The deterministic topology optimization (TO) has become one of the better-known optimization approaches in the engineering design of structures. However, its approximation errors have placed several restrictions on designers as it does not consider inherent uncertainties such as loads, geometrical dimensions, and materials in structures. To remedy this issue, the TO methods were incorporated with reliable design with highly martials involved under uncertainties for structural/mechanical problems. In the current reliability-based topology optimization (RBTO) procedures under multi-source uncertainties, dynamic mean value (DMV) was applied for evaluating the probabilistic constraints. For this aim, a sequential TO and reliability analysis (STORA) was proposed for application in various TO methods. The TO loop coupled bisection method as inverse topology layout applied in reliability loop that the DMV was utilized for evaluating the probabilistic constraint using sufficient descent condition. In the current RBTO model based on STORA, different TO methods named moving iso-surface threshold (MIST), evolutionary structural optimization (ESO), Level-set, and solid isotropic material with penalization (SIMP) are discussed for various problems with continuous design domains. The comparative results are discussed for different TO methods basis bisection approach for TO and RBTO solutions. The results demonstrated that the stable results provided by DMV and different optimal shapes between inverse TO-based bisection and RBTO solutions are captured. The proposed RBTO method using STORA provides safe optimum shapes for almost TO methods with uncertainties in material properties and loads.
Citation: Yiqing Shi, Mahmoud Alfouneh, Chao Yuan. Sequential topology optimization and reliability analysis using bisection: Level-set vs MIST, SIMP and ESO methods with multi-source uncertainties[J]. AIMS Mathematics, 2025, 10(6): 14392-14433. doi: 10.3934/math.2025648
The deterministic topology optimization (TO) has become one of the better-known optimization approaches in the engineering design of structures. However, its approximation errors have placed several restrictions on designers as it does not consider inherent uncertainties such as loads, geometrical dimensions, and materials in structures. To remedy this issue, the TO methods were incorporated with reliable design with highly martials involved under uncertainties for structural/mechanical problems. In the current reliability-based topology optimization (RBTO) procedures under multi-source uncertainties, dynamic mean value (DMV) was applied for evaluating the probabilistic constraints. For this aim, a sequential TO and reliability analysis (STORA) was proposed for application in various TO methods. The TO loop coupled bisection method as inverse topology layout applied in reliability loop that the DMV was utilized for evaluating the probabilistic constraint using sufficient descent condition. In the current RBTO model based on STORA, different TO methods named moving iso-surface threshold (MIST), evolutionary structural optimization (ESO), Level-set, and solid isotropic material with penalization (SIMP) are discussed for various problems with continuous design domains. The comparative results are discussed for different TO methods basis bisection approach for TO and RBTO solutions. The results demonstrated that the stable results provided by DMV and different optimal shapes between inverse TO-based bisection and RBTO solutions are captured. The proposed RBTO method using STORA provides safe optimum shapes for almost TO methods with uncertainties in material properties and loads.
