Optimization-based multitarget stacked machine-learning model for estimating mechanical properties of conventional and fiber-reinforced preplaced aggregate concrete
Published: 14 May 2025| Version 1 | DOI: 10.17632/wt52w9g9p5.1
Contributors:
Majed A. Saleh, Farzin Kazemi, Hakim Abdelgader, Haytham F. IsleemDescription
The dataset employed in the study "Optimization-based multitarget stacked machine-learning model for estimating mechanical properties of conventional and fiber-reinforced preplaced aggregate concrete" consists of 475 data points, with 419 datasets allocated for PAC and 56 datasets designated for FR-PAC. These datasets have been meticulously gathered from both existing literature (389 and 42 for PAC and FR-PAC, respectively) and our experimental work with 30 datasets for PAC and 14 for FR-PAC. To use the dataset, you should refer to the original paper linked below to ensure licensing and credit.
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Institutions
- University of Zintan
- University of Tripoli
- Military Technological College
- Politechnika Gdanska
- Jadara University
Categories
Data Science, Aluminum Silicate, Machine Learning, Machine Learning Algorithm, Concrete Technology, Reinforced Concrete, Steel-Reinforced Concrete, Fiber-Reinforced Composite, Slag, Fly Ash, Fiber Reinforced Concrete, Polypropylene, Infrastructure, Applied Machine Learning