International Construction Specialty Conference of the Canadian Society for Civil Engineering (ICSC) (5th : 2015)

Integrated asset management of water and wastewater infrastructure systems : borrowing from industry… Ganjidoost, Amin; Haas, Carl T.; Knight, Mark A.; Unger, Andre J. A. Jun 30, 2015

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5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   INTEGRATED ASSET MANAGEMENT OF WATER AND WASTEWATER INFRASTRUCTURE SYSTEMS - BORROWING FROM INDUSTRY FOUNDATION CLASSES Amin Ganjidoost1,3, Carl T. Haas1,  Mark A. Knight1 and Andre J.A. Unger2 1 Department of Civil and Environmental Engineering, University of Waterloo, Canada 2 Department of Earth and Environmental Sciences, University of Waterloo, Canada 3 aganjido@uwaterloo.ca  Abstract: Viewing water and wastewater infrastructure systems from a network or functional viewpoint down to an individual component goes hand in hand with life-cycle management. Therefore, three concepts are incorporated: (1) Strategic Planning; (2) Tactical Planning; and (3) Operational Planning. The data relevant to each of these areas are generated and managed by software applications that operate in isolation. A multi-level integration can link and share data among strategic, tactical, and operational asset management plans. The Industry Foundation Classes schema concept is used to develop a framework to support efficient sharing and management of data and planning information among strategic, tactical and operational asset management plans of the water and wastewater infrastructure systems. The proposed multi-level integration framework is comprised of a comprehensive database of water and wastewater infrastructure physical asset inventory, financial and consumer sectors that stores and manages flow of information through strategic, tactical, and operational asset management plans. Data are identified by reference of time and date (temporal) and by physical relation of data to the location of a facility in the water and wastewater infrastructure networks (spatial). The proposed framework enables the integration and interoperation of various domain-specific software applications through developing and maintaining a multi-level integration of strategic, tactical and operational asset management plans based upon the Industry Foundation Classes data model concept. Municipalities and water utilities can use the findings to make optimized asset management decisions.  1 INTRODUCTION Grigg (2012) defines infrastructure asset management as “an information-based process used for life-cycle facility management across organizations”. This paper proposes a multi-level integrated asset management framework to store and manage flow of information through strategic, tactical, and operational asset management plans for water distribution and wastewater collection networks. The data relevant to each of these asset management planning models are generated and managed by software applications that operate in isolation. A multi-level integration should enable water infrastructure stakeholders and software developers to extract and exchange the required data and information from any of the strategic, tactical, and operational asset management planning model using a centralized neutral data file.    Environment Canada (2004) defines the water infrastructure system as comprised of “water treatment plants that purify our water, the water mains in the ground that transport water, and the towers and reservoirs that store water. The term includes the sewer pipes that carry away wastewater and the 299-1 sewage treatment plants that treat wastewater before returning it to the environment …”. The other components of a water infrastructure system that include water and wastewater treatment plants, towers, and reservoirs are outside the scope of this study. 2 BACKGROUND Froese (2003) indicated that information technologies (IT) play an effective role in architecture, engineering, construction, and facilities management (AEC/FM). Various domain-specific software applications are used to facilitate most of the AEC/FM design and management tasks, and the information entered into all of these computer tools describes the same physical project (Froese, 2003).   To facilitate efficient sharing and management of data between AEC/FM, the topic of interoperability has become one of the main areas for research and development in IT for the AEC/FM sectors (Froese, 2003). Froese, (2003) defines Interoperability as “the ability for information to flow from one computer application to the next throughout the lifecycle of a project which relies on the development and use of common information structures”.  