| [1] |
M. Bendsøe, N. Kikuchi, Generating optimal topologies in structural design using a homogenization method, Comput. Method. Appl. M., 71 (1988), 197–224. https://doi.org/10.1016/0045-7825(88)90086-2 doi: 10.1016/0045-7825(88)90086-2
|
| [2] |
M. Habashneh, M. Movahedi Rad, Reliability based geometrically nonlinear bi-directional evolutionary structural optimization of elasto-plastic material, Sci. Rep., 12 (2022), 5989. https://doi.org/10.1038/s41598-022-09612-z doi: 10.1038/s41598-022-09612-z
|
| [3] |
M. Bayat, O. Zinovieva, F. Ferrari, C. Ayas, M. Langelaar, J. Spangenberg, et al., Holistic computational design within additive manufacturing through topology optimization combined with multiphysics multi-scale materials and process modelling, Prog. Mater. Sci., 138 (2023) 101129. https://doi.org/10.1016/j.pmatsci.2023.101129 doi: 10.1016/j.pmatsci.2023.101129
|
| [4] |
B. Wang, Q. Zhu, S. Li, Stabilization of discrete-time hidden semi-Markov jump linear systems with partly unknown emission probability matrix, IEEE T. Automat. Contr., 69 (2023), 1952–1959. https://doi.org/10.1109/TAC.2023.3272190 doi: 10.1109/TAC.2023.3272190
|
| [5] |
D. Meng, H. Yang, S. Yang, Y. Zhang, A. M. P. De Jesus, J. Correia, et al., Kriging-assisted hybrid reliability design and optimization of offshore wind turbine support structure based on a portfolio allocation strategy, Ocean Eng., 295 (2024), 116842. https://doi.org/10.1016/j.oceaneng.2024.116842 doi: 10.1016/j.oceaneng.2024.116842
|
| [6] |
Q. Zhu, H. Wang, Output feedback stabilization of stochastic feedforward systems with unknown control coefficients and unknown output function, Automatica, 87 (2018), 166–175. https://doi.org/10.1016/j.automatica.2017.10.004 doi: 10.1016/j.automatica.2017.10.004
|
| [7] |
M. Habashneh, M. Movahedi Rad, Plastic-limit probabilistic structural topology optimization of steel beams, Appl. Math. Model., 128 (2024) 347–369. https://doi.org/10.1016/j.apm.2024.01.029 doi: 10.1016/j.apm.2024.01.029
|
| [8] |
B. dos Santos, C. da Silva, W. Machado, Uncertainty quantification of the stochastic process "crack size" for the Forman model using the "fast crack bounds" method, Appl. Math. Model., 119 (2023), 500–512. https://doi.org/10.1016/j.apm.2023.03.008 doi: 10.1016/j.apm.2023.03.008
|
| [9] |
Z. Kang, Y. Luo, Non-probabilistic reliability-based topology optimization of geometrically nonlinear structures using convex models, Comput. Method. Appl. M., 198 (2009), 3228–3238. https://doi.org/10.1016/j.cma.2009.06.001 doi: 10.1016/j.cma.2009.06.001
|
| [10] |
L. Wang, B. Ni, X. Wang, Z. Li, Reliability-based topology optimization for heterogeneous composite structures under interval and convex mixed uncertainties, Appl. Math. Model., 99 (2021), 628–652. https://doi.org/10.1016/j.apm.2021.06.014 doi: 10.1016/j.apm.2021.06.014
|
| [11] |
H. Xia, Z. Qiu, An efficient sequential strategy for non-probabilistic reliability-based topology optimization (NRBTO) of continuum structures with stress constraints, Appl. Math. Model., 110 (2022), 723–747. https://doi.org/10.1016/j.apm.2022.06.021 doi: 10.1016/j.apm.2022.06.021
|
| [12] |
X. Zhang, G. Ouyang, A level set method for reliability-based topology optimization of compliant mechanisms, Sci. China E-Technol. Sci., 51 (2008) 443–455. https://doi.org/10.1007/s11431-008-0039-3 doi: 10.1007/s11431-008-0039-3
|
| [13] |
