Postdisaster image‐based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks

Abstract

Reinforced concrete (RC) buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolutional neural networks have been adopted in recent research to rapidly quantify the damage state (DS) of structures. In this article, an advanced object detection neural network, named YOLOv2, is implemented, which achieves 98.2% and 84.5% average precision in training and testing, respectively. The proposed YOLOv2 is used in combination with the classification neural network, which improves the identification accuracy for critical DS of RC structures by 7.5%. The improved classification procedures allow engineers to rapidly and more accurately quantify the DSs of the structure, and also localize the critical damage features. The identified DS can then be integrated with the state‐of‐the‐art performance evaluation framework to quantify the financial losses of critical RC buildings. The results can be used by the building owners and decision makers to make informed risk management decisions immediately after the strong earthquake shaking. Hence, resources can be allocated rapidly to improve the resiliency of the community.

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

“…It was demonstrated that the response frequency bands, vibration modes, and their combination were learned by a deep neural network as essential characteristics that were identifiable from sensor data. Wang and Cha [55] proposed an unsupervised method using an acceleration signal that was obtained from an intact laboratory-scale three-dimensional (3D) steel bridge. The response signal vectors were normalized, and then the continuous wavelet transformation (CWT) and Fast Fourier Transformation (FFT) were applied, which were then fed to a two-dimensional (2D)-CNN autoencoder to extract essential features.…”

Automatic far‐field camera calibration for construction scene analysis

“…To obtain better performance in detecting constructionrelated objects, it is recommended to fine-tune the networks for each specific application. However, as the object detection is not the core objective of this study, interested readers are referred to several articles in the relevant literature for more details (H. Kim, Kim, Hong, & Byun, 2018;Pan & Yang, 2020;Roberts & Golparvar-Fard, 2019;Son et al, 2019).…”

Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance

“…With the development of deep learning, the applications of automatic defect detection on community infrastructures and built environment are increasing. CNNs have been used for rapid structural damage detection and maintenance cost estimation after a serious earthquake so as to provide a reference for owners and decision-makers to make accurate and timely risk management decisions [53]. Region-based CNN (R-CNN) and faster R-CNN have also been used for road damage detection and classification [54].…”