EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 347 (2022) E3S Web Conf., 347 (2022) 01011 References
Open Access
Issue
E3S Web Conf.
Volume 347, 2022
2nd International Conference on Civil and Environmental Engineering (ICCEE 2022)
Article Number 01011
Number of page(s) 10
Section Infrastructure and Building Construction
DOI https://doi.org/10.1051/e3sconf/202234701011
Published online 14 April 2022
  1. United Nations Office for Disaster Risk Reduction (UNDRR), Human Cost of Disasters 2000-2019 (2020) [Google Scholar]
  2. Federal Emergency Management Agency, Post-Disaster Building Safety Evaluation Guidance, FEMA P-2055 (2019) [Google Scholar]
  3. H. Yi, J. Yang, Research Trends of Post Disaster Reconstruction: The Past and the Future, Habitat International, 42, pp. 21–29 (2014) [CrossRef] [Google Scholar]
  4. A. A. Alzughaibi, A. M. Ibrahim, A. M. Eltawil, Y. Na, S. El-Tawil, Post-Disaster Structural Health Monitoring System Using Personal Mobile-Phones, IEEE Topical Conference on Wireless Sensors and Sensor Networks, Jan. 2019, 1–4 (2019) [Google Scholar]
  5. J. O. B. Rotimi, J. L. Masurier, S. Wilkinson, The Regulatory Framework for Effective Post-Disaster Reconstruction in New Zealand, Third International Conference on Post- Disaster Reconstruction: Meeting Stakeholder Interests, 12 (2006) [Google Scholar]
  6. E. Pizzi, M. Acito, C. Del Pero, E. Seghezzi, V. Villa, E. S. Mazzucchelli, Technical-Scientific Support for the Definition of the Project for the Reconstruction of School Buildings Involved in Seismic Events, Buildings for Education: A Multidisciplinary Overview of The Design of School Buildings, Eds. Cham: Springer International Publishing, 193–202 (2020) [Google Scholar]
  7. S. Khanmohammadi, M. Arashpour, Y. Bai, Applications of Building Information Modeling (BIM) in Disaster Resilience: Present Status and Future Trends, 37th ISARC, Kitakyushu, Japan (2020) [Google Scholar]
  8. D. E. Alexander, Principles of Emergency Planning and Management, Sixth impression 2012. Harpenden, Hertfordshire: Terra Publishing (2002) [Google Scholar]
  9. B. Baroudi, R. R. Rapp, Disaster Restoration Projects: A Conceptual Project Management Perspective, in Australasian Journal of Construction Economics and Building - Conference Series 1, 2, 72 (2012) [Google Scholar]
  10. E. L. Quarantelli, The Disaster Recovery Process: What We Know and Do Not Know from Research, Disaster Research Centre, University of Delaware, (1999) [Google Scholar]
  11. M. K. Lindell, Recovery and Reconstruction After Disaster, in Encyclopaedia of Natural Hazards, 812–824 (2013) [Google Scholar]
  12. D. Brunsdon, S. Smith, Summary Notes from the Infrastructure Workshop, NZ Recovery Symposium, Napier, New Zealand, Ministry of Civil Defence & Emergency Management, New Zealand (2004) [Google Scholar]
  13. Ministry of Business, Innovation and Employment (MBIE) New Zealand, Field Guide: Rapid Post Disaster Building Usability Assessment - Earthquakes. (2014) [Google Scholar]
  14. T. Kaminosono, F. Kumazawa, Y. Nakano, Quick Inspection Manual for Damaged Reinforced Concrete Buildings Due to Earthquakes, National Institute of Land and Infrastructure Management, Japan, (2002) [Google Scholar]
  15. M. Maeda, Y. Ito, K. Matsukawa, Revision of Guideline for Post-Earthquake Damage Evaluation of RC Buildings in Japan, the 10th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Anchorage, AK, (2014) [Google Scholar]
  16. J. Moehle, G. Deierlein, A Framework Methodology for Performance-Based Earthquake Engineering, 13th World Conference on Earthquake Eng., 14 (2004) [Google Scholar]
  17. R. Sacks, C. M. Eastman, G. Lee, P. M. Teicholz, BIM handbook Third edition, Hoboken, New Jersey: Wiley (2018) [Google Scholar]
  18. R. Drogemuller, BIM Support for Disaster Response, Risk-informed Disaster Management: Planning for Response, Recovery and Resilience (2013) [Google Scholar]
