This part of the study focuses on identifying and evaluating the Asset Management thinking and highway / pavement related systems embedded within the spectrum of CR’s Life-Cycle planning across their 5 PFI concessions. The anticipated outcome (identified by Task I) is establishing the means to lay the foundations for the HFD maintenance management system by drawing parallels between pre-existing systems and new information and processes requirements. Such an approach will enable a swifter and smoother transition to a proactive maintenance and investment planning system specifically designed and implemented for the aforementioned drainage element.
In practice and from Connect’s perspective, Pavement Management is central to developing, updating and coordinating all activities related to maintaining and rehabilitating pavement and
ancillary assets. The whole system is structured around AM principles that define the what’s and how’s required to achieve a proactive, systematic and engineered management of tangible assets. CR’s PMS is structured around in-service and handback performance requirements (which depict anticipated and agreeable levels of service in a given highway network). Typical tasks and routines associated with the care of assets generally include:
• Inventory of physical assets to include structural characteristics • Inventory of past maintenance work
• Condition surveys targeting pavement / structures serviceability, safety and structural capacity
• Pavement analysis (condition distribution, deterioration modelling)
• Performance monitoring mapped against key contractual performance indicators • Investment Analysis, prioritisation and what-if scenarios to formulate maintenance
budgets
Connect’s annual updates of Life Cycle and AM plans are formulated upon collection and evaluation of condition data pertaining to pavement networks, structures, drainage and ancillary assets. There are different levels of sophistication embedded in each asset - specific DST and there is still work undergoing aiming the development of an all-encompassing highway asset management decision support tool. The Whole Life Plan cycle for pavements can be broken down in tasks listed below:
• Condition Data Collection & Processing
o SCANNER / TRACS (pavement serviceability) o SCRIM (skidding resistance / safety)
o Deflectograph (structural capacity)
o Ground Penetrating Radar / coring (other ad-hoc surveys, if / when required) • Updating DST inventory and condition database
• Updating DST model variables (if and when required)
• Strategy Optimisation (constrained cost minimisation / maximised investment benefit constrained budgets) using proprietary DST IT system
• Preliminary Programs (network and project level)
• DBFO-specific investment committees (project level 2 year project selection) • Updating pavement DST models and final annual maintenance programs.
The process is broken down and visualised in Figure 15. Investment committees are CR’s internal mechanism which was set in place to achieve the alignment of AM objectives and operational output; this in practise institutes a clear line of sight between the strategic side of asset management (client / stake and shareholder expectations, contractual requirements) and the tactical / engineered end of the equation (scheme planning and project delivery).
The annual condition-data collection cycles follow the contractual serviceability/safety and structural capacity requirements as defined in concession specific Operation and Maintenance contracts. These suggest the core asset-condition data to be collected, the data collection intervals and the performance targets pertaining to in service and hand-back requirements for each asset class. Pavement and condition information for the three Highways England concessions (A30/A35 DBFO, A50 DBFO, M1A1 DBFO) and Carlisle Northern Development Route (CNDR) DBFO, are imported into a proprietary database linked to Deighton’s Total Infrastructure Management System (dTims). dTims enables the formulation of decision trees, treatment rules, treatment impacts and investment strategies upon optimisation of investment requirements.
Asphalt Pavement Aggregated Condition Preventive Maintenance / Do nothing Structural Deterioration Structural Overlay (>100mm) • Non-Structural Overlay (40mm) • Retexture • TSCT Condition Score >5 <5 Present Not Present
Figure 14 Simplified Decision Tree for asphalt pavement section
Deterioration models and deterioration projection is carried out externally by focusing on each key condition variable and carrying out regression analysis using historical data; deterioration trends then enable predictive capabilities for each condition index to be generated and embedded within models in the form of linear equations. CR does not adopt any particular mechanistic or empirical methodologies to forecast pavement deterioration. Instead, the annual output of Traffic Speed condition surveys is used as the backbone of condition modelling on a variable-by-variable basis. The DST base table includes inventory information and network sectioning for all pavement assets across each concession. Key information typically stored within the database include type of pavement (Long or Non-Long Life), numbers of lanes, HAPMS referencing, and surfacing material (HRA, SMA, other proprietary surface dressing material). This type of information was generated once and is typically steady with updates focusing on newly constructed sections, which take place rarely in DBFO projects.
