توسعه ضریب تاثیر پویا برای ارزیابی عملکرد پلهای بتنی چند تیر اهنی موجود

The dynamic effect of moving vehicles on bridges is generally treated as a dynamic load allowance (or dynamic impact factor) in many design codes. Due to the road surface deterioration of existing bridges, studies have shown that the calculated impact factors from field measurements could be higher than the values specified in design codes that mainly target at new bridge designs. This paper develops a 3D vehicle–bridge coupled model to simulate the interaction between a bridge and vehicles and investigates the impact factor for multi-girder concrete bridges. The effects of bridge span length, vehicle speed, and road surface condition on the impact factor are examined. Chi-square tests are then performed on the impact factors and it is found that the impact factors obtained under the same road surface condition follow the Extreme-I type distribution. Finally, simple expressions for calculating the impact factors are suggested applicable to both new and existing bridges. Corresponding confidence levels with the proposed impact factors for the five studied bridges indicate that the proposed expressions can be used with considerable confidence. The proposed expressions for impact factor can be used as a modification of the AASHTO specifications when dealing with short bridges and old bridges with poor road surface condition for which the AASHTO specifications may underestimate the impact factor.

The dynamic effect of moving vehicles on bridges is generally treated as a dynamic load allowance (or dynamic impact factor) in many design codes. For example, a value of 0.33 is suggested for the dynamic impact factor by the AASHTO LRFD specifications [1]. In AASHTO standard specifications [2], it is expressed as a function of the bridge length. In other codes, like Canada’s Ontario Bridge Design Code [3] and Australia’s NAASRA Code [4], the impact factor is defined as a function of the first flexural frequency of the bridge. A review of various impact factors for highway bridges implemented by various countries around the world can be found in GangaRao [5]. In the past two decades, significant efforts have been made to investigate the dynamic effect caused by dynamic vehicle loads using different analytical bridge–vehicle models [6], [7], [8], [9], [10], [11], [12] and [13]. Field testing has also been carried out to verify the impact factors specified in the design codes [11], [14], [15] and [16]. However, it has been demonstrated through both analytical studies and field testing that the design codes may underestimate the impact factor under poor road surface conditions [15], [16] and [17]. One of the reasons for the underestimation of the impact factor could be that design codes, like the AASHTO specifications, are mainly providing guidelines for designing new bridges with good road surface condition. Therefore, the code-specified impact factors may not be a problem for bridges with good surface condition. However, for a large majority of old bridges whose road surface conditions have deteriorated due to factors like aging, corrosion, increased gross vehicle weight and so on, caution should be taken when using the code-specified impact factor. As a matter of fact, the average age of bridges in the United States has reached 43 years according to a recent AASHTO report [18]. Therefore, for safety purposes more appropriate impact factors should be provided for performance evaluation of these old bridges. Chang and Lee [8] proposed a function of impact factor for simple-span girder bridges with respect to bridge span length, vehicle traveling speed, and maximum magnitude of surface roughness; however, their study was based on simplified bridge and vehicle models, and more theoretical support was also needed for the proposed impact factor functions. In this paper a 3D vehicle–bridge coupled model is used to analyze the impact factor for multi-girder bridges. The relationship between three parameters, which include the bridge span length, road surface condition, and vehicle speed, and the impact factor is examined by numerical simulations. Chi-square tests are then performed to examine the distribution of the impact factors under the same road surface condition for all the five road surface conditions considered. Based on the results from this study, reasonable expressions for calculating the impact factor are suggested applicable to both new and existing bridges. Corresponding confidence levels with the proposed impact factors for the five studied bridges are provided along with the determined distributions of the impact factors. The proposed expressions can be used as a modification of the AASHTO specifications when dealing with short bridges and old bridges with poor road surface condition for which the AASHTO specifications may underestimate the impact factor.

A 3D vehicle–bridge coupled model was established, and numerical simulations were performed to study the impact factor for multi-girder concrete bridges. The effects of three parameters including the bridge span length, vehicle speed, and road surface condition were investigated. Simple and reasonable expressions for calculating the impact factor were suggested based on a study of the distribution of the impact factors. Corresponding confidence levels with the proposed impact factors for the five studied bridges were provided, indicating that the proposed expressions can be used with considerable confidence. The proposed impact factors were also checked using two other girder bridges, and results confirmed that the proposed impact factors are also appropriate for bridges with different girder spacing and bridge width. The proposed expressions for the impact factor in this study can be used as a modification of the AASHTO specifications when dealing with short bridges and old bridges with poor road surface condition for which the AASHTO specification may underestimate the impact factor. Road surface condition has proven to be a significant factor for bridge dynamic loads by numerous studies in the literature; however, in the current AASHTO codes, the same impact factor is used for all road surface conditions. While this treatment is reasonable for new bridge design, evaluation of existing bridges with a possible deteriorated surface condition requires a separate treatment for different road surface conditions. For future researches, a reliability study may be conducted to check the corresponding reliability index of the proposed impact factor for each road surface condition. It needs to be pointed out that many other factors, including truck suspension characteristics and traffic flow will affect the results and many previous studies have examined the effects of these factors. In the present study, one standard truck is used for the impact factor study. This is consistent with the AASHTO codes that are based on a single standard truck for the impact factor. Selection of suspension characteristics is also a difficult task since such information for many different trucks are not available. In this study, this selection is based on the best data available and the experience of other researchers. However, the same value is used for all road surface conditions and therefore, the relative effect of the road surface conditions on the impact factor is valid.