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

Assessment of the level of service (LOS) of public recreational centre buildings : an uncertainty based… Ruparathna, Rajeev; Hewage, Kasun; Sadiq, Rehan 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   ASSESSMENT OF THE LEVEL OF SERVICE (LOS) OF PUBLIC RECREATIONAL CENTRE BUILDINGS: AN UNCERTAINTY BASED APPROACH Rajeev Ruparathna1,2, Kasun Hewage1 and Rehan Sadiq1 1 School of Engineering, University of British Columbia (Okanagan Campus), Canada. 2 rajeev.ruparathna@ubc.ca Abstract: The federal sustainable development strategy (FSDS) for Canada advocated that public sector operations should aim at shrinking the environmental footprint while enhancing social benefits. In this quest, improving the sustainability performance of public buildings becomes a key constituent since buildings are responsible for the highest portion of the corporate GHG emission and energy usage of public entities. Moreover, public buildings are an important constituent of the socio-economic environment of a local region.  Hence, there is a need for improving the sustainability performance of the future and existing public buildings. Currently, various innovative methods are used by federal, provincial and municipal entities to improve the sustainability performance of public buildings. However, asset management of building has been overlooked from the above studies. There is a lack of comprehensive methods to assess the level of service (LOS) of a building that is crucial for life cycle asset management. To address this problem, this paper proposes an approach to calculate the LOS of a recreational centre building operated by municipal government. Firstly, a LOS framework is formulated for recreational centre building by taking into consideration the key aspects. Secondly, a fuzzy synthetic evaluation method is used to assess the building performance. Thirdly, a case study was conducted to validate the proposed methodology. Results from this approach provide detailed information about the performance of the building assets. This approach facilitates in identifying areas that require immediate attention for improvement. This study provides a novel approach to life cycle asset management of public sector buildings. 1 INTRODUCTION Public buildings represent a key component of the socio-economic environment of any nation (Wright 2006). Despite the numerous benefits to the society, dramatic environmental and social concerns are associated with construction, renovations and operation of buildings (United States Environmental Protection Agency, 2009a; Industry Canada, 2013). There are over 28000 federal buildings and a large number of municipal buildings operating in Canada,  that account for 15% of the Canadian infrastructure portfolio (Mirza, 2007; Environment Canada, 2013). Presently, Canadian building infrastructure stock is aging and have deteriorated considerably (Mirza, 2007). Moreover, there is a  lack of financial resources to replace, repair or rehabilitate current infrastructure stock or to construct new infrastructure facilities, highlighting the importance of a systematic asset management approach for municipal buildings (Federation of Canadian Municipalities 2003). 308-1 A majority of public sector organizations of British Columbia (BC) had signed BC climate action charter and were committed to become carbon neutral by 2012 (Government of British Columbia 2013). In the quest of becoming carbon neutral, municipal governments are compelled to implement programs and policies to reduce the carbon footprint of both corporate and community operations. Federation of Canadian Municipalities, (2011) have stated that public buildings are one of the main contributors to GHG and smog-forming emissions in Canada.  Statistics shows that, buildings account for over 80% of the public sector GHG emissions (Government of British Columbia 2013). Therefore, it is important for public sector organizations to improve the environmental performance of public buildings to comply with the ongoing climate action agenda.  Previous studies revealed that significant reductions in life cycle energy consumption and CO2 emission can be achieved from the building operations phase (Wu et al., 2011; Airaksinen & Matilainen, 2011). However, currently, municipal facilities managers are faced with an onerous task of inspecting, repairing, maintaining, renewing and replacing a diverse portfolio of infrastructure facilities owned by the municipality in the most sustainable way (Vanier and Rahman 2004). Hence, there is an alarming need to focus on life cycle asset management (LCAM) of public buildings to prolong its life cycle as well as comply with contemporary legal and policy obligations. Consequently, many organizations around the world are turning to asset management to ensure optimized utilization of the asset (Félio 2006;  Halfawy 2008). Asset management is the systematic process to maintain a desired service level of an asset at the lowest life cycle cost while complying with  legal obligations and standards (Asset Management BC, 2011; USEPA, 2009b). Assessment of the current level of service (LOS) is a main underlying process in the  life-cycle management of infrastructure assets (Asset Management BC 2011).  