Forecasting and Modeling Bridge Deterioration Using Data Mining Analytics

Rayan Assaad, Islam El-Adaway

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Bridges are considered one of the most important infrastructure systems. Frequent assessments of the conditions of the various bridges' structural parts are crucial to reflect the overall deterioration conditions of bridges. Compared to other structural components, the decks of bridges are more susceptible to harsh deteriorations because they are vulnerable to severe conditions such as: enormous traffic loads and varying temperatures. Transportation agencies experience many challenges in devising methods to predict the deterioration conditions of bridges in a precise manner. Previous research works tried to estimate the deck conditions based on a restricted set of data while other studies incorporated a unique modeling technique with relatively low prediction accuracy. Therefore, existing literature has not yet provided reliable models. To this end, this paper tackles this critical knowledge gap using a multi-step methodology. First, the paper identified the key parameters affecting the conditions of bridge decks. Second, three data mining models were developed for the prediction of deck conditions using artificial neural networks, discriminant analysis, and multiple regression. Third, a comparison between the presented frameworks is performed to select the ultimate predictive model with the highest prediction accuracy. The end result is a model that forecasts and assesses the bridge decks' conditions with a good prediction accuracy based on 22 identified variables. This minimizes efforts, reduces time, and cuts costs related to the site inspection of bridge decks.

Original languageEnglish (US)
Title of host publicationConstruction Research Congress 2020
Subtitle of host publicationComputer Applications - Selected Papers from the Construction Research Congress 2020
EditorsPingbo Tang, David Grau, Mounir El Asmar
PublisherAmerican Society of Civil Engineers (ASCE)
Number of pages10
ISBN (Electronic)9780784482865
StatePublished - 2020
Externally publishedYes
EventConstruction Research Congress 2020: Computer Applications - Tempe, United States
Duration: Mar 8 2020Mar 10 2020

Publication series

NameConstruction Research Congress 2020: Computer Applications - Selected Papers from the Construction Research Congress 2020


ConferenceConstruction Research Congress 2020: Computer Applications
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction


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