Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates
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Karim Moussaceb | Farid Dahmoune | Abdelkader Hammoudi | K. Moussaceb | Cherif Belebchouche | F. Dahmoune | Abdelkader Hammoudi | Cherif Belebchouche
[1] Meysam Najimi,et al. Predicting Rapid Chloride Permeability of Self-Consolidating Concrete: A Comparative Study on Statistical and Neural Network Models , 2013 .
[2] C. Poon,et al. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks , 2013 .
[3] Chao-Lung Hwang,et al. Properties of HPC with recycled aggregates , 2006 .
[4] Mônica Batista Leite,et al. Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks , 2013 .
[5] İlker Bekir Topçu,et al. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic , 2008 .
[6] José Luis Duarte Ribeiro,et al. Modeling of mechanical properties and durability of recycled aggregate concretes , 2012 .
[7] Abdurrahmaan Lotfy,et al. Performance evaluation of structural concrete using controlled quality coarse and fine recycled concrete aggregate , 2015 .
[8] Mohsen Rashki,et al. Using Response Surface Methodology and providing a modified model using whale algorithm for estimating the compressive strength of columns confined with FRP sheets , 2018, Construction and Building Materials.
[9] Łukasz Sadowski,et al. Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust , 2019, Journal of Cleaner Production.
[10] Fangming Deng,et al. Compressive strength prediction of recycled concrete based on deep learning , 2018, Construction and Building Materials.
[11] Antonio José Tenza-Abril,et al. Prediction and sensitivity analysis of compressive strength in segregated lightweight concrete based on artificial neural network using ultrasonic pulse velocity , 2018, Construction and Building Materials.
[12] C. Poon,et al. Use of a CO2 curing step to improve the properties of concrete prepared with recycled aggregates , 2014 .
[13] Saman Soleimani Kutanaei,et al. Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network , 2016 .
[14] Eduardo Júlio,et al. RSM-based model to predict the performance of self-compacting UHPC reinforced with hybrid steel micro-fibers , 2014 .
[15] C. Poon,et al. Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete , 2013 .
[16] J. Brito,et al. Influence of the pre-saturation of recycled coarse concrete aggregates on concrete properties , 2011 .
[17] P. Nimityongskul,et al. Analysis of durability of high performance concrete using artificial neural networks , 2009 .
[18] A. Marí,et al. Influence of Amount of Recycled Coarse Aggregates and Production Process on Properties of Recycled Aggregate Concrete , 2007 .
[19] Jiayuan Wang,et al. Considering life-cycle environmental impacts and society's willingness for optimizing construction and demolition waste management fee: An empirical study of China , 2019, Journal of Cleaner Production.
[20] Khodir Madani,et al. Ultrasound assisted extraction of phenolic compounds from P. lentiscus L. leaves: Comparative study of artificial neural network (ANN) versus degree of experiment for prediction ability of phenolic compounds recovery , 2015 .
[21] José Luis Duarte Ribeiro,et al. Mechanical properties modeling of recycled aggregate concrete , 2010 .
[22] J. de Brito,et al. The influence of the use of recycled aggregates on the compressive strength of concrete: a review , 2015 .
[23] S Marinković,et al. Comparative environmental assessment of natural and recycled aggregate concrete. , 2010, Waste management.
[24] Iman Ferdosian,et al. Eco-efficient ultra-high performance concrete development by means of response surface methodology , 2017 .
[25] Emadaldin Mohammadi Golafshani,et al. Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves , 2018, Journal of Cleaner Production.
[26] Bibhuti Bhusan Mukharjee,et al. Development of sustainable concrete using recycled coarse aggregate and ground granulated blast furnace slag , 2018 .
[27] Wenzhe Li,et al. Assessing recycled pavement concrete mechanical properties under joint action of freezing and fatigue via RSM , 2018 .
[28] A. Al-Hadithi,et al. Production and optimization of eco-efficient self compacting concrete SCC with limestone and PET , 2019, Construction and Building Materials.
[29] M. S. D. Juan,et al. Study on the influence of attached mortar content on the properties of recycled concrete aggregate , 2009 .
[30] J. de Brito,et al. Use of coarse recycled aggregates from precast concrete rejects: Mechanical and durability performance , 2014 .