Submission & tracking

For submitting new manuscripts or tracking the existing ones, login or register to the Submission Tracking System.



Special issue

Special Issue Proposals:

The journal of RESM is open to proposals for special issues on emerging related topics. More info is here.

Research Article

Machine learning approaches for predicting compressive strength of concrete with fly ash admixture

Lomesh Mahajan, Sariputt Bhagat

Department of Civil Engineering, Dr. Babasaheb Ambedkar Technological University, 402103, India



Compressive strength;


Fly ash;


M-L Models;

Prediction Techniques

As worldwide environments differ from place to place, the cementitious composites change their initial characteristics. That's why it's crucial to understand their mechanical qualities for protection. In the case of concrete, the Compressive strength (C.S) is among the most crucial properties. Nowadays Machine learning (M-L) methods have been significant tools to predicting the C.S of concrete rather than traditional methods. In this study, the experimental investigation is compiled and M-L approaches are used to predict the C.S of fly ash-containing concrete. All of the materials in this research were analyzed for their chemical and physical characteristics and supervised machine learning techniques are the focus for predicting concrete C.S. Outcome prediction techniques like Artificial neural networks (A N N), Gene expression programming (G E P), and Decision trees (D-T) were studied. To run the models with proper datasets, concrete samples (cylinders) with varying mix ratios were cast and evaluated at different ages. The 07 input elements (Cement, fly ash, superplasticizer, coarse aggregate, fine aggregate, water, and curing days) were used to forecast the output element C.S. A total of 100 data points were used to predict CS. Furthermore, the experimental evidence is validated by study of Root mean error (RME), Root mean Square error (R M S E). and k-fold Cross validation (R2), The statistical tests were included to see how well the adopted model was performed. The bagging algorithm method outperforms GEP, ANN, and DT in terms of prediction accuracy, as shown by an R2 value of 0.97 vs 0.82, 0.81, and 0.78, respectively.

© 2022 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. 

(More details of the news may be given in the News section)

For more see News...


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.


  • Asos Indeks
  • CiteFactor
  • Cosmos
  • CrossRef
  • Directory of Research Journal Indexing
  • Engineering Journals (ProQuest)
  • EZB Electronic Journal Library
  • Global Impact Factor
  • Google Scholar
  • InfoBase Index
  • International Institute of Organized Research (I2OR)
  • International Scientific Indexing (ISI)
  • Materials Science & Engineering Database (ProQuest)
  • Open Academic Journals Index
  • Publication Forum
  • Research BibleScientific Indexing Service
  • Root Indexing
  • Scopus
  • Ulakbim TR Index (Tubitak)
  • Universal Impact Factor
  • Scope Database


©2014. All rights reserved

Contact :

For publication issues

For administrative issues:

Postal Address:

Kemal Öz Mah. 3. Bilgi Sok., 4A, No:13 Usak/Turkey

Last update

of this page:



Go to main page for last version