Submitted:
08 August 2025
Posted:
11 August 2025
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Abstract
Keywords:
1. Introduction
2. Methodology
2.1. Materials and Properties
2.2. Biochar Production and Properties
2.3. Mix Proportions and Preparation
2.4. Artificial Neural Network (ANN)
3. Results and Discussion
3.1. Compressive Strength
3.2. Artificial Neural Network and Its Evaluation
4. Environmental Impact Results
4.1. Goal and Scope
4.2. Global Warming Potential
4.3. Waste Generation
5. Conclusions
- 1)
- The use of ASA with cement-based materials in concrete production has improved the concrete performance and significantly reduced the carbon footprint. In addition, it has been found that biochar has potential for use in concrete, and that it can be used as an alternative in combination with alumina-silica-based materials such as silica fume and metakaolin.
- 2)
- Using 5% ASA, 10% SF, and 10% MK instead of cement is an optimal alternative for improving the compressive strength of the concrete. In addition, the use of biochar with a higher silica content will also provide a higher increase in strength.
- 3)
- The fact that the strength tends to increase as the ASA usage rate increases up to a certain percentage leads to the conclusion that this material exhibits good hydration and filling effects.
- 4)
- In this study, a time and cost-saving approach was adopted to evaluate the compressive strength of concrete without the need to conduct several experimental tests, and an ANN-based prediction model was developed. The model predicted the performance of concrete with high accuracy, and a strong agreement was obtained between the predicted results and the experimental data. These results emphasize the importance of using alternative materials to cement and show that AI-based approaches are an effective tool for more environmentally sustainable concrete production.
- 5)
- Life cycle assessments have shown significant advantages in terms of GWP and WG.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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| Chemical Composition |
Cement (C) |
Silica Fume (SF) |
Metakaolin (MK) |
| SiO2 | 21.12 | 94.94 | 48 |
| Al2O3 | 5.62 | 0.70 | 47 |
| Fe2O3 | 3.24 | 0.60 | 0.6 |
| CaO | 62.94 | 0.83 | <0.5 |
| MgO | 2.73 | 0.71 | <0.4 |
| SO3 | 2.30 | 0.21 | - |
| Na2O | 0.10 | - | <0.1 |
| K2O | - | - | <2 |
| Physical Composition | |||
| Density (g/cm2) | 3.15 | 2.20 | 2.64 |
| Specific Surface (cm2/g) (Blaine) | 3379 | 96.5%<45μm | 22000 |
| State of the Sample | Unit | Air Dry | Dry |
As Received |
Method of Test |
| Total Moisture | % | - | - | 7.18 | ISO 589 |
| Inherent Moisture | % | 0.96 | - | - | ASTM D 3173 |
| Ash | % | 2.37 | 2.39 | 2.22 | ISO 1171 |
| Volatile Matter | % | 5.35 | 5.40 | 5.01 | ISO 562 |
| Total Sulphur | % | 0.02 | 0.02 | 0.02 | ASTM D 4239 |
| Fixed Carbon | % | 91.32 | 92.21 | 85.59 | ASTM D 3172 |
|
Mix Code |
Cement | Water | ASA | SF | MK |
Fine aggregate |
Coarse-1 aggregate |
Coarse-2 aggregate |
| CS | 298 | 175 | 0 | 24.7 | 14.8 | 447 | 447 | 596 |
| ASA-1-1 | 312 | 175 | 2.7 | 12.3 | 14.8 | 450 | 450 | 600 |
| ASA-1-2 | 294 | 175 | 2.7 | 12.3 | 29.6 | 454 | 454 | 606 |
| ASA-1-3 | 294 | 175 | 2.7 | 24.7 | 14.8 | 446 | 446 | 595 |
| ASA-1-4 | 277 | 175 | 2.7 | 24.7 | 29.6 | 440 | 440 | 586 |
| ASA-1-5 | 277 | 175 | 2.7 | 37.0 | 14.8 | 441 | 441 | 588 |
| ASA-1-6 | 259 | 175 | 2.7 | 37.0 | 29.6 | 435 | 435 | 580 |
| ASA-3-1 | 305 | 175 | 8.0 | 12.3 | 14.8 | 448 | 448 | 598 |
| ASA-3-2 | 287 | 175 | 8.0 | 12.3 | 29.6 | 435 | 435 | 580 |
| ASA-3-3 | 287 | 175 | 8.0 | 24.7 | 14.8 | 444 | 444 | 592 |
| ASA-3-4 | 270 | 175 | 8.0 | 24.7 | 29.6 | 438 | 438 | 584 |
| ASA-3-5 | 270 | 175 | 8.0 | 37.0 | 14.8 | 439 | 439 | 586 |
| ASA-3-6 | 252 | 175 | 8.0 | 37.0 | 29.6 | 433 | 433 | 578 |
| ASA-5-1 | 298 | 175 | 13.4 | 12.3 | 14.8 | 446 | 446 | 595 |
| ASA-5-2 | 280 | 175 | 13.4 | 12.3 | 29.6 | 440 | 440 | 590 |
| ASA-5-3 | 280 | 175 | 13.4 | 24.7 | 14.8 | 442 | 442 | 589 |
| ASA-5-4 | 263 | 175 | 13.4 | 24.7 | 29.6 | 436 | 436 | 581 |
| ASA-5-5 | 263 | 175 | 13.4 | 37.0 | 14.8 | 437 | 437 | 583 |
| ASA-5-6 | 245 | 175 | 13.4 | 37.0 | 29.6 | 431 | 431 | 575 |
| ASA-7-1 | 291 | 175 | 18.8 | 12.3 | 14.8 | 444 | 444 | 592 |
| ASA-7-2 | 273 | 175 | 18.8 | 12.3 | 29.6 | 438 | 438 | 584 |
| ASA-7-3 | 273 | 175 | 18.8 | 24.7 | 14.8 | 440 | 440 | 586 |
| ASA-7-4 | 256 | 175 | 18.8 | 24.7 | 29.6 | 434 | 434 | 578 |
| ASA-7-5 | 256 | 175 | 18.8 | 37.0 | 14.8 | 435 | 435 | 580 |
| ASA-7-6 | 238 | 175 | 18.8 | 37.0 | 29.6 | 429 | 429 | 572 |
| Samples | INPUTS | Experimental Outputs | ANN Outputs | ||||||
| W/C | A/C | SF/C | MK/C | ASA/C | 3th | 7th | 3th | 7th | |
| CS | 0.587 | 5.000 | 0.083 | 0.050 | 0.000 | 22.95 | 26.05 | 22.71 | 26.22 |
| ASA-1-5 | 0.633 | 5.321 | 0.134 | 0.054 | 0.010 | 23.30 | 26.7 | 23.18 | 26.56 |
| ASA-3-5 | 0.649 | 5.433 | 0.137 | 0.055 | 0.030 | 25.30 | 28.00 | 25.15 | 28.11 |
| ASA-5-4 | 0.667 | 5.533 | 0.094 | 0.113 | 0.051 | 27.85 | 29.85 | 27.39 | 29.99 |
| ASA-7-4 | 0.685 | 5.657 | 0.097 | 0.116 | 0.074 | 25.95 | 28.35 | 25.76 | 28.29 |
| ASA-7-5 | 0.685 | 5.676 | 0.145 | 0.058 | 0.074 | 24.10 | 26.80 | 23.68 | 26.79 |
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