Félio & Lounis, (2009) and Federation of Canadian Municipalities, (2002) stated that the LOS is an assessment of the quality of the service provided with respect to the society and economy. Determination of LOS assists decision makers in prioritizing the infrastructure assets in investment planning related to the development, operation, maintenance, rehabilitation, planning, and replacement of municipal infrastructure (Ireland et al. 2008).  Factors related to LOS includes customer relations, quality, consistency of service, capacity, reliability, responsiveness, environmental acceptability, cost, and availability (Federation of Canadian Municipalities, 2002 ; Félio & Lounis, 2009; Ireland et al., 2008). Asset Management BC, (2011) recommends that asset owners should regularly track service levels provided by the infrastructure assets. However, as per author’s knowledge, asset management of buildings has largely been disregarded in North America. Recreational buildings represent an interesting component of the infrastructure portfolio of municipal governments. A recreational centre building can be classified as desire rather than a necessity for an area. However, recreational centre buildings have become an important element for the health and wellbeing of residents. Several GHG inventory reports indicate that recreational buildings account for a significant portion of the corporate energy consumption and the GHG footprint of small and medium municipal governments (Stantec Consulting Ltd. 2011). Recreational centre buildings are comprised of  more building components and systems compared to conventional buildings. Moreover, such buildings also serve as service centres for the public. Hence, recreational centre buildings require more systematic management in maintaining its level of service and to prolong its life cycle. However, asset management of recreational building have been largely overlooked in the literature.  Currently, lack of adequate operational knowledge and understanding among decision-makers is a key challenge in infrastructure management (Federation of Canadian Municipalities 2003). Therefore, better resources are required for LCAM of specialised buildings such as recreational centre buildings. The objective of this paper is to develop a comprehensive LOS index for public recreational centre buildings. This index will assess the performance of the recreational centre building by considering the facility performance and the service level. Fuzzy logic would be used to characterize the imperfect information. This index would provide an objective basis for LCAM decision making for recreational centre buildings. As a proof of concept, the developed LOS index was used in a case study of a model recreational building identified from the literature. Findings of this research could be adopted in assessing the performance of other types of municipal buildings. 308-2 2 LITERATURE REVIEW  Published literature has largely overlooked performance assessment of public recreational centre buildings. Only a handful of studies related to this subject area were observed. Howat and Crilley (2007) developed a performance assessment model for aquatic centres integrating customer service quality, satisfaction, and operational performance. Another study by Howat et al.(2008) studied the relationships between service quality, overall satisfaction and loyalty measures in Australian aquatic buildings. This study revealed that main factors influencing the overall satisfaction are relaxation, staffing and facility presentation. Priyadarsini, (2014) studied energy performance of aquatic centre buildings in Victoria and revealed that energy intensity of aquatic centres ranges from 632 to 2,247 kWh/m2.   Sharma et al. (2008) mentioned that customer expectations, legislative requirements and community are important criteria for assessing the LOS of an infrastructure asset. The stakeholders of recreational centre building have different and conflicting expectations from the municipal infrastructure. Moreover, minimum service standards associated with an asset, financial constraints, and delivery mechanisms should be considered when setting up a target LOS.  