Concrete compressive strength analysis using a combined classification and regression technique

Abstract High performance concrete (HPC) is a complex composite material, and a model of its compressive strength must be highly nonlinear. Many studies have tried to develop accurate and effective predictive models for HPC compressive strength, including linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent reports that a hierarchical structure outperforms a flat one, this study proposes a hierarchical classification and regression (HCR) approach for improving performance in predicting HPC compressive strength. Specifically, the first-level analyses of the HCR find exact classes for new unknown cases. The cases are then entered into the corresponding prediction model to obtain the final output. The analytical results for a laboratory dataset show that the HCR approach outperforms conventional flat prediction models (LR, ANNs, and SVR). Notably, the HCR with a 4-class support vector machine in the first level combined with a single ANNs obtains the lowest mean absolute percentage error.

[1]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[2]  O E Gjorv,et al.  EFFECT OF CONDENSED SILICA FUME ON THE STEEL-CONCRETE BOND , 1990 .

[3]  Hyeran Byun,et al.  A Survey on Pattern Recognition Applications of Support Vector Machines , 2003, Int. J. Pattern Recognit. Artif. Intell..

[4]  R. Zerbino,et al.  High-Strength Concretes Incorporating Different Coarse Aggregates , 1992 .

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[7]  Artur Dubrawski,et al.  HPC Strength Prediction Using Artificial Neural Network , 1995 .

[8]  Heecheon You,et al.  Development of a hierarchical estimation method for anthropometric variables , 2005 .

[9]  Omar Chaallal,et al.  TESTING HIGH-STRENGTH CONCRETE COMPRESSIVE STRENGTH , 1993 .

[10]  Juho Rousu,et al.  Learning hierarchical multi-category text classification models , 2005, ICML.

[11]  V. Malhotra,et al.  Structural Concrete Incorporating High Volumes of ASTM Class Fly Ash , 1989 .

[12]  I-Cheng Yeh,et al.  Knowledge discovery of concrete material using Genetic Operation Trees , 2009, Expert Syst. Appl..

[13]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[14]  Susan T. Dumais,et al.  Hierarchical classification of Web content , 2000, SIGIR '00.

[15]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[16]  Ee-Peng Lim,et al.  Hierarchical text classification and evaluation , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[17]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[18]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[19]  L.W.G. Strijbosch,et al.  Calculating the accuracy of hierarchical estimation , 2010 .

[20]  I-Cheng Yeh,et al.  Modeling of strength of high-performance concrete using artificial neural networks , 1998 .

[21]  Pierre Hellier,et al.  Hierarchical estimation of a dense deformation field for 3-D robust registration , 2001, IEEE Transactions on Medical Imaging.

[22]  Ian Witten,et al.  Data Mining , 2000 .

[23]  Jerzy Hoła,et al.  Application of artificial neural networks to determine concrete compressive strength based on non‐destructive tests , 2005 .

[24]  Mohammad Hossein Fazel Zarandi,et al.  Fuzzy polynomial neural networks for approximation of the compressive strength of concrete , 2008, Appl. Soft Comput..

[25]  Christopher H. Achen.  Two-Step Hierarchical Estimation: Beyond Regression Analysis , 2005, Political Analysis.

[26]  Said Kenai,et al.  APPLICATION OF NEW INFORMATION TECHNOLOGY ON CONCRETE: AN OVERVIEW , 2011 .

[27]  J. Sobhani,et al.  Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models , 2010 .

[28]  J M Calixto,et al.  MECHANICAL PROPERTIES AND DURABILITY OF HIGH-PERFORMANCE CONCRETE , 1999 .

[29]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[30]  Ta-Peng Chang,et al.  A mix proportioning methodology for high‐performance concrete , 1996 .