- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
A Heuristic Approach for Predicting the Geometrical Packing of Cementitious Paste to Reduce CO2 Emissions in Reinforced Concrete Production | ||
| Iraqi Journal of Civil Engineering | ||
| Volume 17, Issue 1, June 2023, Pages 1-18 PDF (1.56 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.37650/ijce.2023.170101 | ||
| Author | ||
| Haider Adel Abdulhameed* | ||
| Civil Engineering Department, University of Technology-Iraq, Baghdad, Iraq | ||
| Abstract | ||
| In recent years, a number of researchers have adopted the wet packing (WP) approach to design different types of concrete mixes. Particle grading is a key to the optimization of the wet compactness density; for that reason, all empty spaces that exist in between large-size particles need to be completely filled with particles of smaller size. Previously-conducted studies in this field have been focused on measuring the particle size distribution’s packing density (PD) of the of granular matrices is the purpose of investigating how to increase the PD of cementitious materials. Thus, literature lacks models capable of predicting the optimal PD value. The current study collected and analyzed 216 datasets in order to construct a model for accurate prediction of PD. The main datasets were organized into two categories: modeling datasets and validation datasets. To configure the model in the best way, a hybrid gravitational search algorithm-artificial neural network (GSA-ANN) was also developed in this study. The findings confirmed ANN as an effective alternative for measuring the ultimate PD of cementitious pastes. ANN provided high levels of accuracy, practicality, and effectiveness in the process of predicting the PD value. Based on the final results, the implementation of the hybrid GSA-ANN technique causes a significant decrease in the number of tests conducted on experimental samples, which results in not only saving time and money, but also reducing the CO2 emission volume. | ||
| Keywords | ||
| Packing density,,; ,،,؛Artificial Neural Network (ANN),,; ,،,؛Cementitious paste,,; ,،,؛Gravitational Search Algorithm (GSA,,; ,،,؛Mix-design | ||
|
Statistics Article View: 1,427 PDF Download: 179 |
||