Estimating Life Expectancies of Highway Assets, Volume 1: Guidebook (2012) / Chapter Skim
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35 In the research leading to development of this guide, various approaches were investigated for estimating life expectancy for a range of highway asset types. The potential methods were gleaned from current practice in not only highway engineering, but many fields that need to measure life expectancy.
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36 estimating Life expectancies of highway assets Method of determining life expectancy When used, implications Section Wait for extreme events Replacement when required due to damage. In some cases historical records may provide guidance on the probability of future hazards.
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Develop Foundation tools: how to Compute Life expectancy Models 37 this also usually means that it is necessary to know for sure that no work was done during the asset's life. When a model requires cross-sectional data in the form of inspection pairs, it is still necessary to know that no work was done between the two inspections in each pair.
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38 estimating Life expectancies of highway assets spanning at least 20 feet. Many agencies also collect the same data for smaller culverts, in some cases as small as 6 feet in diameter (Markow 2007)
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Develop Foundation tools: how to Compute Life expectancy Models 39 by local agencies might not follow the state DOT's procedures in this regard. Table 4-4 shows the definitions of the four condition states used for each type of culvert.
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40 estimating Life expectancies of highway assets condition for culverts is the age when there is a 50% probability of being in a condition state where replacement is normally recommended. Bridge management systems such as Pontis have built-in procedures that can estimate condition state transition times and life expectancy, using this definition, for any type of structural asset including culverts (Cambridge 2003, Thompson and Sobanjo 2010)
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Develop Foundation tools: how to Compute Life expectancy Models 41 states use the NBI or AASHTO inspection conventions, the researchers used a simpler scale consistent with the three states that contributed data: 0: Very poor or serious deterioration, warranting replacement 1: Poor condition 2: Fair; some wear but structurally sound 3: Excellent condition, like new In this scale, state 0 is assumed to be equivalent to an NBI condition rating of 3 or below, or an AASHTO CoRe Element condition state of 4. The researchers found the following variables to have a significant effect on life expectancy: • Material • Coating application • Type of inlet and outlet • Temperature • Precipitation • Freeze/thaw cycles • Soil corrosiveness For larger, box culverts, NBI data were utilized.
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42 estimating Life expectancies of highway assets Box Culverts. For the box culverts in the NBI database (see Section 4.1.8 for further details on NBI condition data)
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Develop Foundation tools: how to Compute Life expectancy Models 43 gramming decision. Therefore, the methods described in this guide focus on condition-based longevity in the absence of changes in the information or standards.
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44 estimating Life expectancies of highway assets during the day and at night. Condition monitoring of sign structures is increasingly done by bridge inspectors, often using hands-on procedures that look for fatigue cracking.
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Develop Foundation tools: how to Compute Life expectancy Models 45 Because of mobilization and traffic control costs, there are economies of scale in replacing all signage along a roadway at the same time (blanket replacement)
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46 estimating Life expectancies of highway assets repairs to damaged posts and panels. For painted sign structures, painting is often performed as a preventive maintenance activity.
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Develop Foundation tools: how to Compute Life expectancy Models 47 different states. To determine asset life, a Markov chain can be calibrated to estimate the transition probability of traffic signs progressing from a subjective rating of "good" to "fair" and ultimately "poor." Alternatives to sign sheeting retroreflectivity, such as physical deterioration of sign structure, lack of color/contrast of sign sheeting, and blistering, cracking and shrinkage of sign sheeting materials, can be duly assessed.
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48 estimating Life expectancies of highway assets 4.1.3.1 Measuring Condition and Performance Agencies typically inspect key components annually and/or when relamping (Markow 2007)
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Develop Foundation tools: how to Compute Life expectancy Models 49 Minnesota DOT noted that a life expectancy of 30 years is plausible for electronic components in the signal cabinet when a preventive maintenance program is in place. 4.1.3.5 Example Analysis The data collection aspect of this research suggests that few agencies track the deterioration of their traffic signals and flashers.
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50 estimating Life expectancies of highway assets The example analysis suggests that pre-timed or semi-actuated traffic signals that were hardwire interconnected or part of a closed loop tend to have longer service lives. On the other hand, signals located in warmer climates, areas with higher wind speeds, located on city streets, supported by a mast arm, or with fiber-optic cables tended to have shorter service lives.
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Develop Foundation tools: how to Compute Life expectancy Models 51 703 – Lighting 1. Lighting standards and supports are properly anchored.
