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Comprehensive Truck Size and Weight Limits Study: Linkage between the Revised Desk Scans and Project Plans Report

Chapter 6: Bridge Comparative Analysis

6.1 Purpose

The purpose of this section is to document how the information and understanding gained through the Desk Scan informed and affected the approach and methodology undertaken in the Bridge Comparative Analysis. The technical methodology was initially provided in the Project Plan and subsequently revised throughout the course of the project as details about available data emerged, as analytical approaches were refined, This involves both  those documents summarized in the revised desk scan that inform with respect to accepted technical approaches and those documents that contain quantitative results pertaining directly to bridge analysis relative to the effects of similar truck configurations and loadings. The bridge task has devolved into four study areas: the first three reflecting the three AASHTO limit states for: Strength, Fatigue and Serviceability; and a fourth sub-study area of the potential effects of the six 'scenario' trucks being studied on bridges. Accordingly, the linkages between the desk scan and the methodology undertaken in the 2014 CTSW Study are considered in that order herein.

6.2 Bridge Analysis Linkages

6.2.1 Structural Impacts Due to Overweight Trucks

6.2.1.1 Strength Limit State

The objective of the Strength Limit Study (Structural Load Rating)is the determination and assessment of the implications of the structural demands on US bridges due to the introduction of the proposed alternative truck configurations with Gross Vehicle Weight (GVW) > 80,000 lb. vs. those due to the control vehicles for the current truck fleet (GVW = 80,000 lb.). This task includes an assessment of one-time bridge costs that may be incurred as a result of not meeting the strength limit state as indicated by the analysis.

In order to achieve the above objective, the first step was to investigate the strength limit state in the bridges of the subject highway networks.

Previous studies were based on the Standard AASHTO Specifications. Based on the result of the desk scan, it was concluded that AASHTO's Load Resistance Factor Rating (LRFR) method (AASHTO 2011, 2013) would be utilized in the 2014 CTSW Study to conform to the latest design/analysis methodology. This represents a significant improvement over past studies in that it provides for a reliability based comparison of the 'scenario' vehicles vs. the control vehicle as compared to a simple structural capacity analysis. 

Previous studies employed WINBasic (2000 CTSW Study) to assess the impacts of large numbers of bridges grouped as simplified bridge models, or analyzed relatively small samples of bridges directly. For this study, it was determined to use the AASHTOWare Bridge Rating (ABrR, (VIRTIS)) analysis program because it handles most bridge types and allows for the analysis of a relatively large sample of real bridges within the constraints of time and budget associated with a study such as this. ABrR was determined to be a ready tool for the analysis of the load rating capacity of 500 representative bridges, selected to conform statistically to the proportion of bridges by bridge type on the NHS. The eleven most common bridge types were included, representing 96% of all bridges on the roadway networks used in developing the Study scenarios. As stated in the 2014 CTSW Study Bridge Project Plan, verified ABrR LRFR bridge models were used for the structural analysis. The majority of the verified bridge models were obtained directly from state DOTs. The remainder were obtained through the help of the Primary Investigator (PI), Mark Mylanarski of NCHRP Project 12-78 (NCHRP Report 700, A Comparison of AASHTO Bridge Load Rating Methods, 2011). Access to some 1500 verified ABrR models that were being used for that study were made available for possible inclusion in this study, with certain restrictions on the publishing of their state and route number. In all, bridge models from 11 states representing different regions in the continental US were included in the study. Bridge models from the following states were included in the study: Alabama, Illinois, Idaho, Louisiana, Michigan, Missouri, New Jersey, New Mexico, New York, Utah, Virginia and South Dakota.

The results of the analysis were recorded for maximum moment and shear and the Rating Factors (RF) for the alternative truck configurations were compared to (normalized relative to) the 80,000 lb. GVW control vehicles. The LFR method was used for girder-floorbeam systems and through trusses since the ABrR software did not currently support the LRFR methodology for these two bridge types.

One-time bridge replacement costs for all scenario vehicles were developed for both highway networks IS and other NHS), regardless of whether some vehicles (triple trailers for instance) may be excluded from certain routes for safety or other reasons. The cost estimates are based on total project costs and not just the direct construction costs. The methodology used is as follows:

  1. Determine the distribution of span lengths in the sample database as percentages separately for IS bridges and other bridges on the NHS. Bridge lengths were taken as the upper limit in the interval (e.g., for 20-40 ft. spans, use 40 ft.). Deck width was taken as 64 ft. (four 12' lanes and two 8' shoulders) for IS bridges and as 48 ft. (three 12' lanes and two 6' shoulders) for other bridges on the NHS.
  2. Calculate the cost of bridge replacement for each span length interval as:

    Cost = Bridge Length x Deck Width x Unit Price for Replacement per ft2

    The Unit Price for replacement was obtained from using a national average that included incidental costs such as mobilization, work zone traffic control and construction inspection. What it did not include were the so called social costs such as construction traffic delays. There are no published average unit costs that can be applied with respect to those social costs.

  3. Determine the percentage of bridges rated less than 1.0 in the structural analysis for each alternative truck configuration (scenario), for each span interval
  4. Determine the actual total number of IS Bridges and Other NHS Bridges in the NBI inventory.
  5. Estimate the number of actual number of bridges in each span interval, using the distributions observed for the sample database.
  6. Determine the projected number of bridges with RF < 1.0 for each scenario, by multiplying the percentage of bridges rated less than 1.0, calculated in STEP 3, by the number of bridges in each span interval, calculated in STEP 5.
  7. Determine the cost of bridge rehabilitations for each span interval for each truck type, separately for IS bridges and other bridges on the NHS, by multiplying the cost calculated for a single bridge for that span interval by the projected number of bridges with RF < 1.0 for each truck scenario.
  8. Add costs from each span interval to determine the total costs for each scenario.
  9. Calculate ?cost for each scenario. ?cost is the difference in the cost of rehabilitations due to an alternative truck configuration and that from the related control vehicle.

6.2.1.2 Fatigue Limit State

Two primary areas of concern with respect to fatigue are: direct, load induced fatigue; and, distortion induced fatigue, often due to out of plane bending. Distortion induced fatigue requires a very rigorous analysis of each specific fatigue detail. During the 2014 CTSW Study scoping/project plan development phase, it was jointly determined with the FHWA that, given the nature of this study, distortion induced fatigue study would not be included.

Therefore, while distortion induced fatigue was included in the desk scan, only direct 'load induced fatigue' was undertaken:

AASHTO published the first fatigue design provisions in 1965. They were completely revised in the 1977 AASHTO Highway Bridge Design Standard Specification, 12th Edition, based on the research results of Dr. John Fisher of Lehigh University and his colleagues. Many specification changes associated with specific details were incorporated annually by AASHTO to improve design as well as fabrication and field performance.  In 1994 the introduction of the AASHTO LRFD Bridge Design Specification incorporated a reliability-based approach with significant changes to the load models for fatigue design.

Load induced fatigue in steel bridges was extensively studied in throughout this period, as reflected in the desk scan. Bridge connection details are grouped into categories A to E' based on their level of fatigue strength/resistance. According to the results of the desk scan, it can be concluded that actual truck traffic closely correlates the effects of the fatigue design truck and that heavy traffic will not cause severe fatigue problems on steel girders with fatigue details of categories A, B or C. Therefore, analysis focused on the categories E and E' (E-prime) will be more meaningful. 

Based on desk scan and the purpose of this study, a study methodology was established as follows:

First, four (4) typical existing steel bridges with fatigue categories E and E' (E-prime) were selected for comparative analysis.  Two (2) of them are simply supported steel girder bridges and the other two (2) are continuous steel girder bridges. All of these four (4) chosen bridges have finite fatigue life cycles per the analysis.

Second, a baseline for comparison was established for the for the two 80,000 lb. Control Vehicle Truck based on the result of desk scan that fatigue life is inversely proportional to the cube of the effective stress range.

Third, results of the other alternatives can be compared with the baseline, as follows:

  1. The desk scan confirmed that fatigue life is inversely proportional to the cube of the effective stress range. Depending on the CAFT limit of AASHTO fatigue prone details, differences in the axle weight and spacing of the vehicle classes and weight groups may result in significantly different incremental fatigue damage to the bridge inventory. The bridge team was tasked to investigate the potential effects on the nation's bridges in the event of the introduction of each of the six proposed 'scenario vehicles'. Inherent in the assessment is the reality that the national adoption or acceptance of any of the proposed alternative truck configurations would likely only constitute a modest increase (relative to the sheer size of the present truck fleet and truck traffic stream) in total loading cycles for any given bridge. This does not negate the possibility of a significantly larger contribution of incremental fatigue damage that could be attributed to that alternative truck configuration for its loading cycles. It does however put the question in perspective. So, as a result of the desk scan it became clear that any significant difference in the fatigue affects attributable to a particular alternative truck configuration must be considered in light of the relative percent of loading cycles assumed to be attributable to that scenario. Accordingly, it was determined to conduct the fatigue assessment as a comparison of the incremental stress ranges resulting at a given fatigue detail location from a single pass of the 'scenario vehicles' vs. the equivalent results from the control vehicle.
  2. To illustrate the fatigue damage potential of each of the proposed 'scenarios vehicles', four (4) typical existing steel bridges were selected for comparative analysis.  Two (2) of them are simply supported steel girder bridges and the other two (2) are continuous steel girder bridges. Steel girders of these bridges are comprised of either rolled shape beams with partial length cover plates or plate girders with horizontal lateral bracings welded to the bottom flanges of the girders.  The analysis was performed in accordance with the AASHTO Manual of Bridge Evaluation (2nd Edition) with 2014 interim revisions and the AASHTO LRFD Bridge Design Specifications (6th Edition). All of these four (4) chosen bridges have finite fatigue life cycles per the analysis.
  3. Utilizing the concept that fatigue life is inversely proportional to the cube of the effective stress range and the assumption that the stress cycles for each truck configuration in the new fleet of trucks is constant, a baseline was established for the two 80,000 lb. Control Vehicle truck configurations and results of the other scenario vehicles were compared with the baseline.

6.2.1.3 Serviceability Limit State

In the 2014 CTSW Study Project Plan, the bridge team had proposed to develop a method to conduct a bridge damage cost responsibility allocation to: 1) assign bridge damage attributable to the Modal Shift Fleets resulting from the potential introduction of each of the Scenario Vehicles onto the national highway networks; and to 2) determine the percentage of vehicles in the existing fleet that are operating excess of the current 80,000 lb. limit. Based on the early desk scan and confirmed by further research and by the FHWA SMEs, it was agreed that there is no generally accepted methodology to accomplish this task. The difficulty is multiplied on a study of a national scale (U.S.).

The goal of the cost responsibility assignment process is to assign bridge cost by truck class, including those of the scenario vehicles. Lacking such a consensus, it was undertaken to develop an axle load based approach, reflecting the generally accepted power formula relationship between axle loads and the resulting bridge damage costs. In a number of states as well as in some other countries, axle load based allocations have been used for bridge costs. These agencies have used various and diverse allocators and exponents to develop expressions of incremental damage resulting from axle loads. As commonly reported in prior studies, 59% to 70% of all bridge capital costs are considered to be non-load induced, or in other words, attributable to environmental factors, aging, and light weight vehicle use. The 2000 FHWA funded "Guidelines for Conducting a State Highway [and bridge] Cost Allocation Study" included examples with as much as 79% assumed to be non-load induced. From the E.U. CATRIN 2008 Deliverable D1, page 30: "Weight dependent [load induced] costs make up between 33% and 46% of all costs." This would equate to 54% to 67% non-load induced damage. Recognition of this factor is fundamental to determining the range of the bridge damage costs that can be expected to result from changes in the truck fleet.

The axle based method investigated was based on a method developed in the Washington D.C DDOT 'District-wide Truck Safety Enforcement Plan', 2010. That approach involves steps to the assignment of cost responsibility to specific truck configurations for the remaining ±41% of Bridge Costs (the load-induced damage costs). However, while the desk scan produced accounts of several state and foreign government agencies applying assumed exponents to allocate bridge damage related costs; the bridge task team did not find a version of the power formula relationship that had passed scientific and engineering scrutiny and was generally accepted and in use.

Given that this specific approach lacked a history of scientific review, was highly dependent on assumptions related to bridge costs, and did not meet the threshold standard of a generally accepted methodology; it was determined that it should not be included in the final report.

6.2.2 Bridge Deck Deterioration, Service Life Preventative Maintenance

The 2014 CTSW Study Bridge Project Plan proposed investigating bridge deck issues in two sub-study areas - Section 1.4.4 Bridge Deck Repair and Section 1.4.5 Replacement Costs and Bridge Deck Preservation and Maintenance Costs.

6.2.2.1 General Technical Approach

The Desk Scan provides a wide array of diverse literature for the Bridge Deck sub-task, however much of the unit cost data was scattered among various DOT web sites. Furthermore, the data format and access was not conducive for inclusion in the desk scan itself. Therefore, as the material was compiled, reviewed and re-worked, it was decided that the two sub-studies Bridge Deck Repairs and Bridge Deck Replacement Costs would be combined into the single undertaking.

