Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength

2013 ◽  
Vol 49 ◽  
pp. 554-563 ◽  
Author(s):  
Jui-Sheng Chou ◽  
Anh-Duc Pham
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
High Performance ◽  
High Performance Concrete ◽  
Concrete Compressive Strength ◽  
Ensemble Approach

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.

scholarly journals Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach

2018 ◽  
Vol 203 ◽  
pp. 06006 ◽  
Author(s):  
Doddy Prayogo ◽  
Foek Tjong Wong ◽  
Daniel Tjandra
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
High Performance ◽  
High Performance Concrete ◽  
Concrete Strength ◽  
Experimental Tests ◽  
Support Vector ◽  
Nonlinear Functional ◽  
Learning Capabilities ◽  
Quality Learning

This study introduces an improved artificial intelligence (AI) approach called intelligence optimized support vector regression (IO-SVR) for estimating the compressive strength of high-performance concrete (HPC). The nonlinear functional mapping between the HPC materials and compressive strength is conducted using the AI approach. A dataset with 1,030 HPC experimental tests is used to train and validate the prediction model. Depending on the results of the experiments, the forecast outcomes of the IO-SVR model are of a much higher quality compared to the outcomes of other AI approaches. Additionally, because of the high-quality learning capabilities, the IO-SVR is highly recommended for calculating HPC strength.

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