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Regression Analysis Models to Predict the 28 -day Compressive Strength Using Accelerated Curing Tests

Journal of Engineering

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Title Regression Analysis Models to Predict the 28 -day Compressive Strength Using Accelerated Curing Tests
 
Creator Al-Hubboubi, Suhair K.
Abbas, Zena K.
 
Description Regression analysis models are adopted by using SPSS program to predict the 28-day compressive strength as dependent variable and the accelerated compressive strength as independent variable. Three accelerated curing method was adopted, warm water (35ºC) and autogenous according to ASTM C C684-99 and the British method (55ºC) according to BS1881: Part 112:1983. The experimental concrete mix design was according to ACI 211.1. Twenty eight concrete mixes with slump rang (25-50) mm and (75-100)mm for rounded and crushed coarse aggregate with cement content (585, 512, 455, 410, 372 and 341)Kg/m3.
      The experimental results showed that the accelerated strength were equal to about (0.356), (0.492) and (0.595) of the 28-day compressive strength for warm water, autogenous and British curing methods respectively. A statistical regression analysis using SPSS program is implemented for the experimental results of the 28-day compressive strength ranging from (16 to 55.2)Mpa and accelerated strength for different curing methods. The linear models with high R2 and F-value are adopted for different curing methods while the Power model with constant is the best model for non parametric analysis.
 
 
Publisher College of Engineering | University of Baghdad
 
Date 2018-01-13
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://joe.uobaghdad.edu.iq/index.php/main/article/view/489
 
Source مجلة الهندسة; مجلد 24 عدد 1 (2018): Journal of Engineering (Eng. J.); 1-19
Journal of Engineering; Vol 24 No 1 (2018): Journal of Engineering (Eng. J.); 1-19
2520-3339
1726-4073
 
Language eng
 
Relation http://joe.uobaghdad.edu.iq/index.php/main/article/view/489/417
 
Rights Copyright (c) 2018 Eng. J.