Sydney Trains has an extensive portfolio of bridges due to the myriad of waterways and transport modes that intersect the network. These structures require significant lifecycle management and while Sydney Trains has been exceeding industry practices for several years, a deficit in sustainment requirements and available funding has developed over the 10-year forward planning horizon. To address this, a bridge optimisation project was initiated, including condition assessment (data), strategy uplift (process), investment optimisation (systems), and cultural enhancement (people). The project has generated over 40% savings in future funding requirements as the benefits and the approach is now being applied to other major asset classes.
Over the last 10 years, bridges have generated the highest funding requirement delta compared to other infrastructure asset classes of Sydney Trains. While a Bridge Management Strategy has been in place, the process of determining investment forecasts involved desktop health assessments and data assumptions. This resulted in bridges being treated and managed as an asset not a group of major components, leading to higher treatment strategy costs.
To improve the bridges strategy, an uplift in condition assessment and data, asset management strategies and delivery practices was initiated in FY21. Sydney Trains engaged structural engineering contractors to undertake bridge assessments including detailed reports and photographs. A range of survey methods were used including drones, barges, boats, and field structural engineers.
As the assessments were completed, the data was processed to optimise the bridge maintenance strategy through a “lifecycle optimisation” process. The aim of this process was to make the maintenance strategy more precise and focused on each bridge component, with the goal of optimising interventions over a 20+ year period while achieving acceptable performance and risk outcomes at the lowest lifecycle cost.
Data was collected and analysed at the component level and bridge component lifecycle strategies were defined and classified into independent and interdependent treatment groupings. Long-term maintenance funding requirements were modelled using specific unit rates associated with components and intervention types and groupings.
By implementing these changes, Sydney Trains has improved its management strategies for bridge assets and the associated funding forecasts across the network. This improved bridge lifecycle management maturity by implementing an approach that links bridge performance to funding strategies at the component level. An indication of the uplift in maturity profile is shown Figure below.
Figure 1 An indication of the uplift in bridge asset maintenance maturity
As a result of these improvements, Sydney Trains has recognised the following change in investment profile over the forward horizon:
Figure 2 Comparison of funding profile and cumulative backlog for Bridges before and after
The efficiencies derived will be repurposed into other project or programs to offset risk and reduce backlog within funding allocation, where Sydney Trains would otherwise need to request additional funds from TfNSW. The efficiency is well in excess of 40% of total funding requirements over the 10-year horizon.
Other intangible benefits identified include:
Beyond bridges, there are a range of multi-million-dollar opportunities that have been identified through ALO that can enhance the sustainability of Sydney Trains maintenance portfolio by realising more value from the available funding.
2.2 Project Management
Sydney Trains Asset Management led a project team consisting the core ALO staff team and specialist professional service providers. For engineering acceptance, an Asset Strategy Council of experienced subject matter experts progressively review the changes to strategy.
The project is divided into three major phases, each containing several tasks:
2.3 New Approach
The ALO approach is bottom-up where data on each individual bridge component determines the need for intervention.
The approach involves three levels of analysis: component, project, and network, which are interconnected and have an impact on each other, as shown in Figure 3.
Figure 3 Three-level ALO bridges maintenance decision making approach
Due to the success of this approach for bridge lifecycle management, the methodology is now being applied to the Slopes portfolio (Cuttings and Embankments), with assessments and strategy analysis underway.
2.4 Best Practice Asset Management Principles Utilisation
This project has developed a methodology that combines component condition data from one-off level 2 condition assessments and defects records from periodical bridge examinations. By doing so, the condition of a bridge component can be accurately identified and the potential for deterioration can be forecasted. This allows for more efficient and consistent scheduling of maintenance activities, reducing costs and the risk of unexpected failures.
As a result of this project, an optimised bridge maintenance plan has been created. This plan details the necessary maintenance activities over the entire lifecycle of bridges and is based on a comprehensive analysis of data, including information on age, conditions, load capacity, environmental factors, and available maintenance funding. By implementing this plan, bridge maintenance can be carried out in a more cost-effective and sustainable way, minimising overall costs and reducing access requirements.
2.5 Value of the Project
The ALO approach is a data-driven and evidence-based method that focuses on detailed information and decision-making processes. It improves transparency and efficiency in funding utilization, and asset lifecycle management, resulting in increased value from.
Using the ALO model, better forecasting of bridge renewal and maintenance funding needs is possible, resulting in a major opportunity for long-term maintenance cost reduction.
These savings improve cost-effectiveness of the rail network sustainment, endorse the value of ALO in Sydney Trains and contribute to the longevity of the networks for future generations.
3. Specific Contribution Made
The project has introduced several noteworthy practices, including:
(1) Components that have been treated are assigned distinct deterioration profiles and initial condition states
Assigning distinct deterioration profiles and initial condition states to treated components, the ALO approach considers previous refurbishments or replacements of components to determine their future condition and remaining useful life accurately.
(2) Interventions are determined based on both component- and bridge-level data
The ALO approach ensures the right timing and scope of component works. Interventions are triggered when a component’s condition drops below a predetermined threshold. Other performance issues, such as insufficient load capacity, narrow deck width, or missing crash barriers, may also require intervention.
Figure 4 Determination of bridge interventions based on both component- and bridge-level data
(3) Bridge components are classified into independent and interdependent categories to match the intervention strategies
In contrast to traditional approaches, the ALO method optimises the timing of component interventions through lifecycle optioneering and bundling. This means grouping together components that require treatment in nearby years to streamline maintenance activities and reduce the number of visits and track possessions needed.
To ensure maintenance strategies are aligned, the approach categorises bridge components into independent and interdependent categories, considering their structural interactions. For example, when replacing a main girder, the headstocks and transoms attached to it may also need to be replaced, even if they are still in fair or good condition. By categorising components and selecting the most suitable interventions based on these categories, this complexity can be addressed.
(4) Timing of component interventions is optimised through bundling
Traditional approaches to bridge asset management often overlook the situation where multiple components require treatment starting in different years. This is a common occurrence in railway bridges, which are long-lasting assets and have key components that may deteriorate at varying rates.
To address this issue, an optimised bundling strategy and task allocation plan can be developed based on a combination of lifecycle cost analysis (LCCA) and risk analysis. This involves considering the risk of deferred interventions, direct intervention costs, indirect costs, and possession costs to determine the most efficient bundling strategy.
For example, some component interventions may be deferred until a later year if other components require significantly more work in future. Alternatively, some interventions may be bundled into a single project, while others are separated into multiple projects to maximise the utilisation of key component’s useful life.
In summary, a more strategic approach to bundling expected interventions can reduce indirect costs, possession costs, and operational disruption while ensuring the optimal use of resources and prolonging the lifespan of bridge components. Figure 5 illustrates the discreteness of bridge components intervention timing and provides examples of different bundling strategies.
Figure 5 Discreteness of bridge components intervention timing
4. General Comments
Photograph 1 – Complex bridge structures inspection using drones equipped with a 360-degree camera
Photograph 2 – Remote digital visual equipment used to fit complex inspection environments
Photograph 3 – Bridge component inspection using a 360-degree camera