Z. Li, L. Wang, G. Xinyu, A level set reliability-based topology optimization (LS-RBTO) method considering sensitivity mapping and multi-source interval uncertainties, Comput. Method. Appl. M., 419 (2024), 116587. https://doi.org/10.1016/j.cma.2023.116587 doi: 10.1016/j.cma.2023.116587
|
| [14] |
Z. Lei, J. Zhang, Y. Liang, G. Chen, D. Yang, Efficient two-phase approach to reliability-based discrete variable topology optimization of continuum structures with multimodal distributions, Comput. Method. Appl. M., 415 (2023), 116237. https://doi.org/10.1016/j.cma.2023.116237 doi: 10.1016/j.cma.2023.116237
|
| [15] |
Y. Zheng, D. Da, H. Li, M. Xiao, L. Gao, Robust topology optimization for multi-material structures under interval uncertainty, Appl. Math. Model., 78 (2020), 627–647. https://doi.org/10.1016/j.apm.2019.10.019 doi: 10.1016/j.apm.2019.10.019
|
| [16] |
H. Simonetti, V. Almeida, F. de Assis das Neves, V. Del Duca Almeida, L. de Oliveira Neto, Reliability-based topology optimization: an extension of the SESO and SERA methods for three-dimensional structures, Appl. Sci., 12 (2022), 4220. https://doi.org/10.3390/app12094220 doi: 10.3390/app12094220
|
| [17] |
G. Zhang, C. Liang, Q. Zhu, Adaptive fuzzy event-triggered optimized consensus control for delayed unknown stochastic nonlinear multi-agent systems using simplified ADP, IEEE T. Automat. Sci. Eng., 22 (2025), 11780–11793. https://doi.org/10.1109/TASE.2025.3540468 doi: 10.1109/TASE.2025.3540468
|
| [18] |
B. Keshtegar, M. Alfouneh, SVR-TO-APMA: Hybrid efficient modelling and topology framework for stable topology optimization with accelerated performance measure approach, Comput. Method. Appl. M., 404 (2023), 115762. https://doi.org/10.1016/j.cma.2022.115762 doi: 10.1016/j.cma.2022.115762
|
| [19] | R. Rubinstein, D. Kroese, Simulation and the Monte Carlo method, John Wiley & Sons, 2016. https://doi.org/10.1002/9781118631980 |
| [20] |
B. Wang, Q. Zhu, S. Li, Stability analysis of discrete-time semi-Markov jump linear systems with time delay, IEEE T. Automat. Contr., 68 (2023), 6758–6765. https://doi.org/10.1109/TAC.2023.3240926 doi: 10.1109/TAC.2023.3240926
|
| [21] | C. Sundararajan, Probabilistic structural mechanics handbook: Theory and industrial applications, Springer Science & Business Media, 2012. |
| [22] |
R. Lopez, A. Beck, Reliability-based design optimization strategies based on FORM: a review, J. Braz. Soc. Mech. Sci. Eng., 34 (2012), 506–514. https://doi.org/10.1590/S1678-58782012000400012 doi: 10.1590/S1678-58782012000400012
|
| [23] |
Z. Hu, R. Mansour, M. Olsson, X. Du, Second-order reliability methods: a review and comparative study, Struct. Multidisc. Optim., 64 (2021), 3233–3263. https://doi.org/10.1007/s00158-021-03013-y doi: 10.1007/s00158-021-03013-y
|
| [24] |
Z. Meng, H. Zhou, H. Hu, B. Keshtegar, Enhanced sequential approximate programming using second order reliability method for accurate and efficient structural reliability-based design optimization, Appl. Math. Model., 62 (2018), 562–579. https://doi.org/10.1016/j.apm.2018.06.018 doi: 10.1016/j.apm.2018.06.018
|
| [25] |
D. Meng, S. Yang, A. de Jesus, S. Zhu, A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower, Renew. Energ., 203 (2023), 407–420. https://doi.org/10.1016/j.renene.2022.12.062 doi: 10.1016/j.renene.2022.12.062
|
| [26] |
