Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during Earthquakes
One of the human concerns has always been to estimate the damage caused before the earthquake and predict the extent of injuries and causalities. An effective model should be developed based on the field survey data for appropriate prediction. In this study, the degree of damage to the structure is...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/4746587 |
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| _version_ | 1832554712481660928 |
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| author | Mohammad Reza Amiri Shahmirani Abbas Akbarpour Nikghalb Rashti Mohammad Reza Adib Ramezani Emadaldin Mohammadi Golafshani |
| author_facet | Mohammad Reza Amiri Shahmirani Abbas Akbarpour Nikghalb Rashti Mohammad Reza Adib Ramezani Emadaldin Mohammadi Golafshani |
| author_sort | Mohammad Reza Amiri Shahmirani |
| collection | DOAJ |
| description | One of the human concerns has always been to estimate the damage caused before the earthquake and predict the extent of injuries and causalities. An effective model should be developed based on the field survey data for appropriate prediction. In this study, the degree of damage to the structure is first determined and the potential damage is then predicted using field data and fuzzy logic (FL). Effective parameters in the model include the structure height, building age, shear wave velocity in the soil, the plan equivalent moment of inertia, distance to the fault, earthquake factor, the number of inhabitants in the building, and the building height-to-width ratio (HWR). The parameters are fuzzily divided into five classifications: bad, relatively bad, medium, relatively good, and good. The model output parameter, which is the degree of damage to the building, is fuzzy and is divided into five classifications: complete damage, extensive damage, moderate damage, slight damage, and no damage. It should be noted that buildings with steel and concrete structures and moment frames, in the night, day, and traffic time scenarios, have very limited type 3 and 4 injuries with 32, 24, and 16 people, respectively, but type 1 and 2 injuries are significant. During the earthquake at night, the number of people with type 1 and 2 injuries is 975607 and 58757, in the event of the earthquake during the day, the number of people with type 1 and 2 injuries is 739096 and 44513, and during the earthquake at traffic time, the number of people with type 1 and 2 injuries is 492731 and 29675, respectively. |
| format | Article |
| id | doaj-art-746a1bc1159b42289dfad1d783bbbfbb |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-746a1bc1159b42289dfad1d783bbbfbb2025-02-03T05:50:44ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/4746587Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during EarthquakesMohammad Reza Amiri Shahmirani0Abbas Akbarpour Nikghalb Rashti1Mohammad Reza Adib Ramezani2Emadaldin Mohammadi Golafshani3Department of Construction Engineering and ManagementDepartment of Construction Engineering and ManagementDepartment of Construction Engineering and ManagementDepartment of Civil EngineeringOne of the human concerns has always been to estimate the damage caused before the earthquake and predict the extent of injuries and causalities. An effective model should be developed based on the field survey data for appropriate prediction. In this study, the degree of damage to the structure is first determined and the potential damage is then predicted using field data and fuzzy logic (FL). Effective parameters in the model include the structure height, building age, shear wave velocity in the soil, the plan equivalent moment of inertia, distance to the fault, earthquake factor, the number of inhabitants in the building, and the building height-to-width ratio (HWR). The parameters are fuzzily divided into five classifications: bad, relatively bad, medium, relatively good, and good. The model output parameter, which is the degree of damage to the building, is fuzzy and is divided into five classifications: complete damage, extensive damage, moderate damage, slight damage, and no damage. It should be noted that buildings with steel and concrete structures and moment frames, in the night, day, and traffic time scenarios, have very limited type 3 and 4 injuries with 32, 24, and 16 people, respectively, but type 1 and 2 injuries are significant. During the earthquake at night, the number of people with type 1 and 2 injuries is 975607 and 58757, in the event of the earthquake during the day, the number of people with type 1 and 2 injuries is 739096 and 44513, and during the earthquake at traffic time, the number of people with type 1 and 2 injuries is 492731 and 29675, respectively.http://dx.doi.org/10.1155/2022/4746587 |
| spellingShingle | Mohammad Reza Amiri Shahmirani Abbas Akbarpour Nikghalb Rashti Mohammad Reza Adib Ramezani Emadaldin Mohammadi Golafshani Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during Earthquakes Shock and Vibration |
| title | Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during Earthquakes |
| title_full | Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during Earthquakes |
| title_fullStr | Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during Earthquakes |
| title_full_unstemmed | Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during Earthquakes |
| title_short | Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during Earthquakes |
| title_sort | buildings causalities and injuries innovative fuzzy damage model during earthquakes |
| url | http://dx.doi.org/10.1155/2022/4746587 |
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