Machine learning approach in investigating carbonation depth of concrete containing Fly ash

[1]  V. Tran Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction , 2022, Journal of Cleaner Production.

[2]  Van Quan Tran Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials , 2022, Construction and Building Materials.

[3]  Van Quan Tran,et al.  Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach , 2022, Construction and Building Materials.

[4]  M. H. Nguyen,et al.  Incorporating industrial by-products into cement-free binders: Effects on water absorption, porosity, and chloride penetration , 2021, Construction and Building Materials.

[5]  M. Shahria Alam,et al.  Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms , 2021 .

[6]  Moncef L. Nehdi,et al.  Machine learning prediction of carbonation depth in recycled aggregate concrete incorporating SCMs , 2021 .

[7]  Saloni,et al.  Sustainable alkali activated concrete with fly ash and waste marble aggregates: Strength and Durability studies , 2021 .

[8]  Liyun Cui,et al.  Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete , 2021, Advances in Civil Engineering.

[9]  Rogério Carrazedo,et al.  Carbonation model for fly ash concrete based on artificial neural network: Development and parametric analysis , 2021 .

[10]  Minh-Ngoc Vu,et al.  Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method , 2020 .

[11]  T. Proske,et al.  Effect of relative humidity on the carbonation rate of portlandite, calcium silicate hydrates and ettringite , 2020, Cement and Concrete Research.

[12]  Mark Alexander,et al.  Performance-based approaches for concrete durability: State of the art and future research needs , 2019, Cement and Concrete Research.

[13]  S. Poyet,et al.  Carbonation of hardened cement pastes: Influence of temperature , 2019, Cement and Concrete Research.

[14]  Jorge de Brito,et al.  Carbonation of concrete made with high amount of fly ash and recycled concrete aggregates for utilization of CO2 , 2019, Journal of CO2 Utilization.

[15]  Qingtao Li,et al.  Effects of micro-environmental climate on the carbonation depth and the pH value in fly ash concrete , 2018 .

[16]  Bakhta Boukhatem,et al.  Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network , 2019, Neural Computing and Applications.

[17]  Ana Carolina Parapinski dos Santos,et al.  CO2 uptake potential due to concrete carbonation: A case study , 2017 .

[18]  S. Singh,et al.  Comparative study of accelerated carbonation of plain cement and fly-ash concrete , 2017 .

[19]  A. Leemann,et al.  Carbonation of concrete: the role of CO2 concentration, relative humidity and CO2 buffer capacity , 2017 .

[20]  Jinrui Zhang,et al.  Long-age wet curing effect on performance of carbonation resistance of fly ash concrete , 2016 .

[21]  S. O. Ekolu,et al.  A review on effects of curing, sheltering, and CO2 concentration upon natural carbonation of concrete , 2016 .

[22]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[23]  Esko Sistonen,et al.  CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods , 2015 .

[24]  C. Andrade,et al.  Recent durability studies on concrete structure , 2015 .

[25]  S. Poyet,et al.  Temperature influence on water transport in hardened cement pastes , 2015 .

[26]  Feng Xing,et al.  Experimental study on effects of CO2 concentrations on concrete carbonation and diffusion mechanisms , 2015 .

[27]  Nicholas H. Florin,et al.  Statistical analysis of the carbonation rate of concrete , 2015 .

[28]  Jianqiao Ye,et al.  Numerical modeling of supercritical carbonation process in cement-based materials , 2015 .

[29]  J. de Brito,et al.  Statistical modelling of carbonation in reinforced concrete , 2014 .

[30]  N. Belie,et al.  A service life based global warming potential for high-volume fly ash concrete exposed to carbonation , 2014 .

[31]  Erik Strumbelj,et al.  Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.

[32]  Alois Knoll,et al.  Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..

[33]  Wang Zhaodong,et al.  Studies on forecasting of carbonation depth of slag high performance concrete considering gas permeability , 2013 .

[34]  C. Andrade,et al.  Natural and accelerated CO2 binding kinetics in cement paste at different relative humidities , 2013 .

[35]  Peng Zhang,et al.  Effect of Fly Ash on Durability of High Performance Concrete Composites , 2013 .

[36]  N. Banthia,et al.  Carbonation in concrete infrastructure in the context of global climate change – Part 1: Experimental results and model development , 2012 .

[37]  Fernando A. Branco,et al.  Statistical analysis of the carbonation coefficient in open air concrete structures , 2012 .

[38]  Mark G. Stewart,et al.  Climate change adaptation for corrosion control of concrete infrastructure , 2012 .

[39]  Ahmed Loukili,et al.  Performance-based design and carbonation of concrete with high fly ash content , 2011 .

[40]  Shaul Mordechai,et al.  Applications of Monte Carlo method in science and engineering , 2011 .

[41]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[42]  Hui-sheng Shi,et al.  Influence of mineral admixtures on compressive strength, gas permeability and carbonation of high performance concrete , 2009 .

[43]  Han-Seung Lee,et al.  A model for predicting the carbonation depth of concrete containing low-calcium fly ash , 2009 .

[44]  Ahmed Loukili,et al.  A performance based approach for durability of concrete exposed to carbonation , 2009 .

[45]  N. De Belie,et al.  Porosity, gas permeability, carbonation and their interaction in high-volume fly ash concrete , 2008 .

[46]  K. Sisomphon,et al.  Carbonation rates of concretes containing high volume of pozzolanic materials , 2007 .

[47]  O. Çopuroğlu,et al.  Effect of global climatic change on carbonation progress of concrete , 2007 .

[48]  S. Tangtermsirikul,et al.  A study on carbonation depth prediction for fly ash concrete , 2006 .

[49]  Jing Wen Chen,et al.  The experimental investigation of concrete carbonation depth , 2006 .

[50]  Koichi Maekawa,et al.  ENHANCED MODELING OF MOISTURE EQUILIBRIUM AND TRANSPORT IN CEMENTITIOUS MATERIALS UNDER ARBITRARY TEMPERATURE AND RELATIVE HUMIDITY HISTORY , 2005 .

[51]  Renato Vitaliani,et al.  Experimental investigation and numerical modeling of carbonation process in reinforced concrete structures - Part II practical applications , 2005 .

[52]  M. L. Laucks,et al.  Carbon Dioxide Uptake by Hydrated Lime Aerosol Particles , 2004 .

[53]  Renato Vitaliani,et al.  Experimental investigation and numerical modeling of carbonation process in reinforced concrete structures Part I: Theoretical formulation , 2004 .

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

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

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

[57]  S. Goñi,et al.  EFFECT OF TEMPERATURE ON THE LEACHING PERFORMANCE OF A SIMULATED CEMENT-BASED IMMOBILIZATION SYSTEM. CALCIUM AND HYDROXYL BEHAVIOUR , 1996 .

[58]  J. G. Cabrera,et al.  Deterioration of concrete due to reinforcement steel corrosion , 1996 .