Deep learning for post‐hurricane aerial damage assessment of buildings

[1]  Hojjat Adeli,et al.  Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes , 2018, Journal of Construction Engineering and Management.

[2]  Kaige Zhang,et al.  Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning , 2018, J. Comput. Civ. Eng..

[3]  Peng Zhao,et al.  Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Khalid M. Mosalam,et al.  Deep Transfer Learning for Image‐Based Structural Damage Recognition , 2018, Comput. Aided Civ. Infrastructure Eng..

[6]  Ilias Bilionis,et al.  Automated building image extraction from 360° panoramas for postdisaster evaluation , 2019, Comput. Aided Civ. Infrastructure Eng..

[7]  Takashi Matsubara,et al.  Advantages of unmanned aerial vehicle (UAV) photogrammetry for landscape analysis compared with satellite data: A case study of postmining sites in Indonesia , 2018 .

[8]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[9]  Xiao Liang,et al.  Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization , 2018, Comput. Aided Civ. Infrastructure Eng..

[10]  Dimitris Samaras,et al.  Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks , 2016, ArXiv.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[13]  Dongho Kang,et al.  Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging , 2018, Comput. Aided Civ. Infrastructure Eng..

[14]  Ambuj Tewari,et al.  Stochastic methods for l1 regularized loss minimization , 2009, ICML '09.

[15]  Nezih Altay,et al.  OR/MS research in disaster operations management , 2006, Eur. J. Oper. Res..

[16]  Amir H. Behzadan,et al.  Deep Convolutional Networks for Construction Object Detection Under Different Visual Conditions , 2020, Frontiers in Built Environment.

[17]  Hojjat Adeli,et al.  A novel machine learning‐based algorithm to detect damage in high‐rise building structures , 2017 .

[18]  Siyuan Xian,et al.  Storm surge damage to residential areas: a quantitative analysis for Hurricane Sandy in comparison with FEMA flood map , 2015, Natural Hazards.

[19]  Jay H. Crandell Statistical assessment of construction characteristics and performance of homes in Hurricanes Andrew and Opal , 1998 .

[20]  Joseph Z. Xu,et al.  Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks , 2019, ArXiv.

[21]  Jean-Paul Pinelli,et al.  Overview of Damage Observed in Regional Construction during the Passage of Hurricane Irma over the State of Florida , 2018, Forensic Engineering 2018.

[22]  David J. Hauser,et al.  Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants , 2015, Behavior Research Methods.

[23]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[24]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[25]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[26]  Xiao Liang,et al.  Uncertainty‐assisted deep vision structural health monitoring , 2020, Comput. Aided Civ. Infrastructure Eng..

[27]  Gary Y.K. Chock Modeling of hurricane damage for Hawaii residential construction , 2005 .

[28]  Jamie B. Kruse,et al.  Spatial dependencies in wind-related housing damage , 2004 .

[29]  Carol J. Friedland,et al.  Remote Sensing Classification of Hurricane Storm Surge Structural Damage , 2007 .

[30]  Norman Kerle,et al.  Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions , 2019, Remote. Sens..

[31]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[32]  Manzhu Yu,et al.  Big Data in Natural Disaster Management: A Review , 2018 .

[33]  Shahriar Akter,et al.  Big data and disaster management: a systematic review and agenda for future research , 2017, Annals of Operations Research.

[34]  Michael K. Lindell,et al.  ASSESSING COMMUNITY IMPACTS OF NATURAL DISASTERS , 2003 .

[35]  Peter J. Bickel,et al.  The Earth Mover's distance is the Mallows distance: some insights from statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[36]  Hojjat Adeli,et al.  NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization , 2017 .

[37]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[38]  Hojjat Adeli,et al.  A novel unsupervised deep learning model for global and local health condition assessment of structures , 2018 .

[39]  Klaus-Robert Müller,et al.  Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models , 2017, ArXiv.

[40]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[41]  Silvio Savarese,et al.  Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Francisco Herrera,et al.  An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics , 2013, Inf. Sci..

[43]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[44]  Georges Dupret,et al.  Bootstrap re-sampling for unbalanced data in supervised learning , 2001, Eur. J. Oper. Res..

[45]  Moncef Gabbouj,et al.  Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks , 2017 .

[46]  Amir H. Behzadan,et al.  Deep learning for site safety: Real-time detection of personal protective equipment , 2020 .

[47]  John P. Wilson,et al.  Conducting disaster damage assessments with Spatial Video, experts, and citizens , 2014 .

[48]  Yi-Zhou Lin,et al.  Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning , 2017, Comput. Aided Civ. Infrastructure Eng..

[49]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[50]  Paul Fronstin,et al.  The Determinants of Residential Property Damage Caused by Hurricane Andrew , 1994 .

[51]  Zheng Yi Wu,et al.  Deep learning‐based multi‐class damage detection for autonomous post‐disaster reconnaissance , 2020, Structural Control and Health Monitoring.

[52]  Hojjat Adeli,et al.  Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete , 2017 .

[53]  Ahsan Kareem,et al.  Automated Poststorm Damage Classification of Low-Rise Building Roofing Systems Using High-Resolution Aerial Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Jie Xu,et al.  Recognition of rust grade and rust ratio of steel structures based on ensembled convolutional neural network , 2020, Comput. Aided Civ. Infrastructure Eng..

[55]  Howie Choset,et al.  xBD: A Dataset for Assessing Building Damage from Satellite Imagery , 2019, ArXiv.

[56]  Amir H. Behzadan,et al.  Convolutional neural networks for object detection in aerial imagery for disaster response and recovery , 2020, Adv. Eng. Informatics.

[57]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[58]  Chris Callison-Burch,et al.  Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription , 2010, NAACL.

[59]  David O. Prevatt,et al.  Linking Building Attributes and Tornado Vulnerability Using a Logistic Regression Model , 2018, Natural Hazards Review.