Reliable and appropriate data and information are essential for measuring and predicting beneficial outcomes of investments in maintenance and repair and for predicting the adverse outcomes of lack of investment. Data and information can be the basis of higher situational awareness during decision-making, of transparency during the planning and execution of maintenance and repair activities, of an understanding of the consequences of alternative investment strategies, and of increased accountability.
A 2004 National Research Council study stated that to implement a portfolio-based facilities asset management program effectively, the following elements are required (NRC, 2004a):
• Accurate data for the entire facilities portfolio to enable life-cycle decision making.
• Models for predicting the condition and performance of the portfolio of facilities.
• Engineering and economic decision-support tools for analyzing tradeoffs among competing investment approaches.
• Performance measures to evaluate the effects of different types of actions (such as maintenance versus renewal) and to evaluate the timing of investments.
This chapter focuses on the data, tools, and technologies that can be used to support portfolio-based facilities management and to support more strategic decision-making about investments in maintenance and repair. It is organized by
data acquisition and tracking systems, indexes and models for measuring outcomes, and predictive models for decision support (Table 3.1).
The costs associated with data collection, analysis, and maintenance can be substantial. Costs will depend on the amount and accuracy of the data collected, how often they are collected, and the cost of the entire process, including data entry, storage, and staff time (NRC, 1998). Once data on a facility are created, it is necessary to update them throughout the facility’s life cycle. The types of data to be maintained, their level of detail, and their currency, integrity, and attributes will depend on the outcomes that they are related to and how important the outcomes are for strategic decision-making. The data on facilities or systems that are mission-critical, for example, might need to be updated more often than data on less strategic facilities.
Because of the costs, the committee believes that “no data before their time” should be an infrastructure-management tenet. Every system and data item should be directly related to decision-making at some level, and off-the-shelf decision-support systems should be fully integrated into decision-making processes. To the greatest extent possible, data should be collected in a uniform manner across federal agencies to provide greater uniformity and in turn support the development of governmentwide performance measures and the greater use of benchmarking for agency practices and investment strategies.
Facilities asset management data should include at least inventory data (number, locations, types, and size of facilities) that are relatively static once collected, and attribute data or characteristics that change (for example, equipment and systems, condition, space utilization, tenants, maintenance history, value, and age) (NRC, 2004a). The systems described below are designed to assist facilities managers to gather and maintain accurate, relevant data about an individual building or structure throughout its life cycle. Some are traditional passive systems that rely primarily on manual entry of data and others collect data automatically in “real time.”
Traditional Passive Facilities Data-Acquisition Systems
Among the systems most commonly used by federal agencies for collection of data on portfolios of facilities are the following:
• Computer-Aided Facility Management Systems. Computer-aided facility management (CAFM) systems have evolved over several decades and through several generations of technology. However, from the beginning, the primary focus of such systems has been space planning and management and asset management. Applications now include energy and lease management, real
TABLE 3.1 Data, Tools, and Technologies to Support Strategic Decision-Making for Investments in Facilities Maintenance and Repair
|Data, Tool, and Technologies||Primary Purpose and Inscription|
|Data Acquisition and Tracking Systems|
|Computer-aided facility management systems (CAFM)||Space planning and management: facilities management|
|Compute rued maintenance management systems (CMMS>||Maintenance-related wort management|
|Building automation systems||Monitoring and control of lighting, heating, ventilation, air-conditioning and other building systems|
|Bat codes||Tracking of equipment, components, or other assets|
|Radio frequency identification systems (RFID)||Real-time asset sacking|
|Sensors||Monitoring of equipment and systems for vibration, strain, energy use. temperature, presence of hazardous materials, and the like|
|Condition assessments||Assessment of the physical condition of facilities systems and components|
|Hand held devices||Allowing facility inspectors to enter work-management, condition, and other information directly into CAFM and CMMS|
|Automated inspections||Inspection of infrastructure, such as roads and railroads, using an array of technologies|
|Nondestructive testing||Monitoring of the condition of systems and infrastructure that are not visible to the human eye|
|Self-configuring systems||Control and other budding systems, such as HVAC. that are able to diagnose a problem and fix it with minimal human intervention|
|Machine vision||An emerging technology for conducting inspections and for developing as-built information to support building information modeling|
|Building information modeling (BIM)||An emerging practice for modeling and exchanging of physical, financial, and other facility-related information throughout a facility's life cycle|
