TAMU-POST: An analysis tool for vehicle impact on in-line pile group. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Mojdeh, Asadollahi Pajouh; Jean-Louis, Briaud
• Editors: Villalobos, Felipe
• Is Part Of: Cogent engineering. Volume 7:Issue 1(2020)
• Publication Date: 2020-01-01
• Subjects: vehicle impact -- soil–pile interaction -- dynamic lateral response -- analytical model -- finite element analysis -- full-scale crash testing; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2020.1735694
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: The tragic consequences of vehicles running into infrastructures have raised the need for perimeter protection. One common perimeter barrier is a set of piles or posts in an in-line geometry as an efficient way to contain or redirect errant vehicles. To date, the design of such barriers relies mostly on performing full-scale crash tests. These crash tests are expensive, and it is often practical to run such tests. In this paper, a general yet simple analysis-design model called TAMU-POST was developed to predict the response of a group of in-line piles impacted by a vehicle. TAMU-POST is based on the finite difference solution to the governing differential equation for a beam supported by piles. The piles are represented by single degrees of freedom consisting of a dashpot, a lumped mass, a spring, and a slider. A large number of computer simulations using a non-linear finite element program LS-DYNA as well as the data obtained from two full-scale crash tests were used to calibrate the proposed model. The design quantities are the barrier deflection, the vehicle dynamic penetration defined as the maximum vehicle intrusion into the barrier, as well as other parameters including the bending moment in the piles and in the beams. A Monte Carlo Simulation analysis was conducted using TAMU-POST to evaluate the probability of failure of a group of in-line piles under a given vehicular impact when considering the inherent uncertainties associated with the input parameters and the model coefficients.