一个强大的LightGBM模型用于混凝土抗拉强度预测,以帮助基于弹性的结构策略
1Pan African University Institute of Basic Sciences, Technology and Innovation, Kenya.
Heliyon
|November 8, 2024
概括
本研究提出了一种准确的,非破坏性的方法,用于使用机器学习来预测混凝土的抗拉强度 (TS). 贝叶斯优化和SHAP可解释性增强了LightGBM模型,以超声波脉冲速度作为关键预测器实现98%的准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 评估混凝土抗拉强度 (TS) 的传统方法存在局限性.
- 非破坏性测试 (NDT) 为TS评估提供了一个创新的替代方案.
- 准确的TS预测对于土木结构设计和基础设施弹性至关重要.
研究的目的:
- 为准确的混凝土TS预测开发一种新的,优化的,非破坏性的方法.
- 整合格拉迪奥,贝叶斯优化 (BO) 和沙普利增量解释 (SHAP) 进行增强的TS预测.
- 为预测具体的TS提供一个全球适用的工具,支持可持续的基础设施发展.
主要方法:
- 使用3460个数据点作为输入参数的超声波脉冲速度 (UPV) 和电电阻 (ER) 开发一个LightGBM模型.
- 贝叶斯优化 (BO) 的应用,用于LightGBM模型的广泛的超参数调整.
- 利用沙普利增量解释 (SHAP) 进行模型解释性和关键输入变量的识别.
- 使用Gradio部署预测模型,以提高可访问性和可用性.
主要成果:
- 优化的LightGBM模型实现了大约98%的预测准确度.
- 贝叶斯优化通过超参数调整显著改善了模型性能.
- SHAP分析确定了超声波脉冲速度 (UPV) 作为混凝土TS预测的最关键变量.
- 格拉迪奥接口使预测工具的访问和应用变得更加容易.
结论:
- 综合方法提供了一个强大而准确的非破坏性工具,用于具体的TS预测.
- 该研究推进了机器学习在土木工程中的应用,以提高基础设施弹性和可持续发展.
- 优化的BO和可解释性的SHAP的新组合为研究人员和行业专业人士提供了实际的好处.
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