The goal of the study was to cover the following topics:

  1. Bridge deck behavior under axle loads and environmental stressors: The desk scan provided literature ranging from design guidelines (e.g., AASHTO manuals) to concrete deck research in the US and Japan.
  2. Qualitative assessment of the effect or the control and scenario vehicle on bridge decks: The desk scan provided very little information about the specific effect of the control and scenario vehicles on bridge decks. However, research studies regarding generic axle loads on bridge decks were investigated and referenced in the study. In addition a few agency officials provided some published data (Indiana DOT) and anecdotal information (NY Bridge Authority).
  3. Preservation and preventative maintenance procedures and practices: The desk scan provided some literature from various DOT agencies on their specific preservation/maintenance policies and procedures. While the general principles were common, specific practices varied widely and were highly dependent on specific economic and environmental factors (truck traffic volume, type of cargo, weather, salt usage, etc.).

6.2.2.2 Available Data and Models

The study was also charged with finding and extracting unit cost data (to be used in the sub-task itself as well as supporting other Bridge sub tasks), financial and deterioration models.

  1. Federal Financial Management Information System (FMIS): FMIS data was obtained from the FHWA for use with regard to bridge cost data. However, the data provided was not in sufficient detail to benefit this sub-task.
  2. Unit Cost Data: There is a wide array of data formats (databases, published bids, and estimating unit costs) provided on the various DOT web sites. When, it was clear what elements were included in those costs, the data was extracted. In all, data from 22 states across the continental US was utilized (from various climatic zones).
  3. It was difficult to find specific "data points" as to what state DOTs are doing to maintain and preserve their bridge decks and ultimately prolong their bridge service life. What the bridge team found was that state DOTs are more or less evolving their policies as technologies, tools and available information (data) is made available. As such, some agencies are moving to integrate their asset management practices and are using internet technologies to update their databases. As a result, much of the data was inaccessible to the general public and required identity authentication for access and retrieval. However, some state DOT agencies provide a portal with links to data warehouses. The process had to be modified to properly access these data warehouses to glean information. Some web sites provided manuals for their highway maintenance practices which also included bridge deck maintenance. For other agencies we found research papers on bridge decks that generally described how a state DOT approaches maintenance and what threshold they use to decide whether to replace a deck or rehabilitate it.  Still further, the bridge team contacted existing client contacts (including from Indiana, New York, Tennessee and South Carolina) to understand their agency maintenance practices and policies, with regards to bridge decks. 
  4. Another issue that arose in finding usable data was that the reporting format, quality, and content varied widely. This was particularly apparent with respect to unit cost data, which in turn required some analysis and interpretation to allow for comparison of the data from state to state. A specific example of unit cost disparity: some sources just reported raw construction costs, while other sources' reported costs were inclusive of all programmatic costs, i.e. would include mobilization, work zone traffic control, construction inspection, etc.
  5. Deterioration Models:
    1. Bridge Deck (Salt induced) Corrosion: The desk scan provided two specific studies that had developed predictive models with respect to salt induced corrosion (Virginia DOT and Michigan DOT). These studies provided a mechanism and timeline for deck deterioration in cold and wet climatic zones where salts are used as de-icing agents. It was desired to investigate the combined effects of truck axle loads in combination with de-icing salts over the service life of bridge decks. However, very little was found in regard to long term studies that investigated both bridge deck stressors together.
    2. Inspection Based Deterioration Models: The desk scan provided a study from the Nebraska Department of Roads (NDOR) which utilized a statistical approach to inspection rating data and correlated it to a timeline showing a non-linear deterioration of bridges (and bridge decks) in  cold wet climatic states where there is heavy truck traffic.
  1. Search for Models:
  2. The AASHTO Standard Specifications for Bridge Design and Construction establish the current philosophy for bridge deck design and for understanding how bridge decks behave under load. The bridge team searched for models and data that would inform as to deterioration mechanisms including: axle loads, (static vs dynamic), repetitive and sustained loading effects on concrete decks and climatic effects.

No direct analysis of impacts to decks was undertaken in this sub-study area. Rather, it constitutes a synopsis of what could be gleaned from the desk scan. In summary however, the bridge team found an overall industry trend of migrating to comprehensive data bases, data gathering, and bridge asset management technologies. By necessity there is a restriction to general access to this information. In the long run, the data quality is expected to improve but we are not there yet. The data driven approach may also provide a metric that can measure bridge deck damage and a link to damage due to specific axle or wheel loads or load groups. The research indicates that as a whole, owner agencies are realizing the benefits of establishing a national bridge database which can be built by all stakeholders with a unified data format and standards for reporting. However the effort is in its mid-term phase and maybe years away from providing meaningful data. Also, more research is needed in establishing the effects of axle loads applied dynamically on bridge decks in different configurations combined with the effects of chloride (or other chemical) contaminations.

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