Novel Framework for the Quantification of Pavement Damages in the Overload Corridors

Abstract

Recent traffic trends and permit issuance show significant mobility demands in the energy sectors across the nation. The increase in the axle loads and frequency of operations of over-weight (OW) trucks resulted in severe damage to transportation infrastructures. Traditionally, the damage imparted by OW vehicles has been quantified by means of the equivalent axle load factors (EALFs) concept. However, because of the nature of assumptions in the development of damage equivalency factors, the field distresses substantially deviate from the prediction models. Therefore, this study aimed to bridge this gap by developing a mechanistic framework to determine damage equivalency factors tailored toward the specific characteristics of OW vehicles operating in the OW corridors, while considering the environmental conditions and the unique features of transportation facilities in the network. To achieve this objective, initially, the authors devised a plan to collect traffic information using portable weigh-in-motion devices at two intervals for 10 representative sites in the energy corridors of Eagle Ford Shale region. Subsequently, a series of nondestructive tests were conducted in the field to determine the material properties of the pavement layers for further numerical simulations. This information was further incorporated into a 3D finite element system to calculate critical input parameters in the modified damage factor models. The proposed mechanistic approach confirmed that the modified damage factors were substantially higher compared with traditional industry-standard values. Further investigation of environmental factors and pavement profiles in this study underscored the significance of these components for accurate assessment of the damage equivalency factors.

Verification of the Axle Load Spectra Databases Using Stationary and Portable Weight-in-Motion Devices in Overload Corridors of East Texas

“…The piezoelectric sensors were inserted into specialized pocket tapes and installed in two sets spaced 8 ft. apart along the surface of the road, as shown in Figures 3a and 3b. The piezoelectric sensors were installed from the edge of the road to mid-lane to register one wheel path and to account for wheel wander [11][12].…”

A Markovian approach to infrastructure life‐cycle analysis: Modeling the interplay of hazard effects and recovery

“… represents the total hours of delay time per day and it is estimated according to Bocchini and Frangopol 32 by incorporating the impacts of partial traffic flow. denotes the average number of daily traffic and represents the average daily truck traffic in terms of number of vehicles 43 . and are coefficients that indicate the equivalent cost of the delay time for cars and trucks, respectively.…”

“…denotes the average number of daily traffic and represents the average daily truck traffic in terms of number of vehicles. 43 and are coefficients that indicate the equivalent cost of the delay time for cars and trucks, respectively. For this highway bridge with three lanes, , , , and are estimated 77,000, 7390, $23.31/hour , and $59.95/hour, respectively.…”

Analysis of Overweight Truck Permit Policy in New Jersey

“…Previous studies recognized the impact of OW vehicles on infrastructure and attempt to estimate the unit damage by means of pavement and bridge performance models. The researchers focused on distribution of the vehicle weight over its axles in these studies, as pavement and bridge wear cannot be simply attributed to the weight of a vehicle (OW or otherwise) (3)(4)(5)(6)(7)(8)(9)(10)(11).…”