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Protocols for Collecting and Using Traffic Data in Bridge Design (2011)

Chapter: Chapter 4 - Conclusions and Suggested Research

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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." 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 4 - Conclusions and Suggested Research." 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 4 - Conclusions and Suggested Research." 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 4 - Conclusions and Suggested Research." 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 4 - Conclusions and Suggested Research." 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 4 - Conclusions and Suggested Research." 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 4 - Conclusions and Suggested Research." 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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106 Conclusions The present HL93 load model and the calibration of the AASHTO LRFD specifications are 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. The model reflects truck configurations and weights taken in the mid-1970s, which primarily consisted of 5-axle semi-trailer trucks. In the past 30 years, truck traffic has significantly increased in volume and weight. Therefore, current AASHTO specified live-load models that are based on past Canadian traffic data may not represent modern and future traffic conditions in some U.S. jurisdictions. Bridge engineers often focus on enhancing the knowledge of member and system resistances with less effort expended on understanding the live-load demand on bridge elements and systems. Enhancement of bridge live-load models needs representative samples of unbiased truck weight data that meet accepted quality standards. It also requires information on the simultaneous presence of multiple trucks on bridges. Traditionally, the latter has been assembled from headway data, and has not been collected in a manner suitable for the development of design live loads. Due to the development of various weigh-in-motion (WIM) technologies, the quality and quantity of WIM data have greatly improved in recent years. Unbiased truckloads are now being collected at normal highway speeds, in large quantity, and without truck driver knowledge. Modern WIM data loggers have the capability to record and report truck arrival times to an accuracy of 0.01 second, sufficient for estimating multiple presence probabilities. This information, however, has not been used to update the bridge design loads. In this regard, the lack of nationally accepted protocols may have been a contributing factor. The goal of this project, therefore, was to develop a set of protocols and methodologies for using available recent 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 LRFD bridge design. The protocols are geared to address the collection, processing, and use of national WIM data to develop and calibrate vehicular loads for LRFD super- structure design, fatigue design, deck design and design for overload permits. 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. Truck traffic data should be collected through WIM systems that simultaneously can collect headway information as well as truck weights and axle weights and axle configurations while remaining hidden from view and unnoticed by truck drivers. Truck data surveys collected at truck weigh stations and pub- licized locations are not reliable for use in live-load modeling because they are routinely avoided by illegal overweight vehicles that could control the maximum loads applied on bridge structures. The selection of WIM system sites should focus on sites where the owners maintain a quality assurance program that regularly checks the data for quality and requires system repair or recalibration when suspect data are identified. Weighing accuracy is sensitive to roadway conditions. Roadway con- ditions at a WIM site can deteriorate after a system is installed and calibrated. Regular maintenance and recalibrations are essential for reliable WIM system performance. Quality infor- mation is more important than the quantity of traffic data collected. It is far better to collect small amounts of well- calibrated data than to collect large amounts of data from poorly calibrated scales. Even small errors in vehicle weight measurements caused by poorly calibrated sensors could result in significant errors in measured loads. For long-duration counts, the scale should be calibrated initially, the traffic characteristics at that site should be recorded, and the scale’s performance should be monitored over time. The state should also perform additional, periodic, on-site calibration checks (at least two per year). These steps will ensure that the data being collected are accurate and reliable. Site-specific calibration is C H A P T E R 4 Conclusions and Suggested Research