|Indexes and Models for Measuring Outcomes|
|Facility condition index (FCI)||A financial index based on a ratio of backlog of maintenance and repair to plan value or current replacement value|
|Condition index||A financial index based on a ratio of repair needs to plant value or current replacement value|
|Engineering-research-based condition indexes||Physical condition indexes based on empirical engineering research and developed from models; indexes have been developed for buildings, some building components (such as roofs) and a variety of infrastructure, including railroad tracks, airfield pavements, and roads|
|Building functionality index||An index to measure building functionality in relation to 14 categories; functionality requirements are independent of condition and are generally related to user requirements (mission), technological efficiency or obsolescence, and regulatory and code compliance|
|Building performance index||An index based on the ratio of a physical building condition index and a building functionality index|
|Predictive Models for Decision Support|
|Service life and remaining service life models||Models to predict the expected service life or remaining service life of systems and components; the purpose of these models is to help determine the appropriate timing of investments in maintenance and repair or replacement|
|Weibull models||Models that estimate the probability of failure of building or infrastructure systems or components|
|Engineering analysis||Analyses (such as fatigue analysis and wear-rate analysis) used to predict the remaining life of a system or component|
|Parametric models for cost estimating or budgeting||Economic-based (such as depreciation) or engineering-based (such as physical condition) models that can be used to develop multiyear maintenance and repair programs and cost estimates for annual budget development|
|Operations research models||An array of decision-support models that have been applied to some types of infrastructure (such as bridges)|
|Simulation models||Models used to analyze the results of “what if?” scenarios; an example is the Integrated Multiyear Prioritization and Analysis Tool (IMPACT), which simulates the annual fiscal cycle of work planning and execution; it can be used to set priorities for maintenance and repair work based on different variables, including budget|
|Proprietary models||Facilities asset models developed for a wide array of applications, including the prediction of outcomes of investments for maintenance and repair developed by private-sector organizations; relatively little information is publically available about how they work and their assumptions, robustness or accuracy|
estate management, maintenance and operations, and geographic information systems (GIS) integration. The latest incarnation of CAFM systems, integrated workplace management systems (IWMS), emphasize the integration of all those applications with an organization’s financial and human resources data systems. All aspects of a facility’s life cycle—including planning, design, financial analysis and management, project management, operations, facilities management, and disposal—are accounted for in IWMS.
• Computerized Maintenance Management Systems. Computerized maintenance management systems (CMMS) have also evolved, with maintenance-related work management as their primary focus. Today’s fully developed CMMS can be used for preventive maintenance scheduling, labor requirements, work-order management, material and inventory management, and vendor management. Most CMMS track facilities-related materials, location, criticality, warranty information, maintenance history, cost and condition, component assembly, and safety information.
One drawback of CMMS and CAFM is that data often are entered manually and this increases the likelihood of error. In addition, the information available to decision-makers can be compromised in that manually updated data may not be entered on a timely basis. However, if the data are accurate, complete, and current, such systems provide a database that can be used for measuring outcomes, for risk analysis, for energy and condition assessment modeling, and for investment-decision support.
Real-Time Active Facility-Data Acquisition Systems
Several technologies bring “intelligence” to facilities, systems, and components; allow for automated data entry into CMMS and CAFM systems; and allow real-time monitoring of the performance of facilities systems and components.
• Building Automation Systems. Building automation systems (BAS) are typically installed to monitor and control lighting, heating, ventilation, and air-conditioning (HVAC) systems; security systems; and life-safety systems, such as fire suppression. These control systems provide real-time feedback in the form of alarms based on operating characteristics (an alarm sounds when specified parameters are exceeded) and records of equipment performance. They are often the best source for early detection of equipment problems.
Traditionally, the data collected by BAS were used only for control purposes. Today, BAS data are being “mined” to provide information so that system operators and facilities managers can understand and assess the performance and condition of systems. For example, advanced sensors can be installed in BAS for HVAC systems (Liu and Akinci, 2009) to track temperature, humidity, pressure, and ventilation rates (ASHRAE, 2009), all of which are related to indoor
environmental quality. The data collected can be used to support more effective decision-making about the daily operations of facilities and their systems (Jones and Bukowski, 2001; Du and Jin, 2007) and to support the health, safety, and productivity of building occupants.
