Deep Learning for Bridge Load Capacity Estimation in Post-Disaster and -Conflict Zones

TL;DR

This paper presents a deep learning approach using a new convolutional neural network architecture to estimate bridge load capacity from crowd-sourced images, aiding maintenance in post-disaster and conflict zones.

Contribution

The study introduces a novel CNN architecture trained on over 6000 bridges and explores dataset variations to improve practical load capacity estimation from images.

Findings

Achieved promising accuracy in load capacity classification

Quantified the impact of dataset variations on prediction metrics

Converted multiclass to binary classification for field use

Abstract

Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance and prevent failure. 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 method to estimate the load carrying capacity from crowd sourced images. A new convolutional neural network architecture is trained on data from over 6000 bridges, which will benefit future research and applications. We tackle 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 optimisation is performed by converting multiclass classification into binary classification to achieve a promising field use performance.