Prediction of Target Mean Strength of Concrete Mixes

Masala Mwiko; Michael Nshumfwa Mulenga

doi:doi:10.11648/j.ajcbm.20220602.13

| Peer-Reviewed

Prediction of Target Mean Strength of Concrete Mixes

Masala Mwiko, Michael Nshumfwa Mulenga

Published in American Journal of Construction and Building Materials (Volume 6, Issue 2)

Received: 4 October 2022 Accepted: 31 October 2022 Published: 15 December 2022

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

In practice cured concrete strength may exhibit strength variation from batch to batch and within batch despite the mixes being of the same proportions and with quality control. On the other hand, structural concrete elements are designed to meet specific characteristic strength. In order to ensure compliance to the specified characteristic strength, concrete mix designers target strengths higher than the specified characteristic strengths, commonly known as Target Mean Strength. This study aimed at establishing the margins between characteristic strength and Target Mean Strength for various local concrete mixes. In order to achieve this overall objective, a semi structured questionnaire was used to identify the popular concrete mix design models in the country. The identified model was then validated through experimental mix designs, concrete mixes, cube casting, curing and strength testing. The mixes were designed for normal concrete classes; C20, C25 and C30. The sample size for each of these classes was guided by the British-DOE method of mix design that demands for standard deviation to be calculated from at least 20 results and the Indian Standard IS 456:2000 that demands a sample size of at least 30 for each concrete class. This study utilized 101 concrete mixes; 31 samples for C20 and 35 for each of the C25 and C30 classes. Three concrete cube specimens were cast for each mix, cured for 28 days and tested for compressive strength at the Civil Engineering Laboratory of the University of Zambia. From the compressive strength results, the probability density function for each class was generated using Microsoft Office excel. The determined standard deviations (s) from the distributions were 8.19, 8.00 and 8.27 MPa for concrete classes of C20, C25 and C30, respectively, which implied margins of 13.43, 13.12 and 13.56 MPa, respectively for 95% reliability (k=1.64). Therefore, the established margins (k x s) can be used for predicting Target Mean Strength of concrete mixes at 95% reliability for the Zambian concrete mixes provided the BS-DOE model is applied and constituent materials are similar to those used in the study.

Published in	American Journal of Construction and Building Materials (Volume 6, Issue 2)
DOI	10.11648/j.ajcbm.20220602.13
Page(s)	93-104
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Characteristic Strength, Target Mean Strength, Standard Deviation, Margin, British-DOE

