卷积神经网络
分类
深度学习
损害赔偿
人工智能
计算机科学
领域(数学)
结构工程
机器学习
模式识别(心理学)
工程类
数学
政治学
纯数学
法学
作者
Mertcan Yilmaz,Gamze Doğan,Musa Hakan Arslan,Alper İlki
标识
DOI:10.1080/13632469.2024.2302033
摘要
The aim of this study was to develop an innovative deep learning based intelligent software (DamageNet) and its mobile applications to classify seismic damage of Reinforced Concrete (RC) elements. Images of 2455 damaged elements that have been exposed to different destructive earthquakes were collected from the "datacenterhub" database. The DamageNet algorithm has been compared with the pretrained convolutional neural networks (CNN) algorithms (VGG16, ResNet-50, MobileNetV2 and EfficientNet) according to performance metrics. With the other models, a maximum test success of 89% was achieved, while with DamageNet a test success of 92% was achieved in damage classification. The mobile application developed based on the DamageNet model was tested in the field after the earthquakes (Mw:7.7 and Mw:7.6) in Kahramanmaraş/Turkey and classification success of 88% was obtained.
科研通智能强力驱动
Strongly Powered by AbleSci AI