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Automated Shear Wall Layout Optimization Framework of Tall Buildings Subjected to Dynamic Wind Loads
https://orcid.org/http://orcid.org/0000-0001-6866-7061
https://orcid.org/http://orcid.org/0000-0003-3530-2511
The structural design procedure of buildings goes through time-consuming iterations, especially at the preliminary design stage. A structural layout is predefined based on the designers’ experience through a trial-and-error approach that might not yield an optimal design. This procedure becomes more complicated for tall buildings where an adequate Main Wind Force Resisting System (MWFRS) should be designed. MWFRS design includes both layout (e.g., cores or peripheral shear walls) and structural element sizes (e.g., reinforcement ratio, shear wall thickness, and shear wall length). This paper presents a multi-objective topology optimization framework for tall buildings subjected to dynamic wind loads to find an optimal shear wall layout based on the required objective functions (e.g., number of shear wall elements). This framework extracts dynamic wind loads time history from a computational fluid dynamic (CFD) model. Then, an automated time history Finite Element Analysis (FEA) is conducted to prepare a database for surrogate model training. An artificial neural network-based surrogate model is built using the prepared database to represent the objective and constraint functions of the optimization problem that can capture the structure response. This model is coupled with a genetic algorithm to identify the optimum layout of shear walls within the predefined architectural and structural constraints. A case study of a 20 stories residential building is presented where constraints of interstorey drift are maintained. The developed framework managed to reduce the weight of the required shear wall elements with an adequate distribution of straining actions and minimum eccentricity.
Automated Shear Wall Layout Optimization Framework of Tall Buildings Subjected to Dynamic Wind Loads
https://orcid.org/http://orcid.org/0000-0001-6866-7061
https://orcid.org/http://orcid.org/0000-0003-3530-2511
The structural design procedure of buildings goes through time-consuming iterations, especially at the preliminary design stage. A structural layout is predefined based on the designers’ experience through a trial-and-error approach that might not yield an optimal design. This procedure becomes more complicated for tall buildings where an adequate Main Wind Force Resisting System (MWFRS) should be designed. MWFRS design includes both layout (e.g., cores or peripheral shear walls) and structural element sizes (e.g., reinforcement ratio, shear wall thickness, and shear wall length). This paper presents a multi-objective topology optimization framework for tall buildings subjected to dynamic wind loads to find an optimal shear wall layout based on the required objective functions (e.g., number of shear wall elements). This framework extracts dynamic wind loads time history from a computational fluid dynamic (CFD) model. Then, an automated time history Finite Element Analysis (FEA) is conducted to prepare a database for surrogate model training. An artificial neural network-based surrogate model is built using the prepared database to represent the objective and constraint functions of the optimization problem that can capture the structure response. This model is coupled with a genetic algorithm to identify the optimum layout of shear walls within the predefined architectural and structural constraints. A case study of a 20 stories residential building is presented where constraints of interstorey drift are maintained. The developed framework managed to reduce the weight of the required shear wall elements with an adequate distribution of straining actions and minimum eccentricity.
Automated Shear Wall Layout Optimization Framework of Tall Buildings Subjected to Dynamic Wind Loads
https://orcid.org/http://orcid.org/0000-0001-6866-7061
https://orcid.org/http://orcid.org/0000-0003-3530-2511
The structural design procedure of buildings goes through time-consuming iterations, especially at the preliminary design stage. A structural layout is predefined based on the designers’ experience through a trial-and-error approach that might not yield an optimal design. This procedure becomes more complicated for tall buildings where an adequate Main Wind Force Resisting System (MWFRS) should be designed. MWFRS design includes both layout (e.g., cores or peripheral shear walls) and structural element sizes (e.g., reinforcement ratio, shear wall thickness, and shear wall length). This paper presents a multi-objective topology optimization framework for tall buildings subjected to dynamic wind loads to find an optimal shear wall layout based on the required objective functions (e.g., number of shear wall elements). This framework extracts dynamic wind loads time history from a computational fluid dynamic (CFD) model. Then, an automated time history Finite Element Analysis (FEA) is conducted to prepare a database for surrogate model training. An artificial neural network-based surrogate model is built using the prepared database to represent the objective and constraint functions of the optimization problem that can capture the structure response. This model is coupled with a genetic algorithm to identify the optimum layout of shear walls within the predefined architectural and structural constraints. A case study of a 20 stories residential building is presented where constraints of interstorey drift are maintained. The developed framework managed to reduce the weight of the required shear wall elements with an adequate distribution of straining actions and minimum eccentricity.
Automated Shear Wall Layout Optimization Framework of Tall Buildings Subjected to Dynamic Wind Loads
Lecture Notes in Civil Engineering
Desjardins, Serge (editor) / Poitras, Gérard J. (editor) / El Damatty, Ashraf (editor) / Elshaer, Ahmed (editor) / Alanani, Magdy (author) / Brown, Tristen (author) / Elshaer, Ahmed (author)
Canadian Society of Civil Engineering Annual Conference ; 2023 ; Moncton, NB, Canada
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2023, Volume 13 ; Chapter: 23 ; 285-299
2024-09-03
15 pages
Article/Chapter (Book)
Electronic Resource
English
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