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

Fiorella Mete, David J. Corr, Michael P. Wilbur, Ying Chen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish (US)
Title of host publicationTransportation Research Record
PublisherSAGE Publications Ltd
Number of pages14
StatePublished - Mar 2022

Publication series

NameTransportation Research Record
ISSN (Print)0361-1981
ISSN (Electronic)2169-4052


  • Bridge and structures management
  • Bridge data QC/QA
  • Data for decision-making
  • Data-driven decisions
  • Executive management issues
  • Infrastructure
  • Infrastructure management and system preservation
  • Policy and organization

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


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