Impact of Overweight Trucks on the Service Life of Bridge Girders

Transportation Research Record: Journal of the Transportation R

Truban

2018

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The AASHTO LRFD Bridge Design Specifications defines Strength II limit state for agencies to consider the load combination by owner-specified special design vehicles, evaluation permit vehicles, or both. The configuration and characteristics of permit vehicles vary from state to state. In addition, the code calibration process performed in 1994 for the development of the live load factors was applied only to the Strength I limit state. In New Jersey, the design permit vehicle was not developed based on actual permit records or weigh-in-motion (WIM) data. Recently, with the development of permit-issuing management and WIM technology, there is a need to evaluate the effectiveness of design permit vehicles. This study aims to develop a live load model for the assessment of Strength II limit state for New Jersey Department of Transportation (NJDOT). Five years of permit vehicle records are provided by NJDOT for the development of the live load model. The distribution of Gross Vehicle Weight is best described as the Generalized Extreme Value distribution. Load effects are simulated for different span lengths. The mean and standard deviation (SD) of the 75-year maximum loads are predicted using different extrapolation approaches. The results show that NJDOT Design Permit Vehicle provides stable mean and SD of bias ratios at 75-year level. In comparison with the current AASHTO live load factor of 1.35, the averages of the bias ratios at the 75-year level are found to be 1.31, 1.23, and 1.16 for the positive moment, shear, and negative moment, respectively.

mentioning

confidence: 99%

Development of Live Load Model for Strength II Limit State in AASHTO LRFD Design Specifications

Lou

Transportation Research Record: Journal of the Transportation R

Truban

2018

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“…A power exponential function of order two is used to fit the deterioration ratings as a continuous function over time, BE ( t ) = exp ( − ρ e · t 2 ) , where ρ e is the element deterioration rate and t is time expressed in years. The fitting is performed using the “statistical method” ( 36 ) as proposed by several researchers ( 18 , 37 – 40 ). This approach is based on the pioneering work of Shina in the 1980s on predicting the service life of bridges ( 36 ).…”

Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Techniques for the Analysis of National Bridge Inventory and Bridge Element Data

Fiorillo

Transportation Research Record: Journal of the Transportation R

2019

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The MAP-21 Act requires information on bridge assets to be at the element level for management operations in the U.S.A. This approach has the objective of improving future predictions of the performance of bridge assets for a more precise evaluation of condition and correct allocation of management funds to keep bridges in a good state of repair. Although bridge element conditions were introduced in the 1990s, the application of such data had never been mandatory for bridge asset management until 2014, therefore, the amount of historical data on bridge element (BE) condition is still limited. On the other hand, National Bridge Inventory (NBI) ratings have been collected since the 1970s and a wide range of data are available. Therefore, it is natural to ask whether BE condition can be predicted using NBI data. In the past, researchers statistically related BE and NBI data, but little has been done to revert NBI to BE. This paper addresses both challenges of mapping BE–NBI condition data using several machine learning techniques. The results of the analysis of these techniques applied to a sample of about 9,000 bridges from northeastern states of the U.S.A. shows that between 79.8% and 100% of the NBI ratings for deck, superstructure, and substructure can be predicted within a rating error of ± 1. The back-mapping operation of NBI time-dependent ratings to BE deterioration profiles for deck, superstructure, and substructure can also be predicted accurately with a probability greater than 50% at the 95% confidence level.

“…Similar to the previous section, the approach developed by Nassif et al (2) was utilized for estimating bridge damage. In this approach, finite element models are developed to estimate the service life of different types of bridge decks and girders for concrete and steel (14,15). Then, the monetary impact of OW trucks on the system of highway bridges is quantified by the bridge life-cycle cost analysis.…”

Section: Estimation Of Bridge Damagementioning

confidence: 99%

Analysis of Overweight Truck Permit Policy in New Jersey

Demiroluk

Transportation Research Record: Journal of the Transportation R

Özbay

et al. 2021

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The roadway infrastructure constantly deteriorates because of environmental conditions, but other factors such as exposure to heavy trucks exacerbates the rate of deterioration. Therefore, decision-makers are constantly searching for ways to optimize allocation of the limited funds for repair, maintenance, and rehabilitation of New Jersey’s infrastructure. New Jersey legislation requires operators of overweight (OW) trucks to obtain a permit to use the infrastructure. The New Jersey Department of Transportation (NJDOT) issues a variety of permits based on the types of goods carried. These permits allow OW trucks to use the infrastructure either for a single trip or for multiple trips. Therefore, one major concern is whether the permit revenue of the agency can recoup the actual cost of damage to the infrastructure caused by these OW trucks. This study investigates whether NJDOT’s current permit fee program can collect enough revenue to meet the actual cost of damage to the infrastructure caused by these heavy-weight permit trucks. The infrastructure damage is estimated by using pavement and bridge deterioration models and New Jersey permit data from 2013 to 2018 containing vehicle configuration and vehicle route. The analysis indicates that although the cost of infrastructure damage can be recovered for certain permit types, there is room for improvement in the permit program. Moreover, based on permit rules in other states, the overall rank of the New Jersey permit program is evaluated and possible revisions are recommended for future permit policies.