Earthquake has become one of the major natural disasters that caused a lot of losses, such as economic losses and human casualties as the main losses. Death tolls in earthquakes arise from three main causes: structural collapses, non-structural causes, and follow-on disasters. The most responsible reason for death tolls in an earthquake is structural collapses. To prevent another death toll after an earthquake occurred, a rapid preliminary assessment of a building is needed to ensure whether the building is safe enough to be occupied or not after the disaster. Nowadays, structural damage recognition is done manually which consists of in-site inspection and/or images inspection. However, manual experts evaluation has some drawbacks which are time-consuming, introduces uncertainty, subjective opinion into the assessment, and also greatly relies on the experiences of the experts. Meanwhile, applications of deep learning (DL) in computer vision have increased significantly in recent years. Convolutional Neural Network (CNN) has been the most popular model of Deep Learning in computer vision. This network is capable of learning amounts of mid-to high‐level image representations, which means CNN has a powerful generalization ability to classify an image. Furthermore, this research proposed TL-EfficientNet which incorporated EfficientNet and Transfer Learning method together to develop the image-recognition classifier model to classify the images of a structural member into several levels of damage based on Taiwan’s code for emergency assessment and evaluation of a building post-disaster using a pre-trained EfficientNet from ImageNet dataset. From result findings and several analyses conducted in further discussions of this research, the proposed model has shown promising results in terms of accuracy and F1 score. Hence, this model conclusively demonstrated a great capability in structural damage recognition using CNN model performance.

Earthquake has become one of the major natural disasters that caused a lot of losses, such as economic losses and human casualties as the main losses. Death tolls in earthquakes arise from three main causes: structural collapses, non-structural causes, and follow-on disasters. The most responsible reason for death tolls in an earthquake is structural collapses. To prevent another death toll after an earthquake occurred, a rapid preliminary assessment of a building is needed to ensure whether the building is safe enough to be occupied or not after the disaster. Nowadays, structural damage recognition is done manually which consists of in-site inspection and/or images inspection. However, manual experts evaluation has some drawbacks which are time-consuming, introduces uncertainty, subjective opinion into the assessment, and also greatly relies on the experiences of the experts. Meanwhile, applications of deep learning (DL) in computer vision have increased significantly in recent years. Convolutional Neural Network (CNN) has been the most popular model of Deep Learning in computer vision. This network is capable of learning amounts of mid-to high‐level image representations, which means CNN has a powerful generalization ability to classify an image. Furthermore, this research proposed TL-EfficientNet which incorporated EfficientNet and Transfer Learning method together to develop the image-recognition classifier model to classify the images of a structural member into several levels of damage based on Taiwan’s code for emergency assessment and evaluation of a building post-disaster using a pre-trained EfficientNet from ImageNet dataset. From result findings and several analyses conducted in further discussions of this research, the proposed model has shown promising results in terms of accuracy and F1 score. Hence, this model conclusively demonstrated a great capability in structural damage recognition using CNN model performance.

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