Highway filter drains: precursors for maintenance management

This paper conceptualises and presents a number of asset management building blocks required to establish holistic management for highway filter drains in the UK roads network. This is accomplished by evaluating current maintenance and management thinking and by identifying how existing strategies are lacking and potentially unsustainable. A condition assessment regime is hence described, tied to a measure of filter drain level of service (drainability) and an asset-specific ageing/renewal model that adopts six discreet condition bands is proposed. For this model to hold true, the Markov process is assumed to represent cumulative damage in a network. Drawing from relevant asset management concepts, a decision support tool to inform and optimise managerial decisions in respect to maintenance planning and resources allocation is also described

Geotechnical asset management for climate change risk

Geotechnical asset management is a process and tool which encourages robust data management, optimised programming and evidenced based decision-making. Although asset management has come a long way since becoming a more accepted practice in the highways sector , and has fundamentally changed the way that assets are maintained, there are aspects that are still evolving as clarity on asset and network need improves and extends into future years. However, in gaining more sight into the future of asset networks, unforeseen risks begin to appear. These risks may not have been known or well understood at the time the asset network was constructed, or may not have been an issue when the asset was originally designed, yet, over the years the changing use of the asset network by users has led to new risks becoming apparent. One of these historically unknown risks is climate change. While knowledge about how climate change is expected to impact assets is improving, the understanding of the scale and scope of assets that will be affected by climate change is less well developed. The tool presented in this research is a risk assessment, which evaluates the risk profile of the effects of climate change on a geotechnical asset as the result of the critical condition impact factors. This risk profile is completed by a scoring the impacting factors on a scorecard, for subsequent inclusion in the final risk score. The likelihood element of the risk assessment uses probability scores taken from the medium emission scenarios presented by the UKCIP 2018. The resultant risk score can then be utilised as a forward planning tool for maintenance, or increased monitoring, where appropriate. Three case studies were assessed to show the practical application of the system. The results of the case studies show that the process works and produces results which aid the planning of maintenance to mitigate for climate change

Future-proofing governance and BIM for owner operators in the UK

Owner operators are managing and maintaining their infrastructure assets. In addition, depending on the national economic activity, they are being reactive or proactive in their response against uncertainty. Findings from this study showed that improvements can be achieved if the concept of future-proofing (FP) of assets – as a structured approach against uncertainty – becomes more explicitly defined. FP is the holistic process of taking security measures against uncertainty and being proactive throughout the organisation and its assets. In combination with information management, it ensures that asset management (AM) strategies will become responsive to a number of future changes in requirements. In this context, it is asserted that both FP and Building Information Modelling (BIM) suffer from a dearth of identification in the context of AM. Through a case study, this paper presents an approach that helps clients to future-proof AM at a strategic level. Furthermore, governance agendas for FP and BIM capabilities for future-proof information have been identified that owner operators and the supply chain can find useful

Highway filter drains maintenance management

Across a large part of the UK highways network the carriageway and pavement foundations are drained by Highway Filter Drains (HFDs). A HFD is a linear trench constructed either at the pavement edge or central reserve, fitted with a porous carrier pipe at the base and backfilled with an initially highly porous aggregate material. This arrangement enables the swift removal of surface runoff and subsurface water from the pavement system minimising road user hazards and eliminating risk of structural damage to the pavement sub-base. The highly porous backfill filters throughout its operational life fines washed from the pavement wearing course or adjacent land. HFDs have been found to be prone to collecting near the basal sections (pipe) or surface layers contaminants or detritus that causes the filter media to gradually block. The process has been defined as HFD clogging and it has been found to lead to reduced drainage capacity and potentially severe drop of serviceability. O&M contractual agreements for DBFO projects usually propose in-service and handback requirements for all assets included in the concession portfolio. Different performance thresholds are thus prescribed for pavements, structures, ancillary assets or street lighting. Similar definitions can be retrieved for drainage assets in such agreements, and these include HFDs. Performance metrics are defined though in a generic language and residual life (a key indicator for major assets that usually drives long-term maintenance planning) is prescribed without indicative means to evaluate such a parameter. Most of pavement maintenance is carried out nowadays using proactive management thinking and engineered assessment of benefits and costs of alternative strategies (what-if scenarios). Such a proactive regime is founded upon data driven processes and asset specific ageing / renewal understanding. Within the spectrum of road management, maintenance Life Cycle Costs are usually generated and updated on an annual basis using inventory and condition data linked to a Decision Support Tool (DST). This enables the assessment and optimisation of investment requirements and projection of deterioration and of treatment impacts aligned to continuous monitoring of asset performance. Following this paradigm shift in infrastructure management, a similar structured methodology to optimise HFD maintenance planning is desired and is introduced in this thesis. The work presented enables the identification of proactive maintenance drivers and potential routes in applying a systemised HFD appraisal and monitoring system. An evaluation of Asset Management prerequisites is thus discussed linked to an overview of strategic requirements to establish such a proactive approach. The thesis identifies condition assessment protocols and focuses on developing the means to evaluate deteriorated characteristics of in service drains using destructive and non-destructive techniques. A probabilistic HFD ageing / renewal model is also proposed using Markov chains. This builds upon existing deterioration understanding and links back to current treatment options and impacts. A filter drain decision support toolkit is lastly developed to support maintenance planning and strategy generation

