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S U M M A R Y This report documents and presents the results of a study to develop a set of protocols and methodologies for using available recent truck traffic data to develop and calibrate live-load models for LRFD bridge design. 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. 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 weigh-in-motion (WIM) data to develop and calibrate vehicular loads for LRFD superstructure design, fatigue design, deck design, and design for overload permits. These protocols, comprised of 13 steps with detailed instructions on their application, are appropriate for national use or use with data specific to a state or local jurisdiction where the truck weight regulations and/or traffic conditions may be significantly different from national standards. 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. The project team recommends that truck traffic data should be collected through WIM systems that can simultaneously collect headway information as well as truck weights, axle weights, and axle configurations while remaining hidden from view and unnoticed by truck drivers. Truck data surveys collected at truck weigh stations and publicized locations are not accurate, because they are normally avoided by illegal overweight vehicles that could control the maximum loads applied on bridge structures. The selection of WIM sites should focus on sites where the owners maintain a quality assurance program that regularly checks the data for quality. WIM devices used for collecting data for live-load modeling should be required to meet performance specifications for data accuracy and reliability. A yearâs worth of recent continuous data is generally recommended for live-load modeling. Quality information is just as important as the quantity of data collected. It is far better to collect limited 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. High-speed WIM is prone to various errors. Such errors need to be recognized and considered in the data review process to edit out unreliable data and unlikely trucks, and to ensure that only quality data are 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. Error filtering procedures provided in Protocols for Collecting and Using Traffic Data in Bridge Design 1
2these protocols should be applied for screening the WIM data prior to use in the live-load modeling and calibration processes. 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 (MP) statistics for various truck loading cases, including: single, following, side-by-side, and staggered. Many modern WIM data loggers currently in use in the United States have the capability to record and report sufficiently accurate truck arrival times for estimating multiple presence probabilities. Studies also have shown that multiple presence statistics are mostly transportable from site to site with similar truck traffic volumes and traf- fic flow. Load effects for single-lane and two-lane loadings may be obtained directly from the WIM data when accurate time arrival stamps are collected. Generalized MP statistics may be used for simulation of maximum load effects where accurate truck arrival time stamps are not available. Trucks arriving at a bridge span are grouped into bins by travel lane and run through moment and shear influence lines with their actual relative positions. The resulting load effects are normalized by dividing by the corresponding load effects for HL93. Legal loads and routine permits are grouped under Strength I. Heavy special permits are grouped under Strength II. There are several possible methods available to calculate the maximum load effect Lmax for a bridge design period (75 years) from truck WIM data collected over a shorter period of time (1 year). 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. The protocols therefore recommend that a yearâs worth of recent continuous data at each site be collected for use in live-load modeling. Various levels of complexity are available for utilizing the truck weight and traffic data to calibrate live-load models for bridge design. A simplified calibration approach (Method I) is first proposed that focuses on the maximum live-load variable, Lmax, for updating the live-load factor for current traffic conditions, in a manner consistent with the LRFD calibration. The ratio, r, for one lane and for two lanes, is used to adjust the LRFD design live-load factor. The ratio, r, is defined as follows: NCHRP Report 368 (Nowak 1999), the LRFD calibration report, provides mean maximum moments and shears for 75 years for simple and continuous spans. An increase in maximum expected live-load based on current WIM data can be compensated in design by raising the live-load factor in a corresponding manner. This simple procedure assumes that the present LRFD calibration and safety indices are adequate for the load data and site-to-site scatter or variability statistics for the WIM data is consistent with values assumed in the LRFD calibration. When implementing the draft protocols using recent WIM data from various states, it became evident that this procedure, while simple to understand and use, had certain limitations when r L from WIM data projections for two lanes L 2 = max max used in existing LRFD calibration for two lanes r L from WIM data projections for one lane L 1 = max max used in existing LRFD calibration for one lane
applied to statewide WIM data. 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. If the variability in the WIM data is much greater than that assumed in the calibration, then the entire LRFD calibration to achieve the target 3.5 reliability index may no longer be valid for that state and a simple adjustment of the live-load factor as given above should not be done. 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. Therefore, a second, more robust reliability-based approach (Method II) is also presented that considers both the recent load data and the site-to-site variations in WIM data in the calibration of live loads for bridge design. The draft recommended protocols were implemented using recent traffic data (either 2005 or 2006) from 26 WIM sites in five states (California, Texas, Florida, Indiana, and Mississippi) across the country. The states and WIM sites were chosen to capture a variety of geographic locations and functional classes, from 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 country with different traffic exposures, load spectra, and truck configurations. Adjustments and enhancements were made to the protocol steps based on the experience gained from this demonstration task. Both calibration methods indicate that the lifetime maximum loading for the one-lane loaded case will govern over the maximum loading for a two-lane loaded case. This could be attributable to the increasing presence of heavy exclusion vehicles and/or routine permits in the traffic stream. The load limit enforcement environment in a state will also 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 long-term WIM data with accurate truck arrival time stamps 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 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 truck weight distribution in each lane allowed the determi- nation of the actual relationship between the truck weights in the main traffic lane (drive lane) and adjacent lanes, and the determination if there is a correlation between the truck properties. This study seems to indicate that there is some negative correlation between the weights of side-by-side trucks. This means that when a heavy truck is in one lane, the other laneâs truck is expected to be lighter. Here again, the conservative assumptions used during the LRFD calibration were not adequately supported by field measurements. The LRFD did not specifically address deck components in the calibration. The proposed approach to calibration of deck design loads is to assume the present LRFD safety targets are adequate for the strength design of decks and establish new nominal loads for axles based on recent WIM data. The LRFD live-load factors will remain unchanged, but the axle loads and axle types will be updated to be representative of current traffic data. Updating the LRFD fatigue load model using recent WIM data is described in the protocols. Damage accumulation laws such as Minerâs rule can be used to estimate the fatigue damage for the whole design period for the truck population at a site. Based upon the results of the WIM study, changes may be proposed to the LRFD fatigue truck model, its axle configuration, and/or its effective weight. Additional studies on truck sorting strategies were performed under NCHRP 12-76(01) to further investigate the truck sorting methodology and the sensitivity of r values to how the trucks are sorted into Strength I. These studies developed more detailed recommendations for grouping 3
4trucks into Strength I and II. Using a stateâs permit and weight regulations to group trucks into Strength I and Strength II was determined to be the most precise and rational approach when using national WIM data. The protocols and methodologies recommended in this report can be followed to obtain live-load models for bridge design using available truck traffic data collected at different U.S. WIM sites. The models will be applicable for ultimate capacity and cyclic fatigue for main members and for bridge decks. The project was not intended to assemble sufficient data to permit recommendations about revisions to the AASHTO HL93 design load.
