Estimating the weight and the failure load of a spaghetti bridge: a deep learning approach

ABSTRACT

In this article, the ability of estimating the weight and the failure load of a structure through image processing has been investigated. To this end, the well-known civil engineering practice, spaghetti bridge, has been used as a test structure. To set up uniform experiments and to simplify the construction process, only 2 dimensional bridges were considered. By defining the failure load as the load that breaks the bridge, in the process of construction and testing, only bridges that were broken have been added to the database. The developed database was employed to train and validate the artificial neural network with three hidden layers particularly designed for this research. Four different activation functions were tested. The results obtained from Logistic sigmoid activation function were comparatively better. The designed artificial neural network was optimised through genetic algorithm. The benefit of using genetic algorithm was that several solutions were obtained. The artificial neural network with the lowest error for both training and validation data was selected. The results indicate that it is possible to estimate the weight and the failure load of a bridge with an acceptable degree of accuracy just by using the image of the bridge.

KEYWORDS:

• Artificial neural network
• bridge
• civil engineering
• deep learning
• failure load

Acknowledgments

The authors would like to thank Professor Derviş Z. Deniz for his valuable comments and suggestions that helped to improve this study and Dr. Ismaeil Fazel for his careful reading and editing recommendations that has improved the text of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.