Crossref
journal-article
Elsevier BV
Construction and Building Materials (78)
References
50
Referenced
276
{'key': '10.1016/j.conbuildmat.2013.08.078_b0005', 'series-title': 'Design and control of concrete mixtures, EB001', 'author': 'Kosmatka', 'year': '2011'}
/ Design and control of concrete mixtures, EB001 by Kosmatka (2011)10.1016/0950-0618(95)92855-B
/ Constr Build Mater / Developments in the application of high-performance concretes by Aïtcin (1995)10.1016/S0008-8846(98)00165-3
/ Cem Concr Res / Modeling of strength of high-performance concrete using artificial neural networks by Yeh (1998)10.1016/S0008-8846(02)00827-X
/ Cem Concr Res / Supplementary cementing materials in concrete: Part I: efficiency and design by Papadakis (2002)10.1016/S0008-8846(00)00345-8
/ Cem Concr Res / Prediction of compressive strength of concrete by neural networks by Ni (2000)10.1061/(ASCE)0887-3801(1995)9:4(279)
/ J Comput Civ Eng / HPC strength prediction using artificial neural network by Kasperkiewicz (1995)10.1016/S0008-8846(02)00787-1
/ Cem Concr Res / Investigations on the compressive strength of silica fume concrete using statistical methods by Bhanja (2002){'key': '10.1016/j.conbuildmat.2013.08.078_b0040', 'series-title': 'Prediction of the strength of mineral-addition concrete using regression analysis', 'first-page': '585', 'author': 'Atici', 'year': '2010'}
/ Prediction of the strength of mineral-addition concrete using regression analysis by Atici (2010)10.1016/j.eswa.2008.07.004
/ Expert Syst Appl / Knowledge discovery of concrete material using Genetic Operation Trees by Yeh (2009)10.1061/(ASCE)CP.1943-5487.0000088
/ J Comput Civ Eng / Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques by Chou (2011)10.3846/13923730.2011.574343
/ J Civ Eng Manage / Application of new information technology on concrete: an overview by Boukhatem (2011)10.1016/j.conbuildmat.2009.10.037
/ Constr Build Mater / Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models by Sobhani (2010)10.1016/j.commatsci.2008.07.012
/ Comput Mater Sci / An improved application technique of the adaptive probabilistic neural network for predicting concrete strength by Lee (2009)10.1016/j.agrformet.2008.10.018
/ Agric For Meteorol / Ensemble data mining approaches to forecast regional sugarcane crop production by Everingham (2009)10.1016/j.asoc.2010.01.024
/ Appl Soft Comput / Enhancing the classification accuracy by scatter-search-based ensemble approach by Chen (2011)10.1016/j.eswa.2008.07.041
/ Expert Syst Appl / Empirical analysis of support vector machine ensemble classifiers by Wang (2009)10.1016/j.ejor.2006.05.029
/ Eur J Oper Res / Improved customer choice predictions using ensemble methods by Wezel (2007)- IBM. PASW Modeler. NY: IBM Corporation, USA; 2010.
10.1016/j.cemconres.2003.08.018
/ Cem Concr Res / Genetic algorithm in mix proportioning of high-performance concrete by Lim (2004)10.12989/cac.2008.5.6.559
/ Comput Concr / Modeling slump of concrete with fly ash and superplasticizer by Yeh (2008){'issue': '5', 'key': '10.1016/j.conbuildmat.2013.08.078_b0105', 'first-page': '365', 'article-title': 'Modeling Portland blast-furnace slag cement high-performance concrete', 'volume': '101', 'author': 'Videla', 'year': '2004', 'journal-title': 'ACI Mater J'}
/ ACI Mater J / Modeling Portland blast-furnace slag cement high-performance concrete by Videla (2004)10.1016/j.conbuildmat.2005.08.009
/ Constr Build Mater / Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks by Pala (2007)10.1016/j.advengsoft.2011.05.016
/ Adv Eng Softw / Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks by Siddique (2011)10.1016/j.eswa.2008.07.017
/ Expert Syst Appl / Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches by Baykasoğlu (2009){'issue': '2', 'key': '10.1016/j.conbuildmat.2013.08.078_b0125', 'first-page': '87', 'article-title': 'Gene expression programming: a new adaptive algorithm for solving problems', 'volume': '13', 'author': 'Ferreira', 'year': '2001', 'journal-title': 'Complex Syst'}
/ Complex Syst / Gene expression programming: a new adaptive algorithm for solving problems by Ferreira (2001)10.1016/j.conbuildmat.2008.01.014
/ Constr Build Mater / Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN by Prasad (2009)10.1016/S0141-0296(03)00004-X
/ Eng Struct / Prediction of concrete strength using artificial neural networks by Lee (2003)10.1061/(ASCE)0899-1561(2006)18:4(597)
