Hamed Mehranfar

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Mehranfar

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Hamed

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0000-0002-4504-0060

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Publications 1 - 10 of 22

The use of BIM in the railway intervention planning process to support the early estimation of future interventions
Chuo, Steven; Mehranfar, Hamed; Adey, Bryan T. (2024)
This report describes the digitalisation of the railway intervention planning process using predictive maintenance algorithms and Building Information Modeling (BIM). It is proposed that this would improve the effectiveness and efficiency of estimating future interventions. This entails an understanding of (1) the current intervention planning process at the SBB, including the stakeholders involved and their associated responsibilities, (2) the digital tools available to facilitate the decision-making for intervention planning of railway infrastructure on the asset, component, and network level, and (3) the coordination between these digital tools to ensure consistency and interaction between these tools and the decision-makers to communicate the results. The intervention planning process is modeled using Business Process Model and Notation (BPMN) 2.0, based on a series of interviews with stakeholders who are involved in decision-making in the planning process. This process requires decisions from the asset managers, network managers, production and project managers, as well as capacity managers. Their tasks and responsibilities are described, and the iterative nature of the interaction amongst these stakeholders is evident. Through BIM visualisations, the report describes the methodologies that facilitate the post-processing and communication of the algorithms’ results. Two methods were used to develop visualisations, both are demonstrated with a case-study of a regional railway line in Switzerland, Line 260. The first method uses the BIM authoring tool Revit, with the algorithm’s results transferred through visual programming plug-in Dynamo. It serves to display the progression of asset condition states, the interventions, and their clustering. The second method uses the BIM authoring tool Navisworks, alongside graphical intervention time schedules generated with the R programming language. It focuses on the scheduling and sequencing of interventions, alongside planning for railway line closures. Both methodologies ensure direct transfer of algorithms’ outputs into practical BIM model applications, enabling rapid iterations and adjustments. Moreover, the report outlines potential strategies to incorporate these digital tools into the railway intervention planning processes. Identified as an area for future research is the current limitation of unidirectional data exchange, where algorithms’ outputs are transferred to the BIM model without a reciprocal flow of information.
Estimation and prediction of track component condition states based on condition data availability to support the digitalized estimation of future interventions
Chuo, Steven; Moghtadernejad, Saviz; Adey, Bryan T.; et al. (2024)
To estimate the future interventions required on a railway portfolio, track managers need to have an overview of their current condition states of all track components and an understanding of how these condition states will evolve in the future. As the exact current condition states of all components on all assets do not exist, as inspections take time and are not done simultaneously on all components of all tracks, this information has to be estimated as best possible. This report introduces a methodology using Bayesian networks to estimate the current condition states in two situations that complement the situation where the current condition state is indeed known. The first is when no data on the condition state of the component exists. This is done considering influencing factors such as environmental condition and traffic loading that would contribute to the deterioration of assets. The second is when historic data on the condition state of the component exists. This is done considering the same influencing factors as when no data exists and this additional data. The proposed methodology is illustrated by using it to estimate the current and future condition states of the components of a railway network in Switzerland, between Biel/Bienne and Zollikofen. As intervention planning processes are becoming increasing digital, the results are visualized in BIM. It is argued that this methodology provides a more consistent and comprehensive overview of the current condition and future condition states than currently exist in practice and that this overview is an integral part of digitalized intervention planning processes. The results of this work help facilitate the use of algorithms that can generate a complete and consistent overview of the type and time of interventions required in the future, an initial overview of the likely lengths and types of possession time required, and the likely costs of intervention.
Estimation and prediction of bridge component condition states based on condition data availability to support the digitalized estimation of future interventions
Chuo, Steven; Moghtadernejad, Saviz; Adey, Bryan T.; et al. (2023)
