Load And Resistance Factor Rating Research Articles

Assessment of an old reinforced concrete open-spandrel arch railway bridge

This paper presents assessment of a 94 years old reinforced concrete (RC) open-spandrel arch bridge with a total length of 285 m having seven major spans of 35 m, a height of 34 m and a vertical slope of 15 ‰. As the bridge was currently subjected to heavy trains having higher axle loads than it was originally designed, assessment of its actual structural condition was necessary. To determine actual in-situ behavior of the bridge, dynamic tests including a series of acceleration measurements along the bridge spans were performed. Then, 3D finite element model of the bridge was generated by using a structural analysis software based on its original constructional drawings. The initial FE model was updated using Response Surface Method (RSM) technique based on the results obtained from measurement data. The updated FE model was used for structural assessment and evaluation of the bridge under UIC-71 live loading. Rating factor and reliability index for each bridge member based on Load and Resistance Factor Rating (LRFR) approach were obtained. Results of the rating and reliability analysis showed that the most critical member of the bridge was the deck, thus strengthening and retrofitting of these deck members might be needed for live loads greater than those induced by UIC-71 train.

Extension of AASHTO Load-Spreading Method to Include the Full Benefits of Pavements for Reliable Load Rating of Buried Culverts

This paper develops a two-step procedure to extend the AASHTO Ad Hoc Method (AAM) for live-load spreading through soil to include the full benefits of pavement overlays. The two-step procedure is called AAMP-θ* (“P” for pavements), which combines the Fox two-layer elasticity solution for peak stress at the pavement–soil interface with the improved AAM-θ* theory for load spreading through soil. The AAMP-θ* procedure is applicable to all culvert analysis tools commonly used for Load and Resistance Factor Rating (LRFR) analysis ranging from simple frame models to sophisticated two-dimensional finite element model (2D-FEM) soil structure models. The AAMP-θ* procedure is rigorously and completely developed wherein all approximations are shown to be conservative so that the predicted benefits of pavements are trustworthy and not overstated. This is why the more accurate AAM-θ* load-spreading method is used instead of the simpler but unconservative AAM-30° method. Application of AAMP-θ* for LRFR load rating of buried culverts is described for simple frame models as well as 2D-FEM pavement–soil structure models. Lastly, the AAMP-θ* procedure is validated by comparing structural response predictions from 2D-FEM solutions with strain-gage measurements in a buried corrugated steel culvert loaded by a three-axle truck. One set of strain-gage measurements was made before, and another set of measurements was made after, asphalt pavement was installed. The correlation between field data and 2D-FEM/AAMP-θ* predictions is remarkable and underscores the bottom-line message that AAMP-θ* should be used when performing LRFR analysis on buried culverts to determine realistic rating factor values and avoid predicting false unsafe values.

Data-driven analytical load rating method of bridges using integrated bridge structural response and weigh-in-motion truck data

Load rating is a widely used approach for evaluating the load-carrying capacity of bridges in an effort to ensure safe bridge operation under expected traffic loads. Load rating often relies on simplified analytical models including empirically derived model parameters that do not reflect bridge-specific information resulting in conservative ratings. To reduce this conservatism, this study proposes a novel data-driven framework that utilizes long-term bridge response data to extract bridge-specific model parameters that can be used within in the Load and Resistance Factor Rating (LRFR) process. The data-driven LRFR (DD-LRFR) framework is empowered by a cyber-physical system (CPS) architecture that uses Internet connectivity to integrate measured bridge responses with truck weights measured by a weigh-in-motion (WIM) station. The CPS architecture uses computer vision of camera images to confirm trucks observed at a WIM station are identical to those observed at a bridge. Bridge response and axle weight data are then used to extract probabilistic models of dynamic load allowances and unit influence lines. The DD-LRFR method is validated using a 20-mile (32.2-km) segment of the I-275 northbound highway in Michigan that is monitored continuously by the CPS architecture. Six girders associated with two bridges along I-275 are rated using the proposed DD-LRFR methodology with rating factors compared to those obtained using conventional and refined load rating methods. The DD-LRFR method yields inventory- and operational-level rating factors that are less conservative than those from the approximate LRFR method and comparable to those using finite element modeling of the bridge.

