Predicting movable bridge deck expansion

Predicting movable bridge deck expansion: A machine learning and asset management perspective

Khedekar, Tejas (TU Delft Civil Engineering and Geosciences; TU Delft Integral Design and Management; INNOCY BBV)

Morales Napoles, O. (mentor)
Nogal Macho, M. (graduation committee)
Schraven, D.F.J. (graduation committee)
van den Eerenbeemt, Thijs (graduation committee)

Delft University of Technology

Civil Engineering | Construction Management and Engineering

2021-01-29

Movable bridge decks experience critical expansion in summer, leading to uncertainty and unpredictability in its availability doe to improper docking and safety hazard. If the bridges are not cooled soon, the inertia of expansion stays, causing prolongation of availability problems. Structural health monitoring of such bridges with a predictive maintenance approach can help plan remedial measures on the exact day and time. For the efficient design of such a structural health monitoring system, a combination of sensor system data and weather API data is tested. A machine learning approach of Gaussian process regression which can give the results on the prediction of critical expansion of bridge deck has been evaluated in this research project. Finally, a check on the transferability of the prediction model is conducted by application on another bridge data and its performance is discussed. Scenario analysis with savings in cost per scenario is also conducted with varying levels of potential unavailability penalty costs, which could be levied on an asset manager of a bridge if the prediction models of sensor system data set or/and weather API data set gives incorrect estimation. Such an analysis is done to justify the use of the prediction models in assumed scenarios to predict expansion of movable bridge deck

Structural health monitoring
Scenario analysis
Gaussian Process Regression
Feature Extraction
Bridge deck expansion
machine learning
Weather API

http://resolver.tudelft.nl/uuid:2f4f73a0-bfd6-498c-8955-6631f7ae12a1

Student theses

master thesis

© 2021 Tejas Khedekar