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首页> 外文会议>International Conference on Rehabilitation and Maintenance in Civil Engineering >Genetic Algorithm Applied for Optimization of Pavement Maintenance under Overload Traffic: Case Study Indonesia National Highway
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Genetic Algorithm Applied for Optimization of Pavement Maintenance under Overload Traffic: Case Study Indonesia National Highway

机译：超负荷交通下路面维护优化的遗传算法：案例研究印度尼西亚国家公路

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摘要

National Road Network which consists of a traditional road structure and modern roads, require planned maintenance and should be in accordance with the needs. The limited choice of available national road network and the deviation of the overloading encourage the government to be more responsive to carry out maintenance management. The institution in charge of road maintenance is often constrained by the limited budget available. A two-objective optimization model considers maximum roughness and minimum maintenance cost for used road network with overload. The study was conducted on the entire national road network in West Java which are paved with flexible pavement. In the proposed approach, data mining model are used for predicting the roughness index over a given period of time. Routine and periodic maintenance are chosen in this study. Multi-objective optimization model was developed based on Genetic Algorithms. Budget constraints and overloading are the two constraints in the developed model. Based on the R-Tools result, the Pareto optimal solutions of the two objective functions are obtained. From the optimal solutions represented by roughness index and cost, an agency more easily obtain the information of the maintenance planning. Results of the developed model has been implemented through the selection of maintenance on the road network scenarios with different levels of overload.
机译：由传统的道路结构和现代公路组成的国家公路网，要求计划维护，应符合需求。可用国家道路网络的有限选择和超载的偏差鼓励政府对进行维护管理的更加响应。负责道路维护的机构往往受到有限预算的限制。两个客观优化模型考虑了使用过载的二手道路网络的最大粗糙度和最低维护成本。该研究是在西爪哇省的整个国家道路网络上进行，铺设了柔性路面。在所提出的方法中，数据挖掘模型用于预测给定时间段的粗糙度指数。本研究中选择了常规和定期维护。基于遗传算法开发了多目标优化模型。预算限制和重载是开发模型中的两个约束。基于R工具的结果，获得了两个目标功能的Pareto最佳解决方案。从粗糙度指数和成本所代表的最佳解决方案，机构更容易获得维护计划的信息。开发模型的结果是通过选择对具有不同水平的道路网络场景的维护来实现。

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