Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete

[1]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[2]  R. F. Stratfull,et al.  CONCRETE VARIABLES AND CORROSION TESTING , 1973 .

[3]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Jinbo Bi,et al.  Regression Error Characteristic Curves , 2003, ICML.

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[8]  Murat Dicleli,et al.  Predicting the shear strength of reinforced concrete beams using artificial neural networks , 2004 .

[9]  George Morcous,et al.  Prediction of Onset of Corrosion in Concrete Bridge Decks Using Neural Networks and Case‐Based Reasoning , 2005 .

[10]  Ao Li,et al.  Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme , 2006, BMC Bioinformatics.

[11]  Manish A. Kewalramani,et al.  Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks , 2006 .

[12]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[13]  Hongbo Zhao.  Slope reliability analysis using a support vector machine , 2008 .

[14]  O. Kisi,et al.  Predicting the compressive strength of steel fiber added lightweight concrete using neural network , 2008 .

[15]  F. Demir.  Prediction of elastic modulus of normal and high strength concrete by artificial neural networks , 2008 .

[16]  Mustafa Saridemir,et al.  Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks , 2009, Adv. Eng. Softw..

[17]  Mustafa Saridemir,et al.  Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic , 2009, Adv. Eng. Softw..

[18]  Hua Liang,et al.  Improved estimation in multiple linear regression models with measurement error and general constraint , 2009, J. Multivar. Anal..

[19]  C. Cai,et al.  Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression , 2009 .

[20]  W. Hartt,et al.  Laboratory investigation of reinforcement corrosion initiation and chloride threshold content for self-compacting concrete , 2010 .

[21]  Lefteris Angelis,et al.  Visual comparison of software cost estimation models by regression error characteristic analysis , 2010, J. Syst. Softw..

[22]  R. Hussain.  Underwater half-cell corrosion potential bench mark measurements of corroding steel in concrete influenced by a variety of material science and environmental engineering variables , 2011 .

[23]  Chris Aldrich,et al.  Interpretation of nonlinear relationships between process variables by use of random forests , 2012 .

[24]  Jian Pei,et al.  Getting to Know Your Data , 2019, An R Companion for the Third Edition of The Fundamentals of Political Science Research.

[25]  J. Maran,et al.  Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L. , 2013 .

[26]  K. Yan,et al.  Prediction of Splitting Tensile Strength from Cylinder Compressive Strength of Concrete by Support Vector Machine , 2013 .

[27]  Faisal Khan,et al.  Analysis of pitting corrosion on steel under insulation in marine environments , 2013 .

[28]  J. Ou,et al.  Quantitative monitoring of pitting corrosion based on 3-D cellular automata and real-time ENA for RC structures , 2014 .

[29]  Mehdi Nikoo,et al.  Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm , 2014, Neural Computing and Applications.

[30]  O. Hodhod,et al.  Modeling the corrosion initiation time of slag concrete using the artificial neural network , 2014 .

[31]  Jui-Sheng Chou,et al.  Machine learning in concrete strength simulations: Multi-nation data analytics , 2014 .

[32]  Sunday O. Olatunji,et al.  Investigating the effect of correlation-based feature selection on the performance of support vector machines in reservoir characterization , 2015 .

[33]  Abul K. Azad,et al.  Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN , 2015 .

[34]  K. Willam,et al.  Carbonation-Induced and Chloride-Induced Corrosion in Reinforced Concrete Structures , 2015 .

[35]  J. Ou,et al.  Corrosion monitoring of the RC structures in time domain: Part I. Response analysis of the electrochemical transfer function based on complex function approximation , 2015 .

[36]  M. Shayanfar,et al.  Corrosion-induced reduction in compressive strength of self-compacting concretes containing mineral admixtures , 2016 .

[37]  Yue Hu,et al.  Prediction of autogenous shrinkage of concretes by support vector machine , 2016 .

