ABSTRACT
Transportation structures such as roads and bridges are exposed to moving traffic loads. Excessive static and dynamic live load effects can cause damage or even collapse of structural components or whole structures. To prevent failures, it is important to provide an adequate safety margin in the design, i.e. load effects are overestimated and load carrying capacity (resistance) is underestimated. In the new generation of design codes, safety reserve is provided in terms of load and resistance factors that are determined in the reliability-based calibration process (Nowak & Collins 2013). The acceptance criterion is closeness to the target reliability index. The code calibration requires the knowledge of statistical parameters of load and resistance; in particular, this applies to live load. It is important to know the cumulative distribution of live load for the considered location (road or bridge) and predict what is the maximum live load that can be expected within a given time period, e.g. a day, a week, a month, a year, and so on. The objective of this paper is to present the development of live load statistics using the weigh-in-motion (WIM) data from several locations in Alabama.
The WIM data obtained from Alabama Department of Transportation (ALDOT) includes station codes, direction of travel code, gross vehicle weight (GVW), vehicle type, axle spacing, axle loads, time of record and also vehicle speed (Table 1). To calculate the load effects such as moment and shear force, the vehicles from the WIM database are “run” over influence lines. The resulting load spectra are pre-sented in form of cumulative distribution functions (CDF) on the normal probability paper. CDF’s plot-ted on the normal probability paper allow for an easier interpretation of the results. If the resulting CDF is in form of a straight line, the corresponding GVW, moment or shear can be considered as a normal random variable. The mean value and standard deviation can be read directly from the graph. Information on construction and interpretation of the normal probability paper can be found in the probability textbooks, e.g. Nowak & Collins (2013). Number of vehicles in the WIM database for years 2009–2014.
Location code
Period of taking records, years
Total number of truck records
911
2006–2007; 2013–2014
1,414,723
915
2006–2007; 2010–2014
1,668,558
918
2006–2007
9,454,026
931
2006–2011; 2014
9,133,511
933
2006–2011; 2013–2014
5,061,730
934
2006–2008; 2013–2014
3,361,605
942
2006–2008;2013–2014
3,249,384
960
2006–2008;2013–2014
1,435,591
961
2006–2008; 2013–2014
5,171,572
963
2006–2008; 2013–2014
6,574,376
964
2006–2011; 2013–2014
3,767,670
965
2006–2008; 2013–2014
6,562,102
US231
2012–2014
452,576
57,307,424