The use of open protocols—open standards by which devices communicate with each other—makes it possible to connect and control devices from multiple BAS developed by multiple vendors. With the arrival of the Internet and Internet Protocol and wireless technology, the performance of systems can now, at least theoretically, be monitored and controlled no matter where they are and no matter who manufactured them.
The reality, however, is that many BAS were installed at different times, by different vendors, and in connection with different generations and types of systems, and this has resulted in a lack of interoperability among hardware, software, and communication protocols. Nevertheless, there are no technologic hurdles that need to be overcome to enable BAS to be used for portfolio-based facilities management and resource planning, or to allow coordinated operations of building equipment and systems to achieve high operating efficiency, while minimizing operating times, and reducing energy use.
• Bar Codes. Bar codes involve an older technology that has evolved and become more complex with greater capabilities. When bar codes are placed on equipment, components, or other assets, information about assets can be automatically scanned into a CMMS or CAFM system.
• Radio Frequency Identification Systems. Radio frequency identification (RFID) systems use two technologies: a radio tag that consists of a microchip that stores data on an object and an antenna that transmits data; and a reader that creates the power for the microchip in the tag for passive RFID tags and then reads and processes the data from the tag. The radiowave data transmitted by the tag are translated into digital information that in turn, can be used by the software to record the status or location of a facility, a system, or another component.
RFID tags can replace bar code systems for traditional inventory applications. Embedding a tab in an asset increases the durability and reusability of the tag. Using RFID tags with sensors substantially increases the number of applications for monitoring performance. Sensors are most often used to monitor motion (vibration) of and strain on facilities systems and components or to monitor temperature. Increasingly, such sensors (often in conjunction with BAS) are being used for energy management systems. Other sensors can be used to detect the presence of radiation, chemicals, or other hazardous materials.
RFID systems are only now beginning to be used for facilities management-related activities. Given their numerous advantages over traditional bar coding and their decreasing size and cost, it seems only a matter of time before they replace bar coding. Although there are stand-alone RFID systems that offer alerts, customized reports, and other features, the integration of RFID systems with real-time
BAS or passive CAFM, CMMS, and IWMS makes possible a greater variety of applications, more efficient use of data, and more efficient operations.1
Condition Assessment Data Collection and Tracking
Condition is an underlying factor in the performance of most facilities, systems, and components. It is also an important predictor of future performance: systems and components that are in good condition will be more reliable and perform better than systems that are deteriorating.
Condition assessments provide reference points for facilities managers on the current condition of facilities, systems, and components. Trends in condition can be used to determine whether facility systems and components are being maintained and are meeting their expected service lives or whether their performance is deteriorating faster than expected.
Information about the condition of facilities, their systems, and their components can be gathered and updated by using different approaches, whose costs vary. The choice of approach will affect the availability, timeliness, and accuracy of data and thus affect the value of the data for strategic decision-making.
In federal agencies, condition-related data are typically acquired through assessments conducted by teams of inspectors on a multiyear cycle. Condition assessments are usually conducted each year for a portion of an agency’s entire portfolio and most facilities are inspected once every 3 to 5 years.
Depending on the information needed and how an organization uses the results, condition assessments can range from detailed assessments of individual components by engineers or technicians of various specialties to walk-through visual inspections by small teams. Condition assessments of any kind help to verify assumptions about facility system conditions and to update real estate records. The consistency and quality of condition assessments among facilities and sites are also important in determining the usefulness of the data collected for decision support and priority-setting.
The costs of condition assessments vary widely, depending on the complexity of the facility and the level of inspection. Cost estimates given in presentations to the committee ranged from $0.07 to $0.60 per square foot of building space when third-party contractors performed the initial condition assessment. For a 500,000 square foot facility, that would translate to $35,000 to $300,000.