/ J Mater Civ Eng / Analysis of strength of concrete using design of experiments and neural networks by Yeh (2006)10.1016/j.conbuildmat.2008.04.015
/ Constr Build Mater / Analysis of durability of high performance concrete using artificial neural networks by Parichatprecha (2009)10.1016/j.conbuildmat.2012.09.026
/ Constr Build Mater / Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks by Dantas (2013)10.1061/(ASCE)0887-3801(2003)17:4(290)
/ J Comput Civ Eng / Study of applying macroevolutionary genetic programming to concrete strength estimation by Chen (2003)10.1007/s00366-011-0208-z
/ Eng Comput / Predicting high-strength concrete parameters using weighted genetic programming by Tsai (2011)10.1016/j.advengsoft.2011.09.014
/ Adv Eng Softw / A new predictive model for compressive strength of HPC using gene expression programming by Mousavi (2012)10.1016/j.autcon.2012.02.001
/ Autom Constr / Concrete compressive strength analysis using a combined classification and regression technique by Chou (2012)10.1016/j.engappai.2012.10.014
/ Eng Appl Artif Intell / High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform by Erdal (2013)10.1016/j.eswa.2012.02.063
/ Expert Syst Appl / Data mining techniques and applications – a decade review from 2000 to 2011 by Liao (2012)10.1016/j.conbuildmat.2008.12.003
/ Constr Build Mater / Neural networks for predicting compressive strength of structural light weight concrete by Alshihri (2009)- SPSS. Clementine 12.0 Algorithm Guide. Chicago, USA: Integral Solutions Limited; 2007.
{'key': '10.1016/j.conbuildmat.2013.08.078_b0195', 'series-title': 'Classification and regression trees', 'author': 'Breiman', 'year': '1984'}
/ Classification and regression trees by Breiman (1984){'issue': '2', 'key': '10.1016/j.conbuildmat.2013.08.078_b0200', 'first-page': '119', 'article-title': 'An exploratory technique for investigating large quantities of categorical data', 'volume': '29', 'author': 'Kass', 'year': '1980', 'journal-title': 'J Roy Stat Soc: Ser C (Appl Stat)'}
/ J Roy Stat Soc: Ser C (Appl Stat) / An exploratory technique for investigating large quantities of categorical data by Kass (1980){'key': '10.1016/j.conbuildmat.2013.08.078_b0205', 'series-title': 'Data mining for business intelligence', 'author': 'Shmueli', 'year': '2007'}
/ Data mining for business intelligence by Shmueli (2007)10.1080/02664769100000005
/ J Appl Stat / A method of choosing multiway partitions for classification and decision trees by Biggs (1991){'key': '10.1016/j.conbuildmat.2013.08.078_b0215', 'series-title': 'Applied linear statistical models', 'author': 'Neter', 'year': '1996'}
/ Applied linear statistical models by Neter (1996)10.2307/2344614
/ J Roy Stat Soc Ser A (General) / Generalized linear models by Nelder (1972){'key': '10.1016/j.conbuildmat.2013.08.078_b0225', 'series-title': 'The nature of statistical learning theory', 'author': 'Vapnik', 'year': '1995'}
/ The nature of statistical learning theory by Vapnik (1995)10.1023/B:STCO.0000035301.49549.88
/ Stat Comput / A tutorial on support vector regression by Smola (2004)10.1016/j.ijforecast.2009.05.029
/ Int J Forecast / MLP ensembles improve long term prediction accuracy over single networks by Adeodato (2011)10.1016/j.ejor.2006.05.029
/ Eur J Oper Res / Improved customer choice predictions using ensemble methods by Wezel (2007)- Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International joint conference on artificial intelligence. Morgan Kaufmann; 1995. p. 1137–43.
10.1007/s00521-011-0734-z
/ Neural Comput Appl / A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems by Gandomi (2012)
Dates
Type | When |
---|---|
Created | 11 years, 11 months ago (Sept. 23, 2013, 12:32 p.m.) |
Deposited | 3 years, 9 months ago (Nov. 15, 2021, 7:15 p.m.) |
Indexed | 1 day, 4 hours ago (Aug. 31, 2025, 6:24 a.m.) |
Issued | 11 years, 9 months ago (Dec. 1, 2013) |
Published | 11 years, 9 months ago (Dec. 1, 2013) |
Published Print | 11 years, 9 months ago (Dec. 1, 2013) |
@article{Chou_2013, title={Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength}, volume={49}, ISSN={0950-0618}, url={http://dx.doi.org/10.1016/j.conbuildmat.2013.08.078}, DOI={10.1016/j.conbuildmat.2013.08.078}, journal={Construction and Building Materials}, publisher={Elsevier BV}, author={Chou, Jui-Sheng and Pham, Anh-Duc}, year={2013}, month=dec, pages={554–563} }