References

[1]	Building Research Establishment. (2010). Design of Normal Concrete Mixes. 2nd Edition, BRE, Garston Watford.
[2]	Skrzypczak, Izabela & Slowk, Marta. (2019). “Economical Aspects Concerning Quality Control of Concrete.” Budownictwo i Architektura. 18.049-056. DOI: 10.24358/Bud_Arch_19_181_05
[3]	American Concrete Institute. (2011). ACI 214R-11: Guide to Evaluation of Strength Test Results of Concrete. Farmington Hills, USA. Reported by ACI Committee 214.
[4]	American Concrete Institute. (2019). ACI 318-19: Building Code Requirements for Structural Concrete. Farmington Hills, USA. Reported by ACI Committee 318.
[5]	Abdelkader, Hakim; Suleiman, Ramadan; Adam, Abdalla; and Khatib, Jamal. (2020). “Concrete Mix Design Using Simple Equation,” BAU Journal-Science and Technology. Vol. 2: Iss. 1, Article 2. Available at: https://digitalcommons.bau.edu.lb/stjournal/vol2/iss1/2
[6]	Shetty, M. S and Jain A. K. (2019). Concrete Technology: Theory and Practice. Ram Naga, New Delhi-110 055: S. Chand.
[7]	British Standard Institution. (2019). BS 8500-1+A2:2019: Concrete Complementary British Standard to BS EN 206: Method of Specifying and guidance for the specifier. London: British Standard Institution. Available at: pdfcoffee.com/qdownload/bs-8500-1-2015a2-2019-pdf-free.html
[8]	Bureau of Indian Standards. (2019). IS 10262: 2019: Concrete Mix Proportioning-Guidelines. Manak Bhavan, 9 Bahadur Shar Zafar Marg. New Delhi-110002: Bureau of Indian Standards.
[9]	Sorate Shekhar M. and Thool Kushal P. (2020). “Experimental Investigation on Mix Design of Concrete by Using IS Method and ACI Method.” International Journal of Creative Research Thoughts (IJCRT). Volume 8: Available at: ijcrt.org/papers/IJCRT2005323.pdf
[10]	Demissew, Abebe. (2022). “Comparative Analysis of Selected Concrete Mix Design Methods Based on Cost-Effectiveness.” Advances in Civil Engineering. Volume 2022: Available at: https://doi.org/ 10.1155/2022/4240774.
[11]	Uche O, Abdulwahab, M, Suleiman, A., & Ismail, Y. (2019). “Prediction Modeling of 28-Day Concrete Compressive Strength Using Artificial Neutral Network.” Arid Zone Journal of Engineering, Technology & Environment. Vol. 15 (3) 692-701. Available at: https://www.azojete.com.ng/index.php/azojete/article/view/48
[12]	British Standard Institution. (1990). BS 812: 1990: Testing Aggregates. London: British Standard Institution.
[13]	British Standard Institution. (1983). BS 1881:1983: Testing Concrete. London: British Standard Institution.
[14]	Alkarkhi, Abbas. (2021). “Z-Test for one sample mean.” ResearchGate. DOI: 10.1016/B978-0-12-824301-5.00007-1.
[15]	Siqi Li, Jinbo Yang, Peng Zhang. (2020). “Water-Cement-Density Ratio Law for the 28-Day Compressive Strength Prediction of Cement- Based Materials.” Advances in Materials Science and Engineering. Doi: 2020.10.115/2020/7302173
[16]	Ministry of Construction, the Republic of the Union of Myanmar and Japan International Cooperation Agency. (2019). Quality Control Manual for Concrete Structure. 1st Edition. Myanmar: Ministry of Construction, the Republic of the Union of Myanmar.
[17]	Jean Michel Torrenti, Frank Dehn. (2019). “On the Relationship Between Mean Compressive Strength and the Characteristic One.” Structural Concrete. 12p. DOI: 10.1002/suco.201900153. hal-02469760
[18]	Japan Society of Civil Engineers. (2010). JSCE Guide line for Concrete No. 16: Standard Specifications for Concrete Structures-2007: “Materials and Construction”. Tokyo: JSCE, Concrete Committee.

Cite This Article

Plain Text BibTeX RIS

APA Style

Masala Mwiko, Michael Nshumfwa Mulenga. (2022). Prediction of Target Mean Strength of Concrete Mixes. American Journal of Construction and Building Materials, 6(2), 93-104. https://doi.org/10.11648/j.ajcbm.20220602.13

Copy | Download

ACS Style

Masala Mwiko; Michael Nshumfwa Mulenga. Prediction of Target Mean Strength of Concrete Mixes. Am. J. Constr. Build. Mater. 2022, 6(2), 93-104. doi: 10.11648/j.ajcbm.20220602.13

Copy | Download

AMA Style

Masala Mwiko, Michael Nshumfwa Mulenga. Prediction of Target Mean Strength of Concrete Mixes. Am J Constr Build Mater. 2022;6(2):93-104. doi: 10.11648/j.ajcbm.20220602.13