Load Rating of a Fully Instrumented Bridge: Comparison of LRFR Approaches

AbstractThis paper presents a comparison of three methods used to load rate the Powder Mill Bridge based on the load and resistance factor rating (LRFR) approach. This is a typical three-span continuous bridge with steel girders in composite action with the RC bridge deck. The three methods are as follows: (1) employing the conventional design office load rating technique using a simplified line girder analysis, (2) using strain measurements from a diagnostic load test to adjust the design office rating to account for in-situ bridge behavior, and (3) using a finite-element (FE) model of the bridge, which accounts for three-dimensional (3D) structural system behavior. Advantages and disadvantages of each method are related to speed, ease of use, reviewability, cost, accuracy, and type of use intended. Similarities and differences in utilizing these three methods are discussed. The advanced load rating methods are shown to produce higher ratings in comparison with the conventional approach.

Load Rating Highway Bridges in the United States: The State of Practice

In the United States, a bridge must meet federal requirements in inspection and load rating set forth in the National Bridge Inspection Standards (NBIS). This article (a) introduces the federal requirements about load rating highway bridges in the United States; (b) discusses the current state of practice in load rating; and (c) provides an overview of the load and resistance factor rating (LRFR) method that Federal Highway Administration (FHWA) is promoting.

A parametric study and rating of steel I-girder bridges subjected to military load classification trucks

This paper presents the flexural behavior and corresponding load rating of simply-supported steel I-girder bridges subjected to military truck loads. The military trucks are categorized by the Military Load Classification (MLC) system according to the North Atlantic Treaty Organization (NATO). A total of 144 load cases are studied for 6 different bridge models based on validated 3-dimensional finite element analysis (FEA) models to examine the deflection, lateral load distribution, and load rating of the bridges. The parameters examined include bridge characteristics (span length, girder spacing, and girder stiffness) and vehicular properties (wheel-line spacing, number of axles, and weight). The response of the bridges under the MLC trucks is compared with that under the standard HS20 trucks of the American Association of State Highway and Transportation Officials Load and Resistance Factor Design (AASHTO LRFD) specifications. Existing predictive models for load distribution factors are evaluated, including the applicability of bridge code provisions for the MLC trucks. The load rating methods based on the Load Factor Rating (LFR) and the Load and Resistance Factor Rating (LRFR) are studied.

Safety assessment of steel-plate girder bridges subjected to military load classification

This paper presents a safety evaluation of steel-plate girder bridges subjected to Military Load Classification (MLC). Three-dimensional finite element models are developed to predict the bridge response, including three different span lengths (L=12m, 24m, and 36m) that represent short- and medium-span bridges. A total of 36 load cases are considered to examine the flexural behavior of the bridges. Evaluation methodologies used here are a critical weight approach and load rating based on the allowable stress rating (ASR), load factor rating (LFR), and load and resistance factor rating (LRFR) methods. The gross weight of the MLC truck is a dominant parameter for the safety of the bridges, rather than longitudinal axle spacing. Load-sharing effects between the girders improve the behavior of the bridge superstructures. Existing empirical formulas developed for nonstandard civilian trucks (e.g., Formula B) exhibit noticeable errors for MLC applications with an average error ranging on the order of 6.7–17.9. The LFR (operating) and the LRFR methods show acceptable rating factors. A safety index of 2.5 is recommended for the evaluation of bridges subjected to MLC trucks. A simplified formula is proposed to promptly assess the critical weight of MLC trucks for short- and medium-span bridges.

Development of State-Specific Load and Resistance Factor Rating Method

Recognizing the limitations of the generic truck weight data and conservative assumptions made during the calibration of live load factors for bridge rating, the AASHTO load and resistance factor rating (LRFR) manual for bridge evaluation provides sufficient flexibility and allows state agencies to adjust the live load factors based on their individual conditions and site-specific or state-specific information. This paper describes a reliability-based process that can be followed to perform such adjustments and illustrates its application using an example in which the procedure was followed during the calibration of a LRFR methodology for New York State bridges. This methodology is applied to the rating of existing bridges, posting of understrength bridges, and checking of permit trucks. The live load models used during the calibration are based on weigh-in-motion data collected from several representative sites. The LRFR live load factors developed using the proposed calibration process would provide uniform and consistent levels of bridge safety and reliability for the bridge classes and configurations targeted. The target reliability levels used during the calibration should reflect the experience gained by state bridge engineers from evaluation of existing bridges under current loading conditions.

