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Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface

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dc.contributor.author Kulasooriya, WKVJB
dc.contributor.author Ranasinghe, RSS
dc.contributor.author Perera, US
dc.contributor.author Thisovithan, P
dc.contributor.author Ekanayake, IU
dc.contributor.author Meddage, DPP
dc.date.accessioned 2023-12-01T08:02:54Z
dc.date.available 2023-12-01T08:02:54Z
dc.date.issued 2023
dc.identifier.citation Kulasooriya, W. K. V. J. B., Ranasinghe, R. S. S., Perera, U. S., Thisovithan, P., Ekanayake, I. U., & Meddage, D. P. P. (2023). Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface. Scientific Reports, 13(1), Article 1. https://doi.org/10.1038/s41598-023-40513-x en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21874
dc.description.abstract This study investigated the importance of applying explainable artificial intelligence (XAI) on different machine learning (ML) models developed to predict the strength characteristics of basalt-fiber reinforced concrete (BFRC). Even though ML is widely adopted in strength prediction in concrete, the black-box nature of predictions hinders the interpretation of results. Among several attempts to overcome this limitation by using explainable AI, researchers have employed only a single explanation method. In this study, we used three tree-based ML models (Decision tree, Gradient Boosting tree, and Light Gradient Boosting Machine) to predict the mechanical strength characteristics (compressive strength, flexural strength, and tensile strength) of basal fiber reinforced concrete (BFRC). For the first time, we employed two explanation methods (Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME)) to provide explanations for all models. These explainable methods reveal the underlying decision-making criteria of complex machine learning models, improving the end user's trust. The comparison highlights that tree-based models obtained good accuracy in predicting strength characteristics yet, their explanations were different either by the magnitude of feature importance or the order of importance. This disagreement pushes towards complicated decision-making based on ML predictions which further stresses (1) extending XAI-based research in concrete strength predictions, and (2) involving domain experts to evaluate XAI results. The study concludes with the development of a “user-friendly computer application” which enables quick st en_US
dc.language.iso en en_US
dc.publisher Nature Publishing Group en_US
dc.title Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface en_US
dc.type Article-Full-text en_US
dc.identifier.year 2023 en_US
dc.identifier.journal Scientific Reports en_US
dc.identifier.issue 1 en_US
dc.identifier.volume 13 en_US
dc.identifier.pgnos 13138(1-15) en_US
dc.identifier.doi https://doi.org/10.1038/s41598-023-40513-x en_US


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