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28 The literature review, risk management primer, state of the practice summary, identification of tools and methodologies, and identification of constraints to adoption completed Phase I of the research effort. Based on those results, Phase II began with three steps: 1. Based on the conclusions of Phase I, develop improved strategies and tools for risk-based asset management and prepare draft implementation guidance. 2. Conduct test scenarios with three or more state DOTs using the draft implementation guid- ance and associated tools. Summarize lessons learned and present recommended changes and improvements to the guidance and tools as appropriate. 3. Prepare draft and final reports and supporting materials detailing the results of the research. A methodical process was followed in selecting studies for implementation. After summariz- ing the tools and techniques to assess asset risks, a presentation was made to the NCHRP 08-118 project panel. At this meeting, the constraints that agencies face in implementing asset risk man- agement were presented (as detailed in SectionÂ 7). These were categorized as general constraints to managing risks to assets, constraints to managing climate change risks, and constraints to wider use of management systems for forecasting asset risks. The selection of the subject areas for which to develop protocols and implement testing took into account all the work that had been completed on the project to that point: the literature review, the interview and survey feedback from state DOTs and management system vendors, the state of the practice, the applicability of the tools and techniques to transportation agencies, and the constraints faced by agencies in applying asset management. The project focused on techniques and tools agencies can use to augment their decision-making processes related to managing asset risks. Five categories were selected for further study: 1. General approaches for deterministic and probabilistic forecasts 2. Strategies to assess risks to bridges, pavements, and other assets using software commonly used in DOTs 3. Techniques, tactics, and proxy indicators for incorporating climate change resilience 4. Strategies to enhance risk assessment and institutionalize risk management 5. Techniques for using simple off-the-shelf tools to assess typical asset risks 8.1 General Approaches for Forecasting Asset Management Variables This category focused on general approaches using deterministic and probabilistic forecasts for key asset management variables to manage asset risks. S E C T I O N 8 Approach Followed for Study Selection and Testing
Approach Followed for Study Selection and Testing 29Â Â The deterministic and probabilistic analyses illustrate the effects that uncertainty and vari- ability could have on long-term forecasts of asset conditions or variables that impact conditions such as revenues, inflation rates, deterioration rates, or unit costs. These analyses can also illus- trate which variables have the greatest effect on forecasted outcomes. With this information, asset management program leaders could answer the following questions: â¢ How much uncertainty surrounds their forecasts? â¢ How could actual outcomes differ from assumed outcomes if key variables change? â¢ How important are frequent updates of forecasts as variables change? Studies considered for inclusion in this category included â¢ Probabilistic and deterministic forecasting to illustrate the impact of financial variability and uncertainty on different revenue sources. Each revenue source is a key input variable that impacts decisions about how much to invest to improve or sustain assets for the 10Â years of the TAMP. â¢ Illustrations of the uncertainty associated with forecasting key variables, such as bridge unit costs, that present the sensitivity of growth rate assumptions surrounding future bridge unit costs. The results of such forecasting, once validated by the state DOTâs SMEs, could help decision makers plan for the best-case, expected, and worst-case scenarios. The techniques can be applied to forecasting other sources of revenues and costs, such as costs of pavement and bridge con- struction components (steel, aggregates, asphalt, etc.) and other key variables that can influence bridge and pavement asset management. 8.2 Use of Management Systems to Forecast Asset Risks The studies within this category differ from those in the general approaches category by using management systems rather than off-the-shelf tools. These studies address â¢ How to use utility functions that include risks and how to incorporate utility functions into existing BMSs and PMSs â¢ How to incorporate risk-level indices into long-term investment strategy planning and opti- mization scenarios â¢ How to incorporate forecasts of deterioration rates and long-term asset conditions Studies considered in this category included the following: â¢ Study 3: Asset-Level Risk IndexâBridge Risk Utility Index â¢ Study 4: Asset-Level Risk IndexâPavement Section Flooding â¢ Study 5: Asset-Level Risk IndexâNon-NBIS Culverts â¢ Study 6: Asset-Level Risk IndexâLandslide Hazard Management â¢ Study 7: Program-Level RiskâPavement Network Analysis â¢ Study 8: Program-Level RiskâBridge Network Analysis These studies were categorized under two approaches to managing risk with existing asset management systems. As shown in FigureÂ 8-1, four of the studies were approached at an asset or location level and two were approached at a program level. Based on the context, the defini- tion of risk may vary. Three studies use an approach wherein risk is defined formally (Risk îµ Threat Probability î³ Vulnerability î³ Consequence) at the asset level, and they follow the same process for test setup. While one study also approached risk at the asset level, it defines risk as a utility-type index.