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52 estimating Life expectancies of highway assets 4.1.4.2 End-of-Life Criteria For electrical components and luminaires, an appropriate end-of-life condition would be a condition state so deteriorated that no economical repair option is available or, similar to Washington State's treatment of traffic signals, an excessive repair or relamping frequency. This is separate from concerns about technological obsolescence, which would not be analyzed in the same way as deterioration.
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Develop Foundation tools: how to Compute Life expectancy Models 53 y gg1 1 0= − × ( )
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54 estimating Life expectancies of highway assets and debris. Most agencies assess retroreflectivity at least annually and at least visually, but, in some cases, use automated equipment.
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Develop Foundation tools: how to Compute Life expectancy Models 55 can perform a lifecycle cost analysis, as in Chapter 5, to determine optimal cleaning intervals to maximize the life expectancy of pavement markings. 4.1.5.4 Published Life Expectancy Values Data on the life expectancy of pavement markings was gathered in Markow (2007)
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56 estimating Life expectancies of highway assets 4.1.6 Curbs, Gutters, and Sidewalks Curb and sidewalk replacement is often driven by functional stimulus such as changes in requirements, changes in land use, urban betterment projects, or related roadway projects such as widening. Condition-related replacement can occur when movement or deterioration cause the asset to exceed a level-of-service standard for accessibility, driven by concern for lawsuits or compliance with the Americans with Disabilities Act (ADA)
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Develop Foundation tools: how to Compute Life expectancy Models 57 4.1.6.4 Published Life Expectancy Values Data on the life expectancy of curbs and sidewalks were gathered in Markow (2007) from a survey of transportation agencies.
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58 estimating Life expectancies of highway assets In pavement management systems, it is common to combine various distresses into a composite pavement condition rating (PCR) (sometimes called Pavement Quality Index or a statespecific name)
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Develop Foundation tools: how to Compute Life expectancy Models 59 tion directly. For the more common deterministic models, it is important to have a measure of regression error in the vicinity of the point where the MTC is reached (Figure 4-9, right side)
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60 estimating Life expectancies of highway assets Published values of age at first overlay for asphalt concrete pavements range from 11 to 20 years; and for reinforced concrete pavements, from 20 to 34 years. The full-depth pavement life for both types of pavements is typically quoted at about 50 years; however, there is little published evidence behind these numbers.
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Develop Foundation tools: how to Compute Life expectancy Models 61 The functional AC overlay average life can be estimated in years. For instance, using the average values in the model, the following result was obtained: t IRI Avg PRE SL Threshold IRI = ( )
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62 estimating Life expectancies of highway assets 4.1.8 Bridges Bridges consist of a collection of separate components, each with its own life expectancy. Based on site characteristics, design considerations, and market conditions, bridge designers attempt to minimize the cost of providing a given crossing for a period of 50 to 100 years.
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Develop Foundation tools: how to Compute Life expectancy Models 63 mandated, including non-bridge structures and bridges or culverts of less than 20 feet in span. Forty of the states use AASHTO's Pontis Bridge Management System to manage and use NBI and CoRe Element data (Thompson 2006)
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64 estimating Life expectancies of highway assets program, it is conceivable that asset life could be extended far beyond its design life, until fatigue, functional requirements, or natural or man-made hazards finally bring its life to an end. 4.1.8.3 Life Extension Interventions Bridge life extension activities can occur at any point in a structure's life.
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Develop Foundation tools: how to Compute Life expectancy Models 65 13 - Concrete Deck - Unprotected w/ AC Overlay 107 - Painted Steel Open Girder/Beam 1. The surfacing on the deck has no patched areas and there are no potholes in the surfacing.
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66 estimating Life expectancies of highway assets a bridge, its deck may be entirely replaced two or more times. It is often possible to replace the entire superstructure.
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Develop Foundation tools: how to Compute Life expectancy Models 67 Element ty pe Life (y rs) Element ty pe Life (y rs)
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68 estimating Life expectancies of highway assets • Retaining walls • Sound barriers • Guiderails and impact attenuators • Rest area facilities • Tunnels • Weigh stations • Maintenance facilities • Highway agency vehicles and equipment 4.1.10 Summary Estimates From the literature, wide ranges in asset life were found, with estimates varying by material/ design type, end-of-life threshold applied, climatic conditions, and levels of applied maintenance. Typical values by asset class were found to be overall bridge life equal to 50–60 years, bridge deck life equal to 25–45 years, culvert life equal to 30–50 years, traffic sign life equal to 10–20 years, pavement markings life equal to 1–5 years, traffic signal life equal to 15–20 years, and roadway lighting life equal to 25–30 years.
