RDL logo
About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide
​
​
Sign inGet started
​
​

About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide

Sign inGet started
RDL logo

Verified research datasets. Instant access. Built for collaboration.

Navigation

About

Aims and Scope

Advisory Board Members

More

Who We Are?

Add Raw Data

User Guide

Legal

Privacy Policy

Terms of Service

Support

Got an issue? Email us directly.

Email: info@rawdatalibrary.netOpen Mail App
​
​

© 2025 Raw Data Library. All rights reserved.
PrivacyTerms
  1. Raw Data Library
  2. /
  3. Publications
  4. /
  5. Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete

Verified authors • Institutional access • DOI aware
50,000+ researchers120,000+ datasets90% satisfaction
Article
en
2024

Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete

0 Datasets

0 Files

en
2024
Vol 10 (4)
Vol. 10
DOI: 10.1016/j.heliyon.2024.e25997

Get instant academic access to this publication’s datasets.

Create free accountHow it works
Access Research Data

Join our academic network to download verified datasets and collaborate with researchers worldwide.

Get Free Access
Institutional SSO
Secure
This PDF is not available in different languages.
No localized PDFs are currently available.
Rawaz Kurda
Rawaz Kurda

Institution not specified

Verified
Dilshad Kakasor Ismael Jaf
Aso A. Abdalla
Ahmed Salih Mohammed
+3 more

Abstract

Tire rubber waste is globally accumulated every year. Therefore, a solution to this problem should be found since, if landfilled, it is not biodegradable and causes environmental issues. One of the most effective ways is recycling those wastes or using them as a replacement for normal aggregate in the concrete mixture, which has high impact resistance and toughness; thus, it will be a good choice. In this study, 135 data were collected from previous literature to develop a model for the prediction of rubberized concrete compressive strength; the database comprised different mixture proportions, the maximum size of the rubber (1-40 mm), and the rubber percentage (0-100%) replacing natural fine and coarse aggregates were among the input parameters in addition to cement content (380-500 kg/m

How to cite this publication

Dilshad Kakasor Ismael Jaf, Aso A. Abdalla, Ahmed Salih Mohammed, Payam Ismael Abdulrahman, Rawaz Kurda, Azad A. Mohammed (2024). Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete. , 10(4), DOI: https://doi.org/10.1016/j.heliyon.2024.e25997.

Related publications

Why join Raw Data Library?

Quality

Datasets shared by verified academics with rich metadata and previews.

Control

Authors choose access levels; downloads are logged for transparency.

Free for Academia

Students and faculty get instant access after verification.

Publication Details

Type

Article

Year

2024

Authors

6

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1016/j.heliyon.2024.e25997

Join Research Community

Access datasets from 50,000+ researchers worldwide with institutional verification.

Get Free Access

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic accessLearn more
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration