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TRANSCRIPT
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