 2021;47:101416.
  • Chen W-H, Wang J-S, Chang M-H, et al. Efficiency improvement of a vertical-axis wind turbine using a deflector optimized by Taguchi approach with modified additive method. Energy Convers Manag. 2021;245:114609.
  • Iqbal MF, Javed MF, Rauf M, et al. Sustainable utilization of foundry waste: forecasting mechanical properties of foundry sand based concrete using multi-expression programming. Sci Total Environ. 2021;780:146524.
  • Sarkhani Benemaran R, Esmaeili-Falak M, Javadi A.A. Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimized models. Int. J. Pavement Eng. 2022;
  • Zhu W, Huang L, Mao L, Esmaeili‐Falak M. Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence‐based algorithms. Structural Concrete. 2022.
  • Yang C, Feng H, Esmaeili‐Falak M. Predicting the compressive strength of modified recycled aggregate concrete. Structural Concrete. 2022.
  • Esmaeili-Falak M. Effect of system’s geometry on the stability of frozen wall in excavation of saturated granular soils [doctoral dissertation]. University of Tabriz; 2017.
  • Sarkhani Benemaran R, Esmaeili-Falak M, Katebi H. Physical and numerical modelling of pile-stabilised saturated layered slopes. Proc Inst Civ Eng Eng. 2020.
  • Mohammed A, Kurda R, Armaghani DJ, et al. Prediction of compressive strength of concrete modified with fly ash: applications of neuro-swarm and neuro-imperialism models. Comput Concr. 2021;27:489–512.
  • Iqbal MF, Liu Q, Azim I, et al. Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. J Hazard Mater. 2020;384:121322.
  • Esmaeili-Falak M, Katebi H, Vadiati M, et al. Predicting triaxial compressive strength and Young’s modulus of frozen sand using artificial intelligence methods. J Cold Reg Eng. 2019; 33(3); 4019007.
  • Shahri SF, Mousavi SR. Bond strength prediction of spliced GFRP bars in concrete beams using soft computing methods. Comput Concr. 2021;27:305–317.
  • Tahwia AM, Heniegal A, Elgamal MS, et al. The prediction of compressive strength and non-destructive tests of sustainable concrete by using artificial neural networks. Comput Concr. 2021;27:21–28.
  • Benemaran RS, Esmaeili-Falak M. Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO. Comput Concr. 2020;26:309–316.
  • Hoang N-D, Chen C-T, Liao K-W. Prediction of chloride diffusion in cement mortar using Multi-Gene genetic programming and multivariate adaptive regression splines. Measurement. 2017;112:141–149. Internet Available fromhttps://linkinghub.elsevier.com/retrieve/pii/S0263224117305365.
  • Gholampour A, Mansouri I, Kisi O, et al. 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(1):295–308.
  • Ashrafian A, Amiri MJT, Rezaie-Balf M, et al. Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods. Constr Build Mater. 2018;190:479–494.
  • Kumar S, Rai B, Biswas R, et al. Prediction of rapid chloride permeability of self-compacting concrete using multivariate adaptive regression spline and minimax probability machine regression. J Build Eng. 2020;32:101490.
  • Ghafoori N, Najimi M, Sobhani J, et al. Predicting rapid chloride permeability of self-consolidating concrete: a comparative study on statistical and neural network models. Constr Build Mater. 2013;44:381–390.
  • Boğa AR, Öztürk M, Topcu IB. Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI. Compos Part B Eng. 2013;45(1):688–696.
  • Najimi M, Ghafoori N, Nikoo M. Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm. J Build Eng. 2019;22:216–226.
  • Abellán-García J, Guzmán-Guzmán JS. Random Forest-based optimization of UHPFRC under ductility requirements for seismic retrofitting applications. Constr Build Mater. 2021;285:122869.
  • Zhang J, Huang Y, Aslani F, et al. A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete. J Clean Prod. 2020;273:122922.
  • Farooq F, Ahmed W, Akbar A, et al. Predictive modeling for sustainable high-performance concrete from industrial wastes: a comparison and optimization of models using ensemble learners. J Clean Prod. 2021;292:126032.
  • Han Q, Gui C, Xu J, et al. A generalized method to predict the compressive strength of high-performance concrete by improved random Forest algorithm. Constr Build Mater. 2019;226:734–742.
  • Sun Y, Li G, Zhang J, et al. Prediction of the strength of rubberized concrete by an evolved random Forest model. Adv Civ Eng. 2019;2019:1–7.
  • Khan MA, Memon SA, Farooq F, et al. Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random Forest. Adv Civ Eng. 2021;1–17.
  • Huang J, Duan T, Zhang Y, et al. Predicting the permeability of pervious concrete based on the beetle antennae search algorithm and random Forest model. Adv Civ Eng. 2020; 2020.
  • Farooq F, Nasir Amin M, Khan K, et al. 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(20):7330.
  • Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
  • Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2:18–22.