107 the only way that the dynamic effects of the pavement leading to the scale can be accounted for in the WIM scale calibration. This calibration process should be executed for a whole range of truck and axle weight types and configurations. In many spans, the maximum lifetime truck-loading event is the result of more than one vehicle on the bridge at a time. Obtaining reliable multiple presence statistics requires large quantities of continuous WIM data with refined time stamps, which may not be available at every site. Studies done using New York WIM data during this project show that there is a strong correlation between multiple presence and ADTT. The multiple presence statistics are mostly transportable from site to site with similar truck traffic volumes and traffic flow and need not be repeated for each site. The site ADTT could serve as a key variable for establishing a site multiple-presence value. The multiple-presence probabilities for permit trucks are significantly different from those used for normal traffic. Information on loads and multiple-presence probabilities for permits need to be obtained locally or regionally through WIM measurements and considered in the Strength II design process since the data used for calibration of national codes are unlikely to be representative of all jurisdictions. Draft Recommended Protocols for Using Traffic Data in Bridge Design An aim of these processes is to capture weight data appro- priate for national use or data specific to a state or local jurisdiction where the truck weight regulations and/or traffic conditions may be significantly different from national stan- dards. The objective is to use data from existing WIM sites to develop live-load models for bridge design. The models will be applicable for the strength, serviceability, and fatigue design of bridge members, including bridge decks and design vehicles for overload permitting. Based on the research findings of this project, step-by-step protocols for collecting and using traffic data in bridge design have been developed. The recommended protocols are summarized as follows. Step 1. Define WIM Data Requirements for Live-Load Modeling This step defines the types of traffic data and WIM sensor calibration statistics needed for live-load modeling of superstructure design load models (Strength I), overload permitting (Strength II), deck design, and calibration of fatigue load models. Step 2. Selections of WIM Sites for Collecting Traffic Data for Bridge Design This step defines the criteria to be used for selecting WIM sites for national, state-specific, route-specific, and site- specific design live-load modeling. National study of truck loads can be conveniently handled by dividing the country into five regions. Representative states are selected from each region and the sites and routes for WIM data collection are selected based on roadway functional classifications. Some of the criteria for selecting WIM sites include remote WIM sites away from weigh stations with free-flowing traffic, sites that can provide a year’s worth of continuous data, sites that have been recently calibrated and are subject to a regular maintenance and quality assurance program, and sites equipped with current sensor and equipment technologies (preferably able to capture and record truck arrival times to the nearest 1/100th of a second or better). Step 3. Quantities of WIM Data Required for Load Modeling There are several possible methods available to calcu- late the maximum load effect for a bridge design period from truck WIM data. The one implemented in these protocols is based on the assumption that the tail end of the histogram of the maximum load effect over a given return period approaches a Gumbel distribution as the return period increases. The method assumes that the WIM data are assembled over a sufficiently long period of time, preferably a year, to ensure that the data are representative of the tail end of the truck weight histograms and to factor in seasonal variations and other fluctuations in the traffic pattern. Sensitivity analyses have shown that the most important parameters for load modeling are those that describe the shape of the tail end of the truck load effects histogram. Step 3 provides recommendations for the quan- tity of WIM data to be collected from each site. Recom- mendations include the following: 1. A year’s worth of recent continuous data at each site to observe seasonal changes of vehicle weights and volumes, 2. If continuous data for a year is unavailable, a minimum of one month of data for each season for each site, and 3. Data from all lanes in both directions of travel. Step 4. WIM Calibration and Verification Tests WIM devices used for collecting data for live-load modeling should be required to meet performance speci- fications for data accuracy and reliability. Field tests to verify that a WIM system is performing within the accuracy required is an important component of data quality assur- ance for bridge load modeling applications. Steps to ensure that the data being collected are accurate and reliable include the following: 1. Initial calibration of WIM equipment; 2. Periodic monitoring of the data reported by WIM systems as a means of detecting drift in the calibration of weight sensors; and 3. Periodic on-site calibration checks for long duration counts, where, in addition to Steps 1 and 2, the scale is

