Showing 3 results for Seismic Life Cycle Cost
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.
A. R. Taghizadeh, S. Gholizadeh,
Volume 16, Issue 1 (1-2026)
Abstract
This paper employs a hybrid approach that integrates a metaheuristic algorithm with a properly trained neural network (NN) to perform seismic life‑cycle cost optimization of reinforced concrete (RC) frames within the framework of performance‑based design. In the proposed hybrid methodology, the center of mass optimization (CMO) metaheuristic algorithm is used to explore the design space. Additionally, a properly trained NN model is employed to estimate the nonlinear seismic response of the RC frames in order to evaluate the design constraints and compute the life‑cycle cost during the optimization process within a reasonable computational time. The efficiency of the proposed hybrid methodology is assessed through two performance‑based design optimization case studies involving 5‑ and 10‑story RC frames. The numerical results demonstrate that the proposed approach is an effective tool for optimizing the life‑cycle cost of RC frames by substantially reducing the computational burden of the optimization process.
