Tall buildings are especially prone to damage caused by winds and earthquakes. In practice, only a single hazard is assumed to dominate the design and is adopted for structural verifications. This is also the case when supplemental damping devices such as tuned mass dampers (TMDs) are adopted. Nevertheless, previous research has shown that from a life-cycle cost (LCC) perspective the dominant hazard concept is misleading, and a multi-hazard approach is necessary. This article proposes a methodology for multi-objective optimization-based design of multiple TMDs (MTMDs) attached to tall buildings subjected to both winds and earthquakes. The LCC related to damage on non-structural components is taken as one of the objective functions to join consistently these hazards. The MTMDs initial cost is selected as the second objective function. The results of these multi-objective optimization problems are Pareto fronts of optimized solutions that may be of interest to stakeholders, including non-technical decision makers. A genetic algorithm is adopted as solution strategy. A realistic case study is presented, and the optimization results are compared with classic literature solutions.

Life-cycle cost-based optimization of MTMDs for tall buildings under multiple hazards

Oren Lavan;Ilaria Venanzi
2021

Abstract

Tall buildings are especially prone to damage caused by winds and earthquakes. In practice, only a single hazard is assumed to dominate the design and is adopted for structural verifications. This is also the case when supplemental damping devices such as tuned mass dampers (TMDs) are adopted. Nevertheless, previous research has shown that from a life-cycle cost (LCC) perspective the dominant hazard concept is misleading, and a multi-hazard approach is necessary. This article proposes a methodology for multi-objective optimization-based design of multiple TMDs (MTMDs) attached to tall buildings subjected to both winds and earthquakes. The LCC related to damage on non-structural components is taken as one of the objective functions to join consistently these hazards. The MTMDs initial cost is selected as the second objective function. The results of these multi-objective optimization problems are Pareto fronts of optimized solutions that may be of interest to stakeholders, including non-technical decision makers. A genetic algorithm is adopted as solution strategy. A realistic case study is presented, and the optimization results are compared with classic literature solutions.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11391/1477462
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