108 subjected to periodic on-site calibration checks at least twice per year, and the calibration statistics are retained for use in filtration of sensor errors. Step 5. Protocols for Data Scrubbing, Data Quality Checks, and Statistical Adequacy of Traffic Data High-speed WIM is prone to various errors that need to be recognized and considered in the data review process to edit out unreliable data and unlikely trucks to ensure that only quality data is made part of the load modeling process. It is also important to recognize that unusual data are not all bad data. The WIM data should therefore be scrubbed to include only the data that meet the quality checks. Filtering protocols provided in this step should be applied for screening WIM data prior to use in the live-load mod- eling and calibration processes. Adjustments to the data scrubbing rules may need to be made to accommodate changes in truck configurations from state to state. Review- ing a sampling of trucks that were eliminated during the data scrubbing process also is recommended to check if the process is performing as intended. Ongoing simple quality checks also are performed on the WIM data to detect any operational problems with the sensors. Step 6. Generalized Multiple-Presence Statistics for Trucks as a Function of Traffic Volume In many spans, the maximum lifetime truck-loading event is the result of more than one vehicle on the bridge at a time. Refined time stamps are critical to the accuracy of multiple-presence statistics for various truck loading cases including single, following, side-by-side, and staggered. However, multiple-presence statistics are mostly trans- portable from site to site with similar truck traffic volumes and traffic flow. A relationship between multiple presence and traffic volume could be developed to utilize the multi- ple presence values from national data to any given site without performing a site-specific analysis. In this step, the relationship between the trucks’ weights in the drive lane and passing lanes must be established 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. Step 7. Protocols for WIM Data Analysis for One-Lane Load Effects for Superstructure Design In this step, single-lane load effects for single truck events and for following truck events for superstructure design are determined. Load effects for following trucks may be obtained directly from the WIM data where accurate time arrival stamps are collected. Generalized multiple-presence statistics obtained in Step 6 may be used for simulation of load effects where accurate truck arrival time stamps are not available. The trucks are grouped into bins by travel lane and run through moment and shear influence lines (or structural analysis program) for simple and two-span continuous spans. The results are normalized by dividing by the corresponding load effects for HL93. Legal loads, illegal overloads, and routine permits are grouped under Strength I. These vehicles are considered to be enveloped by the HL93 load model. Heavy special permits/trip permits are grouped under Strength II. In most states, permit records are either not specific enough or detailed enough to allow separation of permit loads from non-permit loads in a large WIM database. Recommenda- tions for grouping trucks into Strength I and II, based on the studies conducted on truck sorting strategies (Appendix F), are as follows: 1. Using a state’s permit and weight regulations to group trucks into Strength I and Strength II is considered the most precise and rational approach, when using national WIM data. 2. Using number of axles as a means to separate the trip permits from the rest of the traffic is an acceptable approximate sorting approach that may be easier to implement. Typically, trucks with seven or more axles could be grouped into Strength II as trip permits. How- ever, when setting the cutoff for the number of axles, it would be important to take into account the typical axle configurations for routine permits in a state. In some states that have high GVW limits for routine permits, a cut-off limit higher than seven axles for separating routine permits from trip permits would be appropriate. 3. Using GVW as a means to separate the trip permits from the rest of the traffic is also an acceptable approx- imate sorting approach that can be easily implemented. Trucks with GVW=150 kips or more could be grouped into Strength II as trip permits. For certain states this may need to be increased to 200 kips or higher depend- ing on state permit regulations. Step 8. Protocols for WIM Data Analysis for Two-Lane Load Effects for Superstructure Design Determine the number of side-by-side or staggered truck multiple-presence events where trucks are in adjacent lanes in each direction. Run the combined truck with the trucks offset by their actual headway separation through moment and shear influence lines for simple and two-span continuous spans. Estimate the maximum daily load effects for two random trucks simultaneously crossing the bridge. The results are normalized by dividing by the corresponding load effects for HL93. Where accurate time arrival stamps are not available, generalized multiple-presence statistics obtained in Step 6 may be used for simulation of load effects.

109 Legal loads, illegal overloads, and routine permits are grouped under Strength I. These vehicles are considered to be enveloped by the HL93 load model. Heavy special permits/trip permits are grouped under Strength II. In most states, permit records are either not specific enough or detailed enough to allow separation of permit loads from non-permit loads in a large WIM database. As in Step 7, recommendations for grouping trucks into Strength I and II, based on the studies conducted on truck sorting strategies (Appendix F), are as follows: 1. Using a state’s permit and weight regulations to group trucks into Strength I and Strength II is considered the most precise and rational approach, when using national WIM data. 2. Using number of axles as a means to separate the trip permits from the rest of the traffic is an acceptable approximate sorting approach that may be easier to implement. Typically, trucks with seven or more axles could be grouped into Strength II as trip permits. How- ever, when setting the cutoff for the number of axles, it would be important to take the typical axle configurations for routine permits in a state into account. In some states that have high GVW limits for routine permits, a cut-off limit higher than seven axles for separating routine permits from trip permits would be appropriate. 3. Using GVW as a means to separate the trip permits from the rest of the traffic also is an acceptable approximate sorting approach that can be easily implemented. Trucks with GVW=150 kips or more could be grouped into Strength II as trip permits. For certain states, this may need to be increased to 200 kips or higher, depending on state permit regulations. Step 9. Assemble Axle Load Histograms for Deck Design As before, separate trucks into Strength I and Strength II groups for single events and for two-lane loaded cases. For each group, generate axle weight relative frequencies histograms for single, tandem, tridem, and quad axles. Multiple-presence probabilities are determined for side- by-side axle events. Step 10. Filtering of WIM Sensor Errors/WIM Scatter from WIM Histograms Current WIM systems are known to have certain levels of random measurement errors that may affect the accu- racy of the load modeling results. This step proposes an approach to filter out WIM measurement errors from the collected WIM data histograms. To execute the filtering process, calibration data for the WIM system for a whole range of trucks should be obtained. The results of these sensor calibration tests will be the basis for filtering out WIM measurement errors for each WIM data site. The protocols present a procedure to filter out the WIM calibration errors from the measured WIM histograms of gross weights (or load effects) to obtain WIM data histograms that reflect the actual truck weights rather than the measured weights. Step 11. Accumulated Fatigue Damage and Effective Gross Weight from WIM Data Updating the LRFD fatigue load model using recent WIM data is described in this step. Damage accumulation laws such as Miner’s Rule can then be used to estimate the fatigue damage for the whole design period for the truck population at a site. Cumulative fatigue damage from the WIM population is compared to the LRFD fatigue truck to determine the fatigue damage adjustment factor K. Based upon the results of the WIM study, changes may be proposed to the LRFD fatigue truck model, its axle config- uration, and/or its effective weight. Step 12. Lifetime Maximum Load Effect Lmax for Superstructure Design In order to check the calibration of load models and/or load factors for strength design, it is necessary to estimate the mean maximum lifetime loading or load effect Lmax. There are several possible methods available to calculate the maximum load effect for a bridge design period from truck WIM data. Simplified analytical methods or simulations may be used to estimate the maximum loading over a longer period (75 years) from short-term WIM data. The approach implemented in these protocols is found to be one of the easiest methods that provides results comparable to many other computationally intensive methods, including Monte Carlo simulations. This statistical projection method is based on the assumption that the tail end of the histogram of the maximum load effect over a given return period approaches a Gumbel (extreme value) distribution as the return period increases. Step 13. Develop and Calibrate Vehicular Load Models for Bridge Design Various levels of complexity are available for utilizing the site-specific truck weight and traffic data to calibrate live-load models for bridge design. A simplified calibration approach (Method I) is proposed that focuses on the maximum live-load variable, Lmax for updating the live-load model or the load factor for current traffic conditions, in a manner consistent with the LRFD calibration. The ratio, r (Lmax WIM data, divided by Lmax Ontario data) for one lane and for two lanes is used to adjust the live-load factor. This procedure assumes that the present LRFD calibration and safety indices are adequate for the load data and that the site-to-site variability (COV) of the present data and the data then available are consistent. A more robust reliability- based approach (Method II) also is presented that considers

