Abstract
This work presents a two-phase sampling approach to address reliability-based optimization problems in structural engineering. The constrained optimization problem is converted into a sampling problem, which is then solved using Markov chain Monte Carlo methods. First, an exploration phase generates uniformly distributed feasible designs. Thereafter, an exploitation phase is carried out to obtain a set of close-to-optimal designs. The approach is general in the sense that it is not limited to a particular type of system behavior and, in addition, it can handle constrained and unconstrained formulations as well as discrete–continuous design spaces. Three numerical examples involving structural dynamical systems under stochastic excitation are presented to illustrate the capabilities of the approach.
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References
Haftka RT, Gürdal Z (2012) Elements of structural optimization. Springer Science & Business Media. Kluwer, The Netherlands, vol 11
Arora J (2004) Introduction to optimum design. Elsevier, United Kingdom
Enevoldsen I, Sørensen JD (1994) Reliability-based optimization in structural engineering. Struct Saf 15(3):169–196
Gasser M, Schuëller GI (1997) Reliability-based optimization of structural systems. Math Methods Oper Res 46(3):287–307
Schuëller GI, Jensen HA (2008) Computational methods in optimization considering uncertainties–an overview. Comput Methods Appl Mech Eng 198(1):2–13
Schueller GI, Pradlwarter HJ, Koutsourelakis PS (2004) A critical appraisal of reliability estimation procedures for high dimensions. Probab Eng Mech 19(4):463–474
Goller B, Pradlwarter HJ, Schuëller GI (2013) Reliability assessment in structural dynamics. J Sound Vib 332(10):2488–2499
Jerez DJ, Jensen HA, Beer M (2022) Reliability-based design optimization of structural systems under stochastic excitation: an overview. Mech Syst Signal Process 166:108397
Jensen HA, Ferre MS, Kusanovic DS (2010) Reliability-based synthesis of non-linear stochastic dynamical systems: a global approximation approach. Int J Reliab Saf 4(2–3):139–165
Jensen HA, Becerra LG, Valdebenito MA (2013) On the use of a class of interior point algorithms in stochastic structural optimization. Comput Struct 126:69–85
Liu WS, Cheung SH (2017) Reliability based design optimization with approximate failure probability function in partitioned design space. Reliab Eng Syst Saf 167:602–611
Yuan X, Liu S, Valdebenito MA, Faes MG, Jerez DJ, Jensen HA, Beer M (2021) Decoupled reliability-based optimization using Markov chain Monte Carlo in augmented space. Adv Eng Softw 157:103020
Jensen HA, Jerez DJ, Valdebenito M (2020) An adaptive scheme for reliability-based global design optimization: a Markov chain Monte Carlo approach. Mech Syst Signal Process 143:106836
Jensen H, Jerez D, Beer M (2021) A general two-phase Markov chain Monte Carlo approach for constrained design optimization: application to stochastic structural optimization. Comput Methods Appl Mech Eng 373:113487
Jensen HA, Jerez DJ, Beer M (2022) Structural synthesis considering mixed discrete–continuous design variables: A Bayesian framework. Mech Syst Signal Process 162:108042
Jerez DJ, Jensen HA, Beer M, Chen J (2022) Asymptotic Bayesian optimization: a Markov sampling-based framework for design optimization. Probab Eng Mech 67:103178
Angelikopoulos P, Papadimitriou C, Koumoutsakos P (2015) X-TMCMC: adaptive kriging for Bayesian inverse modeling. Comput Methods Appl Mech Eng 289:409–428
Spence SM, Kareem A (2014) Performance-based design and optimization of uncertain wind-excited dynamic building systems. Eng Struct 78:133–144
Bobby S, Spence SM, Bernardini E, Kareem A (2014) Performance-based topology optimization for wind-excited tall buildings: a framework. Eng Struct 74:242–255
Petromichelakis I, Psaros A, Kougioumtzoglou I (2021) Stochastic response analysis and reliability-based design optimization of nonlinear electrome-chanical energy harvesters with fractional derivative elements. ASCE-ASME J Risk Uncert Eng Sys Part B Mech Eng 7:1
Der Kiureghian A (2000) The geometry of random vibrations and solutions by FORM and SORM. Probab Eng Mech 15(1):81–90
