Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization
Geopolymer concrete (GC) emerges as a sustainable alternative yet faces challenges in achieving optimal resource utilization for strength development. Balancing these aspects is crucial for its large-scale adoption as a sustainable material. The type and dosage of precursors, activator, curing, and...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302401291X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846115890555256832 |
|---|---|
| author | Bh Revathi R. Gobinath G Sri Bala T Vamsi Nagaraju Sridevi Bonthu |
| author_facet | Bh Revathi R. Gobinath G Sri Bala T Vamsi Nagaraju Sridevi Bonthu |
| author_sort | Bh Revathi |
| collection | DOAJ |
| description | Geopolymer concrete (GC) emerges as a sustainable alternative yet faces challenges in achieving optimal resource utilization for strength development. Balancing these aspects is crucial for its large-scale adoption as a sustainable material. The type and dosage of precursors, activator, curing, and mixing conditions influence compressive strength, setting time, and workability. Moreover, multiple experimental trials are required for a desirable geopolymer blend. Even the experimental parameters alone do not meet the design principles concerning sustainable construction. This paper presents a study on the mix design and interpretation of machine learning techniques (MLT) with XAI. To train the model, extensive experimental databases using the shapley additive explanations (SHAP) technique rank input factors that impact the strength aspect. The prediction models' performance was compared using coefficient of determination (R2) and root mean square error (RMSE). SHAP interpretations reveal that temperature, Na to Al ratio, and NaOH molarity are the main factors influencing the compressive strength of GC. Further, these parameters were crucial in developing the dense geopolymer matrix. By integrating XAI into the MLT approach, we have also opened new criteria for understanding the complex relationships between geopolymer concrete potential parameters and their compressive strength. |
| format | Article |
| id | doaj-art-82b156e9d1c549d0bd005d47dd5cc4e5 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-82b156e9d1c549d0bd005d47dd5cc4e52024-12-19T10:57:53ZengElsevierResults in Engineering2590-12302024-12-0124103036Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimizationBh Revathi0R. Gobinath1G Sri Bala2T Vamsi Nagaraju3Sridevi Bonthu4Department of Civil Engineering, SR University, Warangal, IndiaDepartment of Civil Engineering, SR University, Warangal, IndiaDepartment of Civil Engineering, SRKR Engineering College, Bhimavaram, India; Centre for Clean and Sustainable Environment, SRKR Engineering College, Bhimavaram, IndiaDepartment of Civil Engineering, SRKR Engineering College, Bhimavaram, India; Centre for Clean and Sustainable Environment, SRKR Engineering College, Bhimavaram, India; Corresponding author. Department of Civil Engineering, SRKR Engineering College, Bhimavaram, India.Department of Computer Science Engineering, Vishnu Institute of Technology, Bhimavaram, IndiaGeopolymer concrete (GC) emerges as a sustainable alternative yet faces challenges in achieving optimal resource utilization for strength development. Balancing these aspects is crucial for its large-scale adoption as a sustainable material. The type and dosage of precursors, activator, curing, and mixing conditions influence compressive strength, setting time, and workability. Moreover, multiple experimental trials are required for a desirable geopolymer blend. Even the experimental parameters alone do not meet the design principles concerning sustainable construction. This paper presents a study on the mix design and interpretation of machine learning techniques (MLT) with XAI. To train the model, extensive experimental databases using the shapley additive explanations (SHAP) technique rank input factors that impact the strength aspect. The prediction models' performance was compared using coefficient of determination (R2) and root mean square error (RMSE). SHAP interpretations reveal that temperature, Na to Al ratio, and NaOH molarity are the main factors influencing the compressive strength of GC. Further, these parameters were crucial in developing the dense geopolymer matrix. By integrating XAI into the MLT approach, we have also opened new criteria for understanding the complex relationships between geopolymer concrete potential parameters and their compressive strength.http://www.sciencedirect.com/science/article/pii/S259012302401291XGeopolymerSHAP analysisSustainable concreteMachine learning |
| spellingShingle | Bh Revathi R. Gobinath G Sri Bala T Vamsi Nagaraju Sridevi Bonthu Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization Results in Engineering Geopolymer SHAP analysis Sustainable concrete Machine learning |
| title | Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization |
| title_full | Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization |
| title_fullStr | Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization |
| title_full_unstemmed | Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization |
| title_short | Harnessing explainable Artificial Intelligence (XAI) for enhanced geopolymer concrete mix optimization |
| title_sort | harnessing explainable artificial intelligence xai for enhanced geopolymer concrete mix optimization |
| topic | Geopolymer SHAP analysis Sustainable concrete Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S259012302401291X |
| work_keys_str_mv | AT bhrevathi harnessingexplainableartificialintelligencexaiforenhancedgeopolymerconcretemixoptimization AT rgobinath harnessingexplainableartificialintelligencexaiforenhancedgeopolymerconcretemixoptimization AT gsribala harnessingexplainableartificialintelligencexaiforenhancedgeopolymerconcretemixoptimization AT tvamsinagaraju harnessingexplainableartificialintelligencexaiforenhancedgeopolymerconcretemixoptimization AT sridevibonthu harnessingexplainableartificialintelligencexaiforenhancedgeopolymerconcretemixoptimization |