Integration and Evaluation of Automated Pavement Distress Data in INDOT’s Pavement Management System

This study was in two parts. The first part established and demonstrated a framework for pavement data integration. This is critical for fulfilling QC/QA needs of INDOT’s pavement management system, because the precision of the physical location references is a prerequisite for the reliable collection and interpretation of pavement data. Such consistency is often jeopardized because the data are collected at different years, and are affected by changes in the vendor, inventory, or referencing system or reference points. This study therefore developed a “lining-up” methodology to address this issue. The applicability of the developed methodology was demonstrated using 2012-2014 data from Indiana’s highway network. The results showed that the errors in the unlined up data are significant as they mischaracterize the true pavement condition. This could lead to the reporting of unreliable information of road network condition to the decision makers, ultimately leading to inappropriate condition assessments and prescriptions. Benefits of the methodology reverberate throughout the management functions and processes associated with highway pavements in Indiana, including pavement performance modeling, optimal timing of maintenance, rehabilitation, and reconstruction (MRR), and assessment of the effectiveness of MRR treatments and schedules. The second part of the study developed correlations for the different types of pavement distresses using machine learning algorithms. That way, the severity of any one type of distress can be estimated based on known severity of other distresses at that location. The 2012-2014 data were from I-70, US-41, and US-52, and the distress types considered are cracking, rutting, faulting, and roughness. Models were developed to relate surface roughness (IRI) to pavement cracks, and between the different crack types, with resulting degrees of confidence that varied across the different crack types and road functional classes. In addition, for each functional class and for each crack type, models were built to relate crack depth to crack width. The concept can be applied to other distress types, such as spalling, bleeding, raveling, depression, shoving, stripping, potholes, and joint distresses, when appropriate data are available

How can the UK road system be adapted to the impacts posed by climate change? By creating a climate adaptation framework

This paper aims to analyse the impacts of climate change to the current and predicted future situations of road transportation in the UK and evaluate the corresponding adaptation plans to cope with them. A conceptual framework of long-term adaptation planning for climate change in road systems is proposed to ensure the resilience and sustainability of road transport systems under various climate risks such as flooding and increased temperature. To do so, an advanced Fuzzy Bayesian Reasoning (FBR) model is first employed to evaluate the climate risks in the UK road transport networks. This modelling approach can tackle the high uncertainty in risk data and thus facilitate the development of the climate adaptation framework and its application in the UK road sector. To examine the feasibility of this model, a nationwide survey is conducted among the stakeholders to analyse the climate risks, in terms of the timeframe of climate threats, the likelihood of occurrence, the severity of consequences, and infrastructure resilience. From the modelling perspective, this work brings novelty by expanding the risk attribute “the severity of consequence” into three sub-attributes including economic loss, damage to the environment, and injuries and/or loss of life. It advances the-state-of-the-art technique in the current relevant literature from a single to multiple tier climate risk modelling structure. Secondly, an Evidential Reasoning (ER) approach is used to prioritise the best adaptation measure(s) by considering both the risk analysis results from the FBR and the implementation costs simultaneously. The main new contributions of this part lie in the rich raw data collected from the real world to provide useful practical insights for achieving road resilience when facing increasing climate risk challenges. During this process, a qualitative analysis of several national reports regarding the impacts posed by climate change, risk assessment and adaptation measures in the UK road sector is conducted for the relevant decision data (i.e. risk and cost). It is also supplemented by an in-depth interview with a senior planner from Highways England. The findings provide road planners and decision makers with useful insights on identification and prioritisation of climate threats as well as selection of cost-effective climate adaptation measures to rationalise adaptation planning. © 2019 Elsevier Lt

Key questions for road investment and spending

This report has been produced by the Road Investment Scrutiny Panel – an assembled group of senior professionals supported by funding from the Rees Jeffreys Road Fund.Roads are the arteries of economic and social prosperity. They also come at a cost to the public purse and in terms of the negative consequences arising from their construction, maintenance and use. As the road network in the UK has expanded, it has supported but also shaped society. In a world beset by global shocks and the climate and nature emergency, weighing up the makeup and scale of our investment in roads and how they are used matters more than ever if we are to secure effective outcomes environmentally, socially and economically.It is a critical time for road investment and expenditure in England, and beyond. The third Road Investment Strategy is under development. The National Policy Statement for National Networks is under review. There are considerable financial pressures facing national and local governments. COP 27 (climate change) and COP 15 (biodiversity) have just taken place.In this report we have identified what we consider to be some of the most important and pressing questions that should be considered in the handling of national and local road investment and expenditure. Our questions relate to decarbonisation, biodiversity, health and social impacts, maintenance and optimisation, and safety. They also concern a call for appropriate consideration of alternative options to address a need for change and for robust decision making in the face of a changing and uncertain world.Not addressing these questions would, we believe, be a false economy while engaging fully with them can enable robust and timely progress.The Panel is comprised of Professors: Glenn Lyons (Chair); Steve Gooding (Co-convenor); Jillian Anable; Nicola Christie; Zoe Davies; Stephen Glaister; Phil Goodwin; and Karen Lucas. The Panel Secretary is Andrew Crudgington