Measurement of Truck Load on Bridges in Detroit, Michigan, Area

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TRANSPORTATION RESEARCH RECORD 1541

Measurement of Truck Load on Bridges in Detroit, Michigan, Area SANGJIN KIM, ANDREJ F. SOKOLIK, AND ANDRZEJ S. NOWAK The objective of the study was to determine the actual truck loads on selected bridges in the Detroit, Michigan, area. Seven representative bridges were selected. The measurements were taken by using a weighin-motion system. For each measured truck, the record included vehicle speed, axle spacing, and axle loads. The variation in the accuracy of the gross vehicle weight (GVW) measurement was estimated to be 65 percent and that of the axle weights was estimated to be 620 percent for most types of trucks. Selected bridges were instrumented, and measurements were taken for 2 or 3 consecutive days. There was a considerable variation in traffic volumes and the weights of trucks, even within a given geographic area. The estimated average daily truck traffic varied from 500 to 1,500 in one direction. The maximum observed truck weights varied from 360 kN (81 kip) to 1100 kN (250 kip). The maximum observed axle weights varied from 90 kN (20 kip) to 225 kN (50 kip). The percentage of trucks exceeding the legal limits in Michigan varied depending on the road. The heaviest GVWs and axle weights were observed on Interstate highways. The largest percentage of overloaded trucks was observed for 11-axle vehicles. The maximum lane moments and shears from the trucks varied between 0.6 and 2.0 times AASHTO load and resistance factor design values. It was found that there are similarities in GVW and lane moment distributions.

The two major concerns of bridge owners are bridge resistance and actual loads. It has been observed that truck loads are strongly site specific. More realistic evaluations of bridges may be possible by using site-specific truck loads. Therefore, the objective of this paper is to present actual truck loads on selected bridges in a major metropolitan area. In 1993 and 1994 a study to measure the actual loads of truck traffic in the Detroit, Michigan, area was carried out by using weigh-inmotion (WIM) equipment (1). The WIM equipment provides several advantages over other technologies and was therefore used for the present study. The WIM system provides unbiased truck data because the instrumentation is invisible to the truck drivers, and therefore they cannot make any effort to avoid the scale. It is portable and accurate, and the data can be collected without the knowledge of the traffic using the bridge. The accuracy of measurements of gross vehicle weight (GVW) is estimated at 95 percent for most types of trucks. The accuracy of axle weights is estimated at 80 percent (2). The testing equipment was provided by the Michigan Department of Transportation (MDOT) and the University of Michigan. The measurements were taken at a total of seven bridge sites. The observed truck traffic and the numbers of trucks and corresponding numbers of axles, parameters of GVW, and axle weight are summarized. The histograms and cumulative distribution functions (CDFs) of GVW and axle weight are also provided.

Department of Civil and Environmental Engineering, 2370 G.G. Brown Building, University of Michigan, Ann Arbor, Mich. 48109-2125.

TESTING PROCEDURE Truck data, such as static axle loads, GVWs, axle spacings, and vehicle speeds, were collected with a WIM data acquisition system. The WIM system operates on a bridge, which serves as a scale. The structure is instrumented and strains are measured, and from the strain data, truck axle loads and GVWs are calculated. The process is repeated for all vehicles passing on the bridge. The equipment was calibrated by using a truck with a known GVW, axle weights, and axle spacings. Traffic control and calibration trucks were provided by MDOT. The system consists of the main processing unit, strain transducers, a portable computer, cables, battery, and lane sensors. The unit is capable of handling up to eight channels through the analog front end (AFE) and two channels for lane sensors. The main component configuration of the WIM system is illustrated in Figure 1. The main processing unit is constructed with three circuit boards that collect, process, and store all data received from the strain transducers and the roadway sensors. The central processing unit is a Motorola MC68000 processor and is connected to the AFE board via a parallel data port. The AFE acts as a signal conditioner and amplifier with a capacity of eight input channels. Before data acquisition the AFE resets the strain signals to zero. The autobalancing of the strain transducers is started when the first axle of the vehicle crosses the first lane sensors. As the truck crosses the two lane sensors, the speed and axle spacings are determined. When the vehicle reaches the bridge the strain sampling is activated. When the last axle of the vehicle exits the instrumented bridge span, the strain sampling is turned off. The data received from the strain transducers are processed by using influence lines to determine GVW and axle weight. The total weighing process takes from 1.7 to 3.0 sec, depending on the instrumented span length, vehicle length, number of axles, and vehicle speed. The WIM equipment operates on 12 V of direct current. All files are stored in a static random access memory (SRAM) that is capable of holding up to 20,000 truck records. Captured strain files may also be stored in SRAM with a maximum of 175 records. The strain transducer used for the system is demountable and is clamped to the upper or lower surface of the bottom flange of the steel girders. All transducers are placed on the girders at the same distance from the abutment, in the middle third of a simple span. Two types of lane sensors were used, depending on the site conditions: tape switches and infrared sensors. Tape switches consist of two metallic strips that are held out of contact in the normal condition. As a vehicle wheel passes over the tape it forces the metallic strips into contact and grounds a switch. If a voltage is impressed across the switch, a signal is obtained at the instant that the vehicle crosses the tape. This signal is fed to a computer, where the speed, axle spacing, and number of axles are determined. The tape switches

