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The ATU Decision Support System (DSS)

The ATU Decision Support System (DSS)

A decision support system to proactively manage

streetworks

Streetworks Issues

Street works are second highest concern of residents and businesses2.2 million reinstatements per year30% fail to meet the two year legal performance criteriaDirect costs to local authorities are about £50m per year17% reduction in the lifetime of a roadAnnual highway maintenance £2.5Bpa Annual carriageway maintenance £1.45Bpa Compensation claims £0.95Bpa Potholes 1.5mUtility openings 1.4m

Time

Money Safety

Key Challenges: Stakeholder Consultation

Lack of a unified decision approach – there is no one authority to plan and manage underground space, dependencies between assets are easily overlooked.

Lack and dispersion of knowledge about asset management – often in the form of ‘tacit knowledge’ developed with extensive experience, new approaches not embedded into routine practice.

Lack of a holistic costs analysis – impact on other assets, as well as economic, environmental and societal impact.

System of Systems Knowledge-driven Approach

The Inter-related Role of Assets

surfacingbinderbasesub-base

capping

subgrade (ground)

utility

road

ground

rain

fall

traffi

c lo

ad

trans

mits

trans

mits

utility

leak

age

road

ground

supp

orts

utility

transmits

supp

orts

Why an Integrated Approach?

Hypothesis: Bad decisions are made for 2 reasons:

1) Lack of knowledge and data2) Narrow interest above general interest

If all the data and knowledge were available AND a holistic approach to decision making is taken better decisions would be made.

Avoiding disruption! Saving money!

A video of the ATU DSS

A quick overview of the DSS and its functionality

A live demo will come later

The ATU-DSS: under the bonnet

Knowledge base (Ontologies + rules)

DSS ARCHITECTURE

Contextual Information Warnings Suggestions

Automated Reasoning

Real World Data

Knowledge base (Ontologies + rules)

DSS ARCHITECTURE

Contextual Information Warnings Suggestions

Automated Reasoning

Real World Data

Computer processable knowledge model of a domain Defines the key concepts to provide a unified view to facilitate data integration and intelligent

decision support Specifies their properties and the relations between them

What is an Ontology?

Computer processable knowledge model of a domain Defines the key concepts to provide a unified view to facilitate data integration and intelligent

decision support Specifies their properties and the relations between them

What is an Ontology?

Define: main concepts related to underground assets

Specify: their dependencies relationships with the natural environment:

(e.g. rain) and human activities (e.g. digging a hole)

Provide: a unified knowledge source supporting decisions related to urban streetworks ‐ diagnosis, planning, maintenance

o how different assets affect each other;o how asset properties and processes affect each othero causes and effects of asset defects or failureso consequences of maintenance/repair activities

ATU ONTOLOGIES: PURPOSE

City infrastructure Asset ontologies road, ground, utilities

Non Asset ontologies triggers, sensors/investigative techniques, environment

• Intra and Inter ontology relationships, e.g. processes

2 classes of ontologies in the ATU-DSS

City infrastructure Asset ontologies road, ground, utilities

Non Asset ontologies triggers, sensors/investigative techniques, environment

• Intra and Inter ontology relationships, e.g. processes

2 classes of ontologies in the ATU-DSS

EnvironmentRoad Ground

Utilities

City infrastructure Asset ontologies road, ground, utilities

Non Asset ontologies triggers, sensors/investigative techniques, environment

• Intra and Inter ontology relationships, e.g. processes

2 classes of ontologies in the ATU-DSS

EnvironmentRoad Ground

Utilities

City infrastructure Asset ontologies road, ground, utilities

Non Asset ontologies triggers, sensors/investigative techniques, environment

• Intra and Inter ontology relationships, e.g. processes

2 classes of ontologies in the ATU-DSS

EnvironmentRoad Ground

Utilities

Trigger

City infrastructure Asset ontologies road, ground, utilities

Non Asset ontologies triggers, sensors/investigative techniques, environment

• Intra and Inter ontology relationships, e.g. processes

2 classes of ontologies in the ATU-DSS

Sensors/investigative

EnvironmentRoad Ground

Utilities

Trigger

Properties: Physical, chemical & mechanical composition of the ground capacity of a road pressure capacity of a pipeProcesses that can change a property infiltration, deformation, pressure

