Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability
Researchers have focused their efforts on investigating the integration of crumb rubber as a substitute for conventional aggregates and cement in concrete. Nevertheless, the manufacture of crumb rubber concrete (CRC) has been linked to the release of noxious pollutants, hence presenting potential en...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402402958X |
| _version_ | 1797259789548388352 |
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| author | Nudrat Habib Muhammad Saqib Taoufik Najeh Yaser Gamil |
| author_facet | Nudrat Habib Muhammad Saqib Taoufik Najeh Yaser Gamil |
| author_sort | Nudrat Habib |
| collection | DOAJ |
| description | Researchers have focused their efforts on investigating the integration of crumb rubber as a substitute for conventional aggregates and cement in concrete. Nevertheless, the manufacture of crumb rubber concrete (CRC) has been linked to the release of noxious pollutants, hence presenting potential environmental hazards. Rather than developing novel CRC formulations, the primary objective of this work is to construct an extensive database by leveraging prior research efforts. The study places particular emphasis on two crucial concrete properties: compressive strength (fc') and tensile strength (fts). The database includes a total of 456 data points for fc' and 358 data points for fts, focusing on nine essential characteristics that have a substantial impact on both attributes. The research employs several machine learning algorithms, including both individual and ensemble methods, to undertake a comprehensive analysis of the created databases for fc' and fts. In order to ascertain the correctness of the models, a comparative analysis of machine learning techniques, namely decision tree (DT) and random forest (RF), is conducted using statistical evaluation. Cross-validation approaches are used in order to address the possible issues of overfitting. Furthermore, the Shapley additive explanations (SHAP) approach is used to investigate the influence of input parameters and their interrelationships. The findings demonstrate that the RF methodology has superior performance compared to other ensemble techniques, as shown by its lower error rates and higher coefficient of determination (R2) of 0.87 and 0.85 for fc' and fts respectively. When comparing ensemble approaches, it can be seen that AdaBoost outperforms bagging by 6 % for both outcome models and individual decision tree learners by 17% and 21% for fc' and fts respectively in terms of performance. The average accuracy of AdaBoost algorithm for both the models is 84%. Significantly, the age and the inclusion of crumb rubber in CRC are identified as the primary criteria that have a substantial influence on the mechanical properties of this particular kind of concrete. |
| first_indexed | 2024-03-07T16:53:03Z |
| format | Article |
| id | doaj.art-6e1eaef504024bd689500a4e0302055b |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T23:15:01Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-6e1eaef504024bd689500a4e0302055b2024-03-17T07:56:48ZengElsevierHeliyon2405-84402024-03-01105e26927Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainabilityNudrat Habib0Muhammad Saqib1Taoufik Najeh2Yaser Gamil3Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan; Corresponding author.Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanOperation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Sweden; Corresponding author.Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, MalaysiaResearchers have focused their efforts on investigating the integration of crumb rubber as a substitute for conventional aggregates and cement in concrete. Nevertheless, the manufacture of crumb rubber concrete (CRC) has been linked to the release of noxious pollutants, hence presenting potential environmental hazards. Rather than developing novel CRC formulations, the primary objective of this work is to construct an extensive database by leveraging prior research efforts. The study places particular emphasis on two crucial concrete properties: compressive strength (fc') and tensile strength (fts). The database includes a total of 456 data points for fc' and 358 data points for fts, focusing on nine essential characteristics that have a substantial impact on both attributes. The research employs several machine learning algorithms, including both individual and ensemble methods, to undertake a comprehensive analysis of the created databases for fc' and fts. In order to ascertain the correctness of the models, a comparative analysis of machine learning techniques, namely decision tree (DT) and random forest (RF), is conducted using statistical evaluation. Cross-validation approaches are used in order to address the possible issues of overfitting. Furthermore, the Shapley additive explanations (SHAP) approach is used to investigate the influence of input parameters and their interrelationships. The findings demonstrate that the RF methodology has superior performance compared to other ensemble techniques, as shown by its lower error rates and higher coefficient of determination (R2) of 0.87 and 0.85 for fc' and fts respectively. When comparing ensemble approaches, it can be seen that AdaBoost outperforms bagging by 6 % for both outcome models and individual decision tree learners by 17% and 21% for fc' and fts respectively in terms of performance. The average accuracy of AdaBoost algorithm for both the models is 84%. Significantly, the age and the inclusion of crumb rubber in CRC are identified as the primary criteria that have a substantial influence on the mechanical properties of this particular kind of concrete.http://www.sciencedirect.com/science/article/pii/S240584402402958XCrumb rubber concrete (CRC)Decision tree (DT)Random forest (RF)Shapley additive explanations (SHAP)Compressive strength (fc’)Tensile strength (fst) |
| spellingShingle | Nudrat Habib Muhammad Saqib Taoufik Najeh Yaser Gamil Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability Heliyon Crumb rubber concrete (CRC) Decision tree (DT) Random forest (RF) Shapley additive explanations (SHAP) Compressive strength (fc’) Tensile strength (fst) |
| title | Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability |
| title_full | Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability |
| title_fullStr | Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability |
| title_full_unstemmed | Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability |
| title_short | Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability |
| title_sort | eco transformation of construction harnessing machine learning and shap for crumb rubber concrete sustainability |
| topic | Crumb rubber concrete (CRC) Decision tree (DT) Random forest (RF) Shapley additive explanations (SHAP) Compressive strength (fc’) Tensile strength (fst) |
| url | http://www.sciencedirect.com/science/article/pii/S240584402402958X |
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