图形用户界面
玄武岩纤维
计算机科学
纤维
接口(物质)
玄武岩
复合材料
材料科学
地质学
并行计算
操作系统
地球化学
最大气泡压力法
气泡
作者
W.K.V.J.B. Kulasooriya,R.S.S. Ranasinghe,Udara Sachinthana Perera,P. Thisovithan,I.U. Ekanayake,D.P.P. Meddage
标识
DOI:10.1038/s41598-023-40513-x
摘要
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 strength prediction of basalt fiber reinforced concrete (BFRC).
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