Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2608
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dc.contributor.authorEkanayake, I.U-
dc.contributor.authorMeddage, D. P. P-
dc.contributor.authorRathnayake, U-
dc.date.accessioned2022-06-13T06:34:07Z-
dc.date.available2022-06-13T06:34:07Z-
dc.date.issued2022-04-
dc.identifier.citationEkanayake, Imesh & Meddage, D. P. P. & Rathnayake, Upaka. (2022). A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Studies in Construction Materials. 16. 1-20. 10.1016/j.cscm.2022.e01059.en_US
dc.identifier.issn2214-5095-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2608-
dc.description.abstractMachine learning (ML) techniques are often employed for the accurate prediction of the compressive strength of concrete. Despite higher accuracy, previous ML models failed to interpret the rationale behind predictions. Model interpretability is essential to appeal to the interest of domain experts. Therefore, overcoming research gaps identified, this research study proposes a way to predict the compressive strength of concrete using supervised ML algorithms (Decision tree, Extra tree, Adaptive boost (AdaBoost), Extreme gradient boost (XGBoost), Light gradient boosting method (LGBM), and Laplacian Kernel Ridge Regression (LKRR). Alternatively, SHapley Additive exPlainations (SHAP) – a novel black-box interpretation approach - was employed to elucidate the predictions. The comparison revealed that tree-based algorithms and LKRR provide acceptable accuracy for compressive strength predictions. Moreover, XGBoost and LKRR algorithms evinced superior performance (R ¼ 0.98). According to SHAP interpretation, XGBoost predictions capture complex relationships among the constituents. On the other hand, SHAP provides unified measures on feature importance and the impact of a variable for a prediction. Interestingly, SHAP interpretations were in accordance with what is generally observed in the compressive behavior of concrete, thus validating the causality of ML predictions.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesCase Studies in Construction Materials;16(5):1-20-
dc.subjectExplainable machine learningen_US
dc.subjectCompressive strengthen_US
dc.subjectTree-based regressionen_US
dc.subjectSHAP explanationen_US
dc.subjectLaplacian kernel Ridge Regressionen_US
dc.titleA novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)en_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.cscm.2022.e01059en_US
Appears in Collections:Department of Civil Engineering
Research Papers - Department of Civil Engineering
Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications

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