To estimate the future interventions required on a bridge portfolio, bridge managers need to have an overview of their current condition states of all bridge components and an understanding of how these condition states will evolve in the future. As the exact current condition states of all components on all assets do not exist, as inspections take time and are not done simultaneously on all components of all bridges, this information has to be estimated as best possible. This report introduces a methodology using Bayesian networks to estimate the current condition states in two situations that compliment the situation where the current condition state is indeed known. The first is when no data on the condition state of the component exists. This is done considering influencing factors such as environmental condition and traffic loading that would contribute to the deterioration of assets. The second is when historic data on the condition state of the component exists. This is done considering the same influencing factors as when no data exists and this additional data. The proposed methodology is illustrated by using it to estimate the current and future condition states of the components of 26 bridges of a 25 km railway network in Switzerland. As intervention planning processes are becoming increasing digital, the results are visualized in BIM. It is argued that this methodology provides a more consistent and comprehensive overview of the current condition and future condition states than currently exist in practice and that this overview is an integral part of digitalized intervention planning processes. The results of this work help facilitate the use of algorithms that can generate a complete and consistent overview of the type and time of interventions required in the future, an initial overview of the likely lengths and types of possession time required, and the likely costs of intervention.
A Methodology for Determining Optimal Component-Level Railway Intervention Programs
Mehranfar, Hamed; Adey, Bryan T.; Chuo, Steven (2024)
Connecting predictive algorithms to BIM to improve the planning process
Chuo, Steven; Mehranfar, Hamed; Adey, Bryan T.; et al. (2024)
Bridge Maintenance, Safety, Management, Digitalization and Sustainability
Bridge managers estimate the intervention requirements, and their associated costs, required possession times and failure risks in years advance. They communicate this information to multiple stakeholders involved in the intervention planning process using reports and tables. As it is difficult for stakeholders to process all the information in short periods of time this process can lead to misinterpretations, which in turn can lead to multiple iterations and discussions. With the rise of algorithms and BIMs to predict, plan, and manage future interventions, there is now an opportunity to use these tools to improve the efficiency of the planning process. This paper presents a methodology to do this, i.e., to demonstrate how predictive algorithms can be connected to BIM to facilitate discussions of the multiple stakeholders involved in the intervention planning process, and how the process can be improved. The methodology is demonstrated on a railway steel truss bridge in Switzerland.
Improvements in providing overviews of future bridge interventions, their possession windows, and costs
Mehranfar, Hamed; Adey, Bryan T.; Chuo, Steven (2024)
Bridge Maintenance, Safety, Management, Digitalization and Sustainability
Bridge managers estimate intervention requirements years in advance. They do this to help ensure that work that needs to be carried out can be planned appropriately in terms of quality and timing. These initial estimates must be done at least 10 years before an intervention is required and perhaps even up to 20 years. These large time periods are required so that detailed investigations can be carried out, decisions can be made as to what should be done, work sites can be planned that exploit synergies between multiple interventions, that intervals can be built into train schedules so that there is enough time to do the work, and then the construction sites can be planned in detail. This paper proposes a methodology to be used by bridge managers to obtain a consistent and complete overview of these future bridge interventions and give indica-tions of the required possession windows required in train schedules, and the approximate costs. The methodology is an improvement on the existing bridge management system methodologies in that it uses nonhomogeneous transition probabilities, instead of homogeneous ones, and ex-plicitly estimates the risks associated with different types of bridge failure as a function of the condition states of the different structural components. Existing methodologies do not explicitly consider failure risk in this manner. The methodology is demonstrated using a 100m-long steel truss bridge in Switzerland. The results are visualized using BIM, which allows bridge managers to better understand the algorithmic results and facilitates discussions between multiple stake-holders of the planning process to prioritize interventions. The use of the methodology is expected to improve both effectiveness and efficiency of the intervention planning process.
Enhancing Railway Track Intervention Planning: Accounting for Component Interactions and Evolving Failure Risks
Mehranfar, Hamed; Adey, Bryan T.; Moghtadernejad, Saviz; et al. (2025)
Infrastructures
