Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques
Foam glass (FG) is characterized by its lightweight structure and exceptional insulating properties, making it a highly suitable material for a wide range of applications. It is produced by foaming molten glass, resulting in a cellular structure that enhances its insulation and impact-resistant prop...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302500177X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832583861392900096 |
|---|---|
| author | Mohamed Abdellatief Leong Sing Wong Norashidah Md Din Ali Najah Ahmed Abba Musa Hassan Zainah Ibrahim G. Murali Kim Hung Mo Ahmed El-Shafie |
| author_facet | Mohamed Abdellatief Leong Sing Wong Norashidah Md Din Ali Najah Ahmed Abba Musa Hassan Zainah Ibrahim G. Murali Kim Hung Mo Ahmed El-Shafie |
| author_sort | Mohamed Abdellatief |
| collection | DOAJ |
| description | Foam glass (FG) is characterized by its lightweight structure and exceptional insulating properties, making it a highly suitable material for a wide range of applications. It is produced by foaming molten glass, resulting in a cellular structure that enhances its insulation and impact-resistant properties. Due to its sustainability and durability, FG is increasingly used in the construction, automotive, and packaging sectors. In this context, the current study proposes a novel approach by developing a thoughtful system for assessing performance and intelligent design utilizing ML models such as Gradient Boosting (GB), Random Forest (RF), Gaussian Process Regression (GPR), and Linear Regression (LR) to predict porosity and compressive strength (CS) of FG. The dataset comprises 214 data points, encompassing input variables such as glass particle diameter, foam agent content, heating rate, holding time, sintering temperature, and dry density, with output parameters of porosity and CS. Data preprocessing involved Pearson correlation analysis to address multicollinearity and reveal nonlinear relationships among variables. Model performance was evaluated through R-values, mean absolute error, and root mean square error metrics, demonstrating that the GPR model achieved superior prediction accuracy with R-values of 0.91 and 0.82 for porosity and CS, respectively. The GB model followed closely, while the RF and LR models showed lower accuracy. Partial dependence plots and global feature importance analyses highlighted density and foam agent content as critical factors influencing FG properties. By achieving the most precise predictions with minimal error distributions, the GPR model offers actionable insights into FG design. These findings enable the optimization of FG production by providing reliable tools for predicting and controlling porosity and CS, reducing material waste, enhancing product quality, and streamlining manufacturing processes. This study demonstrates the potential of advanced ML techniques to bridge the gap between predictive modeling and practical applications in the digital design of FG. |
| format | Article |
| id | doaj-art-a918d3ac7ef448fbbe1446b99a1137d4 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-a918d3ac7ef448fbbe1446b99a1137d42025-01-28T04:14:50ZengElsevierResults in Engineering2590-12302025-03-0125104089Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniquesMohamed Abdellatief0Leong Sing Wong1Norashidah Md Din2Ali Najah Ahmed3Abba Musa Hassan4Zainah Ibrahim5G. Murali6Kim Hung Mo7Ahmed El-Shafie8Department of Civil Engineering, Higher Future Institute of Engineering and Technology in Mansoura, EgyptInstitute of Energy Infrastructure, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, MalaysiaInstitute of Energy Infrastructure, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, MalaysiaDepartment of Engineering, School of Engineering and Technology, Sunway University, No. 5, Jalan, Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, MalaysiaCentre for Promotion of Research, Graphic Era (Deemed to be University), Clementtown, Dehradun, India; Centre of Research Impact and Outcome, Chitkara University, Rajpura 140417, Punjab, India; Corresponding author.Department of Engineering, School of Engineering and Technology, Sunway University, No. 5, Jalan, Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, MalaysiaNational Water and Energy Center, United Arab Emirate University, Al Ain, P.O.Box 15551, United Arab EmiratesFoam glass (FG) is characterized by its lightweight structure and exceptional insulating properties, making it a highly suitable material for a wide range of applications. It is produced by foaming molten glass, resulting in a cellular structure that enhances its insulation and impact-resistant properties. Due to its sustainability and durability, FG is increasingly used in the construction, automotive, and packaging sectors. In this context, the current study proposes a novel approach by developing a thoughtful system for assessing performance and intelligent design utilizing ML models such as Gradient Boosting (GB), Random Forest (RF), Gaussian Process Regression (GPR), and Linear Regression (LR) to predict porosity and compressive strength (CS) of FG. The dataset comprises 214 data points, encompassing input variables such as glass particle diameter, foam agent content, heating rate, holding time, sintering temperature, and dry density, with output parameters of porosity and CS. Data preprocessing involved Pearson correlation analysis to address multicollinearity and reveal nonlinear relationships among variables. Model performance was evaluated through R-values, mean absolute error, and root mean square error metrics, demonstrating that the GPR model achieved superior prediction accuracy with R-values of 0.91 and 0.82 for porosity and CS, respectively. The GB model followed closely, while the RF and LR models showed lower accuracy. Partial dependence plots and global feature importance analyses highlighted density and foam agent content as critical factors influencing FG properties. By achieving the most precise predictions with minimal error distributions, the GPR model offers actionable insights into FG design. These findings enable the optimization of FG production by providing reliable tools for predicting and controlling porosity and CS, reducing material waste, enhancing product quality, and streamlining manufacturing processes. This study demonstrates the potential of advanced ML techniques to bridge the gap between predictive modeling and practical applications in the digital design of FG.http://www.sciencedirect.com/science/article/pii/S259012302500177XSustainable foam glassMachine learningIntelligent designCompressive strengthConstruction materialsPredictive modeling |
| spellingShingle | Mohamed Abdellatief Leong Sing Wong Norashidah Md Din Ali Najah Ahmed Abba Musa Hassan Zainah Ibrahim G. Murali Kim Hung Mo Ahmed El-Shafie Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques Results in Engineering Sustainable foam glass Machine learning Intelligent design Compressive strength Construction materials Predictive modeling |
| title | Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques |
| title_full | Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques |
| title_fullStr | Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques |
| title_full_unstemmed | Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques |
| title_short | Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques |
| title_sort | sustainable foam glass property prediction using machine learning a comprehensive comparison of predictive methods and techniques |
| topic | Sustainable foam glass Machine learning Intelligent design Compressive strength Construction materials Predictive modeling |
| url | http://www.sciencedirect.com/science/article/pii/S259012302500177X |
| work_keys_str_mv | AT mohamedabdellatief sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques AT leongsingwong sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques AT norashidahmddin sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques AT alinajahahmed sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques AT abbamusahassan sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques AT zainahibrahim sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques AT gmurali sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques AT kimhungmo sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques AT ahmedelshafie sustainablefoamglasspropertypredictionusingmachinelearningacomprehensivecomparisonofpredictivemethodsandtechniques |