Prediction of Compressive Strength of Concrete with Decision Trees
Abstract
Full Text:
PDFReferences
Lee, S-C., (2003). "Prediction of concrete strength using artificial neural networks." Engineering Structures, 25, 849–857.
Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N., and Bhatti, M. A., (2006). "Predicting the compressive strength and slump of high strength concrete using neural network." Construction and Building Materials, 20, 769–775.
Topcu, I. B., and Sarıdemir, M., (2008). "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic." Computational Materials Science, 41(3), 305–311.
Rasa, E., Ketabchi, H., and Afshar, M. H., (2009). "Predicting Density and Compressive Strength of Concrete Cement Paste Containing Silica Fume Using Artificial Neural Networks." Sharif University of Technology: Transaction A: Civil Engineering, 16(1), 33-42.
Rao, R. M. P., and Rao, H. S., (2012). "Prediction Of Compressive Strength Of Concrete With Different Aggregate Binder Ratio Using ANN Model." International Journal of Engineering Research and Technology, 1(10), 1-10.
Chou, J-S., Tsai, C-F., Pham, A-D., and Lu, Y-H., (2014). "Machine learning in concrete strength simulations: Multi-nation data analytics." Construction and Building Materials, 73, 771–780.
Garg, N. K., Deo, M. C. and Kumar, V. S., (2008). “Short term prediction of coastal
currents using Model Trees.” Indian National Conference on Advances in Hydraulic Engineering: Hydro 2008, India, 250-256.
Tiryaki, B., (2008). "Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees." Engineering Geology, 99, 51–60.
Hwang, S., Guevarra, I. F., and Yu, B., (2009). "Slope failure prediction using a decision tree: A case of engineered slopes in South Korea." Engineering Geology, 104, 126–134.
Kim, J-W. and Pachepsky, Y. A., (2010). "Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT stream flow simulation." Journal of Hydrology, 394, 305–314.
Ayaz, Y., Kocamaz, A. F., and Karakoc, M. B., (2015). "Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers." Construction and Building Materials, 94, 235–240.
Behnood, A., Olek, J., and Glinicki, M. A., (2015). "Predicting modulus elasticity of recycled aggregate concrete using M5' model tree algorithm." Construction and Building Materials, 94, 137–147.
Gharaei-Manesh, S., Fathzadeh, A., and Taghizadeh-Mehrjardi, R., (2016). "Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran." Cold Regions Science and Technology, 122, 26–35.
Clark, L.A., Pregibon, D., (1991). "Tree-based models." In Chambers, J.M., and Hastie, T.J. (Eds.), "Statistical Models in S." Wadsworth. Pacific Grove, CA, 377–419.
Rodriguez-Galiano,V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M., (2015). "Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines." Ore Geology Reviews, 71, 804–818.
Breiman, L., Friedman, J., Stone, C. J., Olshen, R. A., (1984). "Classification and Regression Trees." Chapman & Hall/CRC, USA.
Quinlan, J.R., (1993). "C4.5 Programs for Machine Learning." Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
Jekabsons G., (2016). "M5PrimeLab: M5' regression tree, model tree, and tree ensemble toolbox for Matlab/Octave." available at http://www.cs.rtu.lv/jekabsons/.
DOI: https://doi.org/10.37628/ijct.v1i2.79
Refbacks
- There are currently no refbacks.