This manuscript proposes a methodology to leverage digitalisation to efficiently generate an overview of required condition-based railway track interventions, possession windows, and expected costs for railway networks at the beginning of the intervention planning process. The consistent and efficient generation of such an overview not only helps track managers in their decision-making but also facilitates the discussion among other decision-makers in later phases of the track intervention planning process, including line planners, capacity managers, and project managers. The methodology uses data of different levels of detail, discrete state modelling for uncertain deterioration of components, and component-level intervention strategies. It dynamically updates the condition estimates of components by capturing the interaction between deteriorating components using Bayesian filters. It also estimates the risks associated with different types of potential service losses that may occur due to sudden events using fault trees as a function of time and the condition of components. An implementation of the methodology is conducted for a 25 km regional railway network in Switzerland. The results suggest that the methodology has the potential to help track managers early in the intervention planning process. In addition, it is argued that the methodology will lead to improvements in the efficiency of the planning process, improvements in the scheduling of preventive interventions, and the reduction in corrective intervention costs upon the implementation in a digital environment.
Benders decomposition to accelerate determination of optimal railway intervention programmes
Mehranfar, Hamed; Adey, Bryan T.; Burkhalter, Marcel; et al. (2023)
Infrastructure Asset Management
An important task of railway asset managers is to develop intervention programmes. These interventions need to be developed considering network-level synergies and constraints, in addition to the condition of the assets and their optimal intervention strategies. Considering these concerns may lead to executing interventions earlier or later than specified in asset intervention strategies to reach optimality. Synergies include the fact that the simultaneous execution of more than one intervention disrupts train movements only once. Constraints include budget limits and not closing parallel lines simultaneously. Although many railway asset managers currently determine intervention programmes in a rather qualitative iterative fashion, there is an increasing interest in exploiting digitalisation to improve the process. This interest has led to a rise in research focused on the development of mixed-integer linear programs to determine optimal programmes more efficiently and effectively. These powerful models, however, still have issues with complicated intervention planning problems, making their use slower than desired. This paper investigates the potential use of Benders decomposition to accelerate the determination of optimal railway intervention programmes for 2.2 km of the Irish Rail network. It is found that the optimal intervention programme is up to 30% faster for the studied example.
Estimation of bridge component condition states with varying data availability
Chuo, Steven; Moghtadernejad, Saviz; Adey, Bryan T.; et al. (2023)
Life-Cycle of Structures and Infrastructure Systems
For computer systems to estimate the type and timing of future interventions on a bridge, and more specifically, on its components, it is important for the bridge managers to understand their current condition states. That information, however, is almost never perfectly available. In this paper, a methodology is developed that accounts for the scenarios of having no or partial inspection data on the bridge components. A Bayesian network is used to estimate the probabilistic condition states of an asset, requiring the utilization of information that is external to an inspection campaign, including the component properties and environment. With partial information available on the bridge and/or component condition state, the Bayesian network takes advantage of the inference capability to draw conclusions on the condition state of interest. The methodology is used to estimate the condition of a railway bridge pier located in Switzerland.
Optimizing Multicomponent Intervention Programs Considering Costs, Interdependencies, Possession Windows, and Service Loss Risks
Mehranfar, Hamed; Adey, Bryan T.; Moghtadernejad, Saviz; et al. (2025)
Journal of Infrastructure Systems
Presently, upon identifying the necessary railway interventions for a given timeframe in the future, e.g., five years, railway managers frequently undertake an iterative process to sequence the required interventions. These iterations often happen from ten to two years ahead of execution. This sequencing requires extensive knowledge regarding the interventions, their costs, service impacts, their geolocation, construction procedures, availability of time and budget, and possibility of delaying interventions without significantly increasing the risk of failures. This paper proposes an algorithm aimed at improving efficiency and effectiveness of determining multicomponent railway intervention programs that results in the lowest service impacts. It considers the required possession windows, costs, and service impacts as well as interdependencies between interventions, service loss risks, and resources. A mixed integer linear programming model is developed to determine optimal intervention programs by maximizing the net benefit. The algorithm is demonstrated on a 25-km railway network in Switzerland. The results show up to 58% improvement in the net benefit of executing multicomponent intervention programs.

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Hamed Mehranfar

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