Machine learning approach in investigating carbonation depth of concrete containing Fly ash
暂无分享,去创建一个
[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 , 2016, Materials and Structures.
[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 .