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TABLE 1 Parameters of Selected Bridges (1 m 5 3.28 ft)

5. M39/M10: M-39 southbound to M-10 southbound. 6. I94/I75: I-94 westbound to I-75 southbound. 7. M153/M39: M-153 over M-39.

TRUCK LOAD MEASUREMENTS Truck Traffic

FIGURE 1

Primary components of WIM system.

are placed perpendicular to the traffic flow and are used to trigger the strain data collection. In slow-traffic areas, because of ineffective tape switches, infrared sensors were used. They consist of an infrared light beam source and a reflector. A source of light is installed on the side of the road and a reflector is installed in the center of the traffic lane. The infrared system is more difficult to install and a truck can easily move the reflector and interrupt the operation. Both the tape switches and the infrared sensors had problems under rainy weather conditions.

SELECTION OF BRIDGES Seven representative bridges in the Detroit area were selected in cooperation with MDOT staff (1). Important factors considered in the selection process included span length, accessibility for the equipment, low dynamic effects, the locations of the bridges, average daily truck traffic (ADTT), placement of roadway sensors, and the presence of traffic control. The basic parameters of the selected bridges are summarized in Table 1. These include span length, number of girders, girder spacing, number of traffic lanes, and ADTT. ADTT was estimated on the basis of the truck measurements performed for the present study. It varied from 500 to 1,500 in one direction. For brevity the following designations have been adopted according to the bridge location and are used throughout this paper. 1. 2. 3. 4.

WY/I94: Wyoming Road over I-94. 194/M10: I-94 eastbound to M-10 northbound. US12/I94: US-12 eastbound to I-94 eastbound. DA/M10: Davison Avenue over M-10.

A summary of the measured truck traffic mix is presented in Table 2. Figure 2 presents a frequency histogram of truck types for all bridge locations. Data are included for vehicles with GVWs of greater than 45 kN (10 kip) for two-axle vehicles or greater than 70 kN (15 kip) for vehicles with three or more axles. For the bridges considered the numbers of trucks with the various numbers of axles are given. The largest number of vehicles was in the two-axle category; this was followed by five-axle trucks. Many two-axle vehicles are of low GVW and do not affect the life of a bridge. An important vehicle type is the five-axle truck, which is the majority of the heavy vehicles (greater than 70 kN). The heaviest vehicles in the Detroit metropolitan area are 11-axle trucks, which constitute a large proportion of vehicles on I94/M10. Truck Weights and Axle Weights The results of field truck measurements are provided on normal probability paper (Figure 3). The vertical axis is the inverse of the standard normal probability function corresponding to the probability exceedance (3). For example, 0 corresponds to a probability of 0.5 (the probability of exceeding the truck weight corresponding to 0 on the vertical axis is 0.5), 1.0 corresponds to 0.159 (the probability of exceeding the truck weight corresponding to 1.0 on the vertical axis is 0.159), and 2 corresponds to 0.02 (the probability of exceeding the truck weight corresponding to 2.0 on the vertical axis is 0.02). The CDFs of GVWs are provided in Figure 3. Depending on the route, the maximum GVWs vary from 360 kN (81 kip) to more than 1100 kN (250 kip), with the average being from 90 kN (20 kip) to TABLE 2 Number of Trucks Weighed

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TABLE 3 Percentage of Overloaded Vehicles and Axles

FIGURE 2

Truck type histogram for all bridges.