Indicators based systems are a popular method of identifying the condition of an infrastructure. There is an overwhelming trend towards an indicator assisted planning and decision making within the Canadian municipalities (Federation of Canadian Municipalities 2003). An indicator provides information to identify the condition or status quo of an object or a service in consideration. Indicators associated with municipal infrastructure could be in a hierarchy reflecting the decision-making structure within the municipalities (i.e. operational indicators, functional indicators to strategic indicators) (Federation of Canadian Municipalities 2003). The Federation of Canadian Municipalities (2003) defines these indicators as defined below. i. Operational indicators: Operational indicator includes data collected by operational crew while performing their   duties or as a part of the inventory process. Operational indicators are expressed as survey results or score boards. ii. Functional indicators: Functional indicators are identified by analyzing the operational indicators that provide an overview of the condition of the infrastructure asset. These indicators are applicable to managerial level decision makers of the municipality.  iii. Strategic indicators: Strategic indicators provide general and abstract information of the infrastructure asset. The top level decision-makers (i.e. city manager, the city board) require this information in the strategic decision-making. These indicators provide a measurement of the quality of life of a municipality or meeting the infrastructure budget.  The funding decision makers related to municipal infrastructure often does not have sound understanding of the condition of the municipal asset (Federation of Canadian Municipalities 2003).  Sudden and unexpected problems create tension within the users, and expedited assessments can result in financial destitution for building owners (Condominium homeowners association, 2010). Therefore, it is important to identify meaningful indicators displaying the performance of infrastructure assets to support the decision-making process. Indicators are expected to assist in decision-making process but are not intended as substitute while exercising judgement related to infrastructure (Federation of Canadian Municipalities 2003). LOS indicators should display, strategic goals, stakeholder goals and organizational goals (Asset Management BC 2011).  3 METHODOLOGY AND FRAMEWORK DEVELOPMENT LOS of a recreational centre building depends on the building performance associated with multiple criteria (i.e. technical, social, environmental, and economic attributes). Hence, LOS assessment is a multi-criteria decision analysis (MCDM) process that incorporates conflicting criteria into the asset management. This LOS framework is based on a fuzzy set MCDM technique that is presented in the next section. Approaches proposed by  Félio & Lounis, (2009) and Khatri et al., (2011) were considered in developing the LOS assessment framework. Figure 1 presents an overview of the LOS framework. 308-3 3.2 Fuzzification of the Performance Indicators Fuzzy set theory is a powerful mathematical model to characterize uncertainty in reality (Zimmermann 2010).  This theory has been used in a vast range of disciplines such as engineering, logistics, management, data processing. Moreover, fuzzy set theory is a powerful tool to be used in decision support due to imperfect information about the reality.  Equation 1 presents the basic definition of the fuzzy set theory. The fuzzy set  is denoted as a set of ordered pairs in a universe of X, where  denotes the objects of X .The membership function, , maps  values to  in the interval 0 to 1.           Equation  1           This approach was used to convert a crisp number to a fuzzy set that is represented by the membership function. Four membership function levels (i.e. poor, satisfactory, good, and excellent) were described for LOS assessment using triangular and trapezoidal shaped membership functions. Excellent is the highest achievable performance level. Good level is the acceptable performance level while satisfactory level requires further performance improvement. Poor is the lowest performance level that requires immediate attention. Fuzzified value would be the places where the performance value intersects with the membership function. As an example for a hypothetical performance value of 55, the fuzzified value would be (0, 0.22, 0.8, 0) (Figure 3).     Figure 3: Membership functions and fuzzification 3.3 Weight calculation and weight assignment. Various subjective methods are used in MCDM to derive the weights (e.g. AHP, ANP). Analytic hierarchy process method (AHP) is a popular method of deriving the weights in MCDM (Khatri et al. 2011).  In AHP method, expert opinion is sought to conduct pairwise comparisons.  The opinions however, should be consistent to ensure the accuracy of the weights.    3.4 Aggregation of performance indicators The aggregation operation consists of combining the lower-level performances to the upper levels. The hierarchical process is presented in Figure 1. For each level, the synthesised performance value is a four-tuple fuzzy number.  