Predicting Pavement Structural Condition Using Machine Learning Methods
Predicting Pavement Structural Condition Using Machine Learning Methods
复制标题
使用机器学习方法预测路面结构状况
DOI：
10.3390/su14148627
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Yuche Chen
中科院分区：
文献类型：
--
作者：
Nazmus Sakib Ahmed;N. Huynh;S. Gassman;R. Mullen;Charles Pierce;Yuche Chen
国家交通部门认识到，需要将路面结构条件纳入其路面性能模型和/或决策过程中，用于选择候选项目，以进行网络级的保护、修复或重建。然而，路面结构状况数据是昂贵的获得。为此，本文开发并评估了两种机器学习方法，随机森林（RF）和极端梯度提升（XGBoost），用于预测柔性路面的结构状况的有效性。其目的是能够预测路面部分的结构状况是否是穷人或不基于年平均日交通量（AADT），卡车的百分比，和速度限制。如果表面曲率指数（SCI 12）高于3.3，则认为路面结构状况较差。这些模型是使用沿着南卡罗来纳州8条主要路线收集的950英里交通速度偏转仪（TSD）数据开发的。机器学习模型的性能与逻辑回归模型的性能进行了比较。当训练好的模型应用于测试数据时，预测结果表明XGBoost和RF模型分别比逻辑回归模型高出12%和8%。XGBoost跑赢RF 4%。XGBoost被认为是三种评估模型中最好的，使用其他不良结构条件阈值对其性能进行了检查;发现其预测准确性在不同场景中具有鲁棒性。AADT和卡车的百分比被认为是显着的因素，而速度限制没有影响路面的结构条件。
State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.