Field Investigation into Effects of Vehicle Speed and Tire Pressure on Asphalt Concrete Pavement Strains

1996 ◽  
Vol 1539 (1) ◽  
pp. 66-71 ◽  
Author(s):  
Karim Chatti ◽  
Hyung B. Kim ◽  
Kyong K. Yun ◽  
Joe P. Mahoney ◽  
Carl L. Monismith
Keyword(s):  
Asphalt Concrete ◽  
Pressure Effect ◽  
Field Investigation ◽  
Vehicle Speed ◽  
Tire Pressure ◽  
Mount Vernon ◽  
Concrete Layer ◽  
Speed Effect ◽  
Longitudinal Strains ◽  
Percent Decrease

An asphalt concrete section on a test track in the PACCAR Technical Center in Mount Vernon, Washington, was fitted with strain gauges at the surface and in pavement cores and tested using an instrumented truck operated at different speeds and with different tire pressures. The field test results are presented. The results indicate that the effects of both vehicle speed and tire pressure–contact area on pavement strains are significant: increasing vehicle speed from 2.7 km/hr (1.7 mi/hr) to 64 km/hr (40 mi/hr) caused a decrease of approximately 30 to 40 percent in longitudinal strains at the bottom of the asphalt concrete layer, which was 137 mm (5.4 in.) thick. The speed effect on transverse strains is lower, causing only a 15 to 30 percent decrease. Reducing tire pressure from 620 kPa (90 psi) to 214 kPa (30 psi) caused a decrease of approximately 20 to 45 percent in the horizontal strains at the bottom of the asphalt concrete layer. The pressure effect on surface strains was significantly lower, causing only a 5 to 20 percent decrease. The speed effect was somewhat reduced at lower pressures, and the pressure effect was reduced at higher speeds.

Analytical Study of Effects of Truck Tire Pressure on Pavements with Measured Tire–Pavement Contact Stress Data

2005 ◽  
Vol 1919 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Randy B. Machemehl ◽  
Feng Wang ◽  
Jorge A. Prozzi
Keyword(s):  
Conventional Method ◽  
Contact Stress ◽  
Asphalt Concrete ◽  
Pressure Effects ◽  
Tire Pressure ◽  
Variance Model ◽  
Truck Tire ◽  
Concrete Layer ◽  
Pavement Structures ◽  
Asphalt Concrete Pavement

Truck tire inflation pressure plays an important role in the tire–pavement interaction process. As a conventional approximation method in many pavement studies, tire–pavement contact stress is frequently assumed to be uniformly distributed over a circular contact area and to be simply equal to the tire pressure. However, recent studies have demonstrated that the tire–pavement contact stress is far from uniformly distributed. Measured tire–pavement contact stress data were input into an elastic multilayer pavement analysis program to compute pavement immediate responses. Two asphalt concrete pavement structures, a thick pavement and a thin pavement, were investigated. Major pavement responses at locations in the pavement structures were computed with the measured tire–pavement contact stress data and were compared with the conventional method. The computation results showed that the conventional method tends to underestimate pavement responses at low tire pressures and to overestimate pavement responses at high tire pressures. A two-way analysis of variance model was used to compare the pavement responses to identify the effects of truck tire pressure on immediate pavement responses. Statistical analysis found that tire pressure was significantly related to tensile strains at the bottom of the asphalt concrete layer and stresses near the pavement surface for both the thick and thin pavement structures. However, tire pressure effects on vertical strain at the top of the subgrade were minor, especially in the thick pavement.

scholarly journals Effect of the Dynamic Load on Stresses in a Deck Pavement with an Interlayer Contact Model

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xuntao Wang ◽  
Jianhu Feng ◽  
Hu Wang ◽  
Shidi Hong ◽  
Xiaohan Cheng ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Dynamic Load ◽  
Asphalt Concrete ◽  
Element Model ◽  
Superposition Method ◽  
Vehicle Speed ◽  
Random Surface ◽  
Bonding Condition ◽  
Concrete Layer ◽  
Road Surface Roughness

Random surface roughness of bridge deck pavement just like random road surface roughness was simulated by the harmony superposition method in this paper. The dynamic load of vehicle was calculated by the random surface roughness of the deck pavement and the quarter-car model. A finite element model of a box girder bridge and its deck pavement was established, and the bonding condition between the adjacent layers was assumed to be contact bonding condition. The stress values of the asphalt concrete layer were calculated and analyzed when surface roughness condition, vehicle speed, and disengaging area changed. Results show that random surface roughness of deck pavement affected the stress trend of the asphalt concrete layer obviously. The appearance of disengaging area would increase the stress values of the asphalt concrete layer and the normal tensile stress value between the asphalt concrete layer and the waterproof layer. This would speed up the damage of the asphalt concrete layer and enlarge further the disengaging area.

