Engineering and Technology Journal (Feb 2009)

Predicting Mechanical Properties of High Performance Concrete by Using Non-destructive Tests

  • Shakir A. Al-Mishhadani,
  • Waleed A. Al-Qaisi,
  • Sura F. Al-Khafaji

DOI
https://doi.org/10.30684/etj.27.3.3
Journal volume & issue
Vol. 27, no. 3
pp. 425 – 444

Abstract

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In this study, high performance concrete mixes were produced by using highrange water reducing agent and also by using 10% silica fume or 10% highreactivity metakaolin as a partial replacement by weight of cement. Three cementcontents (350, 450, and 550) kg/m3 were used through this study. A total of 330(100 mm) cubes, 132 (100×200 mm) cylinders, 132 (100×100×400 mm) prisms,and 66 (150×300 mm) cylinders were casted and cured to the required age of test .All specimens were cured in tap water except 165 cubes, which were submerged inCl ˉ + SO4ˉ ˉ solution at concentration identical to those present in severeaggressive environment to study the effect of this solution on the compressivestrength of high performance concrete mixes. Compressive strength, splittingtensile strength, modulus of rupture, static modulus, rebound number, ultrasonicpulse velocity, dynamic modulus, initial surface absorption, density ,and totalabsorption tests were investigated for all mixes at 7, 28, 90, and 120 days age.Results of the destructive tests (compressive tensile strength, strength, splittingmodulus of rupture, and static modulus) and non–destructive tests (hammer,ultrasonic pulse velocity, and dynamic modulus) are statistically analyzed by usingSPSS Ver.15 software to study the possibility of predicting the mechanicalproperties of high performance concrete by using non–destructive tests. Simple andmultiple linear regression analysis of the obtained results leads to the proposedstatistical models for evaluating the compressive strength, splitting tensilestrength, modulus of rupture, and static modulus by using one or two or three ofthe above mentioned non–destructive tests. Analysis of variance (ANOVA)and t–test was also used to investigate the adequacy of the statistical models.

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