Bridge Rating Using System Reliability Assessment. II: Improvements to Bridge Rating Practices

The current bridge-rating process described in AASHTO Manual for Bridge Evaluation, First Edition permits ratings to be determined by allowable stress, load factor, or load and resistance factor methods. These three rating methods may lead to different rated capacities and posting limits for the same bridge, a situation that has serious implications with regard to public safety and the economic well-being of communities that may be affected by bridge postings or closures. This paper is the second of two papers that summarize a research program to develop improvements to the bridge-rating process by using structural reliability methods. The first paper provided background on the research program and summarized a coordinated program of load testing and analysis to support the reliability assessment leading to the recommended improvements. This second paper presents the reliability basis for the recommended load rating, develops methods that closely couple the rating process to the results of in situ inspection and evaluation, and recommends specific improvements to current bridge-rating methods in a format that is consistent with the load and resistance factor rating (LRFR) option in the AASHTO Manual for Bridge Evaluation.

Reliability-Based Dynamic Load Allowance for Capacity Rating of Prestressed Concrete Girder Bridges

The current highway bridge design in the United States follows the AASHTO-LRFD specifications, which prescribe a dynamic load allowance, IM, of 0.33 for the dynamic effect of truck/tandem loading. Studies have shown that the IM value prescribed by the LRFD code may underestimate this dynamic effect under poor road surface conditions (RSCs). One reason for this underestimation is that the IM value employed in the AASHTO specifications was obtained from the statistical properties of the IM relative to average RSC, as defined by the ISO 1995 standards. In addition, the IM, which is a random variable with certain statistical properties, was modeled as a deterministic constant in the code calibration process. In this paper, the reliability indexes of a selected group of prestressed concrete girder bridges, designed following the AASHTO-LRFD code, are calculated by modeling the IM explicitly as a random variable for different RSCs. It is found that although the calculated bridge reliability indexes are usually above the target reliability index value of 3.5 under above-average RSCs, they can be significantly below the target value of 3.5 when the RSCs are below average. Following the load rating procedure proposed by the AASHTO load and resistance factor rating (LRFR) manual, it is also found that the code-employed IM value may overestimate the rating factors when RSCs are below average. Based on these results, appropriate IM values are suggested for different RSCs to achieve a consistent target reliability index and a reliable load rating. The results presented in this paper are particularly valuable for the rating of existing prestressed concrete girder bridges, for which the actual RSCs can be directly evaluated. The RSCs must be properly taken into account to accurately estimate the actual safety of the considered bridge.

Calibration of Load and Resistance Factor Rating Methodology in New York State

In 2003, AASHTO adopted the Guide Manual for Condition Evaluation and Load and Resistance Factor Rating (LRFR) of Highway Bridges, which was calibrated to provide uniform reliability levels represented by a target reliability index β = 3.5 for inventory ratings and β = 2.5 for operating ratings. These target values were selected to remain consistent with the AASHTO load and resistance factor bridge design specifications and provide average reliability levels similar to those of the allowable stress operating ratings and load factor ratings (LFR). The calibration process was based on a generic live load database considered to be representative of truck weight and traffic spectra at typical U.S. bridge sites. Recognizing the limitations of this generic data set, the LRFR provides sufficient flexibility and allows state agencies to adjust the LRFR load factors on the basis of their individual conditions and site-specific or state-specific information. A reliability-based procedure was developed for calibrating a state-specific NYS-LRFR that would reflect current bridge loading conditions in the state of New York, including the intensity and shape of the truck weight spectra as well as measured truck traffic data. The proposed calibration would lead to bridge ratings consistent with the current practice of the New York State Department of Transportation and provide uniform and consistent levels of bridge safety and reliability. The methodology can be easily adapted by other states on the basis of their unique truck loads and traffic conditions.

