模拟基岩纤维钢筋混凝土的强度特征,使用多种可解释的机器学习与图形用户界面
W K V J B Kulasooriya1, R S S Ranasinghe1, Udara Sachinthana Perera1
1Department of Civil Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka.
Scientific reports
|August 12, 2023
概括
像SHAP和LIME这样的可解释AI (XAI) 方法应用于机器学习 (ML) 模型,预测玄武岩纤维钢筋混凝土 (BFRC) 的强度. 结果揭示了模型的分歧,强调了XAI研究和专家评估在具体科学中的需要.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 模型越来越多地用于预测混凝土强度.
- ML的"黑子"性质阻碍了结果的解释和用户的信任.
- 现有的可解释AI (XAI) 具体研究通常只使用一种解释方法.
研究的目的:
- 研究XAI在BFRC强度预测的ML模型上的应用和重要性.
- 在多个ML模型中比较两个不同的XAI方法 (SHAP和LIME) 提供的解释.
- 提高机器学习预测在混凝土工程中的可解释性和可靠性.
主要方法:
- 开发了三种基于树的ML模型:决策树,梯度增强树和光梯度增强机.
- 应用了两个XAI技术,Shapley增量解释 (SHAP) 和局部可解释的模型不可知解释 (LIME),以分析模型预测.
- 预测的机械强度特征:BFRC的压力,屈曲和拉伸强度.
主要成果:
- 基于树的ML模型在预测BFRC强度特征方面取得了良好的准确性.
- 在模型中,SHAP和LIME提供了不同的特征重要性排名和大小.
- 解释各不相同,表明基于ML预测的决策可能存在复杂性.
结论:
- 对于理解混凝土强度预测中的ML模型行为,XAI方法至关重要.
- 在XAI解释中的差异凸显了进一步研究和领域专家参与的需要.
- 为快速预测BFRC强度而开发了一个用户友好的应用程序.
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