Schuëller GI, Pradlwarter HJ (2007) Benchmark study on reliability estimation in higher dimensions of structural systems—an overview. Struct Saf 29(3):167–182
Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220:671–680
Černý V (1985) Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J Optim Theory Appl 45(1):41–51
Ching J, Chen YC (2007) Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. J Eng Mech 7:816–832
Beck JL, Au SK (2002) Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation. J Eng Mech 128(4):380–391
Wang J, Katafygiotis LS (2014) Reliability-based optimal design of linear structures subjected to stochastic excitations. Struct Saf 47:29–38
Zuev KM, Beck JL (2013) Global optimization using the asymptotically independent Markov sampling method. Comput Struct 126:107–119
Kanjilal O, Papaioannou I, Straub D (2021) Cross entropy-based importance sampling for first-passage probability estimation of randomly excited linear structures with parameter uncertainty. Struct Saf 91:102090
Jensen HA, Vergara C, Papadimitriou C, Millas E (2013) The use of updated robust reliability measures in stochastic dynamical systems. Comput Methods Appl Mech Eng 267:293–317
Chatzi EN, Papadimitriou C (eds) (2016) Identification methods for structural health monitoring. Springer, Berlin, vol 567
Beck JL (2010) Bayesian system identification based on probability logic. Struct Control Health Monit 17(7):825–847
Yuen KV (2010) Bayesian methods for structural dynamics and civil engineering. Wiley, Singapore
Betz W, Papaioannou I, Straub D (2016) Transitional Markov chain Monte Carlo: observations and improvements. J Eng Mechan 142(Issue-5)
Angelikopoulos P, Papadimitriou C, Koumoutsakos P (2012) Bayesian uncertainty quantification and propagation in molecular dynamics simulations: a high performance computing framework. J Chem Phys 137(14):144103
Lophaven SN, Nielsen HB, Søndergaard J (2002) DACE: a Matlab kriging toolbox. In: IMM, informatics and mathematical modelling, The Technical University of Denmark, Lyngby, Denmark, vol 2
Santner TJ, Williams BJ, Notz WI, Williams BJ (2003) The design and analysis of computer experiments, vol 1. Springer, New York
Au SK, Beck JL (2001) Estimation of small failure probabilities in high dimensions by subset simulation. Probab Eng Mech 16(4):263–277
Au SK, Wang Y (2014) Engineering risk assessment with subset simulation. Wiley, Singapore
Jensen HA, Mayorga F, Papadimitriou C (2015) Reliability sensitivity analysis of stochastic finite element models. Comput Methods Appl Mech Eng 296:327–351
Mosqueda G, Whittaker AS, Fenves GL (2004) Characterization and modeling of friction pendulum bearings subjected to multiple components of excitation. J Struct Eng 130(3):433–442
Lomiento G, Bonessio N, Benzoni G (2013) Friction model for sliding bearings under seismic excitation. J Earthquake Eng 17(8):1162–1191
Boore DM (2003) Simulation of ground motion using the stochastic method. Pure Appl Geophys 160(3):635–676
Atkinson GM, Silva W (2000) Stochastic modeling of California ground motions. Bull Seismol Soc Am 90(2):255–274
Anderson JG, Hough SE (1984) A model for the shape of the Fourier amplitude spectrum of acceleration at high frequencies. Bull Seismol Soc Am 74(5):1969–1993
Mavroeidis GP, Papageorgiou AS (2003) A mathematical representation of near-fault ground motions. Bull Seismol Soc Am 93(3):1099–1131
Pradlwarter HJ, Schuëller GI (1993) Equivalent linearization—a suitable tool for analyzing MDOF-systems. Probab Eng Mech 8(2):115–126
AISC (American Institute of Steel Construction) (2001) Manual of steel construction load and resistance factor design. AISC, Chicago
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Jerez, D.J., Jensen, H.A., Beer, M. (2023). A Two-Phase Sampling Approach for Reliability-Based Optimization in Structural Engineering. In: Liu, Y., Wang, D., Mi, J., Li, H. (eds) Advances in Reliability and Maintainability Methods and Engineering Applications. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-28859-3_2
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