Journal of Environmental Treatment Techniques PDF |
2017, Volume 5, Issue 3, Pages: |
J. Environ. Treat. Tech.
ISSN:
Journal weblink: http://www.jett.dormaj.com
Comparison of Live Load Effects for the Design of Bridges
I. Shahid1, S. H. Farooq1, A.K. Noman2, A. Arshad3
3- National University of Sciences and Technology, Islamabad, Pakistan
Received: 06/06/2017 |
Accepted: 08/07/2017 |
Published: 11/07/2017 |
Abstract
Design and lifelong structural performance of bridges is primarily governed by the live load models representing truck traffic. In Pakistan, bridges are designed as per Pakistan Code of Practice for Highway Bridges 1967 (“PHB Code”) and American Associations for State Highway and Transportation officials LRFD (Load and resistance factor design) Bridge Design Specifications (“AASHTO”). Further, National Highway Authority (NHA) has specified legal limits on the live loads to prevent overstressing of bridges. Different states of US had calibrated the AASHTO live load model based on the actual truck weights and traffic volume present in the respective states. In Pakistan,
-level truck traffic of Pakistan. After discussing the different Live Load Models currently in practice for the design of highway bridges in Pakistan, this paper compares the load effects produced by the actual trucks on sample bridges with the load effects of code specified live load models. Three simply supported,
Keywords: Live Load,
1 Introduction1
Estimation of accurate live load due to truck traffic is essential for safe and economical designing of bridges. Main combination of loads for bridge design consists of combination of dead load, live load, environmental load and other loads. Being dynamic, live load is random and unpredictable in nature therefore requires careful consideration in its modelling and estimation.
Live load is divided into static and dynamic components and its sum presents the total live load on bridge structure. In this study only static component was considered. WIM (weigh in motion) is used for collecting the data pertaining to live load due to trucks on bridges. The information include the GVW, axle spacing, axle weight, number of axles and average daily truck traffic (ADTT). Live load effects include the moment, shear and stresses which are used for effective evaluation of a bridge structure. In this study only moment and shear due to single truck on the bridge under consideration is considered. WIM
Corresponding author: A. Arshad, National University of Sciences and Technology, Islamabad, Pakistan.
data was acquired in the raw form. The same was filtered to get the data in required form and was used for analysing the effects of live load on the sample bridges.
2 Review of Live Loads in Context of Pakistan
Design of bridges is primarily governed by the live load models representing truck traffic. In Pakistan, live load models of PHB Code 1967 and AASHTO LRFD code is being practiced for design of bridges. These live loads models are proposed loading keeping in view the objective of covering the worst combination of axle load and axle spacing, likely to arise from the various types of vehicles that are normally expected to use the roads.
2.1Live Loading - PHB CODE 1967.
PHB Code 1967 is primarily based on AASHTO
Bridge Design Specification, 1961. According to PHB code 1967, the highway loading on the bridge consists of a truck train loading and 70 ton military tank. The design live loads are classified as Class A, Class B and Class AA loading.
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2.1.1 Class A Loading (Standard Train Loading)
This load train is reported to have been arrived at after an exhausted analysis of all lorries made in all countries of the world. The loading consists of a train of wheel loads (8- axles) that is composed of a driving vehicle and two trailers of specified axle spacing and loads as shown in Figure 1. This loading in bridge designing is generally adopted on all roads on which permanent bridges and culverts are constructed.
2.1.2 Class B Loading
Class B Loading is similar to Class A loading with a slightly reduced axle loads. This loading is normally adopted for temporary structures and for bridges in specified areas. Example of temporary structures is structures with timber spans. Class B Loading is 60 per cent of Class A Loading as shown in Figure 2. The positions of wheels and axle are same for both Class A and Class B Loading.
Figure 1: Train Loading Class A (PHB CODE, 1967) – 8 Axle
Figure 2: Train Loading Class B (PHB CODE, 1967) – 8Axle
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Figure 3: Military Loading (70 Ton tank)
2.1.3 Class AA Loading (70 ton Military Tank)
Class AA loading is based on the original classification of the defence Authorities. This loading is to be adopted for the design of bridges with certain municipal limits, in certain existing or contemplated industrial area, in other specified areas and along National Highway and State Highways. This loading consists of 70 tons tracked vehicles (Military Tanks) having specified dimensions which are to be observed during the Live load analysis in bridge design. The nose to tail distance between two successive vehicles is not less than 91.4 meter. No other live loads will cover any part of roadway of bridge when this vehicle is crossing the bridge. The minimum clearance between the roadway face of curb and the outer edge of the track shall be assumed 0.3 meter if roadway width is between 3.5 to 4.1 meter, 0.6 meter if roadway width is between 4.1 to 5.5 meter and 1.2 meter if roadway width is greater than 5.5 meter. Bridge designed for Class AA loading should be checked for Class A loading too. As under certain conditions heavier stress may be obtained under Class A loading. Figure 3shows a typical Class AA loading.
2.2AASHTO LRFD Live Loading
AASHTO LRFD live loading commonly known as HL-
93 loading where H stands for Highway and L stands for Loading, was developed in 1993. AASHTO live load model, included in AASHTO Specifications, was developed using truck data from the Ontario Ministry of Transportation, Canada. This is a hypothetical live load model proposed by AASHTO for the analysis of bridges with a design period of 75 years. Reason for proposing this live load model is to prescribe a set of loads such that it produces extreme load effects approximately same as that produced by the exclusion vehicles.
2.2.1 Design truck
It was proposed in 1994 and is commonly called as
4.3(14 feet) to 9 (30 feet) meter in order to influence a maximum positive moment in a span. Design Truck is shown in Figure 4 below.
2.2.2 Design tandem
It consists of two axles weighing 12 tons (25 kips) each spaced at 4 feet as shown in Figure 4.
2.2.3 Design lane
It consists of uniformly distributed load of 0.64 kips/feet (0.94 ton/meter) and is assumed to occupy 10 feet width in traverse direction as given in Figure 4.
2.2.3
a.The combined effect of one design truck with the variable axle spacing with the design lane load as shown in Figure 5, or
b.The combined effect of the designed tandem with the design lane load as shown in Figure 5.
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683, 2012) were considered for filtering out the bad data. Truck record that did not meet the following was eliminated:
a. Total number of axles |
2 |
b. Total number of axles |
12 |
c. Sum of axle spacing is greater than the length of truck. d. Sum of axle weight is greater than GVW of truck.
Maximum numbers of axle were restricted to 12 only with the reason that, trucks above 12 axles resulted in very high load effects. These high values are the representative of a special or permit vehicle. To achieve optimum reliability, special or permit trucks needs to be dealt separately. WIM data was acquired from three different stations the details of which are:
3.1 Sangjani Weigh Station
Sangjani Weigh Station is located on National Highway 5
Figure 5: Location of Weigh Stations
Table 1: Number of vehicles and maximum GVW in each category - Sangjani
Number of
Trucks
Max GVW
(tons)
Truck Configuration (Number of Axles)
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Total |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13* |
14* |
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101022 |
114606 |
9282 |
1787 |
4014 |
13 |
5 |
8 |
3 |
2 |
1 |
1 |
4 |
230743 |
32.43 |
56.59 |
66.82 |
86.30 |
109.30 |
106.05 |
123.70 |
143.80 |
124.80 |
136.00 |
163.40 |
158.92 |
195.18 |
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* Data not included in the Total
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(a) Histogram and PDF of GVW – Sangjani(b) CDF of GVW – Sangjani Figure 6: Histogram and PDF of GVW – Sangjani and CDF of GVW – Sangjani
The results show that maximum GVW recorded at Sangjani from the filtered data is 163.4 tons and its corresponding configuration is 12 axles. Mean GVW for the data recorded at this site is 35.92 tons. Mean GVW of this site is much lower as compared to the mean GVW of Ontario truck data which is 75 tons (Kozikowski and Nowak, 2009).Comparison of GVW of actual truck to GVW of design truck is shown in Figure 7. Result shows that 39.18 percent and 2.17 percent of GVW of actual trucks are higher than GVW of
3.2MullanMansoor (MM) Weigh Station
Three months of truck data was recorded at this site
comprising 116,009 trucks of different configuration. Axle spacing was missing in the data files provided by NHA; therefore it was decided to apply the standard axle spacing measured on ground at Peshawar by Ali et al. (2012).
Unlike the data recorded at Sangjani, the truck configurations were restricted to 6 axles at MM. A total of 11,456 (9.9 per cent) trucks were removed after the application of filter on the raw data. Summary of number of vehicles as per axles and their maximum GVW is summarized in Table 2. Histogram, PDF and CDF of the GVW for MM Weigh station is shown in Figures 8.
The results show that maximum truck GVW is 108.3tons and its corresponding configuration is 6 axles. Average GVW for the data recorded at this site is 39.17 tons. Mean value of this site is larger than Sangjani (35.92 tons) but is lower than the Ontario truck data (75 tons). Comparison of GVW of actual truck to GVW of design truck for this site is shown in Figure 9. Result shows that
27.76per cent and 8.8 per cent of GVW of actual trucks are higher than GVW of
Figure 7: Comparison of GVW of actual trucks to GVW of Design Trucks – Sangjani
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Table 2: Number of vehicles and maximum GVW in each category - MM
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Vehicle type |
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2 |
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3 |
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4 |
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5 |
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6 |
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Total |
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axle |
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axle |
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Axle |
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axle |
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axle |
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No of vehicles |
47593 |
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28908 |
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16287 |
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2274 |
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9491 |
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104553 |
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Max GVW |
42.76 |
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67.14 |
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69.92 |
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83.87 |
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108.29 |
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(tons) |
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(a) Histogram and PDF of GVW – MM(b) CDF of GVW – MM Figure 8: Histogram and PDF of GVW – MM and CDF of GVW – MM
Figure 9: Comparison of GVW of actual trucks to GVW of Design Trucks – MM
3.3Peshawar (Temporary Weigh Station)
A temporary weigh station was established at
Hayatabad in Peshawar to monitor the truck traffic by researchers of UET Peshawar (Ali et al., 2012) in collaboration with Peshawar Development Authority (PDA). Data acquired at this site was limited to very few trucks i:e 411 trucks. The data includes the vehicles up to 6 axles only. Summary of number of vehicles as per axles and their max GVW is summarized in Table 3. CDF of the GVW for Peshawar survey data is shown in Figures 10.
The results show that maximum truck GVW is 88.12 tons and its corresponding configuration of truck is 6 axles. Average GVW for the data recorded at this site is 37.35 tons. Comparing between GVW of actual truck to GVW of design trucks (Class A and
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Table 3: Number of vehicles and maximum GVW in each category - Peshawar
Vehicle type |
2 |
3 |
4 |
5 |
6 |
Total |
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axle |
axle |
axle |
axle |
axle |
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No of vehicles |
154 |
66 |
33 |
3 |
155 |
411 |
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Max GVW (tons) |
30.42 |
37 |
44.93 |
54.37 |
88.12 |
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(a) Histogram and PDF of GVW – Peshawar |
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(b) CDF of GVW – Peshawar |
Figure 10: Histogram and PDF of GVW – Peshawar and CDF of GVW – Peshawar
Figure 11: Comparison of GVW of actual trucks to GVW of Design Trucks - Peshawar
4 Determination of Maximum Moment and Shear Using Influence Lines
For a simply supported bridge, calculation of load effects (moments & shear) involves determination of both the location of point in the beam and the position of loading on the beam. For calculating absolute maximum moments/shear for a large number of trucks, code was developed in a computer program using MS Excel. Code was developed for all the trucks as per number of axles separately. Three sample bridges for each site were selected for Reliability analysis. All these Bridges are simply supported,
a.Muhammad Wala Bridge
b.Mansoor Bridge – MM. Mansoor Bridge is identical to Muhammad Wala Bridge with a clear span of 47.19 meters. This bridge was constructed in 2009. It consists of four
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between girders. This is a three lane bridge, having 180 millimeter deck thickness and 100 millimeter (average) thick wearing surface.
c.
