Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers

被引:68
作者
Ayaz, Yasar [1 ]
Kocamaz, Adnan Fatih [2 ]
Karakoc, Mehmet Burhan [1 ]
机构
[1] Inonu Univ, Dept Civil Engn, Malatya, Turkey
[2] Inonu Univ, Dept Comp Engn, Malatya, Turkey
关键词
Data mining; M5; rule; Tree model M5P; Concrete; Compressive strength; UPV; CONSTRUCTION; PREDICTION; REGRESSION; VELOCITY;
D O I
10.1016/j.conbuildmat.2015.06.029
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Compressive strength and UPV parameters are the methods that are used to determine high-volume mineral admixture concrete quality. But experiments for all levels of these parameters are expensive, difficult and time consuming. For determination of output values, classifiers with model extraction features can be used. In this study, classifiers, with the rule-based M5 rule and tree model M5P in the area of data mining are used to predict the compressive strength and UPV of concrete mixtures after 3, 7, 28 and 120 days of curing. The M5 rule and tree model M5P are tested using the available test data of 40 different concrete mix-designs gathered from literature [1]. The input of the model is a variable data set corresponding to concrete mixture proportions. The findings of this study indicated that the M5 rule and tree model M5P models are sufficient tools for estimating the compressive strength and UPV of concrete. 97% and 87% success is obtained in predicting compressive strength and UPV results, respectively. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:235 / 240
页数:6
相关论文
共 27 条
[1]   Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks [J].
Abdon Dantas, Adriana Trocoli ;
Leite, Monica Batista ;
Nagahama, Koji de Jesus .
CONSTRUCTION AND BUILDING MATERIALS, 2013, 38 :717-722
[2]   The use of GA-ANNs in the modelling of compressive strength of cement mortar [J].
Akkurt, S ;
Ozdemir, S ;
Tayfur, G ;
Akyol, B .
CEMENT AND CONCRETE RESEARCH, 2003, 33 (07) :973-979
[3]   Compressive strength evaluation of structural lightweight concrete by non-destructive ultrasonic pulse velocity method [J].
Alexandre Bogas, J. ;
Gloria Gomes, M. ;
Gomes, Augusto .
ULTRASONICS, 2013, 53 (05) :962-972
[4]   A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals [J].
Alexandridis, Alex ;
Triantis, Dimos ;
Stavrakas, Ilias ;
Stergiopoulos, Charalampos .
CONSTRUCTION AND BUILDING MATERIALS, 2012, 30 :294-300
[5]  
[Anonymous], AGR ECOSYST ENV
[6]  
[Anonymous], CART CLASSIFICATION
[7]  
[Anonymous], DATA MINING PRINCIPL
[8]  
[Anonymous], P WORKSH KNOWL ACQ K
[9]   Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network [J].
Atici, U. .
EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (08) :9609-9618
[10]  
Breiman L., 1984, CLASSIFICATION REGRE