To develop an integrated AEC model structure, model-based systems have been known as the main empowering technology (Halfawy and Froese 2002). Caldas and Soibelman (2003) noted that model-based systems are being utilized more and more to support exchanging information among AEC/FM projects. Industry Foundation Classes (IFCs) are one of the most remarkable of these model-based systems. IFCs have had significant positive impact on integration and interoperability. 2.1 Industry Foundation Classes  The IFCs specification is developed by the International Alliance for Interoperability (IAI). The IFC specification is a neutral data format to describe, exchange, and share information among AEC/FM industry projects (Caldas and Soibelman, 2003). The latest version is IFC4 and is available at buildingSMART International Ltd.  The IFCs data model is substantially built in a hierarchical structure, and its object-oriented design enables complex relationships to exist between entities (Dimyadi et al., 2008). Entities can be physical objects such as watermain pipes, service connections, valves, etc. or conceptual entities such as processes, budgets, scheduling details, etc..   Froese (2003) noted that the scope of the IFCs is limited to the building industry and should be extended to a broader range of civil infrastructure to include the entire built environment. To some extend this has happened in the industrial sector with ISO 15926. This research presents a framework to support efficient sharing and management of data and planning information among strategic, tactical and operational asset management plans for the water and wastewater infrastructure systems, and to enable the integration and interoperation of various domain-specific software applications through developing and maintaining multi-level integrated asset management plans based upon the IFC data model concept. A critical functionality of a multi-level integration of strategic, tactical and operational asset management plans is the requirement to link and manage the inter-dependencies of these data, and to enable different applications to share these data through the use of the integrated asset management model.   Strategic planning is a long-term (10+ years) group of activities including capital planning, operational and maintenance planning, policy planning, risk management, and life-cycle costing at the management level of an organization.  The organization policy levers and the level of service are established at this stage of planning. Embedded in and dependent on strategic planning, a tactical planning (2-10 years) cycle is required to prioritize capital works activities as well as operating and maintenance (O&M) activities, and to flag candidates for capital works and O&M activities. Operational planning is defined as plans that specify details on how overall objectives are to be achieved (Robbins and Coulter, 1996) and to implement tactical plans.  299-2 2.2 EXPRESS Modeling Language  EXPRESS (ISO 10303-11, 1994) is an internationally standardized general-purpose data modeling language in contrast to a domain-specific data modeling language. The data model structure is often represented using the EXPRESS-G notation-a graphical modelling language subset of EXPRESS language (ISO 10303-11, 1994) used for identifying model classes, data attributes and their relationships. Every object which is drawn in EXPRESS-G can be defined in EXPRESS. However, not every object which can be defined in EXPRESS can be drawn in EXPRESS-G (ISO 10303-11, 1994). This section presents the basic symbols used in the EXPRESS-G data modeling language. 2.2.1 Classes Classes are identified in a rectangular box with solid lines and the name of class is enclosed in the box (ISO 10303-11, 1994). Figure 1 shows three examples of classes where Iwis means integrated water infrastructure system. IwisElement IwisWaterDistribution IwisFinance  Figure 1: Classes (Entities)  2.2.2 Data Types EXPRESS-G consists of four main data types as follows:  a) Simple data types  There are seven simple data types: BINARY, BOOLEAN, INTEGER, LOGICAL, NUMBER, REAL, and STRING which are shown in Figure 2. A simple data type is presented as a solid rectangular box with its name enclosed and a double vertical line at the right hand side of the box (Figure 2).  