S. Gholizadeh, S. Tariverdilo,
Volume 14, Issue 3 (6-2024)
Abstract
The primary objective of this paper is to assess the seismic life-cycle cost of optimally designed steel moment frames. The methodology of this paper involves two main steps. In the first step, we optimize the initial cost of steel moment frames within the performance-based design framework, utilizing nonlinear static pushover analysis. In the second step, we perform a life cycle-cost analysis of the optimized steel moment frames using nonlinear response history analysis with a suite of earthquake records. We consider content losses due to floor acceleration and inter-story drift for the life cycle cost analysis. The numerical results highlight the critical role of integrating life-cycle cost analysis into the seismic optimization process to design steel moment frames with optimal seismic life-cycle costs.
A. Hassan Radhi Alhilali, S. Gholizadeh, S. Tariverdilo,
Volume 14, Issue 4 (10-2024)
Abstract
This paper employs neural network models to assess the seismic confidence levels at various performance levels, as well as the seismic collapse capacity of steel moment-resisting frame structures. Two types of shallow neural network models including back-propagation (BP) and radial basis (RB) models are utilized to evaluate the seismic responses. Both neural network models consist of a single hidden layer with a different number of neurons. The prediction accuracy of the trained neural network models is compared using two illustrative examples of 6- and 12-story steel moment-resisting frames. The obtained numerical results indicate that the BP model outperforms the RB model in predicting seismic responses.