30 Risk Assessment Techniques for Transportation Asset Management: Conduct of Research Study 4 and Study 7 address techniques to incorporate risk factors into a state DOTâs existing BMS and PMS. Study 4 illustrates a technique for computing an asset-level risk index for fore- casting risk of pavement flooding. The technique can be incorporated into an agencyâs PMS, and the forecast can be extended to consider the flooding risk to the entire pavement network. Study 7 focuses on pavement network risk by forecasting the impact of risk factors such as inflation, funding levels, and other key pavement variables that influence a state DOTâs ability to achieve a state of good repair. The studies were selected to show the high, expected, and low projected pavement conditions based on different scenarios. Study 3 was considered to illustrate an approach to developing a bridge utility index that can be incorporated into an agencyâs BMS to forecast the threats of flood events, seismic events, and traffic accidents. The results of such forecasts can help inform decisions to invest in mitigation. Study 8 looked at techniques to forecast bridge network risks. This study evaluated funding levels and forecasted the projected bridge conditions using the bridge inspection ratings of various NBI elements. Study 5 presents a methodology to compute a culvert risk index. This study looked at risks from flood events and hurricanes on non-NBIS culverts. This risk index can be incorporated into an agencyâs existing asset management system and can inform decision makers on investments needed in various mitigation actions to manage culvert risks. Study 6 presented an approach to develop a risk index for rockfall, rockslide, debris flow, and other geotechnical hazards along a roadway that pose risk to highway safety and mobility. Risk management using existing asset management systems Risk management using asset- or location-level risk index Risk index defined using formal definition of Risk = Threat Probability Ã Vulnerability Ã Consequence Study 4: Pavement Section Flooding Study 5: Non-NBIS Culverts Study 6: Landslide Hazard Management Risk index defined as utility-type index Study 3: Bridge Risk Utility Index Risk management using program-level inputs (e.g., funding level, inflation rate) Study 7: Pavement Network Analysis Study 8: Bridge Network Analysis Figure 8-1. Categorization of risk index studies.
Approach Followed for Study Selection and Testing 31Â Â 8.3 Tactics for Climate Change Risk and Resilience Two studies developed resources for transportation agencies to fill gaps in existing practice related to assessing and managing risks to transportation assets associated with climate change. The first study includes user-friendly guidance on how to transform the information collected in a typical climate change vulnerability assessment into risk information comparable to other risks. The focus of the study was on how to assess the âlikelihoodâ of climate change risks by developing a customizable template for a likelihood rating rubric that would serve as a start- ing point for evaluating climate risks. With the template rubric, agencies can scale up or down or modify the time frame of analysis as needed. The guidance applies to any climate change and extreme weather threats, such as rising temperatures, changing precipitation, sea level rise, severe storms, flooding, and wildfires. The second study presents an example decision tree for how to move from risk assessment to risk management using an example assetâhazard combination (rising temperature risks to pavements). The focus of the study is to illustrate how decision trees can be used to help deci- sion makers view various alternatives and implications of different choices to inform trade-offs relating to climate change. 8.4 Institutional Strategies for Enhanced Risk Assessment For risk management to be used consistently to improve the management of assets, its use must be institutionalized within transportation agencies. Otherwise, risk management could wane as individual advocates and SMEs retire or move into other positions. The institutional strategy study was considered to show how state DOTs can routinize the management of risks to assets by incorporating it into eight sections of their TAMPs. 8.5 Off-the-Shelf Tools to Demonstrate Typical Risk Assessments Several off-the-shelf tools are commercially available for conducting risk assessments using decision trees. This study was considered to illustrate the use of an Excel add-in tool to decide whether to use a chip seal treatment plan for a pavement during a 10-year TAMP period versus a more expensive, albeit longer-life, option of resurfacing with thin asphalt overlays. 