Condition assessments that are undertaken on a multiyear cycle and conducted for an entire portfolio of facilities can be inefficient and expensive and the
1Bar coding is considered less expensive than RFID technology: it is estimated that bar codes cost $0.005 each whereas passive RFID tags cost more than $0.05 each (Shih, 2009). However, when other variables (such as the speed of collecting data or the cost of RFID scanners versus barcode interrogators and the number of times that an asset inventory is performed) are considered, the cost differentials become smaller (Roberti, 2009). If the inventory system uses multiple bar codes, it is usually a sign that RFID technology will be more cost-effective than traditional bar coding.
information can lose its value for decision-making quickly. Some organizations are now taking a “knowledge-based” approach to condition assessment. The term knowledge-based is used “to indicate that knowledge (quantifiable information) about a facility’s system and component inventory is used to select the appropriate inspection type and schedule throughout a component’s life cycle. Thus, inspections are planned and executed based on knowledge, not the calendar” (Uzarski et al., 2007, p. 2). Because different building components have different service lives, and some may be more important than others with respect to outcomes and risks, some components are inspected more often than others and at different levels of detail. By tailoring the frequency and level of inspections, a knowledge-based approach makes better use of the available resources and provides more timely and accurate data to support investment-related decisions (Uzarski, 2006).
• Hand-Held Devices. Hand-held devices and kiosk terminals placed throughout an organization’s facilities allow inspectors and building operators to enter data on work orders or other building-related actions in nearly real time directly into CAFM and CMMS. That provides a more accurate and up-to-date picture of current maintenance efforts and requirements and of building condition. Hand-held devices, when used by properly trained maintenance staff, reduce the time spent in recording information, improve data accuracy, and allow more time to be dedicated to hands-on maintenance and repair activities.
• Automated Inspections. Technologies exist that replace the human inspector for gathering condition-related data. One example is the International Road Roughness Method, which is in wide use around the world by the highway industry (Gillespie et al., 1986). The railroad industry routinely uses laser optical sensors, accelerometers, displacement transducers, motion detectors, and gyroscopes for measuring track quality under a moving load, deviations from which increase derailment risk and adversely affect operations. The use of those types of technologies allows the collection of more data and higher-quality data at a fraction of the cost of manual data collection (Union Pacific, 2005).
• Nondestructive Testing. Nondestructive testing is sometimes used for collecting condition-related data on components and systems not visible to the human eye. Many technologies can be used for these purposes. Among them are infrared thermography for detecting excessive heat, leaks, delamination, and defective areas and for stress mapping; ultrasonic testing and laser technology for detecting cracks and other defects; and ground-penetrating radar for detecting abnormalities in subsurface systems (NRC, 1998).
Emerging Technologies for Data Acquisition and Tracking
Technologies for various aspects of facilities management are continually evolving and advancing in their capabilities. Three technologies that could substantially improve the acquisition and tracking of data, improve maintenance and repair activities, and provide support for decision-making are self-configuring
systems, machine vision, and building information modeling (BIM). These technologies are described below.
• Self-Configuring Systems. A self-configuring (or self-healing) system is one that is capable of responding to changing contexts in such a way that it achieves a target behavior by regulating itself (Williams and Nayak, 1996). The objective of self-configuring systems is to enable computer systems and applications to manage themselves with minimal but high-level guidance by humans (Parashar and Hariri, 2005).
For example, self-configuring HVAC systems detect and diagnose a problem (such as a damper stuck in a variable-air-volume box) and automatically fix it (Laster and Olatunji, 2007). With continuous monitoring, such systems could recognize that they are going out of commission and then repair themselves, or they could identify ways to reduce energy costs and improve occupancy comfort. The benefits of self-configuring or self-healing systems could include lower operating costs, greater reliability, less downtime, and more efficient operation (Fernandez et al., 2010). Some of those characteristics have been studied in the BAS domain (Ellis and Mathews, 2002; Sallans et al., 2006; Menzel and Pesch, 2008).
• Machine Vision. Machine vision is an emerging technology for conducting facility inspections. Machine vision uses video imaging and computer software to detect component defects, such as cracks. The technology has been used for pavement inspection (Tsai et al., 2010) and railroad-car structural inspection (Schlake et al., 2010) and is under development for railroad-track inspection at the University of Illinois (Resendiz et al., 2010).
In the federal sector, the General Services Administration is developing a set of guidelines for rapid collection of 3-D information by using 3-D imaging technologies (in particular laser scanners) for historical and facility-condition documentation and for collecting as-built information that can be used in the development of building information models.2
• Building Information Modeling. Building information modeling (BIM) is an emerging practice for modeling and exchanging facility information that involves various interoperable technologies and associated sets of processes (Eastman et al., 2008; Smith, 2007). It has the potential to contain and visually display data about physical elements (such as columns, beams, slabs, and walls), nonphysical concepts (such as zones), and the relationships between them. BIM could also provide information about nongeometric properties and attributes, such as material specifcations needed for fabrication, material properties that depict behaviors under different contexts (such as thermal, acoustic, and light refectance), cost, budget, and schedule or even information about parametric rules that depict the connections and distances between objects. Configured in
that way, BIM would incorporate consistent, coordinated, and nonredundant data on a facility (Eastman et al., 2008).
The use of BIM for planning, designing, and constructing facilities is increasing throughout the architecture-engineering-construction industry. The benefits of using BIM for energy simulation, cost estimation, subcontractor coordination, and other applications have been documented. Some federal agencies have begun to require the use of BIM during design and construction (Brucker et al., 2010). A number of BIM guides and roadmaps for future development have been developed by, for example, the Department of Veterans Affairs3, the state of Wisconsin (Wisconsin, 2009), Indiana University (2010), the National BIM standard effort,4 and the Associated General Contractors of America (AGC, 2006).
To date, BIM has been applied to facilities operations and management only sparsely, although using it for building operations, maintenance, and management could yield substantial benefits and long-term cost savings (Wisconsin, 2009). For example, a U.S. Coast Guard facility planning case study recorded a 98 percent reduction in time and effort in producing and updating a facility management database when BIM was used (Eastman et al., 2008; Dempsey, 2009). A case study of the Sydney Australia Opera House identified numerous benefits of using BIM, including consistency in data, providing an integrated source of information for different software applications, and supporting queries for data mining (Ballesty et al., 2007). Additional case studies highlight how BIM can be used for building-systems commissioning, field operations, asset tracking, and energy monitoring (Jordani, 2010).
One of the greatest benefits of using BIM would be having real-time facility-related information in an integrated form that would enable facility operators and managers to have a more holistic understanding of what is happening throughout a facility’s life cycle. BIM would reduce redundant data collection and data reentry and reduce the uncertainty associated with not having the right information when making investment decisions. More accurate, real-time information would bring greater transparency to facility operations, which would increase accountability. Finally, a by-product of BIM is advanced 3-D visualization capabilities, which can help in communicating facility investment requirements and the predicted outcomes of investments of different stakeholders.
Although the benefits of BIM for facilities management and operations are apparent, BIM technology in its current form is best categorized as an information repository. Improved data-exchange standards and software systems are needed to allow full interoperability of data from many systems. Interoperability, in turn, will allow more seamless integration of the data and functionalities needed to support strategic decision-making related to maintenance and repair investments and to document the outcomes.
3The Department of Veterans Affairs BIM guide is available at http://www.cfm.va.gov/til/bim/BIMGuide/lifecycle.htm.
4Information available at http://www.buildingsmartalliance.org/index/php/nbims/about/.
Various federal agencies participate in and support a number of efforts to develop the data and exchange standards, protocols, standard definitions, and data items that are needed if BIM is to reach its full potential as a tool for portfolio-based facilities management. Some of the efforts, including the development of a national BIM standard, are being conducted under the auspices of the National Institute of Building Sciences and involve representatives of federal and private-sector organizations.
An array of indexes derived from models have been developed to measure outcomes related to building and infrastructure condition, functionality, and performance.
Condition Indexes and Models
Facility Condition Index. The facility condition index (FCI) is a well-known and widely used condition index modeled from the ratio of two direct monetary measures: backlog of maintenance and repair (cost of deficiencies) and current replacement value (NACUBO, 1991). Typically when applied to a facility, that ratio ranges from 0 to 1, but it is sometimes multiplied by 100 to expand the range from 0 to 100.
Condition Index. The Federal Real Property Council defines the condition index (CI) as a general measure of a constructed asset’s condition at a specific time. CI is calculated as the ratio of repair needs to plant-replacement value (PRV): CI = (1 — $ repair needs / $ PRV) × 100 (GSA, 2009). Repair needs represents the amount of money necessary to ensure that the constructed asset is restored to a condition substantially equivalent to the originally intended and designed capacity, efficiency, or capability. PRV is the cost of replacing an existing asset at today’s standards (GSA, 2009). Like FCI, CI is a financial measure that is a proxy for physical condition.
