From Analysis of Information Needs towards an Information Model of Railway Infrastructure

Railway is a tightly coupled network, where the operations are directly effected by the condition of rail infrastructure. With the advancement of ICT, a railway network exploit various computerized systems for efficient railway monitoring, maintenance and operations. However, these systems suffer from number of limitations, mainly, the data related to each asset type (e.g. Track, Bridge, etc) are stored in separate database management system. Such scattered and isolated nature of data present the island of information, while making it impossible to perform the sound decision analysis. In this paper, we propose a nework wide information model of railway infrastructure that structure the railway object, specify their properties and identify their inter-relationships. The presented information model supports the railway monitoring, maintenance and operations by providing the layout of railway infrastructure. Structuring data in the form of railway assets, railway risk assessment, railway load management, railway maintenance, and railway failure will provide a solid base to railway stakeholders, e.g. infrastructure managers, to take informed decisions based on data properties

A multi-objective decision making model for risk-based maintenance scheduling of railway earthworks

Aged earthworks constitute a major proportion of European rail infrastructures, the re-placement and remediation of which poses a serious problem. Considering the scale of the networks involved, it is infeasible both in terms of track downtime and money to replace all of these assets. It is, therefore, imperative to develop a rational means of managing slope infrastructure to determine the best use of available resources and plan maintenance in order of criticality. To do so, it is necessary to not just consider the structural performance of the asset but also to consider the safety and security of its users, the socioeconomic impact of remediation/failure and the relative importance of the asset to the network. This paper addresses this by looking at maintenance planning on a network level using multi‐attribute utility theory (MAUT). MAUT is a methodology that allows one to balance the priorities of different objectives in a harmonious fashion allowing for a holistic means of ranking assets and, subsequently, a rational means of investing in maintenance. In this situation, three different attributes are considered when examining the utility of different maintenance options, namely availability (the user cost), economy (the financial implications) and structural reliability (the structural performance and subsequent safety of the structure). The main impact of this paper is to showcase that network maintenance planning can be carried out proactively in a manner that is balanced against the needs of the organization.Geo-engineerin

Network level bridges maintenance planning using Multi-Attribute Utility Theory

Bridge infrastructure managers are facing multiple challenges to improve the availability and serviceability of ageing infrastructure, while the maintenance planning is constrained by budget restrictions. Many research efforts are ongoing, for the last few decades, ranging from development of bridge management system, decision support tools, optimisation models, life cycle cost analysis, etc. Since transport infrastructures are deeply embedded in society, they are not only subject to technical requirements, but are required to meet the requirements of societal and economic developments. Therefore, bridge maintenance planning should accommodate multiple performance goals which need to be quantified by various performance indicators. In this paper, an application of Multi-Attribute Utility Theory (MAUT) for bridge maintenance planning is illustrated with a case study of bridges from the Netherlands road network. MAUT seeks to optimise multiple objectives by suggesting a trade-off among them and finally assigns a ranking to the considered bridges. Moreover, utility functions of MAUT appropriately account for the involved uncertainty and risk attitude of infrastructure managers. The main contribution of this study is in presenting a proof-of-concept on how MAUT provides a systematic approach to improve the decision-making of maintenance planning by making use of available data, accommodating multiple performance goals, their uncertainty, and preferences of infrastructure managers

Providing decision support for transport infrastructure maintenance planning: Through application of multi-criteria and machine learning methods

The importance of maintaining transport infrastructure is increasingly recognized as we witness the aging of infrastructure, an increase in the frequency of extreme weather events, expanding performance demands, and shrinking financial resources. Under these circumstances, transportation agencies are facing competing demands to optimally spend the limited budget and satisfy various performance requirements related to reliability of assets, safety of users, availability of the network and impact on the environment. The multiple performance requirements of infrastructure give rise to several decision-making dilemmas. Aligned within the focus of two European projects, namely DESTination RAIL and COST ACTION TU1406, the objective of this research is to improve the decision-making process of maintenance planning by developing applied decision support methods and predictive models to aid transport infrastructure managers. The developed data-driven decision support methods firstly enabled the optimal maintenance planning of assets over the multi-year period, and secondly used the data from asset management systems for predictive modeling of unseen future events. The proposed approaches explicate the implicit reasoning of experts and pave a way forwards towards evidence-based asset maintenance solutions. This paper-based thesis addresses the challenges of maintenance planning by proposing multi-criteria methods and machine learning models. The proposed multi-criteria methods reduce the preferences of experts into objective data, establish the ranking of discrete assets and create multi-year maintenance plans to facilitate asset managers in deciding which assets to maintain, when to maintain them and what are the consequences of delaying maintenance in terms of budget and performance of assets. The developed predictive models learn from the historical asset management data and facilitate in maintenance planning through predicting the (future) condition states, risk levels, need of maintenance for assets. This research has made progress towards more consistent, explicit, and evidence-based maintenance planning approaches, which makes the decision processes concrete, transparent, and reproducible. The suggested methods specifically concentrated on providing support to infrastructure managers; therefore, the usefulness of the proposed approaches are validated on the real datasets of highway bridges and railway switches. Moreover, where it was possible, the digital tool and code are provided to motivate the implementation of the methods in practice. Finally, these methods eliminate the gap between the appropriate use of historical data and implicit judgment-driven decision-making of experts and pave a way forward towards data-driven resources efficient asset management practices

