Streetworks are the activities conducted in streets such as building or repairing roads, installing or replacing buried utilities, or street furniture. Sustainable streetworks requires an integrated approach taking account of the complex inter-asset relationships between human activities, different city infrastructure assets and the environment. To facilitate decision making by relevant stakeholders, an integrated web-based decision support system is presented in this paper which combines experts' domain knowledge, multiple geospatial datasets and an inference engine for automated inference of potential consequences. Users' feedback collected from two workshops showed that the system is widely considered as a potentially useful tool for practitioners

Clarke, B

Cohn, AG

Dimitrova, V

Magee, DR

Wei, L

White Rose Research Online

This is a repository copy of An Integrated Web-based Decision Support System for Inter-Asset Streetworks Management.White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/128472/Version: Accepted VersionProceedings Paper:Wei, L, Clarke, B, Magee, DR et al. (2 more authors) (Accepted: 2018) An Integrated Web-based Decision Support System for Inter-Asset Streetworks Management. In: 26th GIScience Research UK Conference (GISRUK). 26th GIScience Research UK Conference(GISRUK), 17-20 Apr 2018, Leicester, UK. . (In Press) eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. An Integrated Web-based Decision Support Systemfor Inter-Asset Streetworks ManagementLijun Wei∗1, Barry Clarke2, Derek R. Magee1, Vania Dimitrova1 and Anthony G. Cohn11School of Computing, University of Leeds2School of Civil Engineering, University of LeedsMarch 12, 2018SummaryStreetworks are the activities conducted in streets such as building or repairing roads, in-stalling or replacing buried utilities, or street furniture. Sustainable streetworks requires anintegrated approach taking account of the complex inter-asset relationships between human ac-tivities, different city infrastructure assets and the environment. To facilitate decision making byrelevant stakeholders, an integrated web-based decision support system is presented in this paperwhich combines experts’ domain knowledge, multiple geospatial datasets and an inference enginefor automated inference of potential consequences. Users’ feedback collected from two workshopsshowed that the system is widely considered as a potentially useful tool for practitioners.KEYWORDS: web-mapping; reasoning; decision support system; sustainable streetworks; sub-surface infrastructure1 IntroductionStreetworks are the activities conducted in streets such as building or repairing roads, installing orreplacing buried utilities, or street furniture. It is estimated that the direct costs of streetworks tolocal authorities are about £50M per year in the UK1. Critical urban infrastructure assets, such asroads, ground, and buried utilities (e.g. water, electricity, gas, sewage, communication) are highlyinterconnected. For example, breaking up or opening a road may damage the underlying groundand buried utilities, and a leaking water pipe can erode the surrounding ground, leading to loss ofsupport to the overlying road and eventually causing collapse of the road (Clarke et al., 2017). Inorder to reduce the potential social, economic, and environmental impact, decision making in street-works requires an integrated approach which takes into account the inter-asset dependencies, thelocalised contextual data (e.g. rainfall, traffic loading, soil type) and the state-of-the-art methodsof asset investigation and management. However, in practice, there is no unified decision-making∗L.J.Wei@leeds.ac.uk1Asphalt Industry Alliance (2017) Annual Local Authority Road Maintenance.approach or fully integrated data management in current use across urban streetwork stakehold-ers. Urban infrastructure assets are maintained by different stakeholders who plan and conductstreetworks independently often without unified coordination; relevant data is held in disconnectedplatforms/depositories by different stakeholders, such as the Met Office, the Environment Agency,the British Geological Survey, the Highways England. It is difficult for a decision maker to havethe knowledge of all relevant areas, nor is it easy or quick to gather all relevant data.This paper presents a novel web-based decision support system (DSS), which addresses the highlychallenging problem of inter-asset streetworks management. An integrated approach which combinesthe knowledge of experts from different domains, multiple geospatial datasets held by differentowners, and an inference engine for automated reasoning about potential consequences is presented.A prototype of the proposed decision support system has been developed and demonstrated to awide range of relevant stakeholders for evaluation.The work is conducted within the Assessing The Underworld (ATU) project2 - a large interdisci-plinary UK programme grant that addresses challenges in sustainable management of city infras-tructures taking into account the economic, social and environmental impact of street works.2 System architectureTo determine the software environment of this decision support system, a survey was conductedby the ATU team in 2014 to collect the opinions of relevant practitioners. Results showed that33% of the respondents preferred a Web Mapping System, 24% preferred a standalone GeographicInformation system, 24% preferred a bespoke software, only 18% of them preferred Excel. There-fore, a web-based system architecture was selected for developing this prototype, by combining thefunctionalities of web mapping services and geographic information management.The DSS system is composed of three layers (Figure 1): a) a contextual data and knowledge layerfor storing various background data and experts knowledge; b) a logic layer for data query andautomated reasoning; and c) a web-based user interface layer for events reporting, data visualisationand updating. The main components of this decision support system are described below.Domain Knowledge Models. Based on the literature and discussions with domain experts (e.g.in civil engineering, geophysics and utility management), a suite of knowledge