[38]  Joaquim A. O. Barros,et al.  Corrosion effects on pullout behavior of hooked steel fibers in self-compacting concrete , 2016 .

[39]  A. Gupta,et al.  Enhanced efficiency of ANN using non-linear regression for modeling adsorptive removal of fluoride by calcined Ca-Al-(NO3)-LDH , 2016 .

[40]  Jurg Keller,et al.  Predicting concrete corrosion of sewers using artificial neural network. , 2016, Water research.

[41]  Baris Asikgil,et al.  Regression error characteristic curves based on the choice of best estimation method , 2016 .

[42]  S. Delvasto,et al.  Chloride ion resistance of self-compacting concretes incorporating volcanic materials , 2017 .

[43]  Esko Sistonen,et al.  Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions , 2017 .

[44]  Stéphane P. A. Bordas,et al.  What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences , 2018, International Journal of Data Science and Analytics.

[45]  Jui-Sheng Chou,et al.  The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate , 2017, Eng. Appl. Artif. Intell..

[46]  W. Yodsudjai,et al.  Factors influencing half-cell potential measurement and its relationship with corrosion level , 2017 .

[47]  Nhat-Duc Hoang,et al.  Prediction of chloride diffusion in cement mortar using Multi-Gene Genetic Programming and Multivariate Adaptive Regression Splines , 2017 .

[48]  M. Noorian-Bidgoli,et al.  ICA-ANN, ANN and multiple regression models for prediction of surface settlement caused by tunneling , 2018, Tunnelling and Underground Space Technology.

[49]  Ricardo P Nogueira,et al.  Revisiting the ASTM C876 standard for corrosion of reinforcing steel: On the correlation between corrosion potential and polarization resistance during the curing of different cement mortars , 2018, Electrochemistry Communications.

[50]  Peter de Boves Harrington,et al.  Multiple Versus Single Set Validation of Multivariate Models to Avoid Mistakes , 2018, Critical reviews in analytical chemistry.

[51]  S. Chakraborty,et al.  Analysis of Cotton Fibre Properties: A Data Mining Approach , 2018, Journal of The Institution of Engineers (India): Series E.

[52]  Zhao-Hui Lu,et al.  Empirical model of corrosion rate for steel reinforced concrete structures in chloride-laden environments , 2018, Advances in Structural Engineering.

[53]  S. Siddique,et al.  Prediction of mechanical properties of rubberised concrete exposed to elevated temperature using ANN , 2019 .

[54]  Turhan Bilir,et al.  A novel study for the estimation of crack propagation in concrete using machine learning algorithms , 2019, Construction and Building Materials.

[55]  Nhat-Duc Hoang,et al.  Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network , 2019, Measurement.

[56]  U. Angst.  Predicting the time to corrosion initiation in reinforced concrete structures exposed to chlorides , 2019, Cement and Concrete Research.

[57]  Tuan Nguyen,et al.  Deep neural network with high‐order neuron for the prediction of foamed concrete strength , 2018, Comput. Aided Civ. Infrastructure Eng..

[58]  A. Nazari,et al.  The use of machine learning in boron-based geopolymers: Function approximation of compressive strength by ANN and GP , 2019, Measurement.

[59]  Q. Han,et al.  A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm , 2019, Construction and Building Materials.

[60]  Wei Gao,et al.  Machine learning aided durability and safety analyses on cementitious composites and structures , 2019, International Journal of Mechanical Sciences.

[61]  Sabine Kruschwitz,et al.  A Machine Learning-Based Data Fusion Approach for Improved Corrosion Testing , 2018, Surveys in Geophysics.

[62]  Brandon M. Greenwell,et al.  Hands-On Machine Learning with R , 2019 .

[63]  Jonbi Jonbi,et al.  Modeling the water absorption and compressive strength of geopolymer paving block: An empirical approach , 2020 .

[64]  Abdullah Müsevitoğlu,et al.  Experimental and analytical investigation of chemical anchors’s behaviour under axial tensile , 2020 .