基于粒子群算法的路面施工养护维修作业最佳化研究

Based on particle swarm optimization algorithm work on road construction maintenance repair

针对135个路面路段,采用粒子群算法,以8个路面状况参数(平整度标准差SD、车辙、弯沉、裂缝、坑洞、泛油、修补、推挤)进行路面路段施作养护与维修作业的最佳化优选排序分析。8个路面状况参数中,平整度标准差SD、车辙、弯沉是由仪器量测获得,而裂缝、坑洞、泛油、修补、推挤等五种路面破损参数则是通过工程师的目视调查记录。研究结果发现,粒子群演算法能快速计算各路面路段的整合性路面状况值,并获得路面路段的最佳化优选排序。通过粒子群算法的应用,路面工程师能依据该最佳化的优选排序,针对不同状况的路面路段,有效率的施行适当且实时的养护与维修作业,做好路面管理的工作。过程及采用的分析方法,可提供作为道路主管机关与路面实务业界于排定路面路段养护与维修作业的优选顺序的参考。

For this study,135 road sections,particle swarm algorithm to eight road conditions parameters（ flatness standard deviation SD,rutting,deflection,cracks,potholes,weeping,repair,pushing） road pavement facilities for conservation best of preferred sorting and repair operations analysis.Eight road conditions parameters,flatness standard deviation SD,rutting,deflection is measured by the instrument to obtain,and cracks,potholes,weeping,repair,pushing other five road damage parameter is through visual engineer investigation records. The results found that the particle swarm algorithm can quickly calculate the value of each pavement road conditions integrated sections and get the best of road sections preferably sorted. Through the application of particle swarm optimization,pavement engineers can be based on the best of the preferred sorting for different sections of the road condition,proper and efficient implementation of real-time maintenance and repair work,good road management. The study and analysis method used,may be provided as road authorities and road industry practice reference to scheduled road pavement maintenance and repair jobs in order of preference.