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Research Article

Predicting strength of concrete by ensemble technique

Monali Kirangea1Lomesh Mahajan2

1Department of Computer Engineering, RCPET’s IMRD, Shirpur, 425405, India

2Department of Civil Engineering, R. C. Patel Institute of Technology, Shirpur, Affilated to Dr. Babasaheb Ambedkar Technological University, Lonere, India



Compressive strength;



Ensemble technique;

Decision tree

The developing countries share similar attributes at all the regions. Still 43% of urban population did not escape from slums live hoods. As many developing countries focuses on the infrastructural development and try to improve people living standards. This infrastructural built-up activity consumes lots of concrete and other construction materials. These construction materials possess different properties from place to place. Cementitious composites undergo transformations in their fundamental properties due to regional variations in environmental conditions. Therefore, their mechanical strength computing tools plays crucial role. When topic touches with concrete, one of the most important characteristics is the compressive strength. Predicting the strength of concrete has traditionally been done with using mechanical means, but in recent years few soft computing methods have become important tools. In this research, we apply two methods to compute the Compressive strength of fly ash concrete based on the results of our own experimental findings. To anticipate concrete strength., this study investigated the properties of all the materials involved. The ensemble methodology and the decision tree were two of the success-forecasting methodologies that were investigated, and comparative assessments were made on them. The R2 value for the ensemble methodology was determined to be 0.96, which was much higher than the DT method's 0.76. In addition to k-fold Cross Validation, the findings of the trials are further supported by assessments of root mean square error (RMSE) and root mean error (RME). Ensemble approaches are good for minimizing model variance, improving prediction accuracy. Combining many models to make a single forecast from all their potential predictions eliminates variation.

© 2023 MIM Research Group. All rights reserved.


01/01/2024 Best Paper Award: The winners of 2023 Best Paper Award of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Author Awards section.

01/01/2024 Most Cited Paper Award: The winners of 2023 Most Cited Paper Award of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Author Awards section.

01/01/2024 Reviewer Awards: The winners of 2023 reviewer awards of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Reviewer Awards section.

8/12/2023 Special Issue: Embark on a journey of innovation with the journal of Research on Engineering Structures and Materials as we unveil a compelling opportunity for contributors in our upcoming special issue, "Design, Analysis, and Manufacturing of Composite Vehicle Structures." Led by distinguished Guest Editors Liubov Gavva and Oleg Mitrofanov from Moscow Aviation Institute. For more info see the link.

21/10/2023 Journal Submission System Upgrade Completed: We're delighted to announce that our Journal Submission and Tracking System has undergone a significant upgrade, aimed at enhancing user experience. 

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2023 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.

2023 Best Paper Award:

The paper authored by Ferzan Fidan, Naim Aslan, Mümin Mehmet Koç entitled as “Morpho-structural and compressive mechanical properties of graphene oxide reinforced hydroxyapatite scaffolds for bone tissue applications” is awarded.

2023 Most Cited Paper Award:

The paper authored by Ercan Işık, Ehsan Harirchian, Hüseyin Bilgin, Kirti Jadhav entitled as “The effect of material strength and discontinuity in RC structures according to different site-specific design spectra" is awarded.


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