110 both the recent load data and the site-to-site variations in WIM data in the calibration of live loads. During the development of the step-by-step protocols, recent long-term WIM data collected at several New York WIM sites by NYSDOT were obtained and used to test the validity and applicability of the protocols. The testing process was very helpful in ensuring that the recommended draft pro- tocols were practical and could be effectively implemented using already available national WIM data. Demonstration of Protocols Using National WIM Data The draft recommended protocols were implemented using recent traffic data for a whole year (either 2005 or 2006) from 26 WIM sites in 5 states across the country. The states were California, Texas, Florida, Indiana, and Mississippi. The states and WIM sites were chosen to capture a variety of geographic locations and functional classes, including urban interstates, rural interstates, and state routes. An aim of this task was to give practical examples of using these protocols with national WIM data drawn from sites around the coun- try with different traffic exposures, load spectra, and truck configurations. Adjustments and enhancements were made to several protocol steps based on the experience gained from this demonstration task. The lifetime maximum Lmax and r values (ratio of Lmax values) determined using Step 12 and Method I of Step 13 showed a significant, and consistent, difference between the r values for the one-lane and two-lane events. The r values for one lane events are significantly greater than those for two-lane events. Whereas the maximum r values for two-lane events had a maximum value of 1.184, the one-lane events had a maximum r value among all WIM sites of 2.402 (when sorted using all vehicles with six axles or less as belonging to Strength I). This seems to indicate that the live loading defined in the LRFD specification is fairly adequate in modeling the lifetime max- imum loading on a span with two lanes loaded, but under- estimates the lifetime maximum loading on a span with only one lane loaded. However, as discussed in Step 13, using a single maximum or characteristic value for Lmax for a state would be acceptable if the scatter or variability in Lmax from site to site for the state was equal to or less than the COV assumed in the LRFD calibration. The site-to-site scatter in the Lmax values obtained from recent WIM data showed significant variability from span to span, state to state, and between one- lane and two-lane load effects—well above the overall 20% COV used during the LRFD calibration. For example, the data from Florida show a COV for the moments of simple- span bridges under one-lane loadings that varies from 32.5% for the 20-ft simple span to 22.3% for the 200-ft simple span. (On the other hand, the site-to-site COV statistics for California are lower.) Live-load modeling using Method II was then implemented to reflect the site-to-site variability. The results show that for the California truck traffic con- ditions, the reliability index for one lane is, on average, equal to β = 3.55 which is close to the LRFD target β = 3.50. For two lanes of truck traffic, the average reliability index is β = 4.63. This indicates that for the two-lane loading of California bridges, the current AASHTO LRFD is conservative, produc- ing higher reliability index values than the target β = 3.50 set by the AASHTO LRFD code writers. If the intent is to reduce the reliability index for the two-lane cases to the target β = 3.5, then an adjusted live-load factor γL = 1.20 would result, using the steps provided in the protocols. The reliabil- ity index for Florida for one-lane loading drops to an average of β = 2.58 (when sorted using all vehicles with six axles or less as belonging to Strength I). The two-lane Lmax would lead to an average reliability index β = 3.96. The latter value is still higher than the target β = 3.5 while the one-lane reliability is lower than the target. If the live-load factor is raised to γL = 2.37 then the reliability indices for the Florida sites would in- crease to β = 3.50 for the one-lane cases. For Indiana, the re- liability index for one lane of loading is, on average, equal to β = 3.16 for one-lane loading; the two-lane loading would lead to an average reliability index β = 4.71. The Indiana data show a site-to-site variability in COVs on the order of 11% to 15% for both one-lane and two-lane loadings. Both calibration methods indicate that the live loading defined in the LRFD specification is generally adequate or even conservative in modeling the lifetime maximum loading on a span with two lanes loaded, but it underestimates the lifetime maximum loading for the one-lane loaded case. Studies on truck sorting methods indicate that illegal trucks—not the permits that follow state permit regulations—are likely the biggest drivers of high r values. The load limit enforcement environment in a state will have a more discernible influence on the maximum single-lane loading than the maximum two-lane loading, which results from the presence of two side-by-side trucks. Additionally, with more multiple-presence and WIM data currently available, the projections of Lmax for two-lane events as undertaken in this study are based on actual side-by- side events rather than based on simulations using conservative, assumed side-by-side multiple-presence probabilities as done during the AASHTO LRFD code calibration. The WIM data collected as part of this study show that the actual percentage of side-by-side multiple truck event cases is significantly lower than assumed by the AASHTO LRFD code writers who had to develop their models based on a limited set of multiple- presence data. Knowing the actual truck weight distribution in each lane allowed the determination of the relationship between the truck weights in the main traffic lane (drive lane) and adjacent

111 lanes, and if there is a correlation between the truck properties. This study seems to indicate that there is some negative cor- relation between the weights of side-by-side trucks. This means that when a heavy truck is in one lane, the other lane’s truck is expected to be lighter. Here again, the conservative assump- tions used during the LRFD calibration were not adequately supported by field measurements. Recommendations for Sorting Traffic in the WIM Database into Strength I and Strength II The NCHRP 12-76 study addressed the criteria for sepa- rating traffic data into Strength I and Strength II limit states by recommending that all uncontrolled traffic that consti- tutes normal traffic or service loads at a site be grouped into Strength I and all controlled or analyzed overload permits be grouped into Strength II. Strength I vehicles were taken to include state legal trucks, illegal overloads, and routine permits as they were considered to represent normal service traffic at bridge sites. Only the controlled trip permits or superloads were included in Strength II. In the initial NCHRP 12-76 study, the research team decided to use a simplified approach and group all trucks with six or fewer axles in the Strength I cali- bration as a reasonable, although approximate, way to capture all legal trucks, illegal overloads, and annual permits. Thus, trucks with seven or more axles were considered as controlled or trip permits and included in Strength II. More detailed recommendations for grouping trucks into Strength I and II were developed based on additional research on truck sorting strategies performed under 12-76(01). The detailed recommendations are as follows: 1. Using a state’s permit and weight regulations to group trucks into Strength I and Strength II is considered the most precise and rational approach, when using national WIM data. 2. Using number of axles as a means to separate the trip permits from the rest of the traffic is an acceptable approx- imate sorting approach that may be easier to implement. Trucks with seven or more axles could be grouped into Strength II as trip permits. When setting the cutoff for the number of axles, it would be important to take the typical axle configurations for routine permits in a state into account. In some states that have high GVW limits for routine permits, a cut-off limit higher than seven axles for separating routine permits from trip permits would be appropriate. 3. Using GVW as a means to separate the trip permits from the rest of the traffic also is an acceptable approximate sorting approach that can be easily implemented. Trucks with GVW=150 kips or more could be grouped into Strength II as trip permits. For certain states this may need to be increased to 200 kips or higher depending on state permit regulations. Suggested Research and Improvements in Data Collection Developing and calibrating bridge live-load models requires large amounts of quality WIM data. Improvements in WIM data collection are needed to allow the effective implementa- tion of these protocols. Suggested research on this topic for the future, and recommendations for improving WIM data collection, are as follows: • States should evaluate their WIM data collection equipment to ascertain if it can provide the quantity and quality of data to implement these protocols. It may be necessary to upgrade the WIM technology and data collection system at specific sites selected for data collection. • DOTs should carefully consider the locations of WIM sites within a state. Remote WIM sites away from weigh stations are needed to provide unbiased WIM data. Availability of WIM sites on heavy freight routes, hauling routes, or routes known to have significant permit traffic is important for live-load modeling purposes. • WIM devices should be required to meet ASTM perfor- mance specifications for data accuracy and reliability. The WIM sites should be subject to a regular maintenance and quality assurance program. The system components should be reliable enough to be able to provide a year’s worth of continuous data. • The WIM system should be able to capture and record truck arrival times to the nearest 1/100th of a second or better to allow the determination of truck headway separations. Sensors should be placed in the drive lane and passing lanes at sites used for data collection. • The WIM system should not cut off trucks having more than a certain number of axles or that are heavier than a certain upper weight limit. It is important to record the long heavy superloads that may have a dozen or more axles and weigh over 200 kips. These trucks populate the upper tail of the truck weight distribution and have a significant influence on bridge safety. • WIM calibration and verification testing requirements defined in Step 4 should be implemented as part of an overall quality assurance program. Regular maintenance and periodic recalibration of any WIM system is critical for obtaining reliable traffic data. Initial calibration and periodic recalibration every 6 months are recommended for sites selected for data collection. Calibration of WIM equipment should follow LTPP calibration procedures or ASTM 1318 standards. Periodic monitoring and quality check of the

112 data reported by WIM systems should be performed as a means of detecting drift in the calibration of weight sensors. Calibration statistics should be maintained for each WIM site for use in the error filtration process during data analysis. • Separating permit vehicles from non-permit traffic in large-scale WIM data requires the availability of a reliable electronic permits database/records of special permits authorized in a given period for a state. DOT surveys have indicated that such permit databases are not currently being maintained, at least in an electronic form. States should make necessary changes to their permit operations/ management to create and maintain a comprehensive electronic database of overweight permits authorized that will allow these vehicles to be properly grouped as either Strength I or Strength II for live-load modeling.

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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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