8.6 Study Protocols Protocols were developed for conducting the studies to ease the future implementation and adoption of risk management techniques and tools by transportation agencies. The protocols for demonstrating the pilots were designed to give state DOTs a step-by-step process they could follow to apply the tools or methods in their own agencies. The pilots were developed specifically with state adoption in mind. Each step in the protocol was designed to explain the objective of the tool or method and how to deploy it. The protocol developed for each study includes documenting the following: â¢ Objectives of the study â¢ Description of the strategy or tool that was tested â¢ Methodology used in conducting the pilot
32 Risk Assessment Techniques for Transportation Asset Management: Conduct of Research â¢ DOT organizational unit engaged in the pilot â¢ Who in the DOT could use the pilot and how â¢ Benefit of the pilot for a DOT â¢ Challenges in pilot study setup and use â¢ Resources needed by the DOT to implement the pilot The protocols provide information to transportation agencies on the applicability of each study to their agencyâs asset risk needs. The protocols also satisfy state and federal asset man- agement risk requirements. The protocols include information about the number and types of resources needed, the complexity of each study, and the benefits of implementing the study. Reviewing the study protocol will enable an agency to make an informed decision on the next steps to implementation of the technique or tool to support their asset risk management needs. Transportation agencies can use these tools and techniques to assess risks to infrastructure assets to inform risk-based decisions. Based on the panel feedback, 12 tools and techniques were selected for further studies. Because not all studies could be piloted, not all were selected for pilot testing in a transportation agency. However, protocols were developed so that agencies interested in studies that were not piloted would also have detailed information to implement the study on their own. Each piloted and non-piloted study includes the implementation steps that serve as high-level instructions. Studies also include appendices that provide additional information to help an agency in its implemen- tation efforts. 8.7 Tools and Techniques Selected for Study Tools and techniques aligned with the five categories previously described in this section were tested or demonstrated in the following 12 studies: â¢ Study 1: Using Deterministic Tools to Forecast Revenues at a DOT. â¢ Study 2: Using Probabilistic Tools to Forecast Revenues and Costs at a DOT. â¢ Study 3: Asset-Level Risk IndexâBridge Risk Utility Index. The study demonstrates how a risk index can be developed and calculated, then projected over time under different funding scenarios for all applicable structures using an existing BMS. â¢ Study 4: Asset-Level Risk IndexâPavement Section Flooding. The pilot illustrates the method- ology for defining a pavement section risk index by pavement section using a PMS. â¢ Study 5: Asset-Level Risk IndexâNon-NBIS Culverts. This study illustrates the development of a culvert risk index for small (non-NBIS) culverts. It is developed for implementation in a configurable PMS. â¢ Study 6: Asset-Level Risk IndexâLandslide Hazard Management. This study illustrates how a risk index can be developed for geotechnical hazards along a roadway, such as rockfalls, rock- slides, and debris flows, which pose a major threat to highway safety and mobility. â¢ Study 7: Program-Level RiskâPavement Network Analysis. This study illustrates a method for assessing risks to a pavement network at the program level using an existing PMS. Risk variables include different inflation rates and funding levels. â¢ Study 8: Program-Level RiskâBridge Network Analysis. This study illustrates a framework for evaluating funding risk at the network or program level. The report includes the definition of the scenario being tested and details the identification of the scenario inputs. â¢ Study 9: Crosswalk Between Climate Change Vulnerability and Risk Terminologies. This study provides a crosswalk that explains how resilience terminology correlates to or differs from risk management terminology. â¢ Study 10: Decision Tree for Selecting Climate Risk Management Strategies. This study pro- vides a decision tree for example assetâhazard combinations (e.g., rising temperature risk
