Abstract
This study explores a drilling-based test methodology for nondestructive estimation of in-situ concrete strength. An experimental campaign has been carried out to develop a relationship between the drilling-resistance parameter, DR, and compressive strength of concrete. Rebound hammer (RH) and ultrasonic pulse velocity (UPV) tests were also performed and multivariate regression models complementing DR with RH and UPV data have been developed. A machine learning approach utilizing support vector machines (SVM) was implemented. The experimental data support that combined usage of DR with UPV and/or RH provides a robust tool for compressive strength prediction. Even with the limited data available, the support vector regression model shows promising performance.
| Original language | English |
|---|---|
| Article number | 129700 |
| Journal | Construction and Building Materials |
| Volume | 362 |
| DOIs | |
| Publication status | Published - 2 Jan 2023 |
Bibliographical note
Publisher Copyright:© 2022 Elsevier Ltd
Funding
The authors gratefully acknowledge that the research presented in this paper was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) Project No: 214 M054. The experimental work was performed by the second author as part of his master’s thesis at ITU with additional funding through Scientific Research Projects (BAP) of Istanbul Technical University under Project No. MYL-2019-42522.
| Funders | Funder number |
|---|---|
| International Technological University | |
| Türkiye Bilimsel ve Teknolojik Araştırma Kurumu | 214 M054 |
| Istanbul Teknik Üniversitesi | MYL-2019-42522 |
Keywords
- Concrete strength
- Drilling resistance
- Non-destructive test
- Rebound hammer
- Regression
- Support-vector regression
- Ultrasonic pulse velocity