BINARYBOOLEANINTEGERLOGICALNUMBERREALSTRINGa sequence of 1 and 0 e.g. 1010001true or false (equivalent to 1 or 0)true, false or unknowna sequence of alphanumeric characters e.g. “Pipe”a whole number without decimals e.g. 14a rational number including decimals e.g. 13.28any number either integer or real e.g. 14, 13.28  Figure 2: Simple data types in EXPRESS-G data modeling language  b) Enumeration data type  Data attributes can be described in an enumeration data type when there is a range of possible values and the attribute may only choose one value from the possible range. This data type is shown in a dashed lines rectangular box with a double vertical bar to the right. The name of enumeration data is enclosed into the box. Figure 3 shows an enumeration example for IwisPipeMaterial that enables the IwisPipeMaterial to choose only one type of pipe material.  299-3 IwisPipeMaterialTypeEnum IwisPipeMaterialGenericType   Figure 3: An example of enumeration data type in EXPRESS-G  c) Defined data type   A simple STRING data type can be used to define a data type but there are some types of data that require a detailed description. In this case a defined data type is used to make a clear description for a defined type of data. Figure 4 shows an example of a defined data type in EXPRESS-G. This type of data is shown in a dashed lines rectangular box with its name enclosed into the box.   textWaterUtilitydescription   Figure 4: An example of defined data type in EXPRESS-G  d) Select data type  A select data type enables data attributes to choose the class based upon different purposes. For example, the IwisWaterDistributionElement enables selection of, watermain pipe, valve, hydrant or service connection (Figure 5). Select data type is shown in a rectangular box with dashed lines and a double vertical bar on the left hand side. The name of the data type is written in the box.         IwisServiceConnection IwisValve IwisWatermainPipeIwisHydrant IwisWaterDistributionElement  Figure 5: An example of select data type in EXPRESS-G 2.2.3 Relationships Mandatory and optional relations are two types of attributes that are related to a class. The value of attributes must be given when a Mandatory relation is assigned to a class. However, it is not necessary for the optional attributes. Figure 6 shows an example of a mandatory and optional relation to IwisWaterDistributionElement where TotalLength has a mandatory relation and TotalVolume is considered an optional relation.   IwisWaterDistributionElementREALREALTotalLength TotalVolume   Figure 6: An example of a mandatory and optional relation in EXPRESS-G 299-4 3.1.1 Strategic Planning The implementation steps for strategic planning of a water infrastructure system are categorized as follows (adopted from Uddin et al., 2013): 1. establish policy levers 2. establish level of service performance (consumer satisfaction) policies   3. categorize urban water infrastructure networks needs and funding sources 4. estimate  long-term (10+ years) financial performance 5. prepare long-term (10+ years) capital works  program 6. prepare long-term operating and maintenance program Types of modeling techniques used for this level of planning currently include financial spreadsheets, simple data base management systems (DBMS), system dynamics models, large business oriented models, scenario based models, etc.  3.1.2 Tactical Planning The implementation steps for tactical planning of a water infrastructure system are summarized as follows (adopted from Uddin et al., 2013): 1. prioritize all capital works activities,  2. prioritize all operating and maintenance (O&M) activities,  3. flag specific activities for capital works, and 4. flag specific activities for O&M activities  Types of modeling techniques used for this level of planning currently include deterministic mathematical modeling, simulation, optimization, etc.. 3.1.3 Operational Planning The implementation steps for operational planning of a water infrastructure system are categorized in the three disciplines of (1) engineering and design, (2) construction, and (3) operation and maintenance within a water utility (adopted from Uddin et al., 2013):  1.  Engineering & Design 1.1. perform structural and hydraulic analysis 1.2. analyze cost effectiveness of project level alternatives 1.3. prepare plans and specifications and perform actions 1.4. analyze cost effectiveness of O&M activities 1.5. analyze cost effectiveness of capital works  2. Construction 2.1. perform capital works  3. Operation & Maintenance 3.1. perform structural and hydraulic analysis 3.2. perform O&M activities 3.3. collect condition assessment and financial data  Types of modeling techniques used for this level of planning currently include simulation models, “what-if” scenario models, sensitivity analysis, modeling infeasibilities, opportunity costs and marginal economic value, etc..   299-6 operational models using EXPRESS-G data modeling language. Figure 9 shows a high-level example of the integrated water infrastructure system model structure displaying in EXPRESS-G.                    