0
  • DE
  • EN
  • FR
  • International Database and Gallery of Structures

Advertisement

Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

Author(s):

ORCID
ORCID
Medium: journal article
Language(s): English
Published in: Transportation Research Record: Journal of the Transportation Research Board, , n. 3, v. 2676
Page(s): 454-467
DOI: 10.1177/03611981211052027
Abstract:

Collecting information on heavy trucks and monitoring the bridges which they regularly cross is important for many facets of infrastructure management. In this paper, a two-step algorithm is developed using bridge and truck data, by deploying sequentially unsupervised and supervised machine learning techniques. Longitudinal clustering of bridge data, concerning strain waveforms, is adopted to perform the first step of the algorithm, while image visual inspection and classification tree methods are applied to truck data concurrently in the second step. Both bridge and truck traffic must be monitored for a limited, yet significant, amount of time to calibrate the algorithm, which is then used to build a classification framework. The framework provides the same benefits of two data collection systems while only one needs to be operative. Depending on which monitoring system remains available, the framework enables the use of bridge data to identify the truck’s profile which generated it, or to estimate bridge response given the truck’s information. As a result, the present study aims to provide decision-makers with an effective way to monitor the whole bridge-traffic system, bridge managers to plan effective maintenance, and policymakers to develop ad hoc regulations.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/03611981211052027.
  • About this
    data sheet
  • Reference-ID
    10777894
  • Published on:
    12/05/2024
  • Last updated on:
    12/05/2024
 
Structurae cooperates with
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine