A comparison of machine learning methods for predicting the compressive strength of field-placed concrete

[1]  Joseph R. Kasprzyk,et al.  Computational design optimization of concrete mixtures: A review , 2018, Cement and Concrete Research.

[2]  Philipp Probst,et al.  Hyperparameters and tuning strategies for random forest , 2018, WIREs Data Mining Knowl. Discov..

[3]  A. Zeyad.  Effect of curing methods in hot weather on the properties of high-strength concretes , 2017, Journal of King Saud University - Engineering Sciences.

[4]  Jisong Zhang,et al.  Prediction of Compressive Strength of Ultra-High Performance Concrete (UHPC) Containing Supplementary Cementitious Materials , 2017, 2017 International Conference on Smart Grid and Electrical Automation (ICSGEA).

[5]  Faezehossadat Khademi,et al.  Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression , 2016 .

[6]  Adnan Fatih Kocamaz,et al.  Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers , 2015 .

[7]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[8]  Pijush Samui,et al.  Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine , 2014, KSCE Journal of Civil Engineering.

[9]  Kasım Mermerdaş,et al.  Optimization of concrete mixture with hybrid blends of metakaolin and fly ash using response surface method , 2014 .

[10]  Umit Atici,et al.  Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network , 2011, Expert Syst. Appl..

[11]  K. Sathiyakumari,et al.  Prediction of the Compressive Strength of High Performance Concrete Mix using Tree Based Modeling , 2010 .

[12]  Ahmed M. Azmy,et al.  Neural networks for predicting compressive strength of structural light weight concrete , 2009 .

[13]  Okan Karahan,et al.  Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network , 2009, Adv. Eng. Softw..

[14]  Mustafa Sarıdemir.  Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks , 2009, Adv. Eng. Softw..

[15]  I-Cheng Yeh,et al.  Optimization of concrete mix proportioning using a flattened simplex–centroid mixture design and neural networks , 2009, Engineering with Computers.

[16]  İlker Bekir Topçu,et al.  Prediction of properties of waste AAC aggregate concrete using artificial neural network , 2007 .

[17]  A. Öztas,et al.  Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks , 2007 .

[18]  B. Schölkopf,et al.  Kernel methods in machine learning , 2007, math/0701907.

[19]  M. A. Bhatti,et al.  Predicting the compressive strength and slump of high strength concrete using neural network , 2006 .

[20]  Carlos Videla,et al.  Modeling Portland Blast-Furnace Slag Cement High-Performance Concrete , 2004 .

[21]  Á. Palomo,et al.  Characterisation of fly ashes. Potential reactivity as alkaline cements , 2003 .

[22]  Serhan Ozdemir,et al.  The use of GA-ANNs in the modelling of compressive strength of cement mortar , 2003 .

[23]  Seung-Chang Lee,et al.  Prediction of concrete strength using artificial neural networks , 2003 .

[24]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[25]  Hong-Guang Ni,et al.  Prediction of compressive strength of concrete by neural networks , 2000 .

[26]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[27]  John P Zaniewski,et al.  Materials for civil and construction engineers , 1998 .

[28]  David Darwin,et al.  Effects of Aggregate Type, Size, and Content on Concrete Strength and Fracture Energy , 1997 .

[29]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[30]  Ts Nagaraj,et al.  Analysis of Concrete Strength Versus Water-Cement Ratio Relationship , 1991 .

[31]  Sandor Popovics,et al.  ANALYSIS OF THE CONCRETE STRENGTH VERSUS WATER CEMENT RATIO RELATIONSHIP , 1990 .

[32]  O. Cebeci.  Strength of concrete in warm and dry environment , 1987 .

[33]  George A. F. Seber,et al.  Linear regression analysis , 1977 .

[34]  J. L. Noland,et al.  Building Code Requirements for Reinforced Concrete (ACI 318-71) in Decision Logic Table Format* , 1976 .

[35]  Duff A. Abrams,et al.  Water-Cement Ratio as a Basis of Concrete Quality , 1927 .

[36]  Puneet Gupta,et al.  Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods , 2019, Cement and Concrete Research.

[37]  E. A. Mohamed,et al.  High-Performance Concrete Compressive Strength Prediction Based Weighted Support Vector Machines , 2017 .

[38]  Martin Krzywinski,et al.  Points of Significance: Classification and regression trees , 2017, Nature Methods.

[39]  J. Fox.  Fly Ash Classification – Old and New Ideas , 2017 .

[40]  R. Mendes.  R: The R Project for Statistical Computing , 2016 .

[41]  P. Chatur,et al.  Effectiveness evaluation of regression models for predictive data-mining , 2013 .

[42]  G. Tutz,et al.  An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. , 2009, Psychological methods.

[43]  G. Trtnik,et al.  Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. , 2009, Ultrasonics.

[44]  İlker Bekir Topçu,et al.  Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic , 2008 .

[45]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[46]  L. Breiman.  Random Forests--random Features , 1999 .

[47]  Ali Akbar Ramezanianpour,et al.  Effect of curing on the compressive strength, resistance to chloride-ion penetration and porosity of concretes incorporating slag, fly ash or silica fume , 1995 .

[48]  Monte Carlo,et al.  CS 731 Spring 2011 Advanced Artificial Intelligence Linear Regression , 2022 .