B. Keshtegar, Enriched FR conjugate search directions for robust and efficient structural reliability analysis, Eng. Comput., 34 (2018), 117–128. https://doi.org/10.1007/s00366-017-0524-z doi: 10.1007/s00366-017-0524-z
|
| [27] |
S. Yang, D. Meng, H. Wang, C. Yang, A novel learning function for adaptive surrogate-model-based reliability evaluation, Philos. Trans. R. Soc. A, 382 (2024), 2264. https://doi.org/10.1098/rsta.2022.0395 doi: 10.1098/rsta.2022.0395
|
| [28] |
B. Keshtegar, P. Hao, A hybrid loop approach using the sufficient descent condition for accurate, robust, and efficient reliability-based design optimization, J. Mech. Des., 138 (2016), 121401. https://doi.org/10.1115/1.4034173 doi: 10.1115/1.4034173
|
| [29] |
Y. Luo, M. Zhou, M. Wang, Z. Deng, Reliability based topology optimization for continuum structures with local failure constraints, Comput. Struct., 143 (2014), 73–84. https://doi.org/10.1016/j.compstruc.2014.07.009 doi: 10.1016/j.compstruc.2014.07.009
|
| [30] |
J. Zhang, M. Xiao, P. Li, L. Gao, Quantile-based topology optimization under uncertainty using Kriging metamodel, Comput. Method. Appl. M., 393 (2022), 114690. https://doi.org/10.1016/j.cma.2022.114690 doi: 10.1016/j.cma.2022.114690
|
| [31] |
J. Tang, X. Li, C. Fu, H. Liu, L. Cao, C. Mi, et al., A possibility-based solution framework for interval uncertainty-based design optimization, Appl. Math. Model., 125 (2024), 649–667. https://doi.org/10.1016/j.apm.2023.09.010 doi: 10.1016/j.apm.2023.09.010
|
| [32] |
K. Cho, J. Park, M. Im, S. Han, Reliability-based topology optimization of electro-thermal-compliant mechanisms with a new material mixing method, Int. J. Precis. Eng. Manuf., 13 (2012), 693–699. https://doi.org/10.1007/s12541-012-0090-7 doi: 10.1007/s12541-012-0090-7
|
| [33] |
C. Kim, S. Wang, K. Rae, H. Moon, K. Choi, Reliability-based topology optimization with uncertainties, J. Mech. Sci. Technol., 20 (2006), 494–504. https://doi.org/10.1007/BF02916480 doi: 10.1007/BF02916480
|
| [34] |
B. Youn, K. Choi, Selecting probabilistic approaches for reliability-based design optimization, AIAA J., 42 (2004) 124–131. https://doi.org/10.2514/1.9036 doi: 10.2514/1.9036
|
| [35] |
A. Torii, R. Lopez, A. Beck, L. Miguel, A performance measure approach for risk optimization, Struct. Multidisc. Optim., 60 (2019), 927–947. https://doi.org/10.1007/s00158-019-02243-5 doi: 10.1007/s00158-019-02243-5
|
| [36] |
Z. Meng, B. Keshtegar, Adaptive conjugate single-loop method for efficient reliability-based design and topology optimization, Comput. Method. Appl. M., 344 (2019), 95–119. https://doi.org/10.1016/j.cma.2018.10.009 doi: 10.1016/j.cma.2018.10.009
|
| [37] |
J. Zheng, L. Yuan, C. Jiang, Z. Zhang, An efficient decoupled reliability-based topology optimization method based on a performance shift strategy, J. Mech. Des., 145 (2023), 061705. https://doi.org/10.1115/1.4056999 doi: 10.1115/1.4056999
|
| [38] |
D. Meng, S. Yang, A. De Jesus, T. Fazeres-Ferradosa, S. Zhu, A novel hybrid adaptive Kriging and water cycle algorithm for reliability-based design and optimization strategy: Application in offshore wind turbine monopile, Comput. Method. Appl. M., 412 (2023), 116083. https://doi.org/10.1016/j.cma.2023.116083 doi: 10.1016/j.cma.2023.116083
|
| [39] |
L. Wang, Y. Liu, D. Liu, Z. Wu, A novel dynamic reliability-based topology optimization (DRBTO) framework for continuum structures via interval-process collocation and the first-passage theories, Comput. Method. Appl. M., 386 (2021), 114107. https://doi.org/10.1016/j.cma.2021.114107 doi: 10.1016/j.cma.2021.114107