  19. S. Wilkinson, A. Y. Chang-Richards, Z. Sapeciay, S. B. Costello, Improving Construction Sector Resilience, IJDRBE 7, 173–185 (2016) [Google Scholar]
  20. B. Sertyesilisik, Building Information Modeling as a Tool for Enhancing Disaster Resilience of the Construction Industry, Transactions of the VSB - Technical university of Ostrava, Safety Engineering Series 12, 1, 9–18 (2017) [CrossRef] [Google Scholar]
  21. F. Faqih, T. Zayed, Defect-Based Building Condition Assessment, Building and Environment 191 (2021) [Google Scholar]
  22. M. Messaoudi, N. O. Nawari, BIM-based Virtual Permitting Framework (VPF) for post-disaster recovery and rebuilding in the state of Florida, IJDRR 42, 101349 (2020) [Google Scholar]
  23. N. A. Garyaev, F. Ayoub, Towards Building Information Modelling for Diagnosis, Assessment and Rehabilitation Automation for Existing Buildings, J. Phys.: Conf. Ser. 1425, 012121 (2019) [Google Scholar]
  24. N. Nawari, S. Ravindran, Blockchain and Building Information Modeling (BIM): Review and Applications in Post-Disaster Recovery, Buildings 9, 6, 149 (2019) [CrossRef] [Google Scholar]
  25. E. B. Anil, B. Akinci, O. Kurc, J. H. Garrett, Building-Information-Modeling–Based Earthquake Damage Assessment for Reinforced Concrete Walls, J. Comput. Civ. Eng. 30, 4 (2016) [Google Scholar]
  26. M. M. Torok, M. Golparvar-Fard, K. B. Kochersberger, Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment, J. Comput. Civ. Eng. 28, 5 (2014) [CrossRef] [Google Scholar]
  27. L. Ma, R. Sacks, R. Zeibak-Shini, A. Aryal, S. Filin, Preparation of Synthetic As- Damaged Models for Post-Earthquake BIM Reconstruction Research, J. Comput. Civ. Eng. 30, 3 (2016) [Google Scholar]
  28. D. Liu, X. Xia, J. Chen, S. Li, Integrating Building Information Model and Augmented Reality for Drone-Based Building Inspection, J. Comput. Civ. Eng. 35, 2 (2021) [Google Scholar]
  29. S. Jiang, J. Zhang, Real‐Time Crack Assessment Using Deep Neural Networks with Wall‐Climbing Unmanned Aerial System, Computer‐Aided Civil and Infrastructure Engineering 35, 6, 549–564 (2020) [CrossRef] [Google Scholar]
  30. W. Zhai, H. Shen, C. Huang, W. Pei, Building Earthquake Damage Information Extraction from a Single Post-Earthquake PolSAR Image, Remote Sensing 8, 3, 171 (2016) [CrossRef] [Google Scholar]
  31. Z. Zhou, J. Gong, M. Guo, Image-Based 3D Reconstruction for Post-hurricane Residential Building Damage Assessment, J. Comput. Civ. Eng. 30, 2 (2016) [Google Scholar]
  32. C. Musella, M. Serra, C. Menna, D. Asprone, BIM & AI: advanced technologies for the digitalisation of seismic damages in masonry buildings, in Proc. of the International fib Symposium on Conceptual Design of Structures, Madrid, Spain, (2019) [Google Scholar]
  33. C. Musella, M. Serra, C. Menna, D. Asprone, Building information modeling and artificial intelligence: Advanced technologies for the digitalisation of seismic damage in existing buildings, Structural Concrete (2021) [Google Scholar]
  34. M. Alirezaei, M. Noori, O. Tatari, K. R. Mackie, A. Elgamal, BIM-based Damage Estimation of Buildings under Earthquake Loading Condition, Procedia Engineering, 145, 1051–1058 (2016) [CrossRef] [Google Scholar]
  35. Y. Liu, J. K. W. Yeoh, D. K. H. Chua, Deep Learning–Based Enhancement of Motion Blurred UAV Concrete Crack Images, J. Comput. Civ. Eng. 34, 5, 04020028 (2020) [CrossRef] [Google Scholar]
  36. Y. Liu and J. K. W. Yeoh, Automated crack pattern recognition from images for condition assessment of concrete structures, Automation in Construction 128, 103765 (2021) [CrossRef] [Google Scholar]
  37. R. Kalfarisi, Z. Y. Wu, K. Soh, Crack Detection and Segmentation Using Deep Learning with 3D Reality Mesh Model for Quantitative Assessment and Integrated Visualization, J. Comput. Civ. Eng. 34, 3 (2020) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.