  • Nhu V-H, Hoang N-D, Duong V-B, et al. A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at vinhomes imperia project, Hai Phong city (Vietnam). Eng Comput. 2020;36(2):603–616.
  • Hong H, Pourghasemi HR, Pourtaghi ZS. Landslide susceptibility assessment in Lianhua county (China): a comparison between a random Forest data mining technique and bivariate and multivariate statistical models. Geomorphology. 2016;259:105–118.
  • Chen W, Wang Y, Cao G, et al. A random Forest model based classification scheme for neonatal amplitude-integrated EEG. BioMed Eng OnLine. 2014;13(Suppl 2):S4–S13.
  • Archer KJ, Kimes RV. Empirical characterization of random Forest variable importance measures. Comput Stat Data Anal. 2008;52(4):2249–2260.
  • Biau G, Devroye L, Lugosi G. Consistency of random forests and other averaging classifiers. J Mach Learn Res. 2008;9(9): 2015–2033.
  • Trigila A, Iadanza C, Esposito C, et al. Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology. 2015;249:119–136.
  • Kennedy J, Eberhart R. Particle swarm optimization. 1995:1942–1948. Proc ICNN’95 - Int Conf Neural Networks. IEEE. http://ieeexplore.ieee.org/document/488968/.
  • Babbar A, Prakash C, Singh S, et al. Application of hybrid nature-inspired algorithm: single and bi-objective constrained optimization of magnetic abrasive finishing process parameters. J Mater Res Technol. 2020;9(4):7961–7974.
  • Xue X. Evaluation of concrete compressive strength based on an improved PSO-LSSVM model. Comput Concr. 2018;21:505–511.
  • Shi Y, Eberhart RC. Parameter selection in particle swarm optimization. Int Conf Evol Program. Springer; 1998. p. 591–600.
  • Trelea IC. The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett. 2003;85(6):317–325. https://linkinghub.elsevier.com/retrieve/pii/S0020019002004477.
  • Khoshaim AB, Elsheikh AH, Moustafa EB, et al. Prediction of residual stresses in turning of pure iron using artificial intelligence-based methods. J Mater Res Technol. 2021;11:2181–2194. https://linkinghub.elsevier.com/retrieve/pii/S223878542100168X.
  • Elsheikh AH, Abd Elaziz M, Ramesh B, et al. Modeling of drilling process of GFRP composite using a hybrid random vector functional link network/parasitism-predation algorithm. J Mater Res Technol. 2021;14:298–311.
  • Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016;95:51–67.
  • Aljarah I, Faris H, Mirjalili S. Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. 2018;22(1):1–15.
  • Zhou J, Qiu Y, Armaghani DJ, et al. Predicting TBM penetration rate in hard rock condition: a comparative study among six XGB-based metaheuristic techniques. Geosci Front. 2021;12(3):101091. https://linkinghub.elsevier.com/retrieve/pii/S1674987120302231.
  • Guo H, Zhou J, Koopialipoor M, et al. Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng Comput. 2021;37(1):173–186.
  • Ahmad MW, Mourshed M, Rezgui Y. Trees vs neurons: comparison between random Forest and ANN for high-resolution prediction of building energy consumption. Energy Build. 2017;147:77–89.
  • Chen W, Xie X, Wang J, et al. A comparative study of logistic model tree, random Forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena. 2017;151:147–160.
  • Pham BT, Qi C, Ho LS, et al. A novel hybrid soft computing model using random Forest and particle swarm optimization for estimation of undrained shear strength of soil. Sustainability. 2020;12(6):2218.
  • Qin W, Wang L, Liu Y, et al. Energy consumption estimation of the electric bus based on grey wolf optimization algorithm and support vector machine regression. Sustainability. 2021;13(9):4689.
  • Moayedi H, Mosavi A. Synthesizing multi-layer perceptron network with ant lion Biogeography-Based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings. Sustainability. 2021;13(6):3198.
  • Heidari AA, Mirjalili S, Faris H, et al. Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst. 2019;97:849–872.
  • Bednarz JC. Cooperative hunting Harris’ hawks (Parabuteo unicinctus). Science. 1988;239(4847):1525–1527.
  • Shehabeldeen TA, Elaziz A, Elsheikh M, et al. Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with harris hawks optimizer. J Mater Res Technol. 2019;8(6):5882–5892.
  • Qi C, Chen Q, Fourie A, et al. An intelligent modelling framework for mechanical properties of cemented paste backfill. Miner Eng. 2018;123:16–27.
  • Stone RJ. Improved statistical procedure for the evaluation of solar radiation estimation models. Sol Energy. 1993;51(4):289–291.
  • Behar O, Khellaf A, Mohammedi K. Comparison of solar radiation models and their validation under Algerian climate–the case of direct irradiance. Energy Convers Manag. 2015;98:236–251.
  • Li M-F, Tang X-P, Wu W, et al. General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Convers Manag. 2013;70:139–148.

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