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.

中文翻译：

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估计意大利面条桥的重量和破坏载荷：一种深度学习方法

摘要在本文中，研究了通过图像处理来估计结构的重量和破坏载荷的能力。为此，著名的土木工程实践，意大利面条桥，已被用作测试结构。为了建立统一的实验并简化施工过程，只考虑了二维桥梁。通过将破坏荷载定义为破坏桥梁的荷载，在施工和测试过程中，只有被破坏的桥梁才被添加到数据库中。开发的数据库用于训练和验证具有三个隐藏层的人工神经网络，该网络专为此研究而设计。测试了四种不同的激活函数。Logistic sigmoid 激活函数得到的结果相对较好。通过遗传算法对设计的人工神经网络进行优化。使用遗传算法的好处是可以得到几个解。选择训练和验证数据误差最低的人工神经网络。结果表明，仅使用桥梁图像就可以以可接受的准确度估计桥梁的重量和破坏载荷。