Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network

Xu, Yang; Wei, Shiyin; Bao, Yuequan; Li, Hui

doi:10.1002/stc.2313

Cited by 154 publications

(87 citation statements)

References 52 publications

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“…Overfitting problems, one of the major limitations of conventional NNs, can also be overcome by application of the convolutional operator over multiple layers in the CNN. In the field of structural engineering, CNNs were first introduced to problems related to pixel‐based images, such as image‐based crack detection and image‐based seismic damage identification . For the constitution of an information, the time series of structural response is similar to pixel‐based image data in that they have enormous data and important meaning in data sequence.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of structural engineering, CNNs were first introduced to problems related to pixel-based images, such as image-based crack detection 23 and image-based seismic damage identification. 24 For the constitution of an information, the time series of structural response is similar to pixel-based image data in that they have enormous data and important meaning in data sequence. Thus, CNNs, which are suitable for handling a large volume of data with a sequence characteristic in the input layer, are also appropriate to handle time-history measurement signals such as dynamic structural responses.…”

Section: Introductionmentioning

confidence: 99%

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Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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Summary In this study, a method of predicting the seismic responses of building structures based on a convolutional neural network (CNN) is proposed. In the method, the time histories of acceleration responses previously measured in a building during earthquakes are used in the CNN input layer, with the corresponding time histories of the displacement responses being used in the CNN output layer. The correlations between the features automatically extracted from the acceleration responses by the convolution and pooling operations in the various CNN layers and the displacement responses in the CNN output layer are established through CNN training. The trained CNN predicts the displacement responses for a future earthquake event using only the measured acceleration responses. To verify the validity of the proposed method, a numerical study on the ASCE benchmark model and an experimental study on a reinforced concrete frame structure are conducted. In the numerical study, the structural responses of the ASCE model subject to artificial earthquakes are utilized for CNN training, and the performance of the trained CNN is verified through seismic response prediction. In the experimental study, shaking table tests are performed for various earthquakes to measure the seismic responses of the reinforced concrete test structure. The seismic response prediction performance of the proposed method is examined using CNN trained with the measured seismic responses. Furthermore, the performances of CNNs trained with different numbers of datasets generated by the proposed data generation method based on data overlapping with the same data pool are discussed with related to the number of CNN training iteration number.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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“…Although successful identifications and applications have been achieved by using the above-mentioned methods, challenges still exist. The modeling uncertainties are not considered in the studies [23][24][25][26][27][28] ; however, they inevitably exist in real applications. Furthermore, when the input data (frequencies and mode shapes) are polluted by the white noise, the identification accuracy is greatly affected 23 .…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification by sparse deep belief network using uncertain and limited data

Ding

Hao

2020

Struct Control Health Monit

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Summary The accuracy of structural damage identification is affected by the uncertainties in the vibration measurements and the finite element modeling. This paper proposes a novel approach based on sparse deep belief network (DBN) for structural damage identification with uncertain and limited data. Vibration characteristics, that is, natural frequencies and mode shapes, are extracted as the input to the network, while the output are the damage locations and severities of the structure. DBN is chosen to train the generated data sets and identify structural damages. Restricted Boltzmann Machines (RBMs) are used as building blocks to composite a DBN. To further enhance the capacity of the RBMs, an arctan‐based sparse constraint is utilized to enable the hidden units to become sparse. This is achieved by adding an arctan norm constraint on the whole of the hidden units' activation probabilities. Numerical and experimental studies are conducted to verify the accuracy and performance of the proposed method. Undetermined damage identification is conducted, in which the quantity of input modal data is less than that of the system parameters to be identified. The identification results show that the proposed sparse DBN based on arctan can identify the damage effectively, and its accuracy is better than those obtained by other methods, even when the modeling uncertainty and the measurement noise exist and only limited data is available.

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“…Understanding the nature of the risk to our building inventory is essential for making deci-F I G U R E 1 Sample images collected during post-event building reconnaissance missions (Courtesy of Timothy P. Marshall and Thomas P. Smith, respectively. Such data allow researchers to distil important lessons that will improve the safety and reliability of our buildings, as well as the resilience of our communities (Cha, Choi, & Büyüköztürk, 2017;Cha, Choi, Suh, Mahmoudkhani, & Büyüköztürk, 2018;Gao & Mosalam, 2018;Hoskere, Narazaki, Hoang, & Spencer, 2018;Jahanshahi, Kelly, Masri, & Sukhatme, 2009;Liang, 2019;Li, Yuan, Zhang, & Yuan, 2018;Xue & Li, 2018;Xu, Wei, Bao, & Li, 2019;Yeum & Dyke, 2015). For instance, Figure 1 shows samples of images collected from actual post-hurricane reconnaissance missions (Katrina in 2005 andHarvey in 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Meaningful scenes are captured from particular viewpoints to provide visual evidence of damaged/undamaged buildings or their components. Such data allow researchers to distil important lessons that will improve the safety and reliability of our buildings, as well as the resilience of our communities (Cha, Choi, & Büyüköztürk, 2017;Cha, Choi, Suh, Mahmoudkhani, & Büyüköztürk, 2018;Gao & Mosalam, 2018;Hoskere, Narazaki, Hoang, & Spencer, 2018;Jahanshahi, Kelly, Masri, & Sukhatme, 2009;Liang, 2019;Li, Yuan, Zhang, & Yuan, 2018;Xue & Li, 2018;Xu, Wei, Bao, & Li, 2019;Yeum & Dyke, 2015). In the United States, the National Science Foundation recently funded a unique RAPID facility within the NHERI (Natural Hazards Engineering Research Infrastructure) network that is dedicated to supporting reconnaissance teams as they collect such field data.…”

Section: Introductionmentioning

confidence: 99%

Automated building image extraction from 360° panoramas for postdisaster evaluation

Lenjani

Yeum

Dyke

et al. 2019

Computer aided Civil Eng

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After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain new knowledge and extract lessons from the event. However, in many cases, the images collected are captured without sufficient spatial context. When damage is severe, it may be quite difficult to even recognize the building. Accessing images of the predisaster condition of those buildings is required to accurately identify the cause of the failure or the actual loss in the building. Here, to address this issue, we develop a method to automatically extract pre‐event building images from 360° panorama images (panoramas). By providing a geotagged image collected near the target building as the input, panoramas close to the input image location are automatically downloaded through street view services (e.g., Google or Bing in the United States). By computing the geometric relationship between the panoramas and the target building, the most suitable projection direction for each panorama is identified to generate high‐quality 2D images of the building. Region‐based convolutional neural networks are exploited to recognize the building within those 2D images. Several panoramas are used so that the detected building images provide various viewpoints of the building. To demonstrate the capability of the technique, we consider residential buildings in Holiday Beach in Rockport, Texas, United States, that experienced significant devastation in Hurricane Harvey in 2017. Using geotagged images gathered during actual postdisaster building reconnaissance missions, we verify the method by successfully extracting residential building images from Google Street View images, which were captured before the event.

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Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network

Cited by 154 publications

References 52 publications

Seismic response prediction method for building structures using convolutional neural network

Seismic response prediction method for building structures using convolutional neural network

Structural damage identification by sparse deep belief network using uncertain and limited data

Automated building image extraction from 360° panoramas for postdisaster evaluation

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