|
| [40] |
L. Wang, Z. Li, B. Ni, K. Gu, Non-probabilistic reliability-based topology optimization (NRBTO) scheme for continuum structures based on the parameterized level-set method and interval mathematics, Comput. Method. Appl. M., 373 (2021), 113477. https://doi.org/10.1016/j.cma.2020.113477 doi: 10.1016/j.cma.2020.113477
|
| [41] |
D. Meng, S. Yang, H. Yang, A. De Jesus, J. Correia, S. Zhu, Intelligent-inspired framework for fatigue reliability evaluation of offshore wind turbine support structures under hybrid uncertainty, Ocean Eng., 307 (2024), 118213. https://doi.org/10.1016/j.oceaneng.2024.118213 doi: 10.1016/j.oceaneng.2024.118213
|
| [42] |
T. Cho, B. Lee, Reliability-based design optimization using convex linearization and sequential optimization and reliability assessment method, Struct. Saf., 33 (2011), 42–50. https://doi.org/10.1016/j.strusafe.2010.05.003 doi: 10.1016/j.strusafe.2010.05.003
|
| [43] |
B. Keshtegar, S. Chakraborty, An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search, Reliab. Eng. Syst. Safe., 172 (2018), 195–206. https://doi.org/10.1016/j.ress.2017.12.014 doi: 10.1016/j.ress.2017.12.014
|
| [44] |
S. Yang, D. Meng, Y. Guo, P. Nie, A. M. P. de Jesus, A reliability-based design and optimization strategy using a novel MPP searching method for maritime engineering structures, Int. J. Struct. Integr., 14 (2023), 809–826. https://doi.org/10.1108/IJSI-06-2023-0049 doi: 10.1108/IJSI-06-2023-0049
|
| [45] |
Y. Zhao, Q. Zhu, Stability of highly nonlinear neutral stochastic delay systems with non-random switching signals, Syst. Control Lett., 165 (2022), 105261. https://doi.org/10.1016/j.sysconle.2022.105261 doi: 10.1016/j.sysconle.2022.105261
|
| [46] |
B. Keshtegar, Stability iterative method for structural reliability analysis using a chaotic conjugate map, Nonlinear Dyn., 84 (2016), 2161–2174. https://doi.org/10.1007/s11071-016-2636-1 doi: 10.1007/s11071-016-2636-1
|
| [47] |
D. Meng, S. Yang, Y. Zhang, S. Zhu, Structural reliability analysis and uncertainties-based collaborative design and optimization of turbine blades using surrogate model, Fatigue Fract. Eng. M., 42 (2019), 1219–1227. https://doi.org/10.1111/ffe.12906 doi: 10.1111/ffe.12906
|
| [48] |
S. Zhu, B. Keshtegar, N. Trung, Z. M. Yaseen, D. T. Bui, Reliability-based structural design optimization: hybridized conjugate mean value approach, Eng. Comput., 37 (2021), 381–394. https://doi.org/10.1007/s00366-019-00829-7 doi: 10.1007/s00366-019-00829-7
|
| [49] |
Y. Huang, Y. Lv, Q. Zhu, Stabilization of hybrid stochastic neutral-type systems with non-differentiable delays under high nonlinearity via discrete-time feedback control, Appl. Math. Comput., 495 (2025), 129333. https://doi.org/10.1016/j.amc.2025.129333 doi: 10.1016/j.amc.2025.129333
|
| [50] |
Q. Zhu, Event-triggered sampling problem for exponential stability of stochastic nonlinear delay systems driven by Lexvy processes, IEEE T. Automat. Contr., 70 (2024), 1176–1183. https://doi.org/10.1109/TAC.2024.3448128 doi: 10.1109/TAC.2024.3448128
|
| [51] |
B. Keshtegar, S. Chakraborty, Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints, Reliab. Eng. Syst. Safe., 178 (2018), 69–83. https://doi.org/10.1016/j.ress.2018.05.015 doi: 10.1016/j.ress.2018.05.015
|
| [52] |
