ABSTRACT

Since its inception in 2000, Mississippi’s pavement warranty programme has been used to manage the maintenance activities of the warranty pavement projects. To determine whether a specific maintenance treatment is required on a warranty pavement section, distress measurements are taken and compared against warranty distress thresholds for each distress type. Then, the decisions of what maintenance treatments and scopes should be applied are made by following the warranty specifications. This study aims to identify factors that influence past maintenance decisions and predict future maintenance needs for warranty projects based on past decisions. Statistical methods were employed to identify the relationship between the maintenance decisions and relevant factors based on the historical data of the warranty projects. Moreover, logistic regressions were performed to develop a comprehensive maintenance decision-making prediction model for the warranty pavements. The analysis results showed that the distress measurements at low and medium severity levels in numerical variables and project location and distress type in categorical variables have stronger effects than the others on decisions. The mixed-effects logistic regression model could provide a high accuracy in predicting the remedial actions, which could further provide a trend prediction for the maintenance decision of a warranty pavement section.

摘要

自 2000 年启动以来，密西西比州的路面保修计划一直用于管理保修路面项目的维护活动。为了确定是否需要对保修路面部分进行特定的维护处理，需要进行故障测量并将其与每种故障类型的保修故障阈值进行比较。然后，通过遵循保修规范来决定应该应用哪些维护处理和范围。本研究旨在确定影响过去维护决策的因素，并根据过去的决策预测保修项目的未来维护需求。根据保修项目的历史数据，采用统计方法识别维修决策与相关因素之间的关系。而且，进行逻辑回归以开发保修路面的综合维护决策预测模型。分析结果表明，数值变量中低、中严重程度的危难测量以及分类变量中的项目位置和危难类型对决策的影响要强于其他。混合效应逻辑回归模型可以提供较高的准确度预测补救措施，从而可以进一步为保修路面的维护决策提供趋势预测。分析结果表明，数值变量中低、中严重程度的危难测量以及分类变量中的项目位置和危难类型对决策的影响要强于其他。混合效应逻辑回归模型可以提供较高的准确度预测补救措施，从而可以进一步为保修路面的维护决策提供趋势预测。分析结果表明，数值变量中低、中严重程度的危难测量以及分类变量中的项目位置和危难类型对决策的影响要强于其他。混合效应逻辑回归模型可以提供较高的准确度预测补救措施，从而可以进一步为保修路面的维护决策提供趋势预测。