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Develop Foundation tools: how to Compute Life expectancy Models 69 Moreover, if the goal is to quantify asset longevity in the absence of extenuating circumstances, then it is often more useful to work with condition data directly and quantify the length of the deterioration curve, regardless of whether or not the asset was replaced exactly at the end of the curve. Historical condition data are often easier to find, especially for assets that are still in service and have not yet been replaced.
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70 estimating Life expectancies of highway assets The simplest possible model would be a model which does not have any explanatory variables (Table 4-24)
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Develop Foundation tools: how to Compute Life expectancy Models 71 variation in replacement age is shaped like the normal distribution. Figure 4-14 shows the graph for District D1.
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72 estimating Life expectancies of highway assets Table 4-25 uses the same culverts as in Table 4-24. The only difference in the data set is that barrel length (in feet)
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Develop Foundation tools: how to Compute Life expectancy Models 73 In order to use Microsoft Excel's linear regression capability, it is necessary to make sure it is installed. On the Data ribbon in Microsoft Excel 2007, check for "Data Analysis" in the "Analysis" section on the right side of the Data ribbon (Figure 4-15)
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74 estimating Life expectancies of highway assets Figure 4-16. Manage Office add-ins.
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Develop Foundation tools: how to Compute Life expectancy Models 75 Based on the results reported in this example, the predicted life expectancy of a culvert is computed from the following equation: á = + × + × − ×49 02 5 22 1 0 85 2 09.
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76 estimating Life expectancies of highway assets For the purposes of programming, the method of simple averaging in the preceding example is still the most straightforward way of determining the needed level of investment in each district within any given time frame. The addition of the length variable improves the quality of forecasts for individual culverts, but it does not change the amount of variability within each district, assuming each district has about the same variability of culvert barrel lengths.
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Develop Foundation tools: how to Compute Life expectancy Models 77 Also, certain assets are so long-lived that it may be impossible to exclude enough of them. For example, the typical life span of a bridge currently in service may be 50 years, and the analyst might judge that 70 years gives enough of a safety margin to include 95% of all bridge lifespans.
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78 estimating Life expectancies of highway assets When the condition of an asset is determined, the entire asset might be classified in one of the condition states. Alternatively, the quantity of the asset (e.g., feet of culvert)
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Develop Foundation tools: how to Compute Life expectancy Models 79 The methods for developing Markov deterioration models are described in Chapter 5. But even without going through the process of deterioration modeling, there is a simpler, quickand-easy way of estimating life expectancy using the ideas behind the Markov model.
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80 estimating Life expectancies of highway assets improvement in condition (i.e., where the percent not-failed increased from before to after)
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Develop Foundation tools: how to Compute Life expectancy Models 81 4.2.3 Weibull Survival Probability Model The Markov model described in the preceding section is simple, but for certain applications it may be too simple. The memoryless assumption is often viewed as a weakness because it implies that the rate of deterioration does not increase with age.
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82 estimating Life expectancies of highway assets vary with age. Higher shaping parameters slow the initial rate of deterioration, which then accelerates as the facility gets older.
