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Fardapaper Modeling Slump of Ready Mix Concrete Using .

Fardapaper Modeling Slump of Ready Mix Concrete

2018-12-16  Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks Vinay Chandwani⇑, Vinay Agrawal, Ravindra Nagar Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, Rajasthan, India article info Article history: Available online 6 September 2014 Keywords:

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Modeling slump of ready mix concrete using genetic ...

2015-2-1  The paper explores the usefulness of hybridizing two distinct nature inspired computational intelligence techniques viz., Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for modeling slump of Ready Mix Concrete (RMC) based on its design mix constituents viz., cement, fly ash, sand, coarse aggregates, admixture and water-binder ratio.

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Modeling slump of ready mix concrete using

Home Browse by Title Periodicals Advances in Artificial Neural Systems Vol. 2014 Modeling slump of ready mix concrete using genetically evolved artificial neural networks.

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[PDF] Modeling Slump of Ready Mix Concrete Using ...

DOI: 10.1155/2014/629137 Corpus ID: 5546408. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks @article{Chandwani2014ModelingSO, title={Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks}, author={Vinay Chandwani and V. Agrawal and R. Nagar}, journal={Adv. Artif.

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Modeling slump of ready mix concrete using genetically ...

ResearchArticle Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks VinayChandwani,VinayAgrawal,andRavindraNagar

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Modeling Slump of Ready Mix Concrete Using

2014-11-11  The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks.

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Modeling Slump of Ready Mix Concrete Using

2014-1-1  Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks. Link/Page Citation 1. Introduction Cement concrete is one of the most widely used construction materials in the world today. The material modeling of concrete is a difficult task owing to its composite nature. ...

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Modeling Slump of Ready Mix Concrete using

Workability of concrete measured using a slump test is an indicator to evaluate the life of RMC during its transportation phase and uniformity of concrete from batch to batch. The concrete mix proportions like cement, fly ash, coarse aggregates, fine aggregates, water and admixtures govern the workability or slump value of the concrete.

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Research Article Modeling Slump of Ready Mix Concrete ...

2017-8-3  Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks VinayChandwani,VinayAgrawal,andRavindraNagar Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan, India Correspondence should be addressed to Vinay Chandwani; [email protected]

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Modeling Slump of Ready Mix Concrete Using

Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks . By Vinay Chandwani, Vinay Agrawal and Ravindra Nagar. Cite . BibTex; Full citation; Publisher: Hindawi Limited. Year: 2014. DOI identifier: 10.1155/2014/629137. OAI identifier: Provided by: MUCC ...

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Modeling Slump of Ready Mix Concrete Using

2014-11-11  The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks.

Get Price

Modeling Slump of Ready Mix Concrete using

Workability of concrete measured using a slump test is an indicator to evaluate the life of RMC during its transportation phase and uniformity of concrete from batch to batch. The concrete mix proportions like cement, fly ash, coarse aggregates, fine aggregates, water and admixtures govern the workability or slump value of the concrete.

Get Price

Modeling Slump of Ready Mix Concrete Using

2014-1-1  Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks. Link/Page Citation 1. Introduction Cement concrete is one of the most widely used construction materials in the world today. The material modeling of concrete is a difficult task owing to its composite nature. ...

Get Price

Modeling Slump of Ready Mix Concrete Using

Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks . By Vinay Chandwani, Vinay Agrawal and Ravindra Nagar. Cite . BibTex; Full citation; Publisher: Hindawi Limited. Year: 2014. DOI identifier: 10.1155/2014/629137. OAI identifier: Provided by: MUCC ...

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10.1016/j.eswa.2014.08.048 10.1016/j.eswa ... -

2020-6-11  This potential of ANN has been harnessed for wide applications in modeling the material behavior and properties of concrete. Notable among them are successful implementations in predicting and modeling compressive strength of self compacting concrete ( Uysal Tanyildizi, 2012 ), high performance concrete ( Yeh, 1998 ), recycled aggregate ...