308-5 3.5 Defuzzification of the Aggregated Indexes to Produce the Overall Systems Performance Four-tuple fuzzy number derived from LOS indicators would be made a crisp number through defuzzification operation. A commonly used defuzzification method, centroid method (Equation 2) would be used for defuzzification operation.            Equation  2  Overall LOS index is calculated using Equation 3.           Equation  3 Where,  CT = transpose of a vector of centroid values of the membership functions D = performance of a dimension/category in the framework 4 A CASE STUDY FOR PROOF OF CONCEPT The LOS index developed above was used to assess the LOS of a recreational centre building. Operational data were obtained from primary and secondary sources. Performance level data were obtained from published literature ( e.g. Sydney Water, (2011)).  Table 1 presents the performance values for indicator categories comprised in the index. Due to the time restrictions researchers could not obtain the required data to calculate weights for AHP method.  Therefore, equal weights were assumed for all criteria.   Table 1: Input data for LOS index.     Performance levels LOS dimensions and indicators Operational data Weight Excellent Good Satisfactory Poor Robustness of building components  33%     Condition rating of components 5 50% >8 8-6 6-4 <4 % service life remaining 70 50% >80% 60-80% 60-40% <40% Water-energy use  33%     Electricity use intensity (kwh/ft2/year ) 22 50% <12 12-18 18-30 >30 Natural gas use intensity (kwh/ft2/year ) 40 50% <25 25-35 35-50 >50 Water use intensity ( l/patron/year ) 26 50% <10 10-25 25-40 >40 Economy  34%     Benefit /cost ratio  0.5 50% >0.8 0.8-0.6 0.6-0.4 <0.4 Asset value increase from previous year 1 50% >5% 5-4% 3-2% <1% Security  25%     Spending's for safety and security of the users from O & M expenses 10% 50% >15% 15-12% 12-8% <8% Number of safety incidents 2 50% <1 2-3 3-5 >5 Consistency of service  25%     Number of component breakdowns  4 50% <1 2-3 4-5 >5 Number of days facility was closed for 35 50% <20 20- 30-40 >40 308-6 maintenance 30 Quality of service  25%     Number of complaints received per year 5 33% <1 2-4 5-6 >6 Customer rating  6 33% 10-8 8-6 6-4 <4 Number of staff per active members 0.5 33% >1 1-0.5 0.5-0.25 <0.25 Social equity  25%     % of the community served (in 5km radius) 5 50% >25% 25-20% 20-10% <10% Affordability ( Annual membership cost $) 50 50% <30 30-40 40-50% >50  4.1 Results  Khatri et al., (2011) have defined performance levels for municipal infrastructure. The same was assumed for LOS of recreational centre building.  Table presents the LOS of the recreational building.  Table 2: Performance levels for LOS.   Value range Membership function Centroid value Excellent >80 Trapezoidal 85 Good 80-60 Triangular 60 Satisfactory 60-40 Triangular 33 Poor <40 Triangular 13  Table 3 presents the fuzzification process and results of LOS indicators, dimensions and categories. Results of Table 3 were used to calculate the overall LOS of the recreational centre building. Table 3: LOS of the recreational centre building.  LOS indicators LOS dimension LOS categories Robustness of building components (0,0.21,0.12,0) 49.9 Asset LOS 37.22 (0,0.41,0.35,0.08) (0,0.25,0.75,0) (0,1,0,0) Water-energy use (0,0.2,0.1,0) 45.9 (0,0.9,0,0) (0,0,0.5,0) (0,0.3,0.1,0) Economy (0,0,0.13,0.08) 16.4 (0,0,0.75,0) (0,0,0,0.5) Security (0,0,0.13,0) 16.5 Customer LOS 19.11 (0,0.51,0.61,0.35) (0,0,0.5,0) (0,0,0.5,0) Consistency of service (0,0.6,0.01,0.04) 19.1 (0,0.5,0,0) (0,0,0.1,0.3) 308-7 Quality of service (0,0.04,0.12,0.04) 28.9 (0, 0,0.5,0.5) (0,0.5,0.5,0) (0,0,0.5,0) Social equity (0,0,0,0.19) 12.0 (0,0,0,1) (0,0,0,0.5)  LOS of the recreational centre building = 28.16 Therefore the LOS of the recreational building is poor and needs immediate improvement.  5 DISCUSSION This paper presented an approach for calculating the LOS of municipal recreational centre buildings. An MCDM based systematic framework has been used to calculate the LOS. Fuzzy synthetic method has been used to account for incomplete and qualitative information. The paper presented a case study using literature data to illustrate the underlying concept.  