使用M5P模型对碳酸建筑石遭受模拟酸雨的损害演变的预测
Ali Bameri1, Ebrahim Ghasemi2, Mehdi Nasiri Sarvi1
1Department of Mining Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran.
Scientific reports
|September 30, 2025
概括
机器学习准确地预测了酸雨对建筑石材的破坏. 采用M5 Prime (M5P) 模型,将石材孔隙性确定为关键,其性能优于其他方法,有助于在脆弱地区的材料选择.
科学领域:
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 酸雨对碳酸建筑石构成重大威胁,导致其降解.
- 预测和减轻酸雨损害对于遗产保护和基础设施长寿至关重要.
- 评估石材损坏的现有方法可能耗时且资源密集.
研究的目的:
- 开发和评估一种机器学习模型,用于预测碳酸建筑石的酸雨损害指数 (D).
- 通过可解释机器学习来识别影响酸雨损害的关键因素.
- 为了比较M5 Prime (M5P) 模型与支持向量回归 (SVR) 和随机森林 (RF) 模型的性能.
主要方法:
- 建立了168个数据集的数据库,这些数据来自实验室实验,模拟了在不同pH值下使用硫酸和酸模拟酸雨.
- 使用P波速度量化酸雨损害指数 (D).
- 采用M5 Prime (M5P) 算法,这是一种白盒机器学习技术,用于损害预测和特征重要性分析.
主要成果:
- M5P模型准确地预测了酸雨损害 (D),确定系数 (R2) 为0.891,RMSE为0.021,MAE为0.017.
- 石头的多孔性被确定为影响酸雨损害的最重要因素.
- 与SVR和RF模型相比,M5P模型显示出更高的预测准确性.
结论:
- 机器学习模型M5P提供了一个可靠和准确的框架,用于预测建筑石材中的酸雨损害.
- 这些发现有助于在易受酸雨影响的地区选择耐用石材进行建筑,从而减少了实验测试的需要.
- 这种方法可以帮助建筑行业做出明智的决策,确保材料的耐用性和耐用性免受环境压力因素的影响.
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