Technology-Based Condition Assessment Framework for Railway Infrastructure

应始终监测和分析铁路基础设施的状况，如钢轨、道碴和轨枕，以确保乘客和货物的乘坐安全和乘坐质量。铁路基础设施由多种类型的基础设施组成，其状况很难评估和监测。现有的大多数状态评估模型仅限于特定的铁路基础设施部件和特定的检测技术/技术。因此，应开发一种全面的状况评估工具，以涵盖铁路基础设施部件，使用不同的检测技术，以确保公共服务的安全和质量。本研究旨在开发一种基于技术的铁路基础设施状态评估框架，考虑不同部件的结构和几何缺陷，并通过文献综述和专家意见对每个部件的缺陷进行识别和研究。该模型将使用两个主要输入：(1)部件、缺陷及其类别的相对重要性权重；(2)缺陷严重度。为了获得相对权重，将采用分析网络过程(ANP)技术，该技术考虑了组件之间的相互依赖关系及其缺陷。将使用模糊逻辑统一不同的缺陷/标准，并将语言条件评估评级表转换为严重程度的数字分数。在此基础上，采用加权求和法对权重和严重度进行综合，从而确定铁路基础设施的状况，并对铁路基础设施的整体状况进行评价。本研究所需的数据将从ExpGlobal Inc.、铁路状况分类手册、文献和发放给加拿大各地专业人员的问卷中收集。这种融合的成果还将在使用EXCEL的用户友好的自动化工具中展示。将选择来自Exp Global Inc.的几个案例研究来证明所提议的模型/系统的概念并验证其结果。拟议的模型有助于资产管理公司将铁路状况评估的不准确性降至最低，并对维护、修复和预算分配进行优先排序。

The condition of railway infrastructure, such as rails, ballasts and sleepers, should always be monitored andanalyzed to ensure ride safety and ride quality for both passengers and freight. Railway infrastructure is hard toassess and monitor in terms of its condition due to various types of infrastructure components. The majority ofthe existing condition assessment models are limited to a specific component of railway infrastructure andparticular inspection technique / technology. Therefore, a comprehensive condition assessment tool should bedeveloped to cover the railway infrastructure components using different inspection techniques to ensure thesafety and quality of public service. This research aims at developing a technology-based condition assessmentframework for railway infrastructure considering structural and geometrical defects in different components.The defects of each component will be identified and studied through literature review and experts opinion inthe field. Two main inputs will be used to develop the model: (1) relative weights of importance ofcomponents, defects and their categories and (2) defects severities. To obtain the relative weights, the AnalyticNetwork Process (ANP) technique will be adopted, which considers the inter-dependencies among componentsand their defects. Fuzzy logic will be used to unify different defects / criteria and to translate the linguisticcondition assessment grading scale to a numerical score for severities. Furthermore, the weighted sum meanwill be used to integrate both the weights and severities to determine the condition and to evaluate the overallcondition of the railway infrastructure. The required data for the present research will be collected from ExpGlobal Inc., railway condition classification manuals, literature and questionnaires distributed to professionalsacross Canada. The fruit of this fusion will also be presented in a user-friendly automated tool using EXCEL.Several case studies from Exp Global Inc. will be selected to prove the concept of the proposed model/systemand validate its results. The proposed model helps asset managers in minimizing the inaccuracy of railwaycondition assessment and prioritizing maintenance, rehabilitation and budget allocation.