Currently, LOS of buildings is overlooked by asset managers. Even though performance assessment is common practise for regular buildings, there has not been any sound research focusing on specialized buildings such as recreational centre building. The concept of LOS in buildings has largely been disregarded in practise. Development of decision support tools would support facilities managers for the above purpose. Due to its convenience, indicator based assessment frameworks should be promoted among municipal decision makers and engineering departments. However, it is important to exercise cautious approach as  indicator-based systems are not a silver bullet, and users should be reasonable in their expectations. The proposed framework incorporated multiple criteria associated with the LOS of a recreational building. The LOS is evaluated primarily from the perspective of asset and customers. The LOS of a building is further, assessed considering various underlying dimensions. Therefore, a municipality could identify which aspect is affecting the building performance and take corrective action accordingly. This approach has the capability to customize the LOS index based on the priorities of the municipality. The fuzzy-based approach enables handling data that is incomplete, ambiguous, linguistic, and uncertain.  LOS indicators were identified from the published literature and building rating tools. The number of performance indicators could be improved using further analysis. When selecting indicators for rating systems, it is important to ensure indicators are manageable, meaningful, quantifiable, well defined and aligned with objectives. The fittingness of performance indicators could be further assessed considering relevance, measurability, etc. This approach would provide a more robust set of indicators. The results presented in this paper also illustrate a range of benchmark values for public aquatic centre managers. The case study evaluated the LOS of a recreational centre building. Literature was used to obtain the indicator data to assess the LOS. This analysis identified that LOS of the recreational building considered is poor with a rating of 28.16. Therefore, this building requires immediate improvement. The water-energy performance of the building is satisfactory (49.9) while social equity is the extremely low (12.0). With this information, municipality could improve the LOS of the recreational centre by focusing on the areas that needs immediate attention. During the case study, equal weights were assumed for all the performance categories. The accuracy of LOS would heavily depend on the indicator types considered, the quality of data available and the weights assigned to the performance measures. Hence, the result observed could be different from the actual LOS of the recreational centre building.  308-8   There are several limitations associated with this study. Inclusion of limited number of performance measures for model is the main limitation of this paper. In addition, this framework considers equal weight for the criteria considered. However, it is important to realize that this approach should be tailored to meet the individual needs and priorities of the municipality. Data associated with LOS are uncertain and possesses considerable subjectivity. Several assumptions were used where ever data was not available. This concern could be resolved by using expert interviews and validating the interview responses using literature. Above issues would be corrected in the future publications with improved data collection and MCDM methods. 6 CONCLUSIONS Assessing the LOS of public buildings is a challenge for municipal facilities managers. This paper presents a novel approach for analyzing and calculating the LOS of a recreational centre building. The LOS framework has been developed by integrating systems approach with fuzzy logic.  This approach is reinforced by measures and data that is easily understood and can be used in diagnostic decision-making. This concept could be extended to assess the level of service of other municipal building classes. The credibility of this framework depends on the types of indicators used and the precision of data and weights calculated. Further research is needed to identify more indicators and their interdependencies. Furthermore, it is important to identify sound data for LOS classifications (i.e. excellent, good, satisfactory, and poor).  It is important to realize that when assigning weights, expert judgement could be subjective and be potentially biased. Hence, expert judgments from a diverse group of experts could be obtained to minimize the biases of the LOS index.   The outcomes of this research will inform LCAM of recreational centre buildings in Canada. The approach proposed in this study could be used to assess LOS of complex municipal infrastructure systems. Hence, an extension of the model would provide the Canadian municipalities with an integrated model to assess the LOS while developing holistic infrastructure system.  References Airaksinen, M. and Matilainen, P., 2011. A Carbon Footprint of an Office Building. Energies, 4 (12): 1197–1210. Asset Management BC, 2011. Guide for using the Asset Management BC Roadmap. British Columbia. Building Owners and Managers Association (BOMA) of Canada., 2013. BOMA BESt = Building Environmental Standards [online]. Available from: http://www.bomabest.com/. 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