Culverts are important components of a roadway and should be properly maintained to ensure adequate road surface drainage and public safety. Culvert maintenance greatly relies on culvert inspection which is time consuming and requires a large number of skilled labor hours. Currently, State Departments of Transportation use rigid methods for scheduling culvert inspection based on one or two factors such as culvert size and/or condition. The objective of the research described in the paper is to develop a more intelligent scheduling system for culvert inspection to improve the utilization of limited resources. The proposed intelligent system first predicts the conditions of the culverts that are due for inspection in a given year and based on the prediction results, only schedule inspections for those predicted to be in poor condition. The prediction models utilized a Decision Tree algorithm together with the Synthetic Minority Over-Sampling Technique to deal with the highly imbalanced data in the culvert inventory database. The case study presented in the paper utilized 12,400 culvert records from the Ohio Department of Transportation to train and test the prediction models. The developed prediction models have achieved accuracies over 80% for the training set and 75% for the testing set and satisfactory values for the areas under the curve of 0.8. The case study concluded that by implementing the proposed intelligent culvert inspection scheduling system, the number of culverts needing inspections is reduced by 44%. Implementation of the proposed system could assist state and local agencies with prioritizing inspection of culverts needing attention while maximizing the use of limited resources. While this study is applied to culverts in Ohio, the proposed framework can be used on any similarly available culvert data set worldwide. The paper ends by providing suggestions to improve the quality of the data in culvert inventory databases.

涵洞是道路的重要组成部分，应适当维护，以确保充分的路面排水和公共安全。涵洞的维护在很大程度上取决于涵洞的检查，这很耗时，并且需要大量的熟练工时。当前，美国运输部使用刚性方法基于一个或两个因素（例如涵洞的大小和/或状况）来安排涵洞的检查。本文所述研究的目的是开发一种更加智能的涵洞检查调度系统，以提高有限资源的利用率。所提出的智能系统首先预测给定年份中要检查的涵洞的状况，并根据预测结果，仅安排对预计状况不佳的涵洞进行检查。预测模型利用决策树算法和综合少数群体过采样技术来处理涵洞库存数据库中高度不平衡的数据。本文介绍的案例研究利用了俄亥俄州交通运输部的12,400条涵洞记录来训练和测试预测模型。所开发的预测模型对训练集的准确性达到了80％以上，对于测试集的准确性达到了75％，曲线下面积的满意值达到0.8。案例研究得出结论，通过实施建议的智能涵洞检查调度系统，需要检查的涵洞数量减少了44％。拟议系统的实施可以帮助州和地方机构优先检查需要注意的涵洞，同时最大限度地利用有限的资源。虽然这项研究适用于俄亥俄州的涵洞，但建议的框架可用于全球任何类似可用的涵洞数据集。本文最后提出了一些建议，以提高涵洞清单数据库中数据的质量。