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Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability
Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.
Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0003-4895-5300
Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia.
2024 (Engelska)Ingår i: Heliyon, E-ISSN 2405-8440, Vol. 10, nr 5, artikel-id e26927Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Elsevier, 2024. Vol. 10, nr 5, artikel-id e26927
Nyckelord [en]
Compressive strength (fc’), Crumb rubber concrete (CRC), Decision tree (DT), Random forest (RF), Shapley additive explanations (SHAP), Tensile strength (fst)
Nationell ämneskategori
Samhällsbyggnadsteknik
Forskningsämne
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-104863DOI: 10.1016/j.heliyon.2024.e26927Scopus ID: 2-s2.0-85188221338OAI: oai:DiVA.org:ltu-104863DiVA, id: diva2:1846973
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Validerad;2024;Nivå 2;2024-03-26 (hanlid);

Full text license: CC BY

Tillgänglig från: 2024-03-26 Skapad: 2024-03-26 Senast uppdaterad: 2024-03-26Bibliografiskt granskad

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