Procedure to Estimate Loading from Weigh-in-Motion Data

1996 ◽  
Vol 1536 (1) ◽  
pp. 19-24
Author(s):  
Shie-Shin Wu
Keyword(s):  
Monitoring Program ◽  
Traffic Monitoring ◽  
Traffic Classification ◽  
Traffic Loading ◽  
Motion Data ◽  
Weigh In Motion ◽  
Load Factors ◽  
Different Types ◽  
Truck Load ◽  
Truck Weight

Truck weight data collected from weigh-in-motion (WIM) sites were used to develop a procedure to estimate truck load factors for pavement design purposes. A conceptual procedure that uses WIM data to derive equivalent single-axle load factors for different types of trucks is presented. Sets of load factors can be developed for different types of facilities. An example is provided to illustrate how these factors can be used by engineers to calculate project design loading. Applying these factors to traffic classification counts collected from the statewide traffic monitoring program, engineers can also compute network traffic loading history.

Probabilistic assessment of a design truck model and live load factor from weigh-in-motion data for Mexican Highway bridge design

2015 ◽  
Vol 42 (11) ◽  
pp. 970-974 ◽  
Author(s):  
A.D. García-Soto ◽  
A. Hernández-Martínez ◽  
J.G. Valdés-Vázquez
Keyword(s):  
Bridge Engineering ◽  
Load Factor ◽  
Engineering Practice ◽  
Live Load ◽  
Bridge Design ◽  
Statistical Characterization ◽  
Motion Data ◽  
Weigh In Motion ◽  
Load Factors ◽  
Truck Load

This study is focused on the statistical characterization of live load effects on bridges using weigh-in-motion data from a Mexican highway. A truck load model that is simpler than the design truck model implemented in the current Mexican requirements is suggested for design. The statistics are employed in target-reliability based calibration and verification of load factors in Mexican bridge design. Suggestions that could be useful for the Canadian bridge engineering practice are included.

Axle Load Distribution Characterization for Mechanistic Pavement Design

1998 ◽  
Vol 1629 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Jong R. Kim ◽  
Leslie Titus-Glover ◽  
Michael I. Darter ◽  
Robert K. Kumapley
Keyword(s):  
Load Distribution ◽  
Pavement Design ◽  
Performance Study ◽  
Traffic Loading ◽  
Design Procedures ◽  
Motion Data ◽  
The North ◽  
Load Distributions ◽  
Weigh In Motion ◽  
The 1960S

Proper consideration of traffic loading in pavement design requires knowledge of the full axle load distribution by the main axle types, including single, tandem, and tridem axles. Although the equivalent single axle load (ESAL) concept has been used since the 1960s for empirical pavement design, the new mechanistic-based pavement design procedures under development by various agencies most likely will require the use of the axle load distribution. Procedures and models for converting average daily traffic into ESALs and axle load distribution are presented, as are the relevant issues on the characterization of the full axle load distributions for single, tandem, and tridem axles for use in mechanistic-based pavement design. Weigh-in-motion data from the North Central Region of the Long-Term Pavement Performance study database were used to develop the models for predicting axle load distribution.

Statistical Investigation of Design Live Load by Applying WIM Data with Different Degree of Compliance with Truck Weight Limit

2018 ◽  
Author(s):  
Inyeol Paik ◽  
Kilhwan Jeong
Keyword(s):  
Measurement Data ◽  
Statistical Investigation ◽  
Live Load ◽  
Overload Control ◽  
Safety Level ◽  
Motion Data ◽  
Weigh In Motion ◽  
Single Vehicle ◽  
Live Load Model ◽  
Truck Weight

<p>The live load model applied to the design of the bridge should be defined so that a target safety level can be secured in the actual traffic environment. In this paper, it is presented that the live load could be greatly affected by the measurement data used for the statistical estimation process. For this purpose, a statistical analysis is performed on WIM (weigh-in-motion) data measured during different periods in terms of the overload control at the same spot of an expressway in Korea. The effects by a single vehicle, the back-to-back series vehicles and the side-by-side parallel vehicles are obtained and compared.</p>

State-Specific LRFR Live Load Factors Using Weigh-in-Motion Data

2008 ◽  
Vol 13 (4) ◽  
pp. 339-350 ◽  
Author(s):  
Jordan Pelphrey ◽  
Christopher Higgins ◽  
Bala Sivakumar ◽  
Richard L. Groff ◽  
Bert H. Hartman ◽  
...  
Keyword(s):  
Live Load ◽  
Motion Data ◽  
Weigh In Motion ◽  
Load Factors

Impact of Specialized Hauling Vehicles and Emergency Vehicles on Bridge Load Rating

2021 ◽  
pp. 036119812110145
Author(s):  
Peng Lou ◽  
Chan Yang ◽  
Hani Nassif
Keyword(s):  
Highway Bridges ◽  
Extensive Literature ◽  
Load Rating ◽  
Motion Data ◽  
Weigh In Motion ◽  
Load Factors ◽  
Emergency Vehicles ◽  
Load Effects ◽  
Load Ratings ◽  
Bridge Load Rating

