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Suggested Citation:"Chapter 1 - Background." National Academies of Sciences, Engineering, and Medicine. 2011. Protocols for Collecting and Using Traffic Data in Bridge Design. Washington, DC: The National Academies Press. doi: 10.17226/14521.
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Suggested Citation:"Chapter 1 - Background." National Academies of Sciences, Engineering, and Medicine. 2011. Protocols for Collecting and Using Traffic Data in Bridge Design. Washington, DC: The National Academies Press. doi: 10.17226/14521.
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Suggested Citation:"Chapter 1 - Background." National Academies of Sciences, Engineering, and Medicine. 2011. Protocols for Collecting and Using Traffic Data in Bridge Design. Washington, DC: The National Academies Press. doi: 10.17226/14521.
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5Problem Statement and Research Objective A new vehicular live-load model was developed for the AASHTO LRFD Bridge Design Specifications (AASHTO 1994) because the HS20 truck from the Standard Specifications for Highway Bridges did not accurately represent service-level truck traffic. The HL93, a combination of the HS20 truck and lane loads, was developed using 1975 truck data from the Ontario Ministry of Transportation to project a 75-year live-load occurrence. Because truck traffic volume and weight have increased and truck configurations have become more complex, the 1975 Ontario data do not represent present U.S. traffic loadings. Other design live loads were based on past practice and did not consider actual or projected truck traffic and may not be consistent with the LRFD philosophy. The present HL93 load model and, in fact, the calibration of the AASHTO LRFD specifications, is based on the top 20% of trucks in an Ontario truck weight database assembled in 1975 from a single site over only a 2-week period. It reflects truck configurations and weights taken in the mid-1970s, which primarily consisted of five-axle semi trailer trucks. In the past 30 years, truck traffic has seen significant increases in volume and weight. Updating bridge live-load models needs representative sam- ples of unbiased truck weight data that meet accepted quality standards. One method that has been developed over the last three decades to capture truck loads in an undetected manner and obtain a true, unbiased representation of actual highway loads is known as weigh-in-motion, or WIM, technology. Although the quality and quantity of traffic data has improved in recent years, it has not been used to update the bridge design loads. In addition to information on truck weights and configurations, the design live load is highly influenced by the simultaneous presence of multiple trucks on the bridge. Such information, usually assembled from headway data, traditionally has not been collected in a manner suitable for the development of design live loads. The goal of this project is to develop a set of protocols and methodologies for using available truck traffic data collected at different U.S. sites and recommend a step-by-step pro- cedure that can be followed to obtain live-load models for bridge design. The models will be applicable for the design of bridge members, for both ultimate capacity and cyclic fatigue. The models will be applicable for both main structural mem- bers as well as the design of bridge decks. Scope of Study The original Ontario weight data from one static scale con- tained 10,000 truck events (2-week sample), which is a relatively small sample. More important for highway truck weight forecasting than the small sample size are the considerable site-to-site, seasonal, and other time variations in the truck weight description. These variations are not modeled in a single realization of data from one site. Heavy trucks may have avoided the static weigh station, and the degree to which this avoidance occurred in the recorded sample is also unknown. The Ontario site was assumed to have a high average daily truck traffic (ADTT) of 5,000. With data from only one site, the influence of volume on traffic loading is also unknown. In the LRFD development, it was seen that for two-lane bridges, loading events consisting of two side-by-side trucks govern the maximum load effect. It was calculated that the maximum load effect is equivalent to the effect of a side-by- side occurrence where each truck is about 85% of the mean maximum 75-year truck. A truck having 85% of the weight of the 75-year truck also closely corresponds to the maximum 2-month truck. The calibration of the HL93 load model used the following assumptions for side-by-side vehicle crossings (Nowak 1999): • The total ADTT is assumed to be 5,000 trucks/day. • One out of every five trucks is a heavy truck. • One out of every 15 heavy truck crossings occurs with two trucks side by side. C H A P T E R 1 Background

