Citation

Pamuncak A, Guo W, Soliman Khaled A, Laory I. R. Soc. Open Sci. 2019; 6(12): e190227.

Affiliation

School of Engineering, University of Warwick, Coventry CV4 7AL, UK.

Copyright

(Copyright © 2019, Royal Society Publishing)

DOI

10.1098/rsos.190227

PMID

31903194

PMCID

PMC6936281

Abstract

Many post-disaster and post-conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of ageing and deteriorating bridges increases, it is necessary to quantify their load characteristics in order to inform maintenance and asset databases. The load carrying capacity and the design load are considered as the main aspects of any civil structures. Human examination can be costly and slow when expertise is lacking in challenging scenarios. In this paper, we propose to employ deep learning as a method to estimate the load carrying capacity from crowdsourced images. A convolutional neural network architecture is trained on data from over 6000 bridges, which will benefit future research and applications. We observe significant variations in the dataset (e.g. class interval, image completion, image colour) and quantify their impact on the prediction accuracy, precision, recall and F1 score. Finally, practical optimization is performed by converting multiclass classification into binary classification to achieve a promising field use performance.

© 2019 The Authors.

Language: en

Keywords

bridges; convolutional neural networks; deep learning; design load; load rating