孟增
发布人:任山宏  发布时间:2016-08-11   浏览次数:10267

姓名

孟增

性别

职务

应用力学研究所秘书长

职称

副教授

学历

博士

电子邮件

mengz@hfut.edu.cn

电话


通讯地址

南校区纬地楼619


孟增,现任合肥工业大学土木与水利工程学院工程力学系博士生导师(破格),副教授。研究方向为不确定性结构优化、航天和土木结构分析及优化设计、结构拓扑优化等等。主持包括国家自然科学基金面上项目(2项)、国家自然科学基金青年项目、安徽省自然科学基金杰出青年项目,合肥工业大学优秀青年人才培育计划A在内的项目十余项。获得安徽省力学协会科技进步一等奖(第一完成人)。连续入选全球前2%顶尖科学家榜单(美国斯坦福大学发布)。发表SCI期刊论文80余篇,一作/通讯作者论文50余篇,谷歌被引3300余次,ESI高被引论文6篇,热点论文2篇。任《应用力学学报》和《应用数学和力学》青年编委,南方计算力学协会和随机振动专业委员会青年委员。


简历:(教育背景、工作经历等)

2020.10-至今

合肥工业大学

土木与水利工程学院

博士生导师

2018.01-至今

合肥工业大学

土木与水利工程学院

副教授

2015.10-2017.12

合肥工业大学

土木与水利工程学院

讲师

2011.09-2015.10

大连理工大学

工程力学

博士(硕博连读)

2009.09-2011.09

大连理工大学

工程力学

硕士(硕博连读)

2005.09-2009.09

兰州大学

理论与应用力学

学士


研究领域:

不确定性结构优化、航天和土木结构分析及优化设计、结构拓扑优化等


社会任职:

担任Computer Methods in Applied Mechanics and EngineeringApplied Mathematical ModellingInternational Journal for Numerical Methods in EngineeringReliability Engineering & System SafetyStructural and Multidisciplinary OptimizationProbabilistic Engineering MechanicsAdvances in Structural Engineering等国际期刊审稿人。任《应用力学学报》和《应用数学和力学》青年编委,南方计算力学协会和随机振动专业委员会青年委员


招生计划:

每年拟招收2-3名具有力学、土木、材料等工科背景的同学报考硕士研究生和博士研究生,欢迎有志青年才俊加盟课题组



科研项目:

(1) 极小失效概率下纤维增强复合结构的高保真度可靠性拓扑优化,国家自然科学面上项目,2024-2027,主持。

(2) 功能梯度板壳的高置信度混合不确定性分析及拓扑优化设计,国家自然科学面上项目,2020-2023,主持。

(3) 含缺陷加筋薄壁结构的全局可靠性优化设计方法研究,国家自然科学青年项目,2017-2019,主持。

(3) 复杂代理模型高精度预测方法,事业单位横向课题,2022,主持。

(5) 点阵结构压缩力学性能实验与数值仿真,事业单位横向课题,2022,主持。

(6) 多源不确定性下工业机器人定位精度时变可靠性分析及优化设计,重点实验室开放课题,2020-2023,主持。

(7) 基于二次二阶矩方法的高效可靠度优化算法研究,重点实验室开放课题,2017-2019,主持。

(8) 基于超参数凸模型的高效可靠性优化算法研究,合肥工业大学(优青青年A计划),主持。

(9) 考虑多源时变不确定性的建筑结构防灾优化设计,重点实验室开放课题,2021-2023,主持。

(10) 面向增材制造技术的缺陷识别和不确定性表征优化设计,事业单位课题,2023-2025,主持。

(11) 层级结构的可靠性优化拓扑优化设计方法研究,重点实验室开放课题,2021-2023,主持。

(12) 复合装药爆轰波增强机理研究与验证,事业单位横向课题,2023,参与。


代表论著:

1Meng Z, Kong L, Yi J, et al. Optimum-pursuing method for constrained optimization and reliability-based design optimization problems using Kriging model [J]. Computer Methods in Applied Mechanics and Engineering, 2024, 420: 116704.

2Meng Z, Guo L, Huang B, et al. Concurrent topology optimization design for CNT orientation and CNTRC layout [J]. Applied Mathematical Modelling, 2023, 122: 22-41.

3Meng Z, Qian Q, Xu M, et al. PINN-FORM: a new physics-informed neural network for reliability analysis with partial differential equation [J]. Computer Methods in Applied Mechanics and Engineering, 2023, 414: 116172.

4Meng Z, Yıldız B S, Li G, et al. Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study [J]. Structural and Multidisciplinary Optimization, 2023, 66(8): 191.

5Meng Z, Li C, Pang Y, et al. New bubble sampling method for reliability analysis [J]. Structural and Multidisciplinary Optimization, 2023, 66(8): 180.

6Meng Z, Yang G, Wu Q, et al. Reliability-based topology optimization for fundamental frequency maximization with frequency band constraints [J]. Mechanical Systems and Signal Processing, 2023, 195: 110295.

7Meng Z, Guo L, Li Q. Uncertainty-oriented multi-scale topology optimization of coupled thermo-mechanical continuum structures [J]. Composite Structures, 2023, 315: 116940.

8Meng Z, Yang G, Wang Q, et al. Reliability-based topology optimization of vibrating structures with frequency constraints [J]. International Journal of Mechanics and Materials in Design, 2023, 19(2): 467-481.

9Meng Z, Li C, Hao P. Unified reliability-based design optimization with probabilistic, uncertain-but-bounded and fuzzy variables [J]. Computer Methods in Applied Mechanics and Engineering, 2023, 407: 115925.

10Meng Z, Zhao J, Chen G, et al. Hybrid uncertainty propagation and reliability analysis using direct probability integral method and exponential convex model [J]. Reliability Engineering & System Safety, 2022, 228: 108803.

11Meng Z, Yıldız A R, Mirjalili S. Efficient decoupