PREDICTION OF CEMENT STRENGTH USING SOFT COMPUTING TECHNIQUES

Krystyn J. Van Vliet | MIT DMSE

SMART researchers receive Intra-CREATE grant for personalized medicine and cell therapy. October 1, 2020. Funds will support research on glaucoma through retinal biometrics and neural cell implantation therapy for spinal cord injury.

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Soft Computing for Problem Solving - SocProS , Volume

This two-volume book presents outcomes of the 7th International Conference on Soft Computing for Problem Solving, SocProS 2017. This conference is a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), the Indian Institute of Technology Roorkee, the South Asian University New Delhi and the National Institute of Technology Silchar, and

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Gene expression programming - How is Gene expression

Water temperature prediction in a subtropical subalpine lake using soft computing techniques Ferreira, " Gene expression programming : a new adaptive algorithm for solving problems," Complex Systems, vol.

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PredictingNanobinder-ImprovedUnsaturatedSoilConsistency

pressive strength properties of unsaturated treated soils alone but also enhances the strength with which soil ma- terials are held together or the resistance of soil to defor-

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A Polynomial Model for Concrete Compressive Strength

A Polynomial Model for Concrete Compressive Strength Prediction using GMDH-type Neural Networks and Genetic Algorithm N. Hamid-zadeh1, A. Jamali2, N. Nariman-zadeh2, H. Akbarzadeh1 1Department of Civil Engineering 2Department of mechanical Engineering Faculty, The University of Guilan

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Predicting Nanobinder-Improved Unsaturated Soil

Moreover, evolutionary predictions of the physical and mechanical properties of improved soils, such as unconfined compressive strength (UCS), internal friction, and coefficients of uniformity and gradation, with respect to the percentage of admixture contact (such as cement content) have been carried out by many emerging researchers without

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Improving the Prediction of Cement Compressive Strength by

The dynamic approach of two well-known techniques has been used to predict a cement’s 28-day compressive strength: Multiple linear regression (MLR), and artificial neural networks (ANN). The modeling is based on Portland cement data and utilizes daily physical, chemical analyses, and early strength results at days 1 and 7. Two kinds of models have been built, containing the 1-day strength as

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Athanasia D. Skentou‬ - ‪Μελετητής Google

Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests. PG Asteris, AD Skentou, A Bardhan, P Samui, PB Lourenço. Construction and Building Materials 303, 124450, . :

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PDF] Development of prediction models for shear strength

Abstract In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed

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PDF) Application of neural networks in the prediction of

Application of neural networks in the prediction of compressive strength of high strength concrete. soft computing techniques for for prediction of compressive strength of structural light

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The Role of Artificial Intelligence in Fighting the COVID

The first few months of have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields.

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Quantity of Cement and Sand Calculation in Mortar

For the calculation of cement mortar, let us assume that we use 1m 3 of cement mortar. Procedure for calculation is: 1. Calculate the dry volume of materials required for 1m 3 cement mortar. Considering voids in sands, we assume that materials consists of 60% voids. That is, for 1m 3 of wet cement mortar, 1.6m 3 of materials are required. 2.

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Soft computing techniques for the prediction of concrete

Prediction of concrete compressive strength using six widely used soft computing models. • The generated GP expressions can be utilized to estimate concrete compressive strength directly. • Determination of best performing model using a20-index. • Comparative assessment of results based on earlier studies.

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Application of ANFIS for modeling of microhardness of high

Nazari A and Riahi S. Prediction split tensile strength and water permeability of high strength concrete containing TiO 2 nanoparticles by artificial neural network and genetic programming. A Soft computing based approach for the prediction of ultimate strength of metal plates in compression.

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Computing Creep-Damage Interactions in Irradiated Concrete

To provide a basis of comparison, the mechanical properties of the cement paste and the aggregates are calibrated so that the elastic and strength macroscopic properties of the sample are similar to the one used by Le Pape for a structural analysis of a biological shield (Young's modulus: E = 34 GPa; compressive strength: f c = 40 MPa), using

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ASHU JAIN - IIT Kanpur

Jain, A. (2006), Improved performance of hydrologic and water resources systems using soft computing techniques, Special issue on soft computing - Directions, Vol. 7, Issue 3, published by IIT Kanpur, pp 42-48. 4.

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