利用空间人工智能开发路面性能指数:一个案例研究.
Abdalrhman Milad1, Abdualmtalab Abdualaziz Ali2,3, Zahir Sulaiman Al-Sulaimi4
1Department of Civil and Environmental Engineering, College of Engineering, University of Nizwa, P. O. Box 33, 616, Nizwa, Ad-Dakhliyah, Oman.
Scientific reports
|November 12, 2025
概括
这项研究集成了机器学习 (ML) 和地理信息系统 (GIS) 来预测路面状况. 随机森林模型实现了99.9%的准确性,从而实现了积极的道路维护和优化资源使用.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 地理信息科学 地理信息科学
背景情况:
- 对于现代交通网络来说,传统的路面评估方法是无效的.
- 路面状况指数 (PCI) 和国际度指数 (IRI) 是关键的绩效指标.
- 收集IRI数据比详细的路面应急数据更容易获得.
研究的目的:
- 开发一种集成的机器学习 (ML) 和地理信息系统 (GIS) 方法来评估路面性能.
- 为了对国际度指数 (IRI) 进行分类,以估计柔性路面的路面状况指数 (PCI).
- 通过智能决策支持优化路面维护策略.
主要方法:
- 利用了机器学习算法,包括人工神经网络 (ANN),自适应增强 (AdaBoost),支持矢量机 (SVM),决策树 (DT) 和随机森林 (RF).
- 使用TotalPave智能手机应用程序收集了1042个国际粗度指数 (IRI) 数据点.
- 应用特征选择,空间可视化和不确定性分析,以进行可靠的评估.
主要成果:
- 随机森林 (RF) 模型实现了99.9%的卓越精度和99.9%的F1得分.
- 支持矢量机 (SVM) 的精度最低,为85.8%,F1得分为40.3.3.
- 通过空间分析和验证模型可靠性,确定了关键路面部分.
结论:
- 集成的ML和GIS框架为路面管理系统 (PMS) 提供了一个可扩展和智能的决策支持工具.
- 这种方法使积极的维护规划成为可能,并优化了对运输基础设施的资源配置.
- 该研究展示了一种用于有效评估和预测路面状况的变革性解决方案.
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