DST engineering rules encompass all condition and performance related components of the existing PMS. Here, treatment rules, triggers and impacts are defined and condition forecasting and condition breakdown models are developed and then adopted. Pavement
performance models for different pavement types (in regards to surfacing options), carriageway lanes and geographic locations (environmental and traffic conditions) are hence developed and linked to dTims’ model variables. Strategy optimisation can then be achieved by defining in-service performance constraints and asset-specific hand-back requirements. Such constraints are typically representative of the worst acceptable condition a pavement section can be at any point through its projected service life. KPI’s are hence formulated for structural capacity and safety related performance variables of all pavement sections within a given network. Failing to comply with such performance requirements yields penalties which can in some be in the form of large financial deductions. With DBFO contracts, performance requirements are usually rigidly defined. Different and quantifiable metrics for pavement rutting, texture depth and structural residual life at handback are for example provided in any given O&M contract (in essence data to be assessed and data collection processes are well established well understood and well followed). Optimisation thus takes the form of constrained maintenance cost minimisation – a typical option for any and all pavement management IT systems available in the sector.
Typical dTims outputs include annual proposed project level schemes, forecasted network level condition breakdown under the selected and optimised strategy, and hence overall network level investment requirements. Project selection and subsequent annual programmes are then developed through concession specific investment committees where the output of dTims modelling and generated scheme list is mapped to on-site conditions, scheme selection practicalities and maintenance requirements as proposed by MAC operator pertaining to all other ancillary assets and structures for which condition is usually not measured but rated. Through these committees, maintenance budgets are generated to also include allocations for HFDs and are thus central to the development of all elements coupled to the maintenance management system proposed in the following sections.
• Machine Based Surveys • TRACS / SCANNER • Deflectograph • SCRIM • Visual Surveys • HAPMS Records • Destructive Testing • Performance Targets • In Service Requirements • Hand-back requirements • Client & Shareholder Expectations
Asset Inventory and Condition Data
Regression Analysis Asset Management Requirements DST model Requirements DST Base Table • Network Sectioning • Historical Data • Georeferencing • Construction Information DST Outputs • Asset Condition Reports • Work Plans • Condition Prognosis • Performance Monitoring • Residual Life DST Engineering Rules • Pavement Condition metrics • Deterioration Models • Treatment Rules • Treatment Impacts Strategy Optimisation
• Objectives (cost / benefit) • Variables (condition) • Asset Ageing rules • Constraints (budgets, performance) • Treatment Rules Investment Committees • Asset Managemers • Concession Managers • MAC Operators
Finalised Investment Strategies (short and long term)
Preliminary 1,5,30 Year Program Annual Updates Continuous Improvement O ut p ut s Informs
Technical DST Inputs Asset Management Strategic Requirements
