Drive‐by health monitoring of highway bridges using Bayesian estimation technique for damage classification

Abstract

Drive-by health monitoring (DBHM) is an indirect structural health monitoring strategy that leverages vehicle mounted sensors to detect, locate, and quantify bridge damage. Presently, there exists the need for a multilevel damage classification strategy that is reliable at moderately fast speeds, can quantify physical crack depths, is noise tolerant, can classify damage across the length of a bridge, and does not reference labeled or baseline data. This study presents a novel Bayesian estimation technique that leverages spike and slab prior specifications on an embedded simplified vehicle-bridge model to perform multilevel damage classification without referencing baseline or labeled data. A novel methodology is also proposed that maps crack ratios identified on the simplified model to physical levels of damage. The feasibility of the damage classification and mapping strategy is evaluated through analytical studies for a variety of damage states and operating conditions. Specifically, the classification and mapping of a 0.05 crack ratio is studied across different locations while considering varying levels of noise, vehicle velocities, number of experimental vehicle passes, and model errors. The success of the overall methodology, even in the presence of noise, indicates that the DBHM approach will likely be successful handling physical data. In particular, the feasibility studies demonstrate that the DBHM methodology is capable of leveraging noisy experimental data to reliably detect, locate, and quantify small levels of crack damage across the length of a bridge while the vehicle is traveling at velocities as high as 20.11 m/s.