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Develop Foundation tools: how to Compute Life expectancy Models 83 List of biennial traffic sign inspections Year Age Actual Predict Markov Square of Square of Road of of fraction fraction fraction deviation deviation Log segment insp signs passing passing passing act-pred act-mean likelihood Segment Year Age PassPredicted Markovq DevPred DevMean LogLike Coeff Value RS00001 1994 0 1.00 1.000 1.000 0.0000 0.0976 1.584 Median years 9.88 RS00001 1996 2 1.00 0.966 0.869 0.0012 0.0976 1.496 Shaping param 1.87 RS00001 1998 4 0.99 0.880 0.755 0.0121 0.0914 0.682 Std deviation 0.0819 RS00001 2000 6 0.95 0.761 0.657 0.0356 0.0688 -1.071 Sum LogLike 49.852 RS00001 2002 8 0.89 0.627 0.571 0.0692 0.0410 -3.577 RS00001 2004 10 0.62 0.492 0.496 0.0163 0.0046 0.369 Scaling param 12.025 RS00001 2006 12 0.43 0.369 0.431 0.0037 0.0664 1.309 Markov scaling 14.259 RS00001 2008 14 0.31 0.265 0.375 0.0020 0.1426 1.431 RS00001 2010 16 0.19 0.182 0.326 0.0001 0.2476 1.579 Mean passing 0.6876 RS00002 1998 0 1.00 1.000 1.000 0.0000 0.0976 1.584 SSE 0.3083 RS00002 2000 2 0.96 0.966 0.869 0.0000 0.0742 1.581 SST 3.2848 RS00002 2002 4 0.88 0.880 0.755 0.0000 0.0370 1.584 R-squared 0.9061 RS00002 2004 6 0.73 0.761 0.657 0.0010 0.0018 1.510 RS00002 2006 8 0.64 0.627 0.571 0.0002 0.0023 1.571 RS00002 2008 10 0.51 0.492 0.496 0.0003 0.0315 1.561 RS00002 2010 12 0.42 0.369 0.431 0.0026 0.0716 1.392 RS00003 1996 0 1.00 1.000 1.000 0.0000 0.0976 1.584 RS00003 1998 2 0.97 0.966 0.869 0.0000 0.0797 1.582 RS00003 2000 4 0.91 0.880 0.755 0.0009 0.0495 1.517 RS00003 2002 6 0.71 0.761 0.657 0.0026 0.0005 1.387 RS00003 2004 8 0.58 0.627 0.571 0.0022 0.0116 1.419 RS00003 2006 10 0.41 0.492 0.496 0.0068 0.0771 1.077 RS00003 2008 12 0.34 0.369 0.431 0.0009 0.1208 1.520 RS00003 2010 14 0.21 0.265 0.375 0.0030 0.2281 1.360 RS00004 1998 0 1.00 1.000 1.000 0.0000 0.0976 1.584 RS00004 2000 2 0.95 0.966 0.869 0.0002 0.0688 1.565 RS00004 2002 4 0.87 0.880 0.755 0.0001 0.0333 1.576 RS00004 2004 6 0.73 0.761 0.657 0.0010 0.0018 1.510 RS00004 2006 8 0.54 0.627 0.571 0.0076 0.0218 1.019 RS00004 2008 10 0.44 0.492 0.496 0.0027 0.0613 1.379 RS00004 2010 12 0.31 0.369 0.431 0.0035 0.1426 1.322 RS00005 1996 0 1.00 1.000 1.000 0.0000 0.0976 1.584 RS00005 1998 2 1.00 0.966 0.869 0.0012 0.0976 1.496 RS00005 2000 4 0.91 0.880 0.755 0.0009 0.0495 1.517 RS00005 2002 6 0.83 0.761 0.657 0.0047 0.0203 1.232 RS00005 2004 8 0.71 0.627 0.571 0.0069 0.0005 1.070 RS00005 2006 10 0.51 0.492 0.496 0.0003 0.0315 1.561 RS00005 2008 12 0.46 0.369 0.431 0.0082 0.0518 0.970 RS00005 2010 14 0.33 0.265 0.375 0.0043 0.1279 1.266 RS00006 1998 0 1.00 1.000 1.000 0.0000 0.0976 1.584 RS00006 2000 2 0.95 0.966 0.869 0.0002 0.0688 1.565 RS00006 2002 4 0.79 0.880 0.755 0.0081 0.0105 0.979 RS00006 2004 6 0.61 0.761 0.657 0.0229 0.0060 -0.125 RS00006 2006 8 0.43 0.627 0.571 0.0388 0.0664 -1.311 RS00006 2008 10 0.32 0.492 0.496 0.0297 0.1351 -0.634 RS00006 2010 12 0.29 0.369 0.431 0.0063 0.1581 1.115 Table 4-30. Weibull survival probability model for signs.
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84 estimating Life expectancies of highway assets The value of T can be determined using the Markov model described in the previous example. For this example, however, it is determined using maximum likelihood estimation at the same time as the shaping parameter.
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Develop Foundation tools: how to Compute Life expectancy Models 85 It can be seen in the graph that the Weibull survival probability model is a better fit to the data than the Markov model. Under the Markov model, the R-squared value is only 0.8081, and under the survival probability model, it is 0.9061.
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86 estimating Life expectancies of highway assets For continuous explanatory variables, another approach is to use a linear multivariate model for the scaling parameter, as was done in several of the examples presented earlier in this chapter. The same maximum likelihood estimation technique then can be used for estimation of this model.
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Develop Foundation tools: how to Compute Life expectancy Models 87 • Lack of movement. If none of the inspection pairs show any deterioration, then the models would not work.
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88 estimating Life expectancies of highway assets A good way to determine whether a life expectancy model will work in practice is to start with a quick-and-easy version of the model, and then build it into a prototype of the envisioned application. Microsoft Excel is a good way to do this because development and refinement in Microsoft Excel can be done very quickly.
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