Ensemble Tree Machine Learning Models for Improvement of Eurocode 2 Creep Model Prediction
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References
- [1] ACI COMMITTE 209: Guide for Modeling and Calculating Shrinkage and Creep in Concrete Structures (ACI 209.2R-08), 2008.
- [2] CEN:EN 1992-1-1, Eurocode 2: Design of concrete structures - Part 1-1 : General rules and rules for buildings, 2004.
- [3] GEDAM, B. A. - UPADHYAY, A. - BHANDARI, N. M.: An apt material model to predict creep and shrinkage behaviour of HPC concrete. Sustain Constr Mater Technol, Vol. August, 2013.
- [4] CEB-FIP, MC90: Design of concrete structures. CEB-FIP Model Code 1990, British Standard Institution, 1993.
- [5] BAZANT, Z. P. – BAWEJA, S.: Creep and shrinkage prediction model for analysis and design of concrete structures: Model B3. ACI Spec Publ, Vol. 194, 2000, pp. 1-84.
- [6] GARDNER, N. J – LOCKMAN, M. J..: Design provisions for drying shrinkage and creep of normal-strength concrete. Materials Journal, Vol. 98, 2001.10.14359/10199
- [7] GRANATA, M. F. - MARGIOTTA, P. - ARICI, M.: A Simplified Procedure for Evaluating the Effects of Creep and Shrinkage on Prestressed Concrete Girder Bridges and the Application of European and North American Prediction Models. J Bridg Eng, Vol. 18, Iss. 12, 2013, pp. 128-197.10.1061/(ASCE)BE.1943-5592.0000483
- [8] KRALOVANEC, J. - MORAVCIK, M. - JOST, J.: Analysis of Prestressing in Precast Prestressed Concrete Beams. Civil and Environmental Engineering, Vol. 17, Iss. 1, 2021, pp. 184-191.10.2478/cee-2021-0019
- [9] RAPHAEL, W. - ZGHEIB, E. - CHATEAUNEUF, A.: Experimental investigations and sensitivity analysis to explain the large creep of concrete deformations in the bridge of Cheviré. Case Stud Constr Mater, Vol. 9, 2018, e00176.10.1016/j.cscm.2018.e00176
- [10] RAPHAEL, W. - FADDOUL, R. - GEARA, F. - CHATEAUNEUF, A.: Improvements to the Eurocode 2 shrinkage model for concrete using a large experimental database. Struct Concr, Vol. 13, Iss. 3, 2012, pp. 174-181.10.1002/suco.201100029
- [11] JERGA, J. - KRAJCI, Ľ.: Damage in Concrete and its Detection by Use of Stress-Volumetric Strain Diagram. Civil and Environmental Engineering, Vol. 10, Iss. 1, 2014, pp. 16-25.10.2478/cee-2014-0003
- [12] DAOU, H. - RAPHAEL, W.: A Bayesian regression framework for concrete creep prediction improvement: application to Eurocode 2 model. Res Eng Struct Mater, 2021, pp. 1-19.10.17515/resm2021.272ma0303
- [13] FADDOUL, R. - RAPHAEL, W. - CHATEAUNEUF, A.: Maintenance optimization of series systems subject to reliability constraints. Reliab Eng Syst Saf, Vol. 180, 2018, pp. 179-188.10.1016/j.ress.2018.07.016
- [14] CHEN, P. - ZHENG, W. - WANG, Y. - CHANG, W.: Creep model of high-strength concrete containing supplementary cementitious materials. Constr Build Mater, Vol. 202, 2019, pp. 494-506, https://doi.org/10.1016/j.conbuildmat.2019.01.005.10.1016/j.conbuildmat.2019.01.005
- [15] RAPHAEL, W. - FADDOUL, R. - SELOUAN, A. - CHATEAUNEUF, A.: Information-based formulation for Bayesian updating of the Eurocode 2 creep model. Struct Concr, Vol. 10, Iss. 2, 2009.10.1680/stco.2009.10.2.55
- [16] DIETTERICH, T.: Ensemble Methods in Machine Learning. Int Work Mult Classif Syst, 2000, pp. 1-15.10.1007/3-540-45014-9_1
- [17] BREIMAN, L.: Bagging predictors. Mach Learn, Vol. 24, 1996, pp. 123-140.10.1007/BF00058655
- [18] FREUND, Y. - SCHAPIRE, R. E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J Comput Syst Sci, Vol. 55, Iss. 1, 1997, pp. 119-139, https://doi.org/10.1006/jcss.1997.1504.10.1006/jcss.1997.1504
- [19] BAZANT, Z. – LI, G. H.: Comprehensive database on concrete creep and shrinkage. ACI Mater J, Vol. 105, 2008, pp. 635-637.10.14359/20206
- [20] R CORE TEAM.: R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria, 2021.
- [21] DIETTERICH, T.: An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Mach Learn, Vol. 40, 2000, pp. 139-157.10.1023/A:1007607513941
- [22] THERNEAU, T. - ATKINSON, B.: rpart: Recursive Partitioning and Regression Trees. R package version 4, 2019, pp. 1-15.
- [23] LIANG, M. - CHANG, Z. - WAN, Z. - GAN, Y. - SCHLANGEN, E. - SAVIJA, B.: Interpretable Ensemble- Machine-Learning Models For Predicting Creep Behavior of Concrete. Cem Concr Compos, Vol. 125, 2022.10.1016/j.cemconcomp.2021.104295
- [24] PETERS, A. - HOTHORN, T.: ipred: Improved Predictors. R package version 0.9-9, 2019.
- [25] BREIMAN, L.: Random forests. Mach Learn, Vol. 45, 2001, pp. 5-32.10.1023/A:1010933404324
- [26] WRIGHT, M. N. - ZIEGLER, A.: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J. Stat. Softw., Vol. 77, 2017, pp. 1-17.10.18637/jss.v077.i01
- [27] FRIEDMAN, J. H.: Greedy Function Approximation: A Gradient Boosting Machine. Ann Stat, Vol. 29, Iss. 5, 2001, p. 1189–232, www.jstor.org/stable/2699986.
- [28] GREENWELL, B. - BOEHMKE, B. - CUNNINGHAM, J.: gbm: Generalized Boosted Regression Models. R package version 2.1.8, 2020.
- [29] CHEN, T. - GUESTRIN, C.: XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining New York, NY, USA: Association for Computing Machinery, 2016, pp. 785–94, https://doi.org/10.1145/2939672.293978510.1145/2939672.2939785
- [30] CHEN, T. - HE, T. - BENESTY, M. - KHOTILOVICH, V. - TANG, Y. - CHO, H. - CHEN, K. -MITCHELL, R. - CANO, I. - ZHOU, T. - LI, M. - XIE, J. - LIN, M. - GENG, Y. - LI, Y.: xgboost: Extreme Gradient Boosting. R package version 1.5.0.2, 2021..
Language: English
Page range: 174 - 184
Published on: Jun 29, 2022
Published by: University of Žilina
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year
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© 2022 Hikmat Daou, Wassim Raphael, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.