F. Kong, H. Ni, Q. Zhu, C. Hu, T. Huang, Fixed-time and predefined-time synchronization of discontinuous neutral-type competitive networks via non-chattering adaptive control strategy, IEEE T. Netw. Sci. Eng., 10 (2023), 3644–3657. https://doi.org/10.1109/TNSE.2023.3271109 doi: 10.1109/TNSE.2023.3271109
|
| [53] |
M. Alfouneh, B. Keshtegar, STO-DAMV: Sequential topology optimization and dynamical accelerated mean value for reliability-based topology optimization of continuous structures, Comput. Method. Appl. M., 417 (2023), 116429. https://doi.org/10.1016/j.cma.2023.116429 doi: 10.1016/j.cma.2023.116429
|
| [54] |
L. Mabood, N. Badshah, H. Ali, M. Zakarya, A. Ahmed, A. A. Khan, et al., Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements, Sci. Rep., 12 (2022), 15949. https://doi.org/10.1038/s41598-022-19893-z doi: 10.1038/s41598-022-19893-z
|
| [55] |
L. Mei, Q. Wang, Structural optimization in civil engineering: a literature review, Buildings, 11 (2021), 66. https://doi.org/10.3390/buildings11020066 doi: 10.3390/buildings11020066
|
| [56] |
Z. Zhu, Q. Zhu, Adaptive event-triggered fuzzy control for stochastic highly nonlinear systems with time delay and nontriangular structure interconnections, IEEE T. Fuzzy Syst., 32 (2023), 27–37. https://doi.org/10.1109/TFUZZ.2023.3287869 doi: 10.1109/TFUZZ.2023.3287869
|
| [57] |
Z. Houta, T. Huguet, N. Lebbe, F. Messine, Solid isotropic material with penalization-based topology optimization of three-dimensional magnetic circuits with mechanical constraints, Mathematics, 12 (2024), 1147. https://doi.org/10.3390/math12081147 doi: 10.3390/math12081147
|
| [58] |
O. Sigmund, A 99 line topology optimization code written in Matlab, Struct. Multidisc. Optim., 21 (2001), 120–127. https://doi.org/10.1007/s001580050176 doi: 10.1007/s001580050176
|
| [59] |
K. Liu, A. Tovar, An efficient 3D topology optimization code written in Matlab, Struct. Multidisc. Optim., 50 (2014), 1175–1196. https://doi.org/10.1007/s00158-014-1107-x doi: 10.1007/s00158-014-1107-x
|
| [60] |
S. Rojas-Labanda, M. Stolpe, An efficient second-order SQP method for structural topology optimization, Struct. Multidisc. Optim., 53 (2016), 1315–1333. https://doi.org/10.1007/s00158-015-1381-2 doi: 10.1007/s00158-015-1381-2
|
| [61] |
W. Liao, Q. Zhang, H. Meng, An SQP algorithm for structural topology optimization based on majorization-minimization method, Appl. Sci., 12 (2022), 6304. https://doi.org/10.3390/app12136304 doi: 10.3390/app12136304
|
| [62] |
M. Fanni, M. Shabara, M. Alkalla, A comparison between different topology optimization methods, MEJ Mansoura Eng. J., 38 (2020), 13–24. https://doi.org/10.21608/bfemu.2020.103788 doi: 10.21608/bfemu.2020.103788
|
| [63] |
T. Zuo, C. Wang, H. Han, Q. Wang, Z. Liu, Explicit 2D topological control using SIMP and MMA in structural topology optimization, Struct. Multidisc. Optim., 65 (2022), 293. https://doi.org/10.1007/s00158-022-03405-8 doi: 10.1007/s00158-022-03405-8
|
| [64] |
E. Andreassen, A. Clausen, M. Schevenels, B. S. Lazarov, O. Sigmund, Efficient topology optimization in MATLAB using 88 lines of code, Struct. Multidisc. Optim., 43 (2011), 1–16. https://doi.org/10.1007/s00158-010-0594-7 doi: 10.1007/s00158-010-0594-7
|
| [65] |
Z. Zeng, F. Ma, An efficient gradient projection method for structural topology optimization, Adv. Eng. Softw., 149 (2020), 102863. https://doi.org/10.1016/j.advengsoft.2020.102863 doi: 10.1016/j.advengsoft.2020.102863
