建筑的生态转型:利用机器学习和SHAP用于碎混凝土的可持续性
Nudrat Habib1, Muhammad Saqib2, Taoufik Najeh3
1Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.
Heliyon
|March 11, 2024
概括
这项研究建立了碎混凝土 (CRC) 性能数据库,使用机器学习来预测强度. 随机森林模型在预测压力和拉伸强度方面表现出卓越的表现,将年龄和含量确定为关键因素.
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.
- 环境科学 环境科学
背景情况:
- 粉碎混凝土 (CRC) 提供了一个可持续的替代方案,但由于排放污染物而面临环境问题.
- 现有的CRC特性研究是分散的,需要采取整合方法.
- 这项研究的重点是提高对CRC机械性能,特别是压力和拉伸强度的理解.
研究的目的:
- 从现有研究中创建一个全面的碎混凝土特性数据库.
- 分析九个关键特征对压力强度 (fc') 和拉伸强度 (fts) 的影响.
- 评估各种机器学习算法的性能,以预测CRC的机械性能.
主要方法:
- 编制了一个数据库,包含fc'的456个数据点和fts的358个数据点.
- 应用机器学习算法,包括决策树 (DT) 和随机森林 (RF),用于预测建模.
- 使用统计评估,交叉验证和沙普利增量解释 (SHAP) 进行模型验证和参数影响分析.
主要成果:
- 随机森林 (RF) 展示了卓越的预测性能,R2值为fc'的0.87和fts的0.85.
- AdaBoost的平均准确率为84%,超过了包装和个别决策树模型.
- 年龄和碎含量被确定为影响CRC机械性能最有影响的因素.
结论:
- 机器学习,特别是随机森林,对于预测碎混凝土强度是有效的.
- 开发的数据库和模型为优化CRC配方提供了宝贵的见解.
- 了解年龄和含量的影响对于可持续的CRC应用至关重要.
关键词:
压力强度 (fc) 是指压力强度.粉碎混凝土 (CRC) 的使用决策树 (DT) 是一个决策树.随机森林 (RF) 是一个随机的森林.沙普利的添加剂解释 (SHAP)拉力强度 (fst) 是指拉力强度.更多相关视频
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