Approach Followed for Study Selection and Testing 33Â Â to pavements) to ultimately provide guidance on how to move from risk assessment to risk management. â¢ Study 11: Institutionalizing Risk Management into Asset Management Plans. This study provides strategies to institutionalize risk management into TAMPs. The study used as an example the condition of large bridges in Minnesota to demonstrate how risks to large structures could influence each TAMP section. â¢ Study 12: Probabilistic Decision Tree for Risk Assessment. This study dem onstrates use of an off-the-shelf probabilistic decision tree for risk-based decision making using data available from DOTs. The decision tree was applied to the problem of deciding whether to use a chip seal treatment plan for a pavement during a 10-year TAMP period versus a more expensive, albeit longer-life, option of resurfacing with thin asphalt overlays. 8.8 Studies Selected for Detailed Protocols and Pilot Testing Seven studies were piloted using the protocols: â¢ Study 1: Using Deterministic Tools to Forecast Revenues at a DOT â¢ Study 2: Using Probabilistic Tools to Forecast Revenues and Costs at a DOT â¢ Study 3: Asset-Level Risk IndexâBridge Risk Utility Index â¢ Study 4: Asset-Level Risk IndexâPavement Section Flooding â¢ Study 7: Program-Level RiskâPavement Network Analysis â¢ Study 9: Crosswalk Between Climate Change Vulnerability and Risk Terminologies â¢ Study 11: Institutionalizing Risk Management into Asset Management Plans 8.9 Studies That Were Not Piloted, but for Which Detailed Protocols Were Developed Following are the studies that were not tested but for which protocols were developed: â¢ Study 5: Asset-Level Risk IndexâNon-NBIS Culverts â¢ Study 6: Asset-Level Risk IndexâLandslide Hazard Management â¢ Study 8: Program-Level RiskâBridge Network Analysis â¢ Study 10: Decision Tree for Selecting Climate Risk Management Strategies â¢ Study 12: Probabilistic Decision Tree for Risk Assessment 8.10 How the Protocols and Studies Satisfy the Project Objectives The studies were selected and protocols developed to satisfy NCHRP 08-118 research objec- tives, to help agencies overcome obstacles, and to demonstrate readily available risk management techniques that advance the state of the practice. For example, Studies 1 and 2 demonstrate the use of affordable off-the-shelf tools that could be applied to assess common asset management risks. These included Excel-based tools to fore- cast probabilistic and deterministic risks to key asset management variables. Studies 1 and 2 address the project objective to âdevelop implementation guidance, including practical tools and techniques.â Studies 3 through 8 demonstrate the use of commercial off-the-shelf BMSs and PMSs that incorporate risk utility functions to inform investment decisions at the asset and network levels.
34 Risk Assessment Techniques for Transportation Asset Management: Conduct of Research Studies 3 through 8 achieve the project objectives by â¢ Enhancing risk management techniques by using quantitative and analytical methods â¢ Demonstrating effective processes for âmulti-objective, cross-asset investment decisions under uncertaintyâ â¢ Developing implementation guidance for including practical tools and techniques, as well as possible measures of asset resilience â¢ Demonstrating both program-level and asset-level risk assessment â¢ Building tools and techniques to measure resilience â¢ Building on previous work by capitalizing on the NCHRP 20-07/Task 378 Final Report that demonstrated development of bridge utility indices Studies 9 and 10 use strategies and tactics to incorporate resilience in decisions related to high- risk assets. These studies include a decision tree for climate risk management strategies and a crosswalk between climate change vulnerability and asset management risk terminologies. These studies also help agencies advance strategies for risk mitigation and response and develop tools and techniques for measures of asset resilience. Study 11 demonstrates strategies to integrate risk into all sections of an asset management plan. It demonstrates how risk management can influence the content covered in every section of the TAMP. This study demonstrates qualitative risk techniques; integrates enterprise, network, and program risks; and builds on earlier work such as NCHRP 08-93. Study 12 applies an off-the-shelf probabilistic decision tree to analyze a typical asset manage- ment risk. The example is an assessment of the success or failure of substituting chip seal treat- ments for higher-cost resurfacing treatments.