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Expert Systems with Applications

2020-3-20  Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks Vinay Chandwani⇑, Vinay Agrawal, Ravindra Nagar Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, Rajasthan, India article info Article history: Available online 6 September 2014 Keywords:

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Modelling Slump of Concrete Containing Natural Coarse ...

2021-7-30  modeling ready mix concrete slump using randomised disjoint sets. [36] used seven concrete mix ingredients in modeling the slump and strength of concrete. All these researches yielded satisfactory prediction capability and established that ANN is a powerful and superior modelling

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Concrete slump prediction modeling with a fine-tuned ...

2021-1-22  Chandwani V, Agrawal V, Nagar R (2015) Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks. Expert

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CiteSeerX — Prediction of Slump in Concrete using ...

Neural Network models is constructed, trained and tested using the available test data of 349 different concrete mix designs of High Strength Concrete (HSC) gathered from a particular Ready Mix Concrete (RMC) batching plant. The most versatile Neural Network model is selected to predict the slump in concrete.

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Comprehensive Machine Learning-Based Model for

2021-2-25  Compressive Strength of Ready-Mix Concrete Jiajia Xu, Li Zhou, Ge He, Xu Ji * , Yiyang Dai and Yagu Dang ... consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. ... models to predict the 28-day CS of no-slump concrete, based on the ...

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Expert Systems with Applications

2020-3-20  Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks Vinay Chandwani⇑, Vinay Agrawal, Ravindra Nagar Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, Rajasthan, India article info Article history: Available online 6 September 2014 Keywords:

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10.1016/j.eswa.2014.08.048 10.1016/j.eswa ... -

2020-6-11  This potential of ANN has been harnessed for wide applications in modeling the material behavior and properties of concrete. Notable among them are successful implementations in predicting and modeling compressive strength of self compacting concrete ( Uysal Tanyildizi, 2012 ), high performance concrete ( Yeh, 1998 ), recycled aggregate ...

Get Price

- CORE

The paper present a hybrid artificial neural networks and genetic algorithm approach for modeling slump of ready mix concrete based on its design mix constituents. Genetic algorithms (GA) global search is employed for evolving the initial weights and biases for training of neural networks, which are further fine tuned using the BP algorithm.

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Concrete slump prediction modeling with a fine-tuned ...

2021-1-22  Chandwani V, Agrawal V, Nagar R (2015) Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks. Expert

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CiteSeerX — Prediction of Slump in Concrete using ...

Neural Network models is constructed, trained and tested using the available test data of 349 different concrete mix designs of High Strength Concrete (HSC) gathered from a particular Ready Mix Concrete (RMC) batching plant. The most versatile Neural Network model is selected to predict the slump in concrete.

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Effects of Graded Concrete on Compressive Strengths

Modeling Slump of Ready Mix Concrete using Artificial Neural Network. International Journal of Technology, Volume 6(2), pp. 207–216. Crisfield, M.A., 1981. A Fast Incremental/Iterative Solution Procedure that Handles Snap-through. Computers Structures, Volume 13(1-3), pp. 55–62.

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Concrete Mix Design by Using Python - IRJET

2021-6-11  process. Using python code, mix design for various grades of concrete and combinations of additional materials can be implemented with ease. The work also states the use of mineral admixtures with partial replacement of basic ingredients of concrete. 1.1 Concrete Mix Design Concrete mix design can be defined as a technique of

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(PDF) Investigation of Ready Mixed Concrete

Ready-mixed concrete (RMC) is one of the most common building material for construction industry for nearly all developed and developing countries. Generally, because of the technical requirements, concrete must be mixed in a batch plant and

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Concrete Mix Design - Learn From This How To

2021-7-24  Concrete mix design calculation KPI’s. There are seven categories required to design concrete: the water to cement ratio, total cementitious material, type of SCM, reference standard’s sieving limits maximum aggregate size, air content, and volume. This guide will show a mix design overview using the British Standard and ASTM.

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Prediction of Concrete Properties Using Multiple

The selection of appropriate type and grade of concrete for a particular application is the critical step in any construction project. Workability and compressive strength are the two significant parameters that need special attention. This study aims to predict the slump along with 7-days 28-days compressive strength based on the data collected from various RMC plants.

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