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

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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Abstract

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.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Dorée, Andries G., Supervisor
  • Stipanovic, Irina , Supervisor
Award date12 Sep 2019
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-4822-9
Electronic ISBNs978-90-365-4822-9
DOIs
Publication statusPublished - 2019

Fingerprint

Learning systems
Planning
Asset management
Managers
Decision making
Highway bridges
Aging of materials
Switches
Availability
Concretes

Keywords

  • Maintenance
  • decision making
  • Transport
  • Machine Learning
  • Bridge
  • Maintenance planning

Cite this

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title = "Providing decision support for transport infrastructure maintenance planning: Through application of multi-criteria and machine learning methods",
abstract = "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.",
keywords = "Maintenance, decision making, Transport, Machine Learning, Bridge, Maintenance planning",
author = "{Allah Bukhsh}, Zaharah",
year = "2019",
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language = "English",
isbn = "978-90-365-4822-9",
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school = "University of Twente",

}

Providing decision support for transport infrastructure maintenance planning : Through application of multi-criteria and machine learning methods. / Allah Bukhsh, Zaharah .

Enschede : University of Twente, 2019. 274 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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AB - 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.

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