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    <pubDate>Tue, 23 Jun 2026 06:27:59 GMT</pubDate>
    <dc:date>2026-06-23T06:27:59Z</dc:date>
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      <title>Mathematical modeling for predication of strength of remixed concrete.</title>
      <link>http://localhost:8080/xmlui/handle/123456789/2822</link>
      <description>Title: Mathematical modeling for predication of strength of remixed concrete.
Authors: Bidkar, K. L.; Jadhao, P. D.
Abstract: This paper deals with the methodology, related to application of mathematical model for reuse&#xD;
of partially set old concrete by adding fresh concrete to form serviceable mix by considering&#xD;
their time lags and blend ratios. As Compared to the strength of the freshly prepared concretes&#xD;
the preset concrete obviously gives the reduction in strength. This reduction is further possible&#xD;
to be minimized to a certain extent on blending some quantity of a relatively fresh mix to the&#xD;
existing quantity of the preset mix.&#xD;
In the statistical methods of concrete mix design in civil engineering the most frequently used models&#xD;
are Scheffe’s and Osadebe’s models, which are quite suitable for concrete mix optimization but are&#xD;
greatly limited as a predetermined number of experiments must be carried out in order to formulate&#xD;
them and they can only be applied for mix ratios that fall within the predetermined observation points.&#xD;
Ibearugbulem’s regression model has been formulated as a new model to take care of these&#xD;
inherent problems in Scheffe’s and Osadebe’s. Some modifications were made to obtain the new&#xD;
model. This new model has been tested on concrete cubes for different mix ratios for 28 days&#xD;
compressive strengths. The Fisher f-test shows that the values of compressive cube strength&#xD;
predicted by the new regression model are very close to those from the experiment strength&#xD;
values, with f-value of 3.44 at 95% confidence level. Hence this new model of regression is&#xD;
useful in concrete mix desig</description>
      <pubDate>Mon, 01 Jul 2019 00:00:00 GMT</pubDate>
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      <dc:date>2019-07-01T00:00:00Z</dc:date>
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