Predicting Truck Load Variation Using Q-Truck Model

2014 ◽  
Vol 534 ◽  
pp. 105-110
Author(s):  
Rosnawati Buhari ◽  
Mohd Ezree Abdullah ◽  
Munzilah Md Rohani
Keyword(s):  
Asphalt Concrete ◽  
Radial Strain ◽  
Suspension System ◽  
Vehicle Speed ◽  
Pavement Distress ◽  
Traffic Loads ◽  
Road Roughness ◽  
Heavy Truck ◽  
Truck Load ◽  
Pavement Damage

The study of heavy vehicle forces on pavement is important for both vehicle and pavement. Indeed it was identified several factors such as environment, materials and design consideration affects pavement damage over time with traffic loads playing a key role in deterioration. Therefore, this paper presents dynamically varying tire pavement interaction load, thus enable to assess the strain response of pavements influenced by road roughness, truck suspension system, variation of axle loading and vehicle speed. A 100m pavement with good evenness was simulated to check the sensitivity of the dynamic loads and heavy truck vertical motions to the roughness. The most important performance indicators that are required in pavement distress evaluation are radial strain at the bottom of the asphalt concrete and vertical strain at the subgrade surface was predicted using peak influence function approach. The results show that truck speed is the most important variables that interact with truck suspension system and thus effect of loading time are extremely important when calculating the critical.

Study on the Dynamic Wheel Load of Multi-Axle Vehicle Based on Distribute Loading Weight

2010 ◽  
Vol 159 ◽  
pp. 35-40
Author(s):  
Zhong Hong Dong
Keyword(s):  
Dynamic Load ◽  
Influence Factors ◽  
Vehicle Speed ◽  
Tire Pressure ◽  
Wheel Load ◽  
The Road ◽  
Road Roughness ◽  
Tire Stiffness ◽  
On The Road

To study the dynamic wheel load on the road, a dynamic multi-axle vehicle mode has been developed, which is based on distribute loading weight and treats tire stiffness as the function of tire pressure and wheel load. Taking a tractor-semitrailer as representative, the influence factors and the influence law of the dynamic load were studied. It is found that the load coefficient increases with the increase of road roughness, vehicle speed and tire pressure, yet it decreases with the increase of axle load. Combining the influences of road roughness, vehicle speed, axle load and tire pressure, the dynamic load coefficient is 1.14 for the level A road, 1.19 for the level B road, 1.27 for the level C road, and 1.36 for the level D road.

Characterization of Asphalt Concrete Layer Interfaces

2001 ◽  
Vol 1778 (1) ◽  
pp. 132-139 ◽  
Author(s):  
Stefan A. Romanoschi ◽  
John B. Metcalf
Keyword(s):  
Asphalt Concrete ◽  
Concrete Layer

scholarly journals The Preparation of the Quick-Drying Bitumen Emulsion for the Protection of the Road Surface Asphalt Concrete Layer

2016 ◽  
Vol 32 (6) ◽  
pp. 3129-3134 ◽  
Author(s):  
Victoria Ryabenko ◽  
Elena Chigorina ◽  
Anatoly Razinov ◽  
Yulia Ubaskina ◽  
Ivan Kovtun
Keyword(s):  
Asphalt Concrete ◽  
Road Surface ◽  
The Road ◽  
Bitumen Emulsion ◽  
Concrete Layer

scholarly journals An Enhanced Inverse Filtering Methodology for Drive-By Frequency Identification of Bridges Using Smartphones in Real-Life Conditions

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 499-513
Author(s):  
Nima Shirzad-Ghaleroudkhani ◽  
Mustafa Gül
Keyword(s):  
Surface Roughness ◽  
Smart Cities ◽  
Real Life ◽  
Cost Effective ◽  
Vehicle Speed ◽  
Inverse Filtering ◽  
Novel Approach ◽  
Life Scale ◽  
Frequency Identification ◽  
Speed Effect

This paper develops an enhanced inverse filtering-based methodology for drive-by frequency identification of bridges using smartphones for real-life applications. As the vibration recorded on a vehicle is dominated by vehicle features including suspension system and speed as well as road roughness, inverse filtering aims at suppressing these effects through filtering out vehicle- and road-related features, thus mitigating a few of the significant challenges for the indirect identification of the bridge frequency. In the context of inverse filtering, a novel approach of constructing a database of vehicle vibrations for different speeds is presented to account for the vehicle speed effect on the performance of the method. In addition, an energy-based surface roughness criterion is proposed to consider surface roughness influence on the identification process. The successful performance of the methodology is investigated for different vehicle speeds and surface roughness levels. While most indirect bridge monitoring studies are investigated in numerical and laboratory conditions, this study proves the capability of the proposed methodology for two bridges in a real-life scale. Promising results collected using only a smartphone as the data acquisition device corroborate the fact that the proposed inverse filtering methodology could be employed in a crowdsourced framework for monitoring bridges at a global level in smart cities through a more cost-effective and efficient process.

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