• Of these multiple truck events on the span, one out of 30 occurrences has completely correlated weights. • Using the product of 1/15 and 1/30 means that approximately 1/450 crossings of a heavy truck occurs with two identical heavy vehicles alongside each other. • No field data on multiple presence probabilities and truck weight correlation were provided in the LRFD calibration report. Available literature and published reports show that there is little field data to support these assumptions. • No data on site-to-site variations were provided. • There was no determination of the extent to which over- loaded trucks may have bypassed the static weighing operations. This project will aim to overcome the above-stated limitations in the previous load modeling study and determine the data required for establishing a representative live-load model. The first condition that any set of traffic data should meet before being used for the development of load models is the elimination of bias. Truck data surveys collected at truck weigh stations and publicized locations are not accurate because, normally, they are avoided by illegal overweight vehicles that could control the maximum loads applied on bridge structures. Furthermore, an important parameter that controls the load imposed on the structure is related to the number of simul- taneous vehicles on the bridge, which is determined through data on truck headways under operating conditions. Accurate headway information cannot be obtained from fixed weigh- stations or from truck data collected at highway bypasses. For these reasons it is determined that truck traffic data should be collected through WIM systems that simultaneously can collect headway information as well as truck weights, axle weights, and axle configurations while remaining hidden from view and unnoticed by truck drivers. Simultaneous data on headways and weights is necessary to determine possible correlations between truck positions or the lanes they occupy and their weights or other characteristics such as truck type, size, and numbers of axles. The 1/15 multiple presence assumption was made because of the lack of sufficient real data at the time of the LRFD calibration. Fortunately, the data needed for multiple presence estimates is presently available and already contained in the raw data files captured by many WIM data loggers. The quality and quantity of WIM data have greatly improved in recent years. Due to the development of various WIM technologies, unbiased truckloads are now being collected at normal highway speeds, in large quantity, and without the truck driver’s knowledge. The more advanced load modeling processes will require a more complete set of input data as discussed herein. The maximum lifetime loading requires as an input the percentage of trucks that cross the bridge side by side and the lane-by-lane distribution of truck weights. Assuming that the trucks in each lane have identical distri- bution, as in past simplified approaches, can introduce un- necessary conservatism. Using WIM data could easily improve past estimates or assumptions of various load uncertainties. Some of these uncertainties are now elaborated as follows: 1. Knowing the truck weight distribution in each lane, includ- ing mean, coefficient of variation (COV), and distribution type can improve the input parameters needed for the load modeling process. 2. Estimation of expected maximum loading may require different distributions such as an extremal probability dis- tribution derived from the WIM truckload histograms, rather than the normal distribution. 3. Site-to-site variability of truckloads should be incorporated. LRFD used data from only one site in Ontario. 4. Using unbiased data is very important for the estimation of the maximum load. The data used for the LRFD calibration were obtained using a static scale operated by the Canadian province, and some trucks with excessive overloads may have deliberately bypassed the scales. The data must also not be biased by the presence of weight enforcement activity in the vicinity of the data collection site. 5. With additional WIM data, improved estimates of the tail of the probability distribution of the maximum lifetime effect can be made using extremal distributions and other advanced reliability tools. Determining the probability distribution of the maximum effect is needed for the calibra- tion of the live-load factors. The WIM data must also be separated out—WIM measurement scatter from the actual truck weight scatter. 6. Developing and calibrating bridge live-load models requires large amounts of quality WIM data. High-speed WIM is prone to various errors, which need to be recognized and scrubbed/filtered out in the data review process. 7. A major advance in recent WIM operations is their ability to collect improved headway data for trucks. Clearly the headway assumptions used during the LRFD calibration were not based on actual measurements of multiple presence. Field measurements of truck arrival data to a 0.01-second resolution performed in this project and in NCHRP 12-63 consistently showed much lower side-by-side cases than those assumed in the LRFD. These new multiple presence values can be easily incorporated in a simulation model or a simplified model for estimating the maximum lifetime loading. 8. The data must adequately represent daily and seasonal variations in the truck traffic. Hence, it should be collected for a period of 1 year or at random intervals over extended periods of time. 9. The relationship between the truck weights in the main traffic lane (drive lane) and adjacent lanes must be estab- 6

