An efficient framework for the reliability-based design optimization of large-scale uncertain and stochastic linear systems |
| |
| Affiliation: | 1. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, 48109, USA;2. CRIACIV/Department of Civil and Environmental Engineering (DICA), University of Perugia, via G. Duranti 93, 06125 Perugia, Italy;3. NatHaz Modeling Laboratory, Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, USA;1. Department of Civil & Environmental Engineering, University of Massachusetts, 130 Natural Resources Road, Amherst, MA 01003, USA;2. Department of Civil Engineering & Engineering Mechanics, Columbia University, USA;3. Multifunctional Materials Branch, US Naval Research Laboratory, USA;1. School of Civil Engineering, Hefei University of Technology, Hefei 230009, PR China;2. Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian 116024, PR China;1. Facultad de Ingeniería, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa, Mexico;2. Instituto de Ingeniería, Universidad Nacional Autónoma de México, Ciudad de México, Mexico;3. Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, USA;1. Department of Civil Engineering, Santa Maria University, Valparaiso, Chile;2. Department of Mechanical Engineering, University of Thessaly, GR-38334 Volos, Greece;1. Department of Civil Engineering, Sharif University of Technology, Tehran 46516-34445, Iran;2. Department of Civil Engineering, Amirkabir University of Technology, Tehran 19519-66441, Iran;3. Center of Excellence for Engineering and Management of Civil Infrastructures, School of Civil Engineering, The Univ. of Tehran, Tehran 11155-4563, Iran |
| |
| Abstract: | This paper is focused on the development of an efficient reliability-based design optimization algorithm for solving problems posed on uncertain linear dynamic systems characterized by large design variable vectors and driven by non-stationary stochastic excitation. The interest in such problems lies in the desire to define a new generation of tools that can efficiently solve practical problems, such as the design of high-rise buildings in seismic zones, characterized by numerous free parameters in a rigorously probabilistic setting. To this end a novel decoupling approach is developed based on defining and solving a limited sequence of deterministic optimization sub-problems. In particular, each sub-problem is formulated from information pertaining to a single simulation carried out exclusively in the current design point. This characteristic drastically limits the number of simulations necessary to find a solution to the original problem while making the proposed approach practically insensitive to the size of the design variable vector. To demonstrate the efficiency and strong convergence properties of the proposed approach, the structural system of a high-rise building defined by over three hundred free parameters is optimized under non-stationary stochastic earthquake excitation. |
| |
| Keywords: | Reliability-based design optimization Stochastic loads Reliability analysis Monte Carlo simulation Earthquake engineering Structural optimization High-dimensional problems |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