models/ontologiesand logical rules were developed for this system to describe the main concepts and the complexintra-asset and inter-asset relationships in streetworks. For example, the trigger ontology (Clarkeet al., 2017) describes the categories and properties of events which need some decisions to be madein streetworks, such as human activities (e.g. construction works), abnormal road observations (e.g.road cracks) and pipe observations (e.g. low water pressure). The city infrastructure asset on-tologies (Figure 2) define the vocabulary for describing the conditions of roads, ground (Du et al.,2016), and buried utilities (pipes/cables), which are publicly available from the University of Leedsdata repository3. And the investigation ontology summaries the available geophysical techniques2http://assessingtheunderworld.org/3https://doi.org/10.5518/190(e.g. electromagnetic method, gravity method, seismic method) for shallow (0-5m depth) street-works surveys and their suitability for measuring different infrastructure asset properties.Figure 1: A three-layer system architecture of the decision support system.Figure 2: Inter-linkages between road, ground, utility, environment and investigation ontologies.Integrated Contextual Database. Data in this decision support system is sourced from differentdata owners, preprocessed and integrated into one spatial database. The meteorological data (e.g.amount of daily/hourly rainfall, maximum and minimum air/concrete/soil temperatures) is sourcedfrom the UK Met Office4. When a trigger is reported, the weather observation stations within 10kmto the trigger are retrieved to obtain the historic weather data up to the reporting day, then theobtained data at each time point is averaged as the local weather data of the trigger. The trafficloading data is sourced from the UK Department for Transport (DfT), which collects street-leveltraffic data at thousands of traffic counting points every year5. For a reported trigger, the nearest4Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current).5Department for Transport (DfT) traffic statistics.road segment is first retrieved from the road network database to identify the traffic counting pointfor obtaining the historical traffic data (Figure 3a). Then, the weighted annual traffic on this road iscalculated based on the traffic volumes and the wear factors6 of different types of heavy commercialvehicles like buses, heavy-goods vehicles. The data of buried utilities is sourced from different(a) Road Information (b) Buried utilitiesFigure 3: Examples of retrieved contextual data of a reported trigger (background maps of (a)OpenStreetMap, (b) Google satellite image).asset owners (e.g. United Utilities, National Grid, North West Electricity)7. Multiple attributes ofthe utility assets are recorded in the database, including the utility type, location, depth, material,diameter, year of installation, asset owners, operation status, etc. Based on the location of a trigger,all the buried utilities within a 200m radius are retrieved. An example of the extracted utilitiesaround a reported trigger and their attributes is shown in (Figure 3b). The data of sensitive services(e.g. hospitals, schools) around a trigger (≤ 2km) is fetched from the OpenStreetMap8. To assessthe potential impact on each service, the shortest driving distance from the trigger to each retrievednearby service is calculated using the widely used path planning A* algorithm (Hart et al., 1968).The relevant ground condition data of a trigger (e.g. ground water level; artificial, superficial andsolid geology) is retrieved from multiple datasets of the British Geological Survey (BGS)9.In this prototype, different datasets were sourced for demonstration purposes only. For mainte-nance of the contextual database in a fielded system, the freely available government datasets (e.g.Department of Transport, Environment Agency) can be updated manually regularly (depending onthe temporal resolution of the data) or automatically; the private datasets with licensing can eitherbe updated when changes are reported by the data owners or be directly linked to external datarepositories via WFS, etc.Automated Reasoning Module. Our system has an automated reasoning module which mimicshuman experts to make inferences of the potential consequences of triggers. When a new trigger6UK Design Manual for Roads and Bridges (2006).7For proof-of-concept purpose only.8https://www.openstreetmap.org9http://www.bgs.ac.uk/is added, the localised contextual data is retrieved from the data platform (as described above),mapped to the established ontology concepts and fed into an inference engine, which applies in-ference rules stored in the knowledge base to the known facts to infer new facts using a forwardreasoning method. Then, potential consequences (e.g. road collapse, road deformation, utility move-ment) are identified from the inferred facts and displayed in a matrix table on the user interfacewith the estimated severity and likelihood (Figure 4b).Since the experts we consulted were unable to formulate definitive rules relating different concepts,the system makes use of the qualitative confidences attached to the rules they supplied. Thesequalitative confidences can also be attached to human supplied data. The automated reasoningsystem is able to propagate these qualitative confidences in a reasoning chain (Mahesar et al.,2017). Moreover, even if some of the premises (facts) of an inference rule are currently missing10,the system can still activate the rule by assigning default values to the missing facts and infer thepotential consequences. In this case, the system informs users about the missing facts for derivingeach consequence (marked with red numbers in Figure 4b) and suggests appropriate investigationmethods to obtain this information based on the Investigation ontology.Interactive User Interface. The interactive user interface allows users to a) report new triggers(Figure 4a), such as extreme weather event, pothole, pipe leakage, or planned streetworks; b) visuallyexamine the retrieved localised contextual data (Figure 3); c) inspect the potential consequences,their given/missing facts and reasoning chains (Figure 4b); and d) add or modify the values ofmissing/given facts for recursive inference. A video demonstrating the user interface is available athttp://bit.ly/2mdyIY4.