Prediction of self-healing characteristics of GGBS admixed concrete using Artificial Neural Network
M Chaitanya,P Manikandan,V Prem Kumar,S. Elavenil,V. Vasugi +4 more
- Vol. 1716, Iss: 1, pp 012019
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The article was published on 2020-12-01 and is currently open access. It has received 8 citations till now.read more
Citations
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Influence of Waste Glass Powder as an Aluminosilicate Precursor in Synthesizing Ternary Blended Alkali-Activated Binder
Journal ArticleDOI
Potential utilization of waste glass powder as a precursor material in synthesizing ecofriendly ternary blended geopolymer matrix
Palraj Manikandan,V. V. +1 more
TL;DR: In this paper , an Artificial Neural Network (ANN) framework was developed to assess the workability and mechanical properties of ternary blended geopolymer binder employing WGP replacement levels and varying concentrations of sodium hydroxide solution as input parameters.
Journal ArticleDOI
Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
TL;DR: In this paper , a comparative analysis on the performance of various machine learning models in predicting the self-healing capability of ECC is provided, which includes linear regression (LR), back-propagation neural network (BPNN), classification and regression tree (CART), and support vector regression (SVR).
Journal ArticleDOI
Modeling the capacity of engineered cementitious composites for self-healing using AI-based ensemble techniques
Hisham Alabduljabbar,Kaffayatullah Khan,Hamad Hassan Awan,Rayed Alyousef,Abdeliazim Mustafa Mohamed,S. M. Eldin +5 more
TL;DR: In this article , the capacity of ECC for self-healing, as measured by crackwidth after (CWA) was estimated using three different ensemble machine learning algorithms, namely AdaBoost regressor, decision tree, and bagging regressor.
Journal ArticleDOI
Prospective utilisation of hydrated lime as a performance enhancer in concrete
ES Dhanesh,M. Shanmugasundaram +1 more
TL;DR: In this paper , the effects of using a combination of Ground Granulated Blast Slag (GGBS) and Hydrated Lime (HL) as alternative cement ingredients on the mechanical and microstructural properties of ternary mixed concrete (OPC)+GGBS+HL) were examined.
References
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Journal ArticleDOI
Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network
TL;DR: The results showed that ANN can be an alternative approach for the predicting the compressive strength of ground granulated blast furnace slag concrete using concrete ingredients as input parameters.
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Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates
TL;DR: In this paper, the authors aim at predicting and modeling the 7, 28 and 56 days compressive strength of a concrete containing concrete's recycled coarse aggregates and that, for different range of cement content and slump.
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Sustainable geopolymer concrete using ground granulated blast furnace slag and rice husk ash: Strength and permeability properties
Ankur Mehta,Rafat Siddique +1 more
TL;DR: In this article, the utilization of industrial byproducts such as ground granulated blast furnace slag (GGBS) and rice husk ash (RHA) for the development of sustainable geopolymer concrete was presented and the effect of adding RHA as partial replacement of GGBS on compressive strength, split tensile strength, chloride permeability and sorptivity were investigated up to the age of 90 days.
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Superabsorbent polymers: A review on the characteristics and applications of synthetic, polysaccharide-based, semi-synthetic and ‘smart’ derivatives
TL;DR: A review of different types of superabsorbent polymers (SAPs) together with appropriate strategies elaborated to enable their synthesis can be found in this paper, where the main focus is on polysaccharide-based, semi-synthetic and "smart" SAPs along with their derivatives.
Journal ArticleDOI
A concrete mix proportion design algorithm based on artificial neural networks
Tao Ji,Tingwei Lin,Xujian Lin +2 more
TL;DR: In this paper, a concrete mix proportion design algorithm based on a way from aggregates to paste, a least paste content, Modified Tourfar's Model and ANNs was proposed, which is expected to reduce the number of trial and error, save cost, laborers and time.