Data, Information and Knowledge Condition Driven Management of Highway Filter Drains
HFD deterioration and maintenance planning. Uncharted, reactive and unplanned. Identifying the way forward. 2
What we know; the story so far 3
Operation, Deterioration & Service Life, Maintenance Drivers • A Highway Filter Drain (or French Drain) is a combined drainage system that operates at two levels. • Fast removal of carriageway runoff, and simultaneously • High water tables and subsurface water. • Their design and implementation on projects capitalised on their effectiveness and (initially) long anticipated service life. 4
Operation, Deterioration & Service Life, Maintenance Drivers • Failures are progressive and often offer visual queues that can be identified in network surveys. • Drainage trench (and acc. to environmental and construction conditions) will gradually be clogged by introduced / generated and transported fines – (the fouling material). • Drains will require regular surface cleaning • Drains will require re-construction at 10 year service life point. 5
Operation, Deterioration & Service Life, Maintenance Drivers Asset owners are not realising value. Possible way forward? In terms of planning, management of investments and understanding the factors that drive the deterioration of HFD, our strategies are largely underdeveloped (and mainly reactive). Drainage has been neglected in the past, efforts to implement assessment techniques only partially addressed AM requirements, strategic guides offer fundamental concepts lacking the ‘engineering-end’ of the management equation and empirical and/or time-based approaches offer limited opportunities to evaluate the physical condition of an asset and thus to collect relevant and specific HFD data. 6
Infrastructure Management. Data driven, proactive, engineered 7
Asset management over asset life-cycle Life Extension Asset Care Utilisation Decommission Planning Acquisition Development 8
Pavement Management Decision making in highways is driven by data (collection, processing, interpretation). • Inventory information defines type and location of assets • Condition Data inform maintenance Maintenance planning and define deterioration Data Strategy understanding. • Performance Targets link asset classes to contractual in-service requirements • Budgets • Maintenance rules, treatments and • Inventory LCC • Current Condition impacts define timing and effects of Investment • Performance Targets interventions • Treatment Options requirements • Deterioration Profiles • Generated LCCs present What-If alternatives, optimised lifecycle fund and 9
Operations for Holistic Management Identify Assets Identify Performance Requirements Assess Performance Plan Maintenance Manage Maintenance Operations 10
HFD maintenance management; learning lessons, focusing on condition evaluation, ageing and treatment rules. 11
Aiming for HFD MM Structure and define a framework to Shallow Aggregate Replacement Surface Cleansing introduce holistic management for HFD in Fair - Crusted Poor - Crusted Scarifying UK roads network Surface Cleansing Scarifying Excellent Very Poor Fair Condition Poor Condition Condition Condition • Collect Inventory, maintenance and condition data. Shallow aggregate Replacement Deep aggregate replacement • Establish DST to prioritise investments. DST Maximise network performance of HFD objectives • Optimise maintenance strategies. Planning periods Length of HFD in Highway section Network Condition Distributions • Establish proactive means to drive Inputs and Discount Rates Annual Budgets modelling M&R options and Unit costs Condition transition matrices Deterioration and intervention modelling maintenance decision making. Minimum network performance levels Constraints Maximum yearly budgets Multi year M&R plan Network Condition over planning horizon Outputs / Yearly M&R costs Results Total costs over planning horizon Residual Values 12
