Data, Information and Knowledge Condition Driven Management of - - PowerPoint PPT Presentation

Data, Information and Knowledge

Condition Driven Management of Highway Filter Drains

HFD deterioration and maintenance planning. Uncharted, reactive and unplanned. Identifying the way forward.

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What we know; the story so far

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• A Highway Filter Drain (or French Drain)

is a combined drainage system that

• perates 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.

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Operation, Deterioration & Service Life, Maintenance Drivers

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.

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Asset owners are not realising value. Possible way forward?

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Operation, Deterioration & Service Life, Maintenance Drivers

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

• ffer limited opportunities to evaluate the physical condition of an asset and thus to collect relevant and specific HFD data.

Infrastructure Management. Data driven, proactive, engineered

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Asset management over asset life-cycle

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Planning

Acquisition Development

Asset Care Utilisation Life

Extension

Decommission

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

planning and define deterioration understanding.

• Performance Targets link asset classes

to contractual in-service requirements

• Maintenance rules, treatments and

impacts define timing and effects of interventions

• Generated LCCs present What-If

alternatives, optimised lifecycle fund and

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Data

• Budgets
• Inventory
• Current Condition
• Performance Targets
• Treatment Options
• Deterioration Profiles

Maintenance Strategy LCC Investment requirements

• Identify Assets
• Identify Performance Requirements
• Assess Performance
• Plan Maintenance
• Manage Maintenance Operations

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Operations for Holistic Management

HFD maintenance management; learning lessons, focusing on condition evaluation, ageing and treatment rules.

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Aiming for HFD MM

Structure and define a framework to introduce holistic management for HFD in UK roads network

• Collect Inventory, maintenance and

condition data.

• Establish DST to prioritise investments.
• Optimise maintenance strategies.
• Establish proactive means to drive

maintenance decision making.

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Excellent Condition Fair - Crusted Fair Condition Poor Condition Very Poor Condition Poor - Crusted

Surface Cleansing Scarifying Surface Cleansing Scarifying Deep aggregate replacement Shallow aggregate Replacement Shallow Aggregate Replacement DST

• bjectives

Inputs and modelling Constraints Outputs / Results Maximise network performance of HFD Planning periods Length of HFD in Highway section Network Condition Distributions Discount Rates Annual Budgets M&R options and Unit costs Condition transition matrices Deterioration and intervention modelling Minimum network performance levels Maximum yearly budgets Multi year M&R plan Network Condition over planning horizon Yearly M&R costs Total costs over planning horizon Residual Values

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) 𝑆𝐺𝑊 =

𝑊𝑊𝐺𝑆𝐵−𝑊𝐺 𝑊𝑊𝐺𝑆𝐵

=

𝑓𝑔𝑠𝑊𝐵−𝑁𝐺

𝜍𝑔

𝑓𝑔𝑠𝑊𝐵

=

𝑓𝑔𝑠

𝑁𝐵 𝐻𝐵−𝑁𝐺 𝜍𝑔

𝑓𝑔𝑠

𝑁𝐵 𝐻𝐵

• Correllate the anticipated change of this index

to a level of assumed service.

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Condition Band 𝐒𝐆𝐖 𝐥′ = 𝐋𝐖 𝐋𝐧𝐛𝐲 As new - Excellent 𝑆𝐺𝑊 > 0.7 𝑙′ > 0.6 Good 0.7 < 𝑆𝐺𝑊 > 0.5 0.6 < 𝑙′ > 0.35 Poor 0.5 < 𝑆𝐺𝑊 > 0.3 0.35 < 𝑙′ > 0.05 Very Poor - Spent 𝑆𝐺𝑊 < 0.3 𝑙′ < 0.05

Condition Assessment

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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

expectations and maintenance & rehabilitation interventions

• Establish consistent condition

distribution across network

• Evaluate maintenance backlog
• Define priorities and optimise

investments required within planning horizon

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Benefits of shifting focus to a condition assessment system

Visual Surveys Non Destructive Testing & evaluation Selective Sampling and Sieving

Condition Diagnosis – Intrusive Testing

• Sampling and sieving to identify extend
• f 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

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Quantifying Fouling Levels and assessing service levels

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10 20 30 40 50 60 70 80 90 100 0.01 0.1 1 10 100 Material passing % Nominal aperture size mm C1 C2 C3 C4 10 20 30 40 50 60 70 80 90 100 0.01 0.1 1 10 100 Material passing % Nominal aperture size mm D1 D2 D3 D4

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=.125m3

– 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)

y = -1.1547x + 4.2647 R² = 0.8771 y = -2.8778x + 6.3125 R² = 0.97861 y = -1.5521x + 4.8015 R² = 0.97715 3 4 5 6 7 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

𝜗 𝑆𝐺𝑊

Sand Fouling Clay Fouling Engineered Fouling 10 20 30 40 50 60 70 80 90 100

0.01 0.1 1 10 100

Percentage Passing (%) Nominal Apperture sieve size (mm)

Sand Fouling Clay Fouling Engineered Fouling Type B Aggregate Backfill

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

• f the GPR EM wave

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Geophysical investigation (p.4)

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Ageing & Renewal Rules

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Condition Prognosis; Ageing & Renewal

• Not a unified and universal solution
• Engineering judgment and empirical

understanding of how linear asset performs

• ver c.20 years in our networks are a good

basis for founding the principles.

• HFDs have surpassed the initial 10 yr. service

life projection

• Top-down failure in sections at c. 5 yr.

intervals.

• Assume a linear deterioration and adopt a

mathematical model to simulate ageing of assets

• Define options and impact of maintenance

types and alternatives

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Excellent Condition Fair - Crusted Fair Condition Poor Condition Very Poor Condition Poor - Crusted

Surface Cleansing Scarifying Surface Cleansing Scarifying Deep aggregate replacement Shallow aggregate Replacement Shallow Aggregate Replacement

𝑄 = 1 − 2[𝑜−1

𝑀 ] 𝑜−1 𝑀 𝑜−1 𝑀

1 − 2[𝑜−1

𝑀 ] 𝑜−1 𝑀 𝑜−1 𝑀

1 − 2[𝑜−1

𝑀 ] 𝑜−1 𝑀 𝑜−1 𝑀

1 − [𝑜−1

𝑀 ] 𝑜−1 𝑀

1 = 0.667 0.167 0.167 0.667 0.167 0.167 0.833 0.167 0.667 0.167 0.167 0.833 0.167 1

Thank you !

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