4.1Determination of Maximum Moment
Maximum moment was calculated using influence lines
for all three sites. Similarly, maximum moment was also calculated for
trend was observed at this bridge and maximum value of moment was in order of 2.07 times higher than the moment produced by both
4.2Determination of Absolute Maximum Shear
Same procedure was adopted for calculating maximum
shear for each truck in the data. Normalized shear was also calculated by dividing the truck shear with the design truck shear. Results at Muhammad Wala
Figure 12: CDF of Simple Span Moment – Sangjani
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Figure 13: CDF of Simple Span Moment – MM
Figure 14: CDF of Simple Span Moment – Peshawar
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Figure 15: CDF of Simple Span Shear – Sangjani
Figure 16: CDF of Simple Span Shear – MM
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Figure 17: CDF of Simple Span Shear – Peshawar
5 Results and Discussion
Safety and reliability of bridge infrastructure is a major concern for state highway departments. Bridges that are structurally deficient must be replaced or repaired for the desired function with desired level of safety. Biggest threat to the bridges is the aging effects and the increase in traffic volume. Along with increase in the traffic volume, the traffic also influences the structure by increase in the GVW and axle weights. By accurately predicting the expected load during the entire life time of the bridge and the load carrying capacity, structural deficiency can be avoided. Predicting the accurate load on the bridge is very complicated specially the live load. WIM data can provide the unbiased truck traffic data and it can be a remarkable basis to develop the statistical model of live load. Following conclusions were reached based on the results of this study:
5.1WIM Data
a.15.6 per cent and 9.9 per cent wrong entries were removed respectively from Sangjani weigh station and MM weigh station
b.Wrong recording of these data was due to the limitation of WIM instrument installed by NHA
c.Data recorded at Peshawar was limited to selected trucks which were loaded. All the trucks were not diverted to get the true data.
5.2Live Load Effects
a.Truck Load. Result shows that 39.18 percent and 2.17 percent of GVW of actual trucks are higher than GVW of
b.Moment. 44.80 per cent and 11.66 per cent of actual trucks produce maximum moment higher than that produced by
270.4per cent higher than the moment produced by
c.Shear. 42.8 percent and 12.2 percent of actual trucks produce maximum shear higher than that produced by
5.3Live Load Effects
a.Truck Load. Result shows that 27.76 per cent and 8.8 per cent of GVW of actual trucks are higher than GVW of
b.Moment. 17.93 per cent and 10.6 per cent of actual trucks produce maximum moment higher than that produced by
c.Shear.17.64 percent and 11.2 percent of actual trucks produce maximum shear higher than that produced by
5.4Live Load Effects
a.Truck Load. Result shows that 42.58 per cent and
37.71per cent of GVW of actual trucks are higher than GVW of
b.Moment. 39.17 per cent and 38.69 per cent of actual trucks produce maximum moment higher than that produced by
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c.Shear.44.53percent and 43.55 percent of actual trucks produce maximum shear higher than that produced by
6 Conclusion
This study result indicates that bridges in Pakistan are subjected to extreme load effects under the influence of prevailing traffic trends than they were actually designed for. The results are concluded as:
a. Actual truck traffic of Pakistan is significantly different in axle weights, axle configuration and GVW.
b.Load effects caused by actual truck traffic are much higher than those caused by live load models of PHB Code and AASHTO Specification thus bridges may be significantly overstressed which may reduce the design life of a bridge.
c.Existing live load model of PHB Code is not the true representation of actual truck traffic of Pakistan therefore Live load model needs to be revised and developed as per actual truck traffic in Pakistan.
References
AASHTO “LRFD Bridge Design Specifications.” 6th
Edition 2012, Washington, D.C
Ali, S. M., Javed, M. and Alam, B. (2012).“A Comparative
Study of Live Loads for the Design of Highway
Bridges in Pakistan.” IOSR Journal of Engineering, 2
(2012), p. 96.
Kozikowski, M. (2009), “WIM Based Live Load Model for
Bridge Reliability”, Ph.D Thesis, University of
Nebraska – Lincoln.
Nowak, A. S. (1993), “Live load model for highway
bridges.” Structural Safety, 13,
Nowak, A. S. (1994), “Load model for bridge design code.”
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