270 kN (60 kip). Traffic on I-94 over M-10 is heavier than that on any of the other bridges considered in the study. The percentage of 5- and 11-axle trucks with GVWs larger than the estimated legal limits is provided in Table 3. Most states allow a maximum GVW of 355 kN (80 kip); however, the Michigan legal limit is 730 kN (164 kip) for an 11-axle truck. From Table 3 and Figure 3, it can be seen that a number of illegally loaded trucks were measured during the data collection at several of the sites. Figures 4, 5, and 6 present GVW histograms of 2-, 5-, and 11-axle vehicles, respectively, for all bridges. There is a distinct difference in the GVWs for each axle group. The histograms indicate that the heaviest are 11-axle trucks and the lightest are 2-axle trucks.

FIGURE 3

GVW distributions (1 kN 5 0.225 kip).

The performance of a bridge deck or road pavement is affected by axle load rather than GVW. The CDFs of axle weights are plotted on normal probability paper in Figure 7. As with the GVW, the heaviest value was observed on I94/M10. The maximum axle weights varied from 90 kN (20 kip) to 225 kN (50 kip), and average values varied from 30 kN (7 kip) to 60 kN (14 kip). For 5- and 11axle trucks, the percentage of vehicles with axle weights larger than the estimated legal limit is also provided in Table 3. The axle weight limits varied from 58 kN (13 kip) to 80 kN (18 kip), depending on axle spacing and truck configuration, but only the maximum legal limit was used as a criterion in Table 3. The results of the measurements indicate that a considerable number of axle weights exceed the legal limit. The CDFs of steering axle weights are plotted on normal probability paper in Figure 8. Comparison of the steering axle weight CDFs and all axle weight CDFs (Figures 7 and 8) indicates a significant difference in both variation and magnitude. Steering axles had much smaller variations and maximum axle weights. The standard deviations of steering axle weight varied from 9 kN (2 kip) to 22 kN (5 kip), and the maximum varied from 58 kN (13 kip) to

FIGURE 4 GVW histogram for two-axle vehicles, all bridges (1 kN 5 0.225 kip).

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FIGURE 5 GVW histogram for five-axle vehicles, all bridges (1 kN 5 0.225 kip).

98 kN (22 kip). On the contrary, the standard deviations of all axle weight varied from 13 kN (3 kip) to 36 kN (8 kip), and the maximum varied from 90 kN (20 kip) to 225 kN (50 kip).

EFFECT OF BRIDGE LIVE LOAD From the bridge safety point of view, it is more important to consider load effect rather than loads, because bridge damage is caused by load effect rather than the load itself. A given GVW may have different effects depending on the distributions of the axle weights, the axle spacing, and the number of axles. The effect of truck loads on bridges can be evaluated by consideration of lane moments and shears. Static lane moment and shear CDFs are presented for a typical simple span of 27 m (90 ft) in Figures 9 and 10 for each bridge. Each truck in the data base is analytically driven across the bridge to determine the maximum static bending moment and shear per lane for various simple span lengths. For an easier comparison, the moments and shears are divided by the corresponding load and resistance factor design (LRFD) moment and shear, which are cal-

FIGURE 6 GVW histogram for 11-axle vehicles, all bridges (1 kN 5 0.225 kip).

FIGURE 7

Axle weight distributions (1 kN 5 0.225 kip).

FIGURE 8 Steering axle weight distributions (1 kN 5 0.225 kip).

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culated according to the AASHTO LRFD specifications (4). Only CDFs for a span of 27 m (90 ft) are provided here, because the CDFs for other spans had similar distributions. The lane moments in Figure 9 indicate a wide variation among bridges. The maximum values of the lane moment-to-LRFD moment ratio varied from 0.6 at M153/M39 to 2.0 at I94/M10. All sites had a median lane moment between 0.16 and 0.34 times the LRFD moment, which corresponds to an inverse normal value of 0. The variation of lane shears in Figure 10 is similar to that of lane moments. For I94/M10 the extreme value exceeded 2.0. For other bridges the maximum shears varied from 0.65 at M153/M39 to 1.5 at I94/I75. GVW AND LANE MOMENT DISTRIBUTION RELATIONSHIP

FIGURE 9 (90 ft).