Introduction to Rehabilitation of Bridges

Many countries are giving increasing importance to rehabilitating structures, because replacing them is wasteful. There are few standardized methods and procedures to evaluate and rehabilitate an existing structure, however. Peru decided to use the manual load and resistance factor rating (LRFR), because Peruvian bridge structural designs have long used AASHTO specifications. In Peru, the LRFR manual is being applied cautiously in the evaluation of existing bridges. An application of the new standards for the Ochape Bridge located in the northeast of the country is illustrated. This information includes monitoring, reinforcement, and retrofit process control through the final stage. Peruvian engineers should be encouraged to share experiences in the assessment, rehabilitation, and reinforcement of existing bridges and thus enable Peru to refine and apply the LRFR manual according to local conditions.

Bridge rating using in-service data in the presence of strength deterioration and correlation in load processes

This paper presents a probability-based methodology for load rating bridges that can accommodate detailed site-specific in-service structural deterioration and response data in a load and resistance factor rating (LRFR) format. The use of site-specific structural response allows the elimination of a substantial portion of modelling uncertainty in live load characterization. Inclusion of structural ageing allows the bridge owner the choice to rate for longer intervals than, say, the usual two-year inspection cycle. This methodology allows the live load-effect sequence on bridges to be statistically stationary with a weakened mixing-type dependence that asymptotically decreases to zero with increasing separation in time, instead of making the common assumption of independent and identically distributed sequences of live loads. In addition, uncertainties in field measurement, modelling uncertainties and Bayesian updating of the empirical distribution function are considered to obtain an extreme value distribution of the time-dependent maximum live load. Gross section loss due to corrosion occurring with a random rate governed by an exponentiated Ornstein-Uhlenbeck type stochastic noise is considered. An illustrative example utilizes in-service peak strain data from ambient traffic collected on a high-volume steel girder bridge. In-service load and ageing resistance factor rating (ISLARFR) equations corresponding to plastic collapse of critical girder cross-section over a range of service lives are developed.

Comparative Study of Fatigue Provisions for the AASHTO Fatigue Guide Specifications and LRFR Manual for Evaluation

The recently developed Manual for condition evaluation and load and resistance factor rating (LRFR) of highway bridges in 2003 provides an alternative procedure for practicing engineers to evaluate the fatigue life of steel bridge structures. Although the evaluation manual maintains several aspects used in the AASHTO fatigue guide specification in 1990, it also utilizes formulas and values specified in the AASHTO LRFD bridge design specifications in 1998. A comparative study of the fatigue lives provided by the procedures in the Evaluation manual and the Guide specifications was performed using a life prediction of 14 steel bridges with different structural configurations and various fatigue details. It has been shown that longer predicted fatigue lives are typically obtained when using the Evaluation manual. The ratio of the finite evaluation fatigue lives for the two procedures was found to be in a range of 0.99–2.14.

An overweight permit analysis system for bridge management

With the increased overweight traffic and freight transport due to the industrial growth, the risk of accidents has increased accordingly. Under such circumstances, the Seoul Metropolitan Government (SMG) has faced with the impending need to develop solutions associated with the traffic permit for commercial vehicles with oversized/overweight. Therefore, a practical and reli ability-based Overweight PERmit Analysis System (OPERAS) was developed in this paper due to this issuance for the SMG. The procedure taken for the development of the system includes data survey, structural analysis and load carrying capacity evaluation, and sa fety assessment of bridge structures on permit routes for the overweight vehicles. Also, optimum routes for overweight vehicles were selected among a number of road routes of Seoul. The modified Load and Resistance Factor Rating (LRFR) approach based on the AASHTO method for permit vehicles was utilized to evaluate the bridge load rating for permit vehicle. Load factors of permit vehicle for bridge LRFR evaluation were calibrated based on the reliability-based Moses’ model with Korean Standard rating load Single and multiple trips for permit vehicle can be evaluated using the developed system. The system automatically uptates the permit analysis results for bridges on routes according to the structural and traffic condition.