|
| [66] |
J. Gao, Z. Luo, L. Xia, L. Gao, Concurrent topology optimization of multiscale composite structures in Matlab, Struct. Multidisc. Optim., 60 (2019), 2621–2651. https://doi.org/10.1007/s00158-019-02323-6 doi: 10.1007/s00158-019-02323-6
|
| [67] |
M. Rad, M. Habashneh, J. Lógó, Reliability based bi-directional evolutionary topology optimization of geometric and material nonlinear analysis with imperfections, Comput. Struct., 287 (2023), 107120. https://doi.org/10.1016/j.compstruc.2023.107120 doi: 10.1016/j.compstruc.2023.107120
|
| [68] |
Z. Zhu, Q. Zhu, Adaptive neural prescribed performance control for non-triangular structural stochastic highly nonlinear systems under hybrid attacks, IEEE T. Automat. Sci. Eng., 22 (2024), 6543–6553. https://doi.org/10.1109/TASE.2024.3447045 doi: 10.1109/TASE.2024.3447045
|
| [69] | X. Huang, M. Xie, Evolutionary topology optimization of continuum structures: methods and applications, John Wiley & Sons, 2010. https://doi.org/10.1002/9780470689486 |
| [70] |
X. Huang, Y. Xie, A further review of ESO type methods for topology optimization, Struct. Multidisc. Optim., 41 (2010), 671–683. https://doi.org/10.1007/s00158-009-0447-4 doi: 10.1007/s00158-009-0447-4
|
| [71] |
G. Rozvany, O. Querin, Combining ESO with rigorous optimality criteria, Int. J. Veh. Des., 28 (2002), 294–299. https://doi.org/10.1504/IJVD.2002.001991 doi: 10.1504/IJVD.2002.001991
|
| [72] |
Y. Liu, F. Jin, Q. Li, S. Zhou, A fixed-grid bidirectional evolutionary structural optimization method and its applications in tunnelling engineering, Int. J. Numer. Method. Eng., 73 (2008), 1788–1810. https://doi.org/10.1002/nme.2145 doi: 10.1002/nme.2145
|
| [73] | M. Abdi, Evolutionary topology optimization of continuum structures using X-FEM and isovalues of structural performance, University of Nottingham, 2015. |
| [74] |
Z. Zhuang, Y. Xie, Q. Li, S. Zhou, A 172-line Matlab code for structural topology optimization in the body-fitted mesh, Struct. Multidisc. Optim., 66 (2023), 11. https://doi.org/10.1007/s00158-022-03464-x doi: 10.1007/s00158-022-03464-x
|
| [75] |
G. Kazakis, N. Lagaros, Multi-scale concurrent topology optimization based on BESO, implemented in MATLAB, Appl. Sci., 13 (2023), 10545. https://doi.org/10.3390/app131810545 doi: 10.3390/app131810545
|
| [76] |
L. Tong, J. Lin, Structural topology optimization with implicit design variable-optimality and algorithm, Finite Elem. Anal. Des., 47 (2011), 922–932. https://doi.org/10.1016/j.finel.2011.03.004 doi: 10.1016/j.finel.2011.03.004
|
| [77] |
M. Alfouneh, V. Hoang, Z. Luo, Q. Luo, Topology optimization for multi-layer multi-material composite structures, Eng. Optim., 55 (2023), 773–790. https://doi.org/10.1080/0305215X.2022.2034801 doi: 10.1080/0305215X.2022.2034801
|
| [78] |
W. Chen, X. Su, S. Liu, Algorithms of isogeometric analysis for MIST-based structural topology optimization in MATLAB, Struct. Multidisc. Optim., 67 (2024), 43. https://doi.org/10.1007/s00158-024-03764-4 doi: 10.1007/s00158-024-03764-4
|
| [79] |
M. Alfouneh, V. Hoang, Heat flux topology optimization treatment of vibrational damped cellular composite flexible structures, Optim. Eng., 24 (2023), 1747–1772. https://doi.org/10.1007/s11081-022-09751-2 doi: 10.1007/s11081-022-09751-2
|
| [80] |