IwisServiceConnection IwisValve IwisWatermainPipeIwisManhole IwisLateral IwisSanitarySewerIwisWaterDistributionElementIwisWastewaterCollectionElement(ABS)Iwisdescription textIwisPhysicalAsset IwisFinanceIwisConsumerREALREALTotalLength TotalVolume REALREALTotalLength TotalVolume IwisHydrant IwisTotoalIncome IwisTotalExpendituresIwisPipeMaterialTypeEnumGenericType IwisWaterDemandIwisPriceElasticityIwisPublicPolicy  IwisWaterFeeIwisRevenueIwisSewageFee IwisAnnualWaterConsumptionREALUnitCost REALUnitCost  Figure 9: A High-level example of the integrated water infrastructure system (Iwis) model structure in EXPRESS-G   299-8 4 CONCLUSIONS This paper reviews the basic concepts of Industry Foundation Classes and the EXPRESS-G data modeling language. A framework is proposed to integrate strategic, tactical and operational asset management planning models for the water and wastewater infrastructure systems.  Further work is needed to completely develop the proposed multi-level integration framework. It is important that the developed neutral IWIS data files are reviewed by the water infrastructure industry experts and software developers to ensure that the specification is validated universally by the agreement of a wide cross section of water infrastructure industry experts and does not rely on a particular region. It should also be demonstrated with various currently utilized software packages. The proposed framework is the first known approach for data integration, sharing, and exchange between strategic, tactical, and operational asset management planning models of the water and wastewater infrastructure systems.  In practice, this framework should enable water infrastructure stakeholders and software developers to extract and exchange the required data and information from any of the strategic, tactical, and operational planning model using a centralized IWIS neutral data file. Acknowledgements We gratefully acknowledge the financial support provided by the Natural Science and Engineering Research Council of Canada, the University of Waterloo, and the Centre for Advancement of Trenchless Technology located at the University of Waterloo.  References Caldas, C., and Soibelman, L.  2003. Integration of Construction Documents in IFC Project Models. Construction Research Congress: 1-8. Dimyadi, J., Spearpoint, M. and Amor, R. 2008. Sharing Building Information using the IFC Data Model for FDS Fire Simulation. Fire Safety Science (9): 1329-1340. Environment Canada. 2004. Freshwater website: Water efficiency/conservation - sustaining our infrastructure, Retrieved April 12, 2013, from http://www.ec.gc.ca/water/en/manage/effic/e_ sustin.htm. Froese, T. 2003. Future directions for IFC-based interoperability, ITcon, Special Issue IFC-Product models for the AEC arena, (8): 231-246. Grigg, N. S. 2012. Water, Wastewater, and Stormwater Infrastructure Management, IWA Publishers, Boca Raton, Florida, 343 p. Halfawy, M. R., and Froese, T. 2002. A model-based approach for implementing integrated project systems. In 9th International Conference on Computing in Civil and Building Engineering, Taipei, Taiwan (2): 1003-1008. ISO 10303-11 .1994. Industrial automation systems and integration - Product data representation and exchange - Part 11: Description methods: The EXPRESS language reference manual. Rehan, R., Knight, M.A., Haas, C.T., and Unger, A.J.A. 2011. Application of system dynamics for developing financially self-sustaining management policies for water and wastewater systems. Water Research 45 (16): 4737-4750. Rehan, R., Knight, M.A., Unger, A.J.A, and Haas, C.T. 2013. Development of a system dynamics model for financially sustainable management of municipal watermain networks. Water Research I-22. Rehan, R., Knight, M.A., Unger, A.J.A, and Haas, C.T. 2014. Financially sustainable management strategies for urban wastewater collection infrastructure: development of a system dynamics model. Tunneling and Underground Space Technology 39:116–129. Robbins, S. P, and Coulter, M. K. 1996. Management, 7th Ed, McGraw-Hill, Business & Economics - 770 p/p. 214. Uddin, W., Hudson, W., and Haas, R. 2013. Public Infrastructure Asset Management. McGraw Hill Professional, New York.    299-9 

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