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Automated classification of civil structure defects based on convolutional neural network

Frontiers of Structural and Civil Engineering 2021年第2期15卷 305-317页

作者： pierclaudio savino Francesco TONDOLO Department of Structural Geotechnical and building EngineeringPolitecnico di TorinoTorino10129Italy

Today,the most commonly used civil infrastructure inspection method is based on a visual assessment conducted by certified inspectors following prescribed ***,the increase in aggressive environmental and load conditions,coupled with the achievement of many structures of the life-cycle end,has highlighted the need to automate damage identification and satisfy the number of structures that need to be *** overcome this challenge,this paper presents a method for automating concrete damage classification using a deep convolutional neural *** convolutional neural network was designed after an experimental investigation of a wide number of pretrained networks,applying the transfer-learning *** and validation were conducted using a database built with 1352 images balanced between“undamaged”,“cracked”,and“delaminated”concrete *** increase the network robustness compared to images in real-world situations,different image configurations have been collected from the Internet and on-field bridge *** GoogLeNet model,with the highest validation accuracy of approximately 94%,was selected as the most suitable network for concrete damage *** results confirm that the proposed model can correctly classify images from real concrete surfaces of bridges,tunnels,and pavement,resulting in an effective alternative to the current visual inspection techniques.

关键词： concrete structure infrastructures visual inspection convolutional neural network artificial intelligence

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