Reliability-Based Load and Resistance Factor Rating Using In-Service Data

Traditional bridge evaluation techniques are based on design-based deterministic equations that use limited site-specific data. They do not necessarily conform to a quantifiable standard of safety and are often quite conservative. The newly emerging load and resistance factor rating (LRFR) method addresses some of these shortcomings and allows bridge rating in a manner consistent with load and resistance factor design (LRFD) but is not based on site-specific information. This paper presents a probability-based methodology for load-rating bridges by using site-specific in-service structural response data in an LRFR format. The use of a site-specific structural response allows the elimination of a substantial portion of modeling uncertainty in live load characterization (involving dynamic impact and girder distribution), which leads to more accurate bridge ratings. Rating at two different limit states, yield and plastic collapse, is proposed for specified service lives and target reliabilities. We consider a conditional Poisson occurrence of identically distributed and statistically independent (i.i.d.) loads, uncertainties in field measurement, modeling uncertainties, and Bayesian updating of the empirical distribution function to obtain an extreme-value distribution of the time-dependent maximum live load. An illustrative example uses in-service peak-strain data from ambient traffic collected on a high-volume bridge. Serial independence of the collected peak strains and of the counting process, as well as the asymptotic behavior of the extreme peak-strain values, are investigated. A set of in-service load and resistance factor rating (ISLRFR) equations optimized for a suite of bridges is developed. Results from the proposed methodology are compared with ratings derived from more traditional methods.

Load Rating of Prestressed Concrete Girder Bridges

In light of the adoption of the load and resistance factor design (LRFD) philosophy by the AASHTO Subcommittee on Bridges and Structures, research efforts are under way to facilitate the transition from load factor rating (LFR) to load and resistance factor rating (LRFR) in New Mexico. Five prestressed concrete girder bridges, courtesy of the New Mexico bridge inventory, were rated with the BRASS-GIRDER and BRASS-GIRDER (LRFD) structural software. The objectives for this study were to evaluate and verify the BRASS (bridge rating and analysis of structural systems) software, to identify the source of dissension between LFR and LRFR rating factors, and to examine any trends in the rating factors as affected by bridge geometry. The comparison of LFR and LRFR focused on both flexure and shear for the strength limit state. The LRFR method generally yielded lower rating factors for flexure, with the longer-span bridges demonstrating a larger deviation between LFR and LRFR. The live load effects were identified as the major factor contributing to the difference in flexure ratings; the dead load effects and flexural resistance had little effect. The LRFR rating factors for shear also were generally lower than those produced by LFR. The discrepancy in the shear ratings was caused by both the live load effects and shear resistance. The dead load effects contributed little to the variation in LFR and LRFR rating factors for shear. Overall, the shear ratings controlled over those based on flexure.

Load Rating of Prestressed Concrete Girder Bridges: Comparative Analysis of Load Factor Rating and Load and Resistance Factor Rating

In light of the adoption of the load and resistance factor design (LRFD) philosophy by the AASHTO Subcommittee on Bridges and Structures, research efforts are under way to facilitate the transition from load factor rating (LFR) to load and resistance factor rating (LRFR) in New Mexico. Five prestressed concrete girder bridges, courtesy of the New Mexico bridge inventory, were rated with the BRASS-GIRDER and BRASS-GIRDER (LRFD) structural software. The objectives for this study were to evaluate and verify the BRASS (bridge rating and analysis of structural systems) software, to identify the source of dissension between LFR and LRFR rating factors, and to examine any trends in the rating factors as affected by bridge geometry. The comparison of LFR and LRFR focused on both flexure and shear for the strength limit state. The LRFR method generally yielded lower rating factors for flexure, with the longer-span bridges demonstrating a larger deviation between LFR and LRFR. The live load effects were identified as the major factor contributing to the difference in flexure ratings; the dead load effects and flexural resistance had little effect. The LRFR rating factors for shear also were generally lower than those produced by LFR. The discrepancy in the shear ratings was caused by both the live load effects and shear resistance. The dead load effects contributed little to the variation in LFR and LRFR rating factors for shear. Overall, the shear ratings controlled over those based on flexure.

Load and Resistance Factor Rating Using Site-Specific Data

Traditional techniques for bridge evaluation are founded on design-based deterministic equations that use limited site-specific data; they do not necessarily conform to a quantifiable standard of safety and often are quite conservative. The newly emerging method of load and resistance factor rating (LRFR) addresses some of these shortcomings and allows bridge rating in a manner consistent with load and resistance factor design but is not based on site-specific information. A probability-based method for load rating of bridges using site-specific in-service structural response data in an LRFR format is presented. The use of site-specific structural response data eliminates a substantial portion of modeling uncertainty in live load characterization (involving dynamic impact and girder distribution) and yields more accurate bridge ratings. Rating at two limit states—yield and plastic collapse—is proposed for specified service lives and target reliabilities. This method considers a conditional Poisson occurre...