lished to determine whether passing trucks’ characteristics are similar to those in the main traffic lane and if there is a correlation between the truck properties. Here again, the assumptions used during the LRFD calibration were not adequately supported by field measurements. The availability of the current WIM data along with headway information and lane of occupancy will allow us to determine the level of correlation (if any) between the trucks in each lane. The relationship between truck traffic patterns and headways should be related to ADTT. Specifically, data should be collected to determine how the number of side- by-side events varies with ADTT. The goal of this project is to develop a set of protocols and methodologies for using available current truck traffic data collected at different U.S. sites and recommend a step-by-step procedure that can be followed to obtain live-load models for bridge design. The protocols are geared to address the collec- tion, processing, and use of national WIM data to develop and calibrate vehicular loads for LRFD superstructure design, fatigue design, deck design, and design for overload permits. Various levels of complexity are available for utilizing truck weight and traffic data to calibrate live-load models for bridge design. A simplified calibration approach that focuses on the maximum live-load variable, Lmax, for updating the load factor and a more robust reliability-based approach that considers the site-to-site variations in WIM data in the calibration of live loads are proposed. The study also gives practical examples of implementing these protocols with recent national WIM data drawn from states/sites around the country with different traffic exposures, load spectra, and truck configurations. This will give a good cross-section of WIM data for illustrative purposes. This task also allowed the updating and/or refinement of the protocols based on its applicability to WIM databases of varying quality and data standards currently being collected by the states. This report discusses the results of the demonstration studies in detail. Introduction This report, prepared in accordance with Task 9 requirements for this project, documents the findings of Tasks 1 through 8. It contains four chapters and six appendices (the appendices are not printed herein but can be found at www.TRB.org). Chapter 1 gives a review of the problem statement, the re- search objective, and scope of study. Chapter 2 describes the research tasks, findings of the literature search and survey of states, a state-of-the-art summary, and the process to develop and calibrate bridge design live-load models. Chapter 3 pro- vides the draft recommended protocols for using traffic data in bridge design and the results of the demonstration of the draft protocols using national WIM data. Chapter 4 contains the conclusions and recommendations for future research. Appendix A includes the results of the demonstration of the draft protocols performed in Task 8 using recent national WIM data from five states. Appendix B summarizes the main features of technical publications most relevant to this project that were compiled during the literature search. Appendix C contains the questionnaires used in the surveys and tabulated responses. Appendix D summarizes the find- ings of Task 2, which investigated potential processes for developing live-load models for bridge design. Appendix E illustrates an implementation of the error filtering algorithm described in the protocols, using recent WIM data. Appendix F discusses the results of truck sorting methods for grouping trucks into Strength I and Strength II, and their influence on “r” values. 7

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TRB’s National Cooperative Highway Research Program (NCHRP) Report 683: Protocols for Collecting and Using Traffic Data in Bridge Design explores a set of protocols and methodologies for using available recent truck traffic data to develop and calibrate vehicular loads for superstructure design, fatigue design, deck design, and design for overload permits.

The protocols are geared to address the collection, processing, and use of national weigh-in-motion (WIM) data. The report also gives practical examples of implementing these protocols with recent national WIM data drawn from states/sites around the country with different traffic exposures, load spectra, and truck configurations. The material in this report will be of immediate interest to bridge engineers.

This report replaces NCHRP Web-Only Document 135: Protocols for Collecting and Using Traffic Data in Bridge Design.

Appendices A through F for NCHRP Report 683 are available only online. These appendices are titled as follows.

Appendix A—Survey Questionnaires & Responses

Appendix B—Main Features of Selected Studies

Appendix C—National WIM Data Analyses

Appendix D—Potential Processes to Develop and Calibrate Vehicular Design Loads

Appendix E—Implementation of WIM Error Filtering Algorithm

Appendix F—Truck Sorting Strategies & Influence on “r” Values

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