使用统计和机器学习方法绘制风险运输基础设施资产的地图
1Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA. rakesh.salunke@jsums.edu.
Scientific reports
|January 29, 2026
概括
绘制脆弱的高速公路堤和斜坡 (HWS) 对于交通基础设施至关重要. 这项研究开发了一种机器学习方法,以识别有风险的HWS,改善资产管理和防止山体滑坡.
科学领域:
- 地质技术工程地质技术工程
- 运输基础设施管理 运输基础设施管理
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 高速公路堤防和坡道 (HWS) 是重要的,但经常被忽视的运输资产.
- 高温水库易受山体滑坡的影响,极端降雨事件加剧了这种情况.
- 准确地绘制易受伤害的HWS对于有效的基础设施管理至关重要.
研究的目的:
- 开发和评估机器学习模型,用于绘制有风险的高速公路堤和斜坡的地图.
- 为积极的资产管理创建可靠的易受伤害的HWS库存.
- 确定影响HWS故障的关键因素.
主要方法:
- 利用远程传感数据的数字海拔模型 (DEM) 来得出因果因素.
- 开发了包括随机森林在内的监督机器学习模型.
- 使用地质技术,地形学和水文数据训练模型,以已知的HWS故障位置作为地面真相.
- 使用AUC,F1得分和准确度指标评估模型性能.
主要成果:
- 随机森林模型获得了完美的分数 (AUC,F1,精度=1.0).
- 为了平衡预测准确度,确定了0.75的最佳概率值.
- 影响HWS故障的关键因素被确定为高度,与溪流的距离,NDVI和降水.
结论:
- 开发的基于GIS的机器学习方法有效地绘制了大面积的脆弱HWS.
- 这种方法使得有针对性的干预和优化了基础设施维护的资金使用.
- 运输机构可以采用这种方法来进行战略地质技术资产管理.
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