Copy | Download

@article{10.11648/j.ajcbm.20220602.13,
  author = {Masala Mwiko and Michael Nshumfwa Mulenga},
  title = {Prediction of Target Mean Strength of Concrete Mixes},
  journal = {American Journal of Construction and Building Materials},
  volume = {6},
  number = {2},
  pages = {93-104},
  doi = {10.11648/j.ajcbm.20220602.13},
  url = {https://doi.org/10.11648/j.ajcbm.20220602.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcbm.20220602.13},
  abstract = {In practice cured concrete strength may exhibit strength variation from batch to batch and within batch despite the mixes being of the same proportions and with quality control. On the other hand, structural concrete elements are designed to meet specific characteristic strength. In order to ensure compliance to the specified characteristic strength, concrete mix designers target strengths higher than the specified characteristic strengths, commonly known as Target Mean Strength. This study aimed at establishing the margins between characteristic strength and Target Mean Strength for various local concrete mixes. In order to achieve this overall objective, a semi structured questionnaire was used to identify the popular concrete mix design models in the country. The identified model was then validated through experimental mix designs, concrete mixes, cube casting, curing and strength testing. The mixes were designed for normal concrete classes; C20, C25 and C30. The sample size for each of these classes was guided by the British-DOE method of mix design that demands for standard deviation to be calculated from at least 20 results and the Indian Standard IS 456:2000 that demands a sample size of at least 30 for each concrete class. This study utilized 101 concrete mixes; 31 samples for C20 and 35 for each of the C25 and C30 classes. Three concrete cube specimens were cast for each mix, cured for 28 days and tested for compressive strength at the Civil Engineering Laboratory of the University of Zambia. From the compressive strength results, the probability density function for each class was generated using Microsoft Office excel. The determined standard deviations (s) from the distributions were 8.19, 8.00 and 8.27 MPa for concrete classes of C20, C25 and C30, respectively, which implied margins of 13.43, 13.12 and 13.56 MPa, respectively for 95% reliability (k=1.64). Therefore, the established margins (k x s) can be used for predicting Target Mean Strength of concrete mixes at 95% reliability for the Zambian concrete mixes provided the BS-DOE model is applied and constituent materials are similar to those used in the study.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Prediction of Target Mean Strength of Concrete Mixes
AU  - Masala Mwiko
AU  - Michael Nshumfwa Mulenga
Y1  - 2022/12/15
PY  - 2022
N1  - https://doi.org/10.11648/j.ajcbm.20220602.13
DO  - 10.11648/j.ajcbm.20220602.13
T2  - American Journal of Construction and Building Materials
JF  - American Journal of Construction and Building Materials
JO  - American Journal of Construction and Building Materials
SP  - 93
EP  - 104
PB  - Science Publishing Group
SN  - 2640-0057
UR  - https://doi.org/10.11648/j.ajcbm.20220602.13
AB  - In practice cured concrete strength may exhibit strength variation from batch to batch and within batch despite the mixes being of the same proportions and with quality control. On the other hand, structural concrete elements are designed to meet specific characteristic strength. In order to ensure compliance to the specified characteristic strength, concrete mix designers target strengths higher than the specified characteristic strengths, commonly known as Target Mean Strength. This study aimed at establishing the margins between characteristic strength and Target Mean Strength for various local concrete mixes. In order to achieve this overall objective, a semi structured questionnaire was used to identify the popular concrete mix design models in the country. The identified model was then validated through experimental mix designs, concrete mixes, cube casting, curing and strength testing. The mixes were designed for normal concrete classes; C20, C25 and C30. The sample size for each of these classes was guided by the British-DOE method of mix design that demands for standard deviation to be calculated from at least 20 results and the Indian Standard IS 456:2000 that demands a sample size of at least 30 for each concrete class. This study utilized 101 concrete mixes; 31 samples for C20 and 35 for each of the C25 and C30 classes. Three concrete cube specimens were cast for each mix, cured for 28 days and tested for compressive strength at the Civil Engineering Laboratory of the University of Zambia. From the compressive strength results, the probability density function for each class was generated using Microsoft Office excel. The determined standard deviations (s) from the distributions were 8.19, 8.00 and 8.27 MPa for concrete classes of C20, C25 and C30, respectively, which implied margins of 13.43, 13.12 and 13.56 MPa, respectively for 95% reliability (k=1.64). Therefore, the established margins (k x s) can be used for predicting Target Mean Strength of concrete mixes at 95% reliability for the Zambian concrete mixes provided the BS-DOE model is applied and constituent materials are similar to those used in the study.
VL  - 6
IS  - 2
ER  -

Copy | Download

Author Information

Masala Mwiko

Department of Civil and Environmental Engineering, School of Engineering, The University of Zambia, Lusaka, Zambia
Michael Nshumfwa Mulenga

Department of Civil and Environmental Engineering, School of Engineering, The University of Zambia, Lusaka, Zambia

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Masala Mwiko, Michael Nshumfwa Mulenga. (2022). Prediction of Target Mean Strength of Concrete Mixes. American Journal of Construction and Building Materials, 6(2), 93-104. https://doi.org/10.11648/j.ajcbm.20220602.13

Copy | Download

ACS Style

Masala Mwiko; Michael Nshumfwa Mulenga. Prediction of Target Mean Strength of Concrete Mixes. Am. J. Constr. Build. Mater. 2022, 6(2), 93-104. doi: 10.11648/j.ajcbm.20220602.13

Copy | Download

AMA Style

Masala Mwiko, Michael Nshumfwa Mulenga. Prediction of Target Mean Strength of Concrete Mixes. Am J Constr Build Mater. 2022;6(2):93-104. doi: 10.11648/j.ajcbm.20220602.13

