Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach
Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li<sub>2</sub>CO<sub>3...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/16/2/654 |
| _version_ | 1797439278984200192 |
|---|---|
| author | Sai Akshay Ponduru Taihao Han Jie Huang Aditya Kumar |
| author_facet | Sai Akshay Ponduru Taihao Han Jie Huang Aditya Kumar |
| author_sort | Sai Akshay Ponduru |
| collection | DOAJ |
| description | Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li<sub>2</sub>CO<sub>3</sub> content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders’ compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science. |
| first_indexed | 2024-03-09T11:49:30Z |
| format | Article |
| id | doaj.art-708aa1006b7645978fbf019e796a9982 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T11:49:30Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-708aa1006b7645978fbf019e796a99822023-11-30T23:16:19ZengMDPI AGMaterials1996-19442023-01-0116265410.3390/ma16020654Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven ApproachSai Akshay Ponduru0Taihao Han1Jie Huang2Aditya Kumar3Department of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USADepartment of Materials Science and Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USACalcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li<sub>2</sub>CO<sub>3</sub> content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders’ compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science.https://www.mdpi.com/1996-1944/16/2/654calcium aluminate cementXGBoost modelanalytical modelcompressive strengthphase assemblage |
| spellingShingle | Sai Akshay Ponduru Taihao Han Jie Huang Aditya Kumar Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach Materials calcium aluminate cement XGBoost model analytical model compressive strength phase assemblage |
| title | Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach |
| title_full | Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach |
| title_fullStr | Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach |
| title_full_unstemmed | Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach |
| title_short | Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach |
| title_sort | predicting compressive strength and hydration products of calcium aluminate cement using data driven approach |
| topic | calcium aluminate cement XGBoost model analytical model compressive strength phase assemblage |
| url | https://www.mdpi.com/1996-1944/16/2/654 |
| work_keys_str_mv | AT saiakshayponduru predictingcompressivestrengthandhydrationproductsofcalciumaluminatecementusingdatadrivenapproach AT taihaohan predictingcompressivestrengthandhydrationproductsofcalciumaluminatecementusingdatadrivenapproach AT jiehuang predictingcompressivestrengthandhydrationproductsofcalciumaluminatecementusingdatadrivenapproach AT adityakumar predictingcompressivestrengthandhydrationproductsofcalciumaluminatecementusingdatadrivenapproach |