The ATU Decision Support System (DSS)

Transcription

1 The ATU Decision Support System (DSS)

2 The ATU Decision Support System (DSS) A decision support system to proactively manage streetworks

3 Streetworks Issues Street works are second highest concern of residents and businesses 2.2 million reinstatements per year 30% fail to meet the two year legal performance criteria Direct costs to local authorities are about 50m per year 17% reduction in the lifetime of a road Annual highway maintenance 2.5Bpa Annual carriageway maintenance 1.45Bpa Compensation claims 0.95Bpa Potholes 1.5m Utility openings 1.4m Money Time Safety

4 Key Challenges: Stakeholder Consultation System of Systems Knowledge-driven Approach 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.

5 surfacing binder base sub-base The Inter-related Role of Assets capping utility subgrade (ground) rainfall road road supports transmits ground ground utility transmits traffic load supports transmits leakage utility

6 Why an Integrated Approach? Hypothesis: Bad decisions are made for 2 reasons: 1) Lack of knowledge and data 2) 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!

7 A video of the ATU DSS A quick overview of the DSS and its functionality A live demo will come later

8 The ATU-DSS: under the bonnet

9 DSS ARCHITECTURE Contextual Information Warnings Suggestions Automated Reasoning Knowledge base (Ontologies + rules) Real World Data

10 DSS ARCHITECTURE Contextual Information Warnings Suggestions Automated Reasoning Knowledge base (Ontologies + rules) Real World Data

11 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

12 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

13 ATU ONTOLOGIES: PURPOSE 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 other o causes and effects of asset defects or failures o consequences of maintenance/repair activities

14 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

15 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 Road Environment Ground Utilities

16 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 Road Environment Ground Utilities

17 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 Road Environment Ground Trigger Utilities

18 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 Sensors/investigative Road Environment Ground Trigger Utilities

19 Ontology Structure: Asset ontologies Properties: Physical, chemical & mechanical composition of the ground capacity of a road pressure capacity of a pipe Processes that can change a property infiltration, deformation, pressure

20 Ontology Structure: Asset ontologies Properties: Physical, chemical & mechanical composition of the ground capacity of a road pressure capacity of a pipe Processes that can change a property infiltration, deformation, pressure property property process process

21 Ontology Structure: Asset ontologies Properties: Physical, chemical & mechanical composition of the ground capacity of a road pressure capacity of a pipe Processes that can change a property infiltration, deformation, pressure property property process process Infrastructure Asset Utility Ground Road Pipe Cable Drainage Duct

22 ATU ONTOLOGY: ROAD

23 ATU ONTOLOGIES: ROAD

24 ATU ONTOLOGIES: ROAD Hierarchy of road processes and properties

25 ATU ONTOLOGIES: ROAD Hierarchy of road processes and properties Relationships between properties and processes

26 ATU ONTOLOGIES: ROAD Hierarchy of road processes and properties And similarly for the other ontologies Relationships between properties and processes

27 Trigger Ontology 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)

28 Inference and Rules Knowledge Base Facts relating to Scenario (instances of ontology classes) RoadCracking=active TrafficLoad=high Real World Data Rule Base Expert derived rules If RoadCracking=active and TrafficLoad=high Then RoadCrackingIntensity increases Inference Engine Derives new facts

29 Inference and Rules Knowledge Base Facts relating to Scenario (instances of ontology classes) RoadCracking=active TrafficLoad=high Real World Data Rule Base Expert derived rules If RoadCracking=active and TrafficLoad=high Then RoadCrackingIntensity increases Inference Engine Derives new facts

30 Inference and Rules Knowledge Base Facts relating to Scenario (instances of ontology classes) RoadCracking=active TrafficLoad=high RoadCrackingIntensity increases Real World Data Rule Base Expert derived rules If RoadCracking=active and TrafficLoad=high Then RoadCrackingIntensity increases Inference Engine Derives new facts

31 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

32 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

33 Key Challenge: Explaining observations We may have a rule If Pipe leaking is active and subgrade is sand Then 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.

34 Summary Ontologies 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.

35 ATU DSS A live demo

36 Summary so far You ve seen: the current prototype the challenges the vision A demo of the live system The underlying technology Coffee After the break: the future A new case study with Balfour Beatty A discussion on future