Novel machine learning applications on fly ash based concrete: An overview
暂无分享,去创建一个
[1] T. Madhavi,et al. Durabilty and Strength Properties of High Volume Fly Ash Concrete , 2014 .
[2] Tara C. Kandpal,et al. Potential of fly ash utilisation in India , 2002 .
[3] Paul Ziehl,et al. Investigation of early compressive strength of fly ash-based geopolymer concrete , 2016 .
[4] Johannes Urpelainen,et al. Environmental Justice in India: Incidence of Air Pollution from Coal-Fired Power Plants , 2020 .
[5] Rajendra Kumar Sharma,et al. Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete , 2018 .
[6] Shuaishuai Lin,et al. Short-term wind power prediction based on data mining technology and improved support vector machine method: A case study in Northwest China , 2018, Journal of Cleaner Production.
[7] Jui-Sheng Chou,et al. Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques , 2011, J. Comput. Civ. Eng..
[8] A. K. Sinha,et al. Leaching behavior of lignite fly ash with shake and column tests , 2007 .
[9] B. Rajagopalan,et al. A comparison of machine learning methods for predicting the compressive strength of field-placed concrete , 2019, Construction and Building Materials.
[10] Vladimir Vapnik,et al. Support-vector networks , 2004, Machine Learning.
[11] Kejin Wang,et al. Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks , 2019, Neural Computing and Applications.
[12] Edward J. Garboczi,et al. The Past, Present, and Future of the Computational Materials Science of Concrete | NIST , 2000 .
[13] Paratibha Aggarwal,et al. EFFECT OF BOTTOM ASH AS REPLACEMENT OF FINE AGGREGATES IN CONCRETE , 2007 .
[14] Deepak Choudhary,et al. Learning Algorithms Using BPNN & SFS for Prediction of Compressive Strength of Ultra-High Performance Concrete , 2019, Machine Learning Research.
[15] Ardeshir Bahreininejad,et al. Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .
[16] Manas Ranjan Senapati,et al. Fly ash from thermal power plants - waste management and overview , 2011 .
[17] Yuhang Wang,et al. Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models , 2020 .
[18] Mehrdad Arashpour,et al. Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer , 2020 .
[19] Hossien Riahi-Madvar,et al. Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry , 2019, Engineering Applications of Computational Fluid Mechanics.
[20] Binh Thai Pham,et al. Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete , 2019, Materials.
[21] Dale P Bentz,et al. CEMHYD3D:: a three-dimensional cement hydration and microstructure development modelling package , 1997 .
[22] Jorge de Brito,et al. Toxicity and environmental and economic performance of fly ash and recycled concrete aggregates use in concrete: A review , 2018, Heliyon.
[23] Ian Flood,et al. Towards the next generation of artificial neural networks for civil engineering , 2008, Adv. Eng. Informatics.
[24] Ashfia Siddique,et al. An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete , 2020, Construction and Building Materials.
[25] Jiyang Fu,et al. Review on Application of Artificial Intelligence in Civil Engineering , 2019, Computer Modeling in Engineering & Sciences.
[26] A. K. Sinha,et al. Management of Lignite Fly Ash for Improving Soil Fertility and Crop Productivity , 2007, Environmental management.
[27] William Stafford Noble,et al. Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.
[28] Thierry Sedran,et al. Le logiciel BétonlabPro 3 , 2007 .
[29] Emadaldin Mohammadi Golafshani,et al. Estimating the optimal mix design of silica fume concrete using biogeography-based programming , 2019, Cement and Concrete Composites.
[30] Amir Hossein Alavi,et al. New machine learning prediction models for compressive strength of concrete modified with glass cullet , 2019, Engineering Computations.
[31] Abobakr Khalil Al-Shamiri,et al. Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete , 2020, Materials.
[32] F. Moghadas Nejad,et al. Designing sustainable concrete mixture by developing a new machine learning technique , 2020, Journal of Cleaner Production.
[33] Serkan Suba,et al. Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique , 2009 .
[34] A. Chang,et al. Physical properties of fly ash-amended soils , 1977 .
[35] Fereidoon Moghadas Nejad,et al. Developing a novel machine learning method to predict the compressive strength of fly ash concrete in different ages , 2019 .
[36] Chiho Kim,et al. Machine learning in materials informatics: recent applications and prospects , 2017, npj Computational Materials.
[37] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[38] R. Gujar,et al. Prediction and validation of alternative fillers used in micro surfacing mix-design using machine learning techniques , 2019, Construction and Building Materials.
[39] P. Sarker,et al. A comprehensive review on the applications of coal fly ash , 2015 .
[40] Lynne Moore,et al. European Research on Intelligent Computing in Civil Engineering , 2003 .
[41] Quang Dang Nguyen,et al. Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches , 2020 .
[42] Sharda Dhadse,et al. Fly ash characterization, utilization and Government initiatives in India Œ A review , 2008 .
[43] Madan Somvanshi,et al. A review of machine learning techniques using decision tree and support vector machine , 2016, 2016 International Conference on Computing Communication Control and automation (ICCUBEA).
[44] Togay Ozbakkaloglu,et al. Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods , 2018, Construction and Building Materials.
[45] Ian Flood,et al. Neural Networks in Civil Engineering. I: Principles and Understanding , 1994 .
[46] R. Sett. Flyash: Characteristics, Problems and Possible Utilization , 2017 .
[47] Mümine Kaya Keleş,et al. An overview: the impact of data mining applications on various sectors , 2017 .
[48] Bhupinder Singh,et al. Geopolymer concrete: A review of some recent developments , 2015 .
[49] Pariwat Ongsulee,et al. Artificial intelligence, machine learning and deep learning , 2017, 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE).
[50] Wei Dongfang,et al. Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach , 2020, Construction and Building Materials.
[51] Surabhi. Fly ash in India : Generation vis-à-vis Utilization and Global perspective , 2017 .
[52] Mohammadreza Koopialipoor,et al. Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples , 2019, Engineering with Computers.
[53] Ardeshir Bahreininejad,et al. Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems , 2015, Appl. Soft Comput..
[54] M. Jain,et al. Fly ash – waste management and overview : A Review , 2014 .
[55] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[56] Hamid Eskandari-Naddaf,et al. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete , 2020 .
[57] K. M. Liew,et al. Exploring mechanical performance of hybrid MWCNT and GNMP reinforced cementitious composites , 2021, Construction and Building Materials.