The M77/GSO DBFO posed a number of unique challenges in the formulation and optimisation of WLPs and investment strategies. Due to limited available condition data and pavement deterioration that failed to be represented by typical regression equations, a maintenance planning approach driven by visual assessment and transition probability matrices was defined as a course of action in recent years. The approach tackled the ongoing issue of out-of sync machine-based data and actual on-site conditions but failed to capture condition projection in a systemised and engineered manner. As part of the project deliverables and in line with Tasks 1b and 8 (see Section 3.3) a probabilistic condition projection methodology and a pavement DST was built to support proactive maintenance planning. This would enable the development of short-to medium term investment strategies and also lay the foundation for the development of the HFD proactive maintenance system. The whole process integrating inventory data, visual condition information, discrete condition bands, DST set up and typical outputs is presented in Figure 16. Five condition grades were used to assess pavement condition ranging from Very Good to Very Poor [𝑉𝑉𝑉𝑉, 𝑉𝑉, 𝐹𝐹, 𝑃𝑃, 𝑉𝑉𝑃𝑃]. A visual assessment was undertaken in 2016 and a base condition array displaying the condition distribution across the M77 route was hence developed. Deterioration was then projected by adopting a Markovian Transition Probability matrix for each surfacing type found in the network (currently SMA, HRA). Transition probabilities were defined based on engineering judgement and prior network knowledge and anticipated service life of surfacing material options. The TPM for HRA surfacing material adopting a service life 𝑛𝑛 = 16 years and linear deterioration was thus calculated as shown below:
𝑃𝑃 = � 𝑝𝑝11 𝑝𝑝12 ⋯ 𝑝𝑝1𝑛𝑛 𝑝𝑝21 𝑝𝑝22 … 𝑝𝑝2𝑛𝑛 ⋮ … ⋱ ⋮ 𝑝𝑝𝑛𝑛1 𝑝𝑝𝑛𝑛2 ⋯ 𝑝𝑝𝑛𝑛𝑛𝑛 � = 𝑉𝑉𝑉𝑉 𝑉𝑉 𝐹𝐹 𝑃𝑃 𝑉𝑉𝑃𝑃 ⎣⎢ ⎢ ⎢ ⎡0.765 0.2350 0.765 0.2350 00 00 00 0 0 0.765 0.235 0 0 0 0 0 0.765 0.235 0 0 0 0 0 0.765 0.235⎦⎥ ⎥ ⎥ ⎤
Since the DST aims at identifying major resurfacing investment requirements, two maintenance options are used (40mm HRA or SMA) assuming like-for-like interventions (ie replacement of SMA surfacing with SMA surfacing) and maintenance costs are extracted from the DBFO management accounts and discounted to match average 2007 prices. To simulate the effect of maintenance treatments a generalised deterioration / renewal mathematical model (conceptualised by De La Garza and Krueger (2007)) is used as presented in the equation that follows:
𝐶𝐶𝐶𝐶𝑛𝑛𝐶𝐶𝑖𝑖𝛥𝛥𝑖𝑖𝐶𝐶𝑛𝑛𝑖𝑖(𝛥𝛥 + 1) = 𝐶𝐶𝐶𝐶𝑛𝑛𝐶𝐶𝑖𝑖𝛥𝛥𝑖𝑖𝐶𝐶𝑛𝑛𝑖𝑖(𝛥𝛥) − 𝐶𝐶𝐶𝐶𝑛𝑛𝐶𝐶𝑖𝑖𝛥𝛥𝑖𝑖𝐶𝐶𝑛𝑛𝐷𝐷 𝑖𝑖(𝛥𝛥) 𝑖𝑖𝑗𝑗 + 𝐶𝐶𝐶𝐶𝑛𝑛𝐶𝐶𝑖𝑖𝛥𝛥𝑖𝑖𝐶𝐶𝑛𝑛𝑖𝑖(𝛥𝛥) 𝐷𝐷𝑖𝑖𝑖𝑖 + � 𝑅𝑅𝑗𝑗𝑖𝑖(𝛥𝛥) − � 𝑅𝑅𝑖𝑖𝑖𝑖(𝛥𝛥)
In principle, the equation determines the annual change of the asset condition by calculating the effects of maintenance treatments on the various condition levels and then ageing the resulting condition distributions according to the adopted deterioration rates. Deterioration rates (denoted by 𝐷𝐷𝑖𝑖𝑖𝑖and 𝐷𝐷𝑖𝑖𝑗𝑗) were based on the TPM for each surfacing material, while 𝑅𝑅𝑗𝑗𝑖𝑖 and 𝑅𝑅𝑖𝑖𝑖𝑖 denote the upstream condition changes due to maintenance interventions (i.e. the impact of maintenance on current condition. In this case the two maintenance options reset a deteriorated section to a Very Good / as new condition. All this has been embedded and structured in the excel based toolkit to monitor and follow deterioration and project and output network level performance and maintenance needs.
Outputs related to the excel based DST developed for CR include an annual maintenance spend tracker, network level performance breakdown for SMA / HRA wearing courses and annual treatment lengths for each surfacing material used in the concession. The tool-kit has
been communicated with Transport Scotland and will be central for maintenance planning purposes to supplement dTims optimisation and analysis for the following years.
1. Condition Bands adopted
3. Markov Transition Probabilities
2. Inventory & Condition Assessment
4. Network Ageing
5. Base Condition (starting point)
6. Solver Optimisation problem set up
7 . D S T out put s