Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques
Recent global events have underscored the critical need for advancing research on buildings and fortifying their resilience against the escalating threat of natural hazards. One paramount task in this pursuit is the rapid and precise assessment of existing buildings' vulnerability to natural hazard...
Αποθηκεύτηκε σε:
| Μορφή: | Online |
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| Γλώσσα: | Αγγλικά |
| Έκδοση: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20240514_9783725804856_319 |
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| _version_ | 1869527029176598528 |
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| collection | Directory of Open Access Books |
| description | Recent global events have underscored the critical need for advancing research on buildings and fortifying their resilience against the escalating threat of natural hazards. One paramount task in this pursuit is the rapid and precise assessment of existing buildings' vulnerability to natural hazard activities—a crucial endeavor that demands simplicity, efficiency, and cost-effectiveness. The dispersion of big data and the complexity of conducting a detailed construction analysis can hinder the expeditious identification of vulnerable structures, especially in the face of a large-scale mitigation campaign. This Special Issue focuses on the development and application of modern computational techniques in the assessment of a building’s vulnerability to natural hazards. This collection explores innovative methods, such as artificial neural networks and fuzzy logicmachine learning, which have demonstrated unparalleled efficiency in dealing with big data and capturing non-linear relationships among various parameters affecting a building's resilience against earthquakes, floods, and other natural disasters. The articles within this Special Issue delved into the practical implementation of these soft computational techniques, offering insights into their reliability and applicability. By bridging the gap between traditional construction analysis and the urgency of identifying vulnerable buildings, this Special Issue aims to pave the way for a fast and reliable methodology that aligns with the demands of our dynamic urban landscapes. |
| format | Online |
| id | doab-20.500.12854ir-137723 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1377232024-05-14T14:15:06Z Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques Lahmer, Tom Harirchian, Ehsan Novelli, Viviana response spectrum rapid damage assessment remote sensing deep learning historical heritage finite element analysis damage assessment minaret rapid assessment machine learning seismic vulnerability Django damage classification seismic fragility analysis dual surrogate model Kriging model active learning mega-frame with vibration control substructure architectural seismic risk probability of exceedance strengthening steel reinforced concrete mechanical steel stitches shear Eastern Turkey adaptive pushover design spectra Bitlis simplified design rules earthquake linear method nonlinear method performance analysis AHP building attributes flood risk assessment GIS building material concrete compressive strength neural network slime mold algorithm Pazarcık-Elbistan earthquakes Diyarbakir pushover analysis damage limits Eurocode thema EDItEUR::M Medicine and Nursing::MK Medical specialties, branches of medicine::MKG Pharmacology::MKGW Psychopharmacology Recent global events have underscored the critical need for advancing research on buildings and fortifying their resilience against the escalating threat of natural hazards. One paramount task in this pursuit is the rapid and precise assessment of existing buildings' vulnerability to natural hazard activities—a crucial endeavor that demands simplicity, efficiency, and cost-effectiveness. The dispersion of big data and the complexity of conducting a detailed construction analysis can hinder the expeditious identification of vulnerable structures, especially in the face of a large-scale mitigation campaign. This Special Issue focuses on the development and application of modern computational techniques in the assessment of a building’s vulnerability to natural hazards. This collection explores innovative methods, such as artificial neural networks and fuzzy logicmachine learning, which have demonstrated unparalleled efficiency in dealing with big data and capturing non-linear relationships among various parameters affecting a building's resilience against earthquakes, floods, and other natural disasters. The articles within this Special Issue delved into the practical implementation of these soft computational techniques, offering insights into their reliability and applicability. By bridging the gap between traditional construction analysis and the urgency of identifying vulnerable buildings, this Special Issue aims to pave the way for a fast and reliable methodology that aligns with the demands of our dynamic urban landscapes. 2024-05-14T14:14:58Z 2024-05-14T14:14:58Z 2024 book ONIX_20240514_9783725804856_319 9783725804856 9783725804863 https://directory.doabooks.org/handle/20.500.12854/137723 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8952 https://mdpi.com/books/pdfview/book/8952 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0486-3 10.3390/books978-3-7258-0486-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725804856 9783725804863 250 open access |
| spellingShingle | response spectrum rapid damage assessment remote sensing deep learning historical heritage finite element analysis damage assessment minaret rapid assessment machine learning seismic vulnerability Django damage classification seismic fragility analysis dual surrogate model Kriging model active learning mega-frame with vibration control substructure architectural seismic risk probability of exceedance strengthening steel reinforced concrete mechanical steel stitches shear Eastern Turkey adaptive pushover design spectra Bitlis simplified design rules earthquake linear method nonlinear method performance analysis AHP building attributes flood risk assessment GIS building material concrete compressive strength neural network slime mold algorithm Pazarcık-Elbistan earthquakes Diyarbakir pushover analysis damage limits Eurocode thema EDItEUR::M Medicine and Nursing::MK Medical specialties, branches of medicine::MKG Pharmacology::MKGW Psychopharmacology Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques |
| title | Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques |
| title_full | Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques |
| title_fullStr | Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques |
| title_full_unstemmed | Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques |
| title_short | Building's Vulnerability Assessment against Natural Hazards by Using Modern Computational Techniques |
| title_sort | building s vulnerability assessment against natural hazards by using modern computational techniques |
| topic | response spectrum rapid damage assessment remote sensing deep learning historical heritage finite element analysis damage assessment minaret rapid assessment machine learning seismic vulnerability Django damage classification seismic fragility analysis dual surrogate model Kriging model active learning mega-frame with vibration control substructure architectural seismic risk probability of exceedance strengthening steel reinforced concrete mechanical steel stitches shear Eastern Turkey adaptive pushover design spectra Bitlis simplified design rules earthquake linear method nonlinear method performance analysis AHP building attributes flood risk assessment GIS building material concrete compressive strength neural network slime mold algorithm Pazarcık-Elbistan earthquakes Diyarbakir pushover analysis damage limits Eurocode thema EDItEUR::M Medicine and Nursing::MK Medical specialties, branches of medicine::MKG Pharmacology::MKGW Psychopharmacology |
| topic_facet | response spectrum rapid damage assessment remote sensing deep learning historical heritage finite element analysis damage assessment minaret rapid assessment machine learning seismic vulnerability Django damage classification seismic fragility analysis dual surrogate model Kriging model active learning mega-frame with vibration control substructure architectural seismic risk probability of exceedance strengthening steel reinforced concrete mechanical steel stitches shear Eastern Turkey adaptive pushover design spectra Bitlis simplified design rules earthquake linear method nonlinear method performance analysis AHP building attributes flood risk assessment GIS building material concrete compressive strength neural network slime mold algorithm Pazarcık-Elbistan earthquakes Diyarbakir pushover analysis damage limits Eurocode thema EDItEUR::M Medicine and Nursing::MK Medical specialties, branches of medicine::MKG Pharmacology::MKGW Psychopharmacology |
| url | ONIX_20240514_9783725804856_319 |