A hybrid data analytics approach for high-performance concrete compressive strength prediction

2020 ◽  
Vol 3 (2) ◽  
pp. 158-168 ◽  
Author(s):  
Serhat Simsek ◽  
Mehmet Gumus ◽  
Mohamed Khalafalla ◽  
Tahir Bachar Issa
Keyword(s):  
Compressive Strength ◽  
Data Analytics ◽  
High Performance ◽  
High Performance Concrete ◽  
Strength Prediction ◽  
Concrete Compressive Strength ◽  
Hybrid Data

scholarly journals Optimised Mix Design for Normal Strength and High Performance Concrete Using Particle Packing Method

2011 ◽  
Vol 57 (4) ◽  
pp. 357-371 ◽  
Author(s):  
S. Gopinath ◽  
A. Ramachandra Murthy ◽  
D. Ramya ◽  
Nagesh R. Iyer
Keyword(s):  
Compressive Strength ◽  
High Performance ◽  
High Performance Concrete ◽  
American Concrete Institute ◽  
Particle Packing ◽  
Mix Design ◽  
Concrete Compressive Strength ◽  
Normal Strength Concrete ◽  
Normal Strength ◽  
Packing Method

Abstract This paper presents the details of optimized mix design for normal strength and high performance concrete using particle packing method. A critical review of mix design methods have been carried out for normal strength concrete using American Concrete Institute (ACI) and Bureau of Indian Standards (BIS) methods highlighting the similarities and differences towards attaining a particular design compressive strength. Mix design for M30 and M40 grades of concrete have been carried out using ACI, BIS and particle packing methods. Optimization of concrete mix has been carried out by means of particle packing method using EMMA software, which employs modified Anderson curve to adjust the main proportions. Compressive strength is evaluated for the adjusted proportions and it is observed that the mixes designed by particle packing method estimates compressive strength closer to design compressive strength. Further, particle packing method has been employed to optimize the ingredients of high performance concrete and experiments have been carried out to check the design adequacy of the desired concrete compressive strength.

Study on the Compressive Strength and Mixing of Ultra-High Performance Concrete

2013 ◽  
Vol 357-360 ◽  
pp. 825-828
Author(s):  
Su Li Feng ◽  
Peng Zhao
Keyword(s):  
Compressive Strength ◽  
High Performance ◽  
High Performance Concrete ◽  
High Strength ◽  
Strength Properties ◽  
Test Method ◽  
Test Results ◽  
Concrete Compressive Strength ◽  
Ultra High Performance Concrete ◽  
Mixture Ratio

The test in order to obtain liquidity, higher intensity ultra-high performance concrete(UHPC), in the course of preparation, high intensity quartz sand to replace the ordinary sand,reasonable mixture ratio control low water-cement ratio,the incorporation of part of the test piece ofsteel fibers, produced eight specimens . In the ordinary molding and the standard conservation 28d thecase, the ultra-high-performance concrete compressive strength of more than 170MPa.Thepreparation of the test method and test results will provide the basis for further study of the law of themechanical properties of ultra high strength properties of concrete.

High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods

2020 ◽  
pp. 118-140
Author(s):  
Melda Yucel ◽  
Ersin Namlı
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Prediction Model ◽  
High Performance Concrete ◽  
Prediction Method ◽  
Structural Features ◽  
Strength Prediction ◽  
Concrete Compressive Strength ◽  
Learning Methods ◽  
Machine Learning Methods

In this chapter, prediction applications of concrete compressive strength values were realized via generation of various hybrid models, which are based on decision trees as main prediction method, by using different artificial intelligence and machine learning techniques. In respect to this aim, a literature research was presented. Used machine learning methods were explained together with their developments and structural features. Various applications were performed to predict concrete compressive strength, and then feature selection was applied to prediction model in order to determine primarily important parameters for compressive strength prediction model. Success of both models was evaluated with respect to correct and precision prediction of values with different error metrics and calculations.

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