The Federal Highway Administration (FHWA) mandated states to adopt specialized hauling vehicles (SHVs) and emergency vehicles (EVs) in 2013 and 2016, respectively, in the load rating of bridges. Both the AASHTO single-unit trucks (SUs) and EVs are specially configured so that they may result in high load effects and can adversely affect bridge load rating factors. This paper investigates the impacts of rating these vehicles on the states’ bridge load ratings. An extensive literature review of the states’ load rating policies is performed. To determine whether any state can possibly be exempted from the new load ratings for SUs and EVs for Interstate highway bridges, the load effects of various state legal vehicles are analyzed and compared with those of SUs and EVs. The results of the study indicate the inevitability of executing the new load rating analysis for SUs and EVs for the vast majority of states. Weigh-in-motion data are processed to screen the potential EV traffic fleeting on the highway, and the calibrated live load factors are proposed for EVs accordingly. The load effects are found to be smaller than those FHWA originally assigned, improving the rating factors. Lastly, this paper proposes a screening tool to help state agencies to convert the known rating factors to the rating factors of SUs and EVs so that the load rating work can be prioritized for the bridges that are vulnerable to SUs and EVs.

scholarly journals Machine learning of Truck Traffic Classification groups from Weigh-in-Motion data

2021 ◽  
pp. 100178
Author(s):  
Narges Tahaei ◽  
Jidong J. Yang ◽  
Mi Geum Chorzepa ◽  
S. Sonny Kim ◽  
Stephan A. Durham
Keyword(s):  
Machine Learning ◽  
Traffic Classification ◽  
Truck Traffic ◽  
Motion Data ◽  
Weigh In Motion

Evasion of Weight-Enforcement Stations by Trucks

1997 ◽  
Vol 1570 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Wiley Cunagin ◽  
W. A. Mickler ◽  
Charles Wright
Keyword(s):  
Traffic Monitoring ◽  
Department Of Transportation ◽  
Weigh In Motion ◽  
Florida Department ◽  
Low Levels ◽  
Axle Loads ◽  
Truck Weight ◽  
Enforcement Activity ◽  
Avoidance Problem ◽  
Northeast Corner

Highway engineers have become increasingly concerned about the deterioration of the nation’s highway pavements. They suspect that overweight trucks are a primary cause of the problem. Before 1979, the data needed to assess the magnitude of the overweight-truck problem did not exist. Consequently, several studies were conducted to address the magnitude and location of overweight trucking. These studies included efforts to improve truck weight-enforcement programs and to assess the feasibility of using weigh-in-motion (WIM) equipment for weight enforcement. The Florida Department of Transportation (FDOT) undertook the study documented in this paper. The objective of the study was to assess the magnitude, expressed in equivalent single-axle loads (ESALs), of the problem of avoidance of weight-enforcement stations by overweight trucks. The study also considered whether bypassing traffic was local or interstate. FDOT selected the I-95 corridor in the northeast corner of Florida. Two permanent weight-enforcement stations and four bypass route locations were used as traffic monitoring sites. The results of the study indicate that the numbers of overweight vehicles decrease with increasing enforcement activity, but that vehicles attempt to bypass permanent truck weight-enforcement stations. In general, the violations at the permanent weight-enforcement stations were minor, whereas those on the bypass routes were much more severe. These results, when considered with the WIM data and the experience of truck weight-enforcement officers, suggest that only intensive enforcement activity can reduce violations to low levels. Information for enforcement, especially in the definition and quantification of the scale avoidance problem, is provided. Recommendations are made concerning corridor areas and extended and random enforcement operations.

A Strategy for Design of Marine Pollution Monitoring Studies

1986 ◽  
Vol 18 (4-5) ◽  
pp. 15-26 ◽  
Author(s):  
D. A. Segar ◽  
E. Stamman
Keyword(s):  
Information Needs ◽  
Marine Pollution ◽  
Monitoring Program ◽  
Pollution Monitoring ◽  
Systematic Evaluation ◽  
Management Information ◽  
Different Types ◽  
Management Context ◽  
Definition Of ◽  
Scientific Testing

Most historical marine pollution monitoring has proven useless in a management context. A strategy for development of effective marine pollution monitoring programs is outlined. This strategy is based on the following steps: 1) systematic evaluation of the management information needs, 2) identification of the hypothetical impacts associated with those management concerns, and 3) investigation of the feasibility of monitoring those effects such that the existence, or absence, of a specified level of effects can be established in a statistically-valid manner. There are two fundamentally different types of monitoring program: site-specific and regional. These two types of program differ markedly in scope and approach when designed through application of this strategy. The strategy requires development of null hypotheses which address management concerns and which are amenable to scientific testing. In order for the program to be successful, the null hypotheses selected for inclusion in a marine pollution monitoring program must address levels of effect which are predefined to be environmentally significant. The definition of environmentally significant effect levels is a difficult process which must be primarily the responsibility of the managerial community.

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