A Methodology for Development of Design Permit Vehicles

Open Access
Shah, Meet M
Graduate Program:
Civil Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
November 17, 2016
Committee Members:
  • Jeffrey A. Laman, Thesis Advisor
  • Konstantinos Papakonstantinou, Committee Member
  • Ali M Memari, Committee Member
  • Influence Lines
  • Bridge
  • Bridge Analysis
  • Permit Vehicle
  • Design Live Load
This study proposes a methodology for the establishment of a design permit vehicle that will predict loading effects (shear and moment) caused by a population of special hauling permit vehicles, specifically from the heaviest state-issued superload permits. This methodology utilizes databases obtained from the Pennsylvania Department of Transportation and a forecasting of vehicle loads. A fundamental objective of this study was to develop analytical tools to evaluate vehicle data files, both weigh in motion (WIM) and superloads. Primary analytical tools were developed to enable an automated procedure to simulate the passage of large numbers of vehicles, including WIM database vehicles, and permit database vehicles, over several bridge configurations to obtain the maximum moments and shears. The main objective was to construct a permit design vehicle model that envelopes 98% of WIM and superload vehicles. WIM and superload vehicles were processed by characterizing the population by vehicle width, number of axles, axle spacings, axle loads, axle group loads, and gross vehicle weight (GVW). Characterization for a given vehicle class includes averages, standard deviations, 95th percentiles, and maximums. Additionaly, a procedure to forecast the maximum vehicle effects on highway bridge for AASHTO specified design life is proposed. This procedure required processing of the WIM and superload vehicles by modeling individual and group axle weight as Generalized Extreme Value Distribution. A permit design vehicle was developed demonstrating the proposed methodology. Individual axle loads and axle groups were observed to follow the generalized extreme value distribution.