New Truck Loading Model For Rural Bridges In Saskatchewan

ABSTRACT In rural municipalities in Saskatchewan, there are approximately 1700 bridges, many of which were constructed in the 1950s to 1960s and are therefore nearing the end of their expected service life. Evaluations for those bridges are therefore needed to make cost-effective decisions on rehabilitation or replacement to maintain the transportation network for the local regions. However, the lack of existing information on estimating the critical load effects occurring on short span bridges, on low traffic roadways, and over specific reference periods makes it difficult to perform the bridge evaluations with an adequate degree of confidence. This study focuses on aspects of the estimation of traffic load effects based on the use of a truck loading model to generate the nominal critical load effects in bridges. In order to be truly representative of actual traffic, the selected truck model must reproduce the critical static, dynamic and total load effects that may occur on rural bridges in Saskatchewan over specific reference periods. Since the traffic conditions in rural Saskatchewan differ from more populated regions, data used in the analysis for this study were all collected in the local regions. A probabilistic and statistical approach was used to introduce a new design truck model designated as SRL-895. Weigh-in-motion (WIM) data from six locations across Saskatchewan (SK) were evaluated and used to determine the most critical WIM station, which was then used in combination with traffic count data in rural areas in SK to simulate extreme truck loading configurations that caused extreme static load effects on bridges over a specific reference period. This new truck loading model provides bias factors that are much more consistent for various load effects, span lengths, and reference periods compared to those provided by the truck model specified in the Canadian Highway Bridge Design Code. The dynamic component of critical load effects measured from rural bridge tests was considered as a random variable in an estimation of the critical dynamic load effect over specific reference periods. This method improves the randomness of sampling from a bridge test. The inclusion of specific reference periods in the method used for the estimation of the critical dynamic load effects makes it consistent with that used for the estimation of the critical static load effects. Total critical load effects in bridge girders were estimated by combining simultaneously all relevant factors that have different contributions to the total load effect over a specific reference period, including static and dynamic effects, as well as the distribution of load effects across the width of the bridge. This method permits the determination of a live load factor that can be used for calculations of total load effect, instead of combining live load factor for calculation of critical static load effects and adding a separate dynamic load allowance as is specified in CSA S6-19. The emphasis of this study was on the development of a rational methodology for establishing a combination of site-specific traffic characteristics with field measurements in a rigorous and consistent statistical approach. The proposed method addressed several deficiencies that have been identified with methods currently found in the literature with the aim of having more accurate estimates for critical load effects in bridges based on available data sources for the local region under consideration.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Saskatchewan

Year dc:date.issued

2021

Author dc:creator

• Doan, Kien T.

Advisors dc:contributor.advisor

• Sparling, Bruce
• Feldman, Lisa

Committee members dc:contributor.committeemember

• Hawkes, Chris
• Wegner, Leon
• Boulfiza, Moh
• Chowdhury, Nurul

• Rural bridges
• truck loading model
• traffic load effects
• static load effects
• dynamic load effects
• total traffic load effects.

Handle dc:identifier.uri

https://hdl.handle.net/10388/13560

OAI identifier oai:identifier

oai:harvest.usask.ca:10388/13560