Defining function, failure and quantifying levels of service • Deterioration of HFD is driven by introduction of foreign particles within the filter trench. • Other studies - Evaluation through selective sampling and sieving but no quantitative representation of foreign material. • Early Eng.D work - What to measure, how to define distress indices and how to make them accountable for the asset’s performance. • Field Work and routine maintenance - 2 failure modes with different progression; bottom up and top down. • Each unique failure mode defines specific treatment rules; scarifying and surface cleansing for top down, aggregate replacement for bottom up. • Requirement for scarifying may be defined upon evaluating historic maintenance records; empirical evidence suggests drainage trenches will be presented with localised siltation and vegetation growth 13
Defining function, failure and quantifying levels of service • Define a condition index (here Free voids Ratio) 𝑓 𝑔𝑠 𝑊 𝐵 − 𝑁𝐺 𝑁𝐵 𝐻𝐵 − 𝑁𝐺 𝑓 𝑔𝑠 𝑊 𝑊𝐺𝑆𝐵 −𝑊 𝐺 𝜍𝑔 𝜍𝑔 𝑆 𝐺𝑊 = = = 𝑁𝐵 𝑊 𝑊𝐺𝑆𝐵 𝑓 𝑔𝑠 𝑊 𝐵 𝑓 𝑔𝑠 𝐻𝐵 • Correllate the anticipated change of this index to a level of assumed service. 𝐥 ′ = 𝐋 𝐖 Condition Band 𝐒 𝐆𝐖 𝐋 𝐧𝐛𝐲 𝑙 ′ > 0.6 As new - Excellent 𝑆 𝐺𝑊 > 0.7 Good 0.7 < 𝑆 𝐺𝑊 > 0.5 0.6 < 𝑙′ > 0.35 Poor 0.5 < 𝑆 𝐺𝑊 > 0.3 0.35 < 𝑙′ > 0.05 𝑙 ′ < 0.05 Very Poor - Spent 𝑆 𝐺𝑊 < 0.3 14
Condition Assessment 15
Benefits of shifting focus to a condition assessment system Agencies that have used enhanced approaches to assessing condition of transportation assets will benefit from the use of data to support decision making. Availability of objective, engineered and quantified indices will accomplish: • Improve link between customer Selective Sampling and expectations and maintenance & Sieving rehabilitation interventions Non • Establish consistent condition Destructive distribution across network Testing & evaluation • Evaluate maintenance backlog • Define priorities and optimise Visual investments required within planning Surveys horizon 16
Condition Diagnosis – Intrusive Testing • Sampling and sieving to identify extend of fouling. • Localised but detailed – requirement to remove samples (ABS approach or manual extraction). • Classification of fouling in laboratory . • Layer by layer approach (define layer depth) to evaluate progression of sedimentation in trench and identify right treatment 17
Quantifying Fouling Levels and assessing service levels 100 100 90 90 80 80 Material passing % Material passing % 70 70 60 60 D1 C1 50 50 D2 C2 40 40 D3 C3 30 30 D4 C4 20 20 10 10 0 0 0.01 0.1 1 10 100 0.01 0.1 1 10 100 Nominal aperture size mm Nominal aperture size mm 18
Condition Diagnosis - Non Destructive Evaluation • Why GPR • Machine Based surveys and non-destructive evaluation are embedded within pavement management systems. • GPR is a prominent option that has has a long history of applications for both pavement and railway trackbed related condition surveys. • What is being offered • A network based condition evaluation technology that has shown promise in identifying changing fouling and moisture conditions in railway ballast surveys. • A technology that is sufficiently understood by highways asset managers. • An evaluation approach with known limitations – but also a lot of potential. 19
Geophysical investigation (p.1) Aim: Study Dielectric Dispersion Variables: Fouling Levels and water content EkkoPulse 500Mhz & 1Ghz antennae used Box Dimensions: .5x.5x.5m=.125m 3 – Add aggregate in layers, compact and scan with both antennae. – Remove portion of aggregate fill (usually about half) add fouling material, mix, compact and fill tank again – Scan box and collect raw data
Geophysical investigation (p.2) Sand Fouling Clay Fouling Engineered Fouling Type B Aggregate Backfill 100 7 90 Sand Fouling 80 Clay Fouling 6 Percentage Passing (%) y = -2.8778x + 6.3125 70 R² = 0.97861 Engineered Fouling 60 𝜗 5 y = -1.5521x + 4.8015 50 R² = 0.97715 40 4 30 y = -1.1547x + 4.2647 R² = 0.8771 20 3 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 10 𝑆 𝐺𝑊 0 0.01 0.1 1 10 100 Nominal Apperture sieve size (mm)
Geophysical investigation (p.3) HFDs are constructed in a way that lacks the superimposed course philosophy of pavement systems. The interpretation of raw data will thus be more challenging than for data extracted from pavement surveys Trenches are though initially designed to be extremely porous – large voids in medium will affect how signal travels through and scatters or reflects within the medium The combination of fouling and moisture and the elimination of available void space will leave a signature on reflected signal and frequency response of the GPR EM wave 22
Geophysical investigation (p.4) 23
Ageing & Renewal Rules 24
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