W. Chen, L. Tong, S. Liu, Concurrent topology design of structure and material using a two-scale topology optimization, Comput. Struct., 178 (2017), 119–128. https://doi.org/10.1016/j.compstruc.2016.10.013 doi: 10.1016/j.compstruc.2016.10.013
|
| [81] |
J. Sethian, A. Wiegmann, Structural boundary design via level set and immersed interface methods, J. Comput. Phys., 163 (2000), 489–528. https://doi.org/10.1006/jcph.2000.6581 doi: 10.1006/jcph.2000.6581
|
| [82] | G. Gordon, R. Tibshirani, Karush-kuhn-tucker conditions, Optimization, 10 (2012), 725. |
| [83] | E. Madenci, I. Guven, The finite element method and applications in engineering using ANSYS®, Springer, 2015. https://doi.org/10.1007/978-1-4899-7550-8 |
| [84] |
X. Liu, F. Gasco, W. Yu, J. Goodsell, K. Rouf, Multiscale analysis of woven composite structures in MSC. Nastran, Adv. Eng. Softw., 135 (2019), 102677. https://doi.org/10.1016/j.advengsoft.2019.04.008 doi: 10.1016/j.advengsoft.2019.04.008
|
| [85] |
P. Wei, Z. Li, X. Li, M.Y. Wang, An 88-line MATLAB code for the parameterized level set method based topology optimization using radial basis functions, Struct. Multidisc. Optim., 58 (2018), 831–849. https://doi.org/10.1007/s00158-018-1904-8 doi: 10.1007/s00158-018-1904-8
|
| [86] | S. Zhang, P. Li, Y. Zhong, J. Xiang, Structural topology optimization based on the level set method using COMSOL, CMES Comput. Model. Eng. Sci., 101 (2014), 17–31. |
| [87] |
M. Yaghmaei, A. Ghoddosian, M. M. Khatibi, A filter-based level set topology optimization method using a 62-line MATLAB code, Struct. Multidisc. Optim., 62 (2020), 1001–1018. https://doi.org/10.1007/s00158-020-02540-4. doi: 10.1007/s00158-020-02540-4
|
| [88] |
Z. Houta, T. Huguet, N. Lebbe, F. Messine, Solid isotropic material with penalization-based topology optimization of three-dimensional magnetic circuits with mechanical constraints, Mathematics, 12 (2024), 1147. https://doi.org/10.3390/math12081147 doi: 10.3390/math12081147
|
| [89] |
E. Demirci, A. R. Yıldız, A new hybrid approach for reliability-based design optimization of structural components, Mater. Test., 61 (2019), 111–119. https://doi.org/10.3139/120.111291. doi: 10.3139/120.111291
|
| [90] |
B. Keshtegar, P. Hao, A hybrid self-adjusted mean value method for reliability-based design optimization using sufficient descent condition, Appl. Math. Model., 41 (2017), 257–270. https://doi.org/10.1016/j.apm.2016.08.031 doi: 10.1016/j.apm.2016.08.031
|
| [91] |
Z. Meng, Y. Pang, Y. Pu, X. Wang, New hybrid reliability-based topology optimization method combining fuzzy and probabilistic models for handling epistemic and aleatory uncertainties, Comput. Method. Appl. M., 363 (2020), 112886. https://doi.org/10.1016/j.cma.2020.112886 doi: 10.1016/j.cma.2020.112886
|
| [92] |
M. Alfouneh, J. Ji, Q. Luo, Damping design of harmonically excited flexible structures with graded materials to minimize sound pressure and radiation, Eng. Optim., 53 (2021), 348–367. https://doi.org/10.1080/0305215X.2020.1735381 doi: 10.1080/0305215X.2020.1735381
|
| [93] |
R. Picelli, R. Sivapuram, Y. M. Xie, A 101-line MATLAB code for topology optimization using binary variables and integer programming, Struct. Multidisc. Optim., 63 (2021), 935–954. https://doi.org/10.1007/s00158-020-02719-9 doi: 10.1007/s00158-020-02719-9
|
| [94] | R. A. Feijóo, A. A. Novotny, C. Padra, E. Taroco, The topological-shape sensitivity method and its application in 2D elasticity, J. Comput. Method. Sci. Eng., 4 (2004), 397–428. |