Ontology Structure: Asset ontologies

Properties: Physical, chemical & mechanical composition of the ground capacity of a road pressure capacity of a pipeProcesses that can change a property infiltration, deformation, pressure

Ontology Structure: Asset ontologies

property

process

property

process

Properties: Physical, chemical & mechanical composition of the ground capacity of a road pressure capacity of a pipeProcesses that can change a property infiltration, deformation, pressure

Ontology Structure: Asset ontologies

Infrastructure Asset

Utility

Pipe Cable Drainage

Ground Road

Duct

property

process

property

process

ATU ONTOLOGY: ROAD

ATU ONTOLOGIES: ROAD

ATU ONTOLOGIES: ROAD

Hierarchy of road 

processes and 

properties

ATU ONTOLOGIES: ROAD

Hierarchy of road 

processes and 

properties

Relationships between properties 

and processes

ATU ONTOLOGIES: ROAD

Hierarchy of road 

processes and 

properties

Relationships between properties 

and processesAnd similarly for the other ontologies

• A trigger, external to the asset, starts a chain reaction

• The triggers can be• Environmental(e.g. excessive rainfall, reduction in temperature, change in traffic load)• Observation (e.g. crack in road, pothole, drop in water pressure)• Management(e.g. planned maintenance, utility replacement)

Trigger Ontology

Knowledge BaseFacts relating to Scenario

(instances of ontology classes)RoadCracking=activeTrafficLoad=high

Inference and Rules

Rule BaseExpert derived rules…

If RoadCracking=active andTrafficLoad=high Then

RoadCrackingIntensity increases

…

Inference EngineDerives new facts

Real World Data

Knowledge BaseFacts relating to Scenario

(instances of ontology classes)RoadCracking=activeTrafficLoad=high

Inference and Rules

Rule BaseExpert derived rules…

If RoadCracking=active andTrafficLoad=high Then

RoadCrackingIntensity increases

…

Inference EngineDerives new facts

Real World Data

Knowledge BaseFacts relating to Scenario

(instances of ontology classes)RoadCracking=activeTrafficLoad=high

RoadCrackingIntensityincreases

Inference and Rules

Rule BaseExpert derived rules…

If RoadCracking=active andTrafficLoad=high Then

RoadCrackingIntensity increases

…

Inference EngineDerives new facts

Real World Data

Key Challenge: Rules not always certain

Conclusions of many rules are only (very) likely rather than definite.

If Pipe Leak is small then[Very Likely]

Soil Wetting increased

How to represent likelihoods? Unable to obtain quantitative probabilities (e.g. 0.9) Preferred linguistic terms:

Definite | Very Likely | Likely | Unlikely | Very Unlikely

How to reason with/combine such uncertain knowledge? Propagate these symbolic values Special algorithms for comparing “uncertainty vectors”

Key Challenge: Missing information/facts

A rule might not get invoked because some the truth of some of its antecedents might not be known (yet)

But the rule might predict a critical undesirable consequence

We allow facts to be assumed: the rule can be invoked

Assumed facts are flagged for checking later by user

Key Challenge: Explaining observations

We may have a rule

If Pipe leaking is activeand subgrade is sandThen Road cracking is active

If we observe road cracking and we know subgrade is sand, then an explanation may be that there is a pipe leak.

SummaryOntologies encode knowledge about the domain

Rules encode expert heuristic knowledge

Inference engine uses rules to infer new facts

System handles missing knowledge, uncertain knowledge

User can verify assumptions and alter facts dynamically and the inference will recompute as necessary.

ATU DSS

A live demo

Summary so far

You’ve seen: the current prototype the challenges the visionA demo of the live systemThe underlying technology

Coffee

After the break: the futureA new case study with Balfour BeattyA discussion on future