(a) Interface for reporting new triggers⇒(b) Interface of potential consequencesFigure 4: (Left) The interface for reporting new triggers (map: c©OpenStreetMap contributors, CCBY-SA); (right) Potential consequences of a reported trigger.3 User FeedbackThe prototype of this decision support system was demonstrated to a wide range of potential usersin two workshops in 2017 to collect users’ feedback. Open-ended questions were asked regarding10Note that at least one fact in the antecedent of a rule must be present and not assumed.the usefulness of the demonstrated system, the specific functionalities that interested the users andwhat improvement is needed for future development of the system.Results showed that 56% of the respondents considered this system as a potentially useful tool forpractitioners (e.g. incident managers, survey company developers, constructors, asset owners, localauthority) to mitigate risks, prioritise and justify their expenses and activities; 17% also consid-ered the system to be useful for the general public. The potential benefits for training novice orjunior staff in streetworks were also mentioned by one of the participants. Regarding the specificfunctionalities of the system that interested the users, 56% of them were interested in the abilityof the automated reasoning module to help determine the impact of an incident in a short periodof time, identify potential consequences from seemingly insignificant triggers and potentially re-duce the streetworks disruptions. 50% of them were excited about the integrated data platformthat brought various critical contextual data together. As for future improvements of the sys-tem, adding additional contextual data sources, such as bus routes, agriculture data, archaeologicaldata, was suggested by most of the participants. It was also recommended to develop a smart phoneapplication for easier access to the system.4 ConclusionThis paper has presented an integrated web-based decision support system for inter-asset streetworksmanagement. This system comprises: 1) multiple domain knowledge models and logical rules whichencode the experts’ knowledge of road, ground, utilities, human activities, environment and theirinter- and intra- relationships; 2) an integrated geospatial database which contains various criticalcontextual datasets; and 3) an automated reasoning module which allows inference of potentialconsequences of reported triggers based on encoded experts’ knowledge and localised contextualdata. Users’ feedback collected from two workshops showed that the system is widely consideredas a potentially useful tool for practitioners to quickly obtain contextual data and infer potentialconsequences of triggers based on multi-domain knowledge and inter-asset relationships. Largerscale user evaluation with ATU partner organisations which are involved in underground assetmanagement is being conducted to gain deeper insights into the utility of the DSS.5 AcknowledgementsFinancial support from The UK Engineering and Physical Sciences Research Council (EPSRC) forthe Assessing the Underworld (ATU) grant (EP/K021699/1) is gratefully acknowledged. We alsothank all our colleagues in ATU project for useful discussions. The contribution of all the industrypartners and other stakeholders involved in the research is also gratefully acknowledged.6 BiographiesLijun Wei is a postdoctoral research fellow in the School of Computing at the University ofLeeds. Her research interests include underground utility and archaeology sites mapping, high-definition map generation for intelligent vehicles localisation/navigation, multi-sensor fusion anddecision support systems.Barry Clarke is a Professor in the School of Civil Engineering at the University of Leeds. He hasbeen actively involved at a strategic level in the construction industry and is a past President ofthe institution. His research interests include construction processes and the performance of theground.Derek R. Magee is a Lecturer in Computing at the University of Leeds. His research interestsare in the area of qualitative and quantitative data analysis, in particular remote sensing, buriedasset management and medical imaging.Vania Dimitrova is an Associate Professor in the School of Computing at the University of Leeds.Her research focuses on developing intelligent systems that augment human intelligence, includingknowldge capture and modelling, behaviour modelling, adaptation and personalisation.Anthony G. Cohn is a Professor in the School of Computing at the University of Leeds and isa Fellow of the Royal Academy of Engineering, AAAI, and EurAI. He has received DistinguishedService awards from IJCAI and AAAI. His research interests cover AI, Robotics, Sensor Fusion,and Decision Support Systems.ReferencesClarke, B. G., Magee, D., Dimitrova, V., Cohn, A. G., Du, H., Mahesar, Q., Sadeghioon, A. M.,Rogers, C. D., Gunn, D., Enwisle, D., Reeves, H., Collins, R., and Stirling, R. (2017). A decisionsupport system to proactively manage subsurface utilities. In International Symposium for NextGeneration Infrastructure, Institution of Civil Engineers, London, 11-13 Sep, 2017.Du, H., Dimitrova, V., Magee, D., Stirling, R., Curioni, G., Reeves, H., Clarke, B. G., and Cohn,A. G. (2016). An ontology of soil properties and processes. In Proceedings of 15th InternationalSemantic Web Conference, Kobe, Japan, October 17-21, 2016, pages 30–37.Hart, P. E., Nilsson, N. J., and Raphael, B. (1968). A formal basis for the heuristic determinationof minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2):100–107.Mahesar, Q., Dimitrova, V., Magee, D., and Cohn, A. G. (2017). Uncertainty management forrule-based decision support systems. In Proceedings of 29th IEEE International Conference onTools with Artificial Intelligence (ICTAI2017), 06-08 Nov 2017, Boston, MA, USA.

An Integrated Web-based Decision Support System for Inter-Asset Streetworks Management

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An Integrated Web-based Decision Support System for Inter-Asset Streetworks Management

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