Copy | Download

@article{10.11648/j.ajcbm.20220602.13,
  author = {Masala Mwiko and Michael Nshumfwa Mulenga},
  title = {Prediction of Target Mean Strength of Concrete Mixes},
  journal = {American Journal of Construction and Building Materials},
  volume = {6},
  number = {2},
  pages = {93-104},
  doi = {10.11648/j.ajcbm.20220602.13},
  url = {https://doi.org/10.11648/j.ajcbm.20220602.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcbm.20220602.13},
  abstract = {In practice cured concrete strength may exhibit strength variation from batch to batch and within batch despite the mixes being of the same proportions and with quality control. On the other hand, structural concrete elements are designed to meet specific characteristic strength. In order to ensure compliance to the specified characteristic strength, concrete mix designers target strengths higher than the specified characteristic strengths, commonly known as Target Mean Strength. This study aimed at establishing the margins between characteristic strength and Target Mean Strength for various local concrete mixes. In order to achieve this overall objective, a semi structured questionnaire was used to identify the popular concrete mix design models in the country. The identified model was then validated through experimental mix designs, concrete mixes, cube casting, curing and strength testing. The mixes were designed for normal concrete classes; C20, C25 and C30. The sample size for each of these classes was guided by the British-DOE method of mix design that demands for standard deviation to be calculated from at least 20 results and the Indian Standard IS 456:2000 that demands a sample size of at least 30 for each concrete class. This study utilized 101 concrete mixes; 31 samples for C20 and 35 for each of the C25 and C30 classes. Three concrete cube specimens were cast for each mix, cured for 28 days and tested for compressive strength at the Civil Engineering Laboratory of the University of Zambia. From the compressive strength results, the probability density function for each class was generated using Microsoft Office excel. The determined standard deviations (s) from the distributions were 8.19, 8.00 and 8.27 MPa for concrete classes of C20, C25 and C30, respectively, which implied margins of 13.43, 13.12 and 13.56 MPa, respectively for 95% reliability (k=1.64). Therefore, the established margins (k x s) can be used for predicting Target Mean Strength of concrete mixes at 95% reliability for the Zambian concrete mixes provided the BS-DOE model is applied and constituent materials are similar to those used in the study.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Prediction of Target Mean Strength of Concrete Mixes
AU  - Masala Mwiko
AU  - Michael Nshumfwa Mulenga
Y1  - 2022/12/15
PY  - 2022
N1  - https://doi.org/10.11648/j.ajcbm.20220602.13
DO  - 10.11648/j.ajcbm.20220602.13
T2  - American Journal of Construction and Building Materials
JF  - American Journal of Construction and Building Materials
JO  - American Journal of Construction and Building Materials
SP  - 93
EP  - 104
PB  - Science Publishing Group
SN  - 2640-0057
UR  - https://doi.org/10.11648/j.ajcbm.20220602.13
AB  - In practice cured concrete strength may exhibit strength variation from batch to batch and within batch despite the mixes being of the same proportions and with quality control. On the other hand, structural concrete elements are designed to meet specific characteristic strength. In order to ensure compliance to the specified characteristic strength, concrete mix designers target strengths higher than the specified characteristic strengths, commonly known as Target Mean Strength. This study aimed at establishing the margins between characteristic strength and Target Mean Strength for various local concrete mixes. In order to achieve this overall objective, a semi structured questionnaire was used to identify the popular concrete mix design models in the country. The identified model was then validated through experimental mix designs, concrete mixes, cube casting, curing and strength testing. The mixes were designed for normal concrete classes; C20, C25 and C30. The sample size for each of these classes was guided by the British-DOE method of mix design that demands for standard deviation to be calculated from at least 20 results and the Indian Standard IS 456:2000 that demands a sample size of at least 30 for each concrete class. This study utilized 101 concrete mixes; 31 samples for C20 and 35 for each of the C25 and C30 classes. Three concrete cube specimens were cast for each mix, cured for 28 days and tested for compressive strength at the Civil Engineering Laboratory of the University of Zambia. From the compressive strength results, the probability density function for each class was generated using Microsoft Office excel. The determined standard deviations (s) from the distributions were 8.19, 8.00 and 8.27 MPa for concrete classes of C20, C25 and C30, respectively, which implied margins of 13.43, 13.12 and 13.56 MPa, respectively for 95% reliability (k=1.64). Therefore, the established margins (k x s) can be used for predicting Target Mean Strength of concrete mixes at 95% reliability for the Zambian concrete mixes provided the BS-DOE model is applied and constituent materials are similar to those used in the study.
VL  - 6
IS  - 2
ER  -

Copy | Download