Lane moment distributions for span length of 27 m

FIGURE 10 (90 ft).

Lane shear distributions for span length of 27 m

It has been observed that the CDF of GVW (Figure 3) and the CDF of lane moment (Figure 9) are similar in shape. The similarity can also be found between GVW and lane shear distribution and between lane moment and lane shear distribution. The GVW-versuslane moment distribution is plotted in Figure 11 for I94/M10. As the span increases the lane moment becomes more closely a function of GVW rather than axle weight and axle spacing. It can be observed that the relationship between GVW and lane moment distribution is linear for span lengths of greater than or equal to 18 m (60 ft). For a span length of 9 m (30 ft), the linearity digresses from a GVW of 530 kN (120 kip), which coincides with the maximum observed GVW of five-axle vehicles and a lane moment ratio of 1.0. It can be concluded that this digression is mostly caused by 11-axle vehicles and a few vehicles with 6, 7, 8, 9, or 10 axles. This relationship can be useful for estimating the lane moment distribution from the GVW distribution, because sometimes, the GVW distribution is available but the moment or shear distribution is not. Another possible application is the estimation of moment distribution from strain measurements without disturbing normal traffic. Moments and strains have a linear relationship through the stiffness and elastic modulus of the bridge elements. One of the most difficult problems in collecting vehicle weight data was to control normal traffic during the installation of axle sensors. Even though the installation takes a short time, it would not be a trivial task to block normal urban or highway traffic. Strain measurement does not require lane sensors, and therefore, there is no need for traffic control.

FIGURE 11 Relationship between GVW and moment ratio distribution for I94/M10 (1 m 5 3.3 ft).

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The distribution of lane moments, which is, in turn, estimated from strain measurements, can be a relatively accurate measure of the distribution of GVW, and vice versa. However, the method should be used with caution since the relationship between GVW and lane moment distribution depends on axle weight, axle spacing, span length, and truck traffic content.

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shear varied between 0.6 and 2.0 times the LRFD value. It was found that there are similarities in GVW and lane moment distributions. ACKNOWLEDGMENTS The research presented here was sponsored by MDOT and the Great Lakes Center for Truck Transportation Research, which is gratefully acknowledged.

CONCLUSION The tests were carried out on bridges located on various types of roads in the Detroit area; some carry surface street traffic, whereas others carry highway loads. Truck loads on bridges are strongly site specific, even within a given geographic area. There was considerable variation in traffic volumes and the weights of trucks. The estimated ADTT varied from 500 to 1,500 in one direction. The observed truck weights were often heavier than the legal limits, especially the axle weight limit. The maximum GVWs varied from 360 kN (81 kip) to 1100 kN (250 kip). Most vehicles with fewer than five axles had GVWs of less than 530 kN (120 kip). Eleven-axle trucks had the heaviest GVWs. The maximum axle weights varied from 90 kN (20 kip) to almost 225 kN (50 kip). Axle weights show much smaller variation than GVWs from site to site for each vehicle type. Steering axles had much smaller variations and maximum axle weights than other axles. Maximum values of lane moment and

REFERENCES 1. Nowak, A. S., S. Kim, J. A. Laman, V. Saraf, and A. F. Sokolik. Truck Loads on Selected Bridges in the Detroit Area. Report UMCE 94-34. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, 1994. 2. Nowak, A. S., J. A. Laman, and H. Nassif. Effect of Truck Loading on Bridges. Report UMCE 94-22. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, 1994. 3. Benjamin, J. R., and C. A. Cornell. Probability, Statistics, and Decision for Civil Engineers. McGraw-Hill Book Company, New York, 1970. 4. AASHTO LRFD Bridge Design Specifications. AASHTO, Washington, D.C., 1994. The results presented here are those of the authors and do not necessarily reflect the opinions of the sponsors. Publication of this paper sponsored by Committee on Dynamics and Field Testing of Bridges.