A systematic literature review on requirement prioritization techniques and their empirical evaluation

[Context and Motivation] Many requirements prioritization approaches have been proposed, however not all of them have been investigated empirically in real-life settings. As a result, our knowledge of their applicability and actual use is incomplete. [Question/problem] A 2007 systematic review on requirements prioritization mapped out the landscape of proposed prioritization approaches and their prioritization criteria. To understand how this sub-field of requirements engineering has developed since 2007 and what evidence has been accumulated through empirical evaluations, we carried out a literature review that takes as input publications published between 2007 and 2019. [Principle ideas/results] We evaluated 102 papers that proposed and/or evaluated requirements prioritization methods. Our results show that the newly proposed requirements prioritization methods tend to use as basis fuzzy logic and machine learning algorithms. We also concluded that the Analytical Hierarchy Process is the most accurate and extensively used requirement prioritization method in industry. However, scalability is still its major limitation when requirements are large in number. We have found that machine learning has shown potential to deal with this limitation. Last, we found that experiments were the most used research method to evaluate the various aspects of the proposed prioritization approaches. [Contribution] This paper identified and evaluated requirements prioritization techniques proposed between 2007 and 2019, and derived some trends. Limitations of the proposals and implications for research and practice are identified as well

Predictive Maintenance for Infrastructure Asset Management

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Multi-year maintenance planning framework using multi-attribute utility theory and genetic algorithms

This paper introduces a comprehensive framework for the development of optimal multi-year maintenance plans for a large number of bridges. A maintenance plan is said to be optimal when, within the given budget, a maximum number of bridges can be maintained in the best possible year, achieving maximum performance with minimum socio-economic impact. The framework incorporates heuristic rules, multi-attribute utility theory, discrete Markov chain process, and genetic algorithms to find an optimal balance between limited budgets and performance requirements. The applicability of the proposed framework is illustrated on an extensive case study of highway bridges. The framework enables asset owners to execute various planning scenarios under different budget and performance requirements, where each resulting plan is optimal. The focus of this study has mainly been on highway bridges, however the framework is general and can be applied to any other infrastructure asset type

SOA and EDA: a comparative study - similarities, difference and conceptual guidelines on their usage

Changing business requirements and new technologies trigger the business stakeholders to shift their approach from many small isolated systems to a single connected system. Integration of isolated systems is partially supported by service oriented architecture (SOA) and event driven architecture (EDA), each of which provides a set of system design guidelines. Since the purpose of both architectures is similar, the stakeholders have to make a choice on which architecture to use. The objective of this paper is to investigate the differences between SOA and EDA and provide conceptual guidelines on which architecture to consider for a given set of requirements. Apart from literature, we have considered various online resources (blogs, forums) that argue about differences and similarities between SOA and EDA. To clarify the design principles of both architectures, we present a case study of a learning management system (LMS)

Predictive maintenance using tree-based classification techniques:a case of railway switches

\u3cp\u3eWith growing service demands, rapid deterioration due to extensive usage, and limited maintenance due to budget cuts, the railway infrastructure is in a critical state and require continuous maintenance. The infrastructure managers have to come up with smart maintenance decisions in order to improve the assets’ condition, spend optimal cost and keep the network available. Currently, the infrastructure managers lack the tools and decision support models that could assist them in taking (un) planned maintenance decisions effectively and efficiently. Recently, many literature studies have proposed to employ the machine learning techniques to estimate the performance state of an asset, predict the maintenance need, possible failure modes, and such similar aspects in advance. Most of these studies have utilised additional data collection measures to record the assets’ behaviour. Though useful for experimentation, it is expensive and impractical to mount monitoring devices on multiple assets across the network. Therefore, the objective of this study is to develop predictive models that utilise existing data from a railway agency and yield interpretable results. We propose to leverage the tree-based classification techniques of machine learning in order to predict maintenance need, activity type and trigger's status of railway switches. Using the data from an in-use business process, predictive models based on the decision tree, random forest, and gradient boosted trees are developed. Moreover, to facilitate in models interpretability, we provided a detail explanation of models’ predictions by features importance analysis and instance level details. Our solution approach of predictive models development and their results explanation have wider applicability and can be used for other asset types and different (maintenance) planning scenarios.\u3c/p\u3