Engineering-Research-Based Condition Indexes. Engineering-research-based indexes measure the physical condition of facilities, their systems, and their components. These types of indexes are based on empirical engineering research and are the driving engines for the sustainment management systems (SMS) (decision-support systems and asset-management systems) developed by the U.S. Army Corps of Engineers. The indexes can be applied to airfield pavements (Shahin et al., 1976; Shahin, 2005), roads and streets (Shahin and Kohn, 1979), railroad track (Uzarski et al., 1993), roofing (Shahin et al., 1987), and building components (Uzarski and Burley, 1997).
Each index follows a mathematical weighted-deduct-density model in which a physical condition-related starting point of 100 points is established. Some number of points is then deducted on the basis of the presence of various distress types
(such as broken, cracked, or otherwise damaged systems or components), their severity (effect), and their density (extent). The deductive values were based on a consensus of many building operators, engineers, and other subject matter experts. Risk assessment and consequence are incorporated into the severity definitions and the actual deductions to be taken for each combination. For example, a “high” severity generally denotes health, life-safety, or structural integrity problems or mission impairment. Inspectors need only collect distress data and they do not make judgments concerning physical condition. The computed building condition index (BCI) will be plus or minus 5 points of the expert-group consensus with 95 percent confidence on a 0 to 100 scale.
Those indexes are computed at a facility hierarchy level typically associated with maintenance and repair activities (for example, logical pavement portion, logical roof portion, and air-handling unit). Logical management units are based on component type, material or equipment type, location, age, and other discerning factors. Maintenance and repair needs are correlated to the numerical BCI scale. In general, the lower the BCI value, the greater the risk of physical failure. Different BCI scale ranges (such as 86 to 100 or 71 to 85) signify the relative risk. The indexes can also be rolled up to determine the condition of a system, facility, entire portfolio, or portfolio subsets to support reporting and managerial requirements.
To maximize the usefulness of the indexes, condition standards need to be established, that is, the point at which the component condition drops below a minimum desired value whereby a mission is adversely affected or the risk of mission impairment becomes unacceptable and triggers a maintenance and repair requirement. The minimum value is a variable and depends on the facility, mission, risk tolerance, redundancy, occupancy, location, and other factors. Representatives of the U.S. Navy told the committee that they are working on creating those types of standards for their facilities portfolio. The committee notes that determining the minimum values is not a trivial matter for any organization and that additional research is needed to facilitate such a determination.
Building Functionality Index and Model
Functionality is a broad term that applies to an entire facility and its capacity to support an organization’s programs and mission effectively. Functionality is related primarily to user requirements (mission), technical obsolescence, and regulatory and code compliance, and it is independent of condition. A building functionality index (BFI) for buildings and building functional areas (such as administration, laboratory, storage, and production) has been developed by the U.S. Army (Grussing et al., 2009). It follows the same form, format, and rating-scale development theory as engineering-research-based physical condition indexes. However, rather than accounting for distresses, functionality issues are considered with severity (effect) and how widespread the issue is. The numerical BFI scale
(0 to 100) is correlated to modernization needs. The model addresses 65 specific functionality issues, which are grouped into 14 general functionality categories, as shown in Table 3.2.
Building Performance Index and Model
A building performance index (BPI) has been developed that combines the BCI and the BFI into a measure of the overall quality of a building. The BPI is derived mathematically by taking the sum of ⅔ of the lowest of the BCI and BFI values and ⅓ of the higher of the two values. The ⅔ to ⅓ split was derived through regression analysis and is intended to serve as a measure of rehabilitation needs.
TABLE 3.2 Building Functionality Index Categories and Descriptions
|Location||Suitability of building location to mission performance|
|Building size and configuration||Suitability of building or area size and layout to the mission|
|Structural adequacy||Ability of structure to support seismic, wind, snow, and mission-related loads|
|Access||Ability of building or area to support required entry, navigation, and egress|
|Americans with Disabilities Act (ADA)||Level of compliance with the ADA|
|Antiterrorism and force protection (AT/FP)||Compliance with AT/FP requirements|
|Building services||Suitability of power, plumbing, telecommunication, security, and fuel distribution|
|Comfort||Suitability of temperature, humidity, noise, and lighting for facility occupants|
|Efficiency and obsolescence||Energy efficiency, water conservation, and HVAC zoning issues|
|Environmental and life-safety||Asbestos abatement, lead paint, air quality, fire protection, and similar issues|
|Missing and improper components||Availability and suitability of components necessary to support the mission|
|Aesthetics||Suitability of interior and exterior building appearance|
|Maintainability||Ease of maintenance for operational equipment|
|Cultural resources||Historic significance and integrity issues that affect use and modernization|
SOURCE: Grussing et al., 2009.
Outcomes of maintenance and repair investments are measurable (either directly or through a model) by taking before and after measurements to gauge the effects of the investment. However, predictive models are needed to estimate the outcomes before investment (or in the absence of investment, that is, the do-nothing case). The prediction can be compared with the measured post investment value to determine whether the expected outcome was realized. Such predictions are crucial for performing a consequence analysis of maintenance and repair alternatives.
Modeling approaches have been developed to predict the remaining service lives of facility systems and components and to estimate the probability of system failure, and they support risk-based decision making related to the timing of maintenance and repair investments. Cost and budget models are also available to support the development of multiyear maintenance and repair programs.
Models of Service Life and Remaining Service Life
Service life is the expected usable life of a component. At the end of the service life, replacement or major rehabilitation or overhaul is required. Remaining service life is the time from today to when the service life will be expended.
Service life is based on a number of factors, which may include manufacturer’s test data, actual in-service data, and opinion based on experience. Service life typically is expressed as 5, 10, 15, 25, or 50 years. In reality, service life is in a range because of operating environments, the magnitude and timing of maintenance, use, abuse, and other factors.
Knowing the service life and the remaining service life of a component is important for making decisions about the timing of investments and for planning maintenance and repair work. Service-life and remaining-service-life models can also consider risk. If risk tolerance is high, maintenance and repair investments can be planned for the year in which the service life is expected to expire (or beyond). If risk tolerance is low, maintenance and repair may be planned to occur before the remaining service life expires. As risk tolerance decreases, maintenance and repair activities will be implemented sooner rather than later in relation to remaining service life.
Efforts have been made to determine the lives of facilities as a whole. Such organizations as the Bureau of Economic Analysis and Marshall and Swift have published facility service-life information. Facility service lives are generally based on economic depreciation rather than physical condition and performance. Such values are useful for planning overall facility recapitalization and modernization, for computing commercial tax liability, and for appraising value ( Whitestone, 2001).
The BUILDER Sustainment Management System5 calculates service life on
5Information available at http://www.erdc.usace.army.mil/pls/erdcpub/docs/erdc/images/ERDC_FS_Product_BUILDER.pdf.
the basis of the predicted component-section condition index (CSCI) (see discussion of Weibull models below) and a standard CSCI value denoting a physical condition whereby the component should be replaced. Remaining service life (RSL) is the difference between the current age and the predicted service life. RSL is adjusted on the basis of the revised predicted CSCI value resulting from the most recent condition survey inspection (Uzarski et al., 2007).
Remaining Maintenance Life Model. Remaining maintenance life (RML) measures the time remaining before maintenance and repair should be accomplished (Uzarski et al., 2007). With the CSCI and a minimum acceptable condition standard needed to support a mission fully, RML can be predicted in the same manner as RSL (Uzarski, 2004).
Weibull Models. Weibull models estimate the probability of failure and are widely used to estimate the weak link in a system. They have been used in the railroad industry for predicting defect formation in rails (Orringer, 1990). The presence of defects and the defect rate (defects/mile) are criteria for planning rail-defect testing and rail replacement. Because of public-safety concerns, the risk tolerance for defect-caused rail breaks that result in derailments is very low.
Weibull models have also been applied to predicting the engineering-based CSCI in buildings (Grussing et al., 2006). In recognition of a probability that a component section will fail faster or slower than expected, a Weibull model was used in the BUILDER SMS to predict a “current” CSCI. The CSCI was predicted to overcome the fact that building components are not all inspected at the same time and that years may pass between inspections. The prediction model provides real-time condition reporting, including the rollup CIs. It is also used to predict future CSCI and rollup CIs.
Because of the variance in actual versus predicted service life of systems and components, service life must be adjusted in accordance with actual condition data. With the Weibull-based CSCI prediction model, the adjusted service life, RSL, and RML can be computed.
Engineering Analysis. Traditional engineering analyses and models are also used to predict remaining component life. Examples include fatigue analysis, wear-rate analysis, and corrosion effects on structural strength. Typically, components are analyzed only on an as-needed basis. Computations of stress or strain coupled with material properties (such as strength and dimensions) and operating conditions are often used in a model for estimating an outcome.
Cost and Budget Models
Predicting outcomes of maintenance and repair investments requires estimates of costs or budget needs and consequences. The traditional approach involves using cost estimates developed for individual projects. The expected outcomes of a list of projects can be married to the costs of the projects and the outcome of a maintenance and repair investment can be predicted. However, this approach has
several problems. First, when the overall program and cost are being developed with planned outcomes, the projects to support the program may or may not have already been developed and their costs estimated. Second, the estimated costs may have been developed 2 years previously or earlier and may need to be updated. Third, developing project level cost estimates can be expensive so it typically is not done unless there is high certainty that the project will be funded. Finally, the costing approach is not particularly sensitive to what-if consequence analyses. Any change would require the cost estimator to recompute the cost with the changes incorporated.
To overcome the issues related to project cost estimating, parametric cost or budget models have been developed. Parametric models use cost correlated to particular measures to provide a reasonable estimate that is sufficiently accurate for planning purposes. They may be economic-based (involving, for example, depreciation or average service life) or engineering-based (involving, for example, actual and predicted condition or adjusted service life). Detailed project estimates are completed later in the project planning and execution process, when there is greater certainty about the availability of funding.
Examples of parametric models include the facility sustainment model developed for the Department of Defense (Whitestone and Jacobs, 2001) and the associated recapitalization and operating cost models (Lufkin et al., 2005). Those models are economics-based rather than engineering-based. The BUILDER SMS system uses an engineering-based parametric cost model based on component replacement cost and the CSCI. In general, parametric cost models are particularly useful for developing multiyear maintenance and repair programs.
Operations Research Models. Operations research (OR) models have been applied to management of some types of infrastructure, such as bridges (Golabi, 1997). However, although OR models are well-suited to maintenance and repair investments, they are seldom applied. With OR techniques (there are many), an objective function (such as minimizing energy consumption) could be established subject to budget, labor, and other constraints. Multiple criteria could be considered, and an optimal mix of projects and a prediction of the outcomes could be identified.
On the basis of an agency’s goals and needs (such as resource allocation, maintenance and repair scheduling, and logistics), OR models could be used for a variety of normative analyses. For example, in many situations in which inspection and collection of data are expensive, OR models could be used to find the optimal frequency of inspection or the need for collecting more data or samples. The key tradeoff in this type of analysis involves making a decision based on the available information versus collecting more data before making a decision.
Stochastic optimization models are used in situations in which decision-makers are faced with uncertainty and must determine whether to act now or to wait and see. There is uncertainty in future facility deterioration, budget levels, the effects of maintenance and repair actions, and so on. Stochastic optimization
models evaluate managerial recourse, and this provides an opportunity to fx problems if worse-case scenarios occur.
Another class of OR model, Markov decision process (MDP), attempts to link facility condition and optimal long-term maintenance strategy. MDP models are used for management of networks of facility systems and components (such as pavements and bridges). The application of MDP models in facility asset management is limited, in that, for example, facility condition may result from different deterioration processes and require different remedial actions. Deterioration processes and required remedial actions are difficult to define in a model.
Simulation Models. Simulation models are used to analyze the results of what-if scenarios and can be used in conjunction with OR models. Although much research is needed for simulation (deterministic and probabilistic) modeling with regard to facility maintenance and repair funding and consequence analysis (pertaining to outcomes), the U.S. Army Engineer Research and Development Center, Construction Engineering Research Laboratory (ERDC-CERL) has developed the deterministic Integrated Multiyear Prioritization and Analysis Tool (IMPACT) simulation model.6 The IMPACT model simulates the annual fiscal cycle of work planning and execution and displays building, system, and component condition indexes up to 10 years into the future. The model, in part, sets priorities for maintenance and repair work and their assumptions and assigns funding on the basis of such variables as policies, standards, and budgets.
Proprietary Models. The private sector has long been active in facility-asset management and has collected large amounts of facility data. Some companies have used the data to develop asset management models. Generally, the models purport to predict condition and budgets, and outcomes related to both. However, such models are proprietary, so little about how they work, or about their assumptions, robustness, and accuracy, is publically known or peer-reviewed.
6Information available at http://www.cecer.army.mil/td/tips/product/details.cfm?ID=738.