本文基于路面评价指标中车辙深度指数和行驶质量指数来评价路面的损坏情况, 使用关联规则挖掘环境、交通、路面等影响因素与路面状况之间的关联程度. 针对关联规则Apriori算法复杂度和耗时的缺点, 提出一种不生成候选集的方法来产生频繁集的改进Apriori算法, 并通过实验对比证明改进的Apriori算法能够有效提升速度和性能. 使用改进的Apriori算法分析路面评价指标及其影响因素之间的强关联规则, 得到不同环境路面损坏的主要成因. 本文结论能够对路面养护提供科学可靠的支持, 可为路面养护部门提供合理的养护建议与数据支撑.
Based on the rutting depth index and driving quality index in the pavement evaluation index, the pavement damage was evaluated in this study. The association rules were used to mine the degree of association between influencing factors such as environment, traffic, and road surface and road surface conditions. Aiming at the shortcomings of the complexity and time-consuming of the association rule Apriori algorithm, an improved Apriori algorithm that does not generate candidate sets to generate frequent sets was proposed. The experiments show that the improved Apriori algorithm can effectively improve the speed and performance. The improved Apriori algorithm was used to analyze the strong association rules between evaluation indexes and influencing factors, and the main causes of pavement damage in different environments were obtained. The conclusion of this paper can provide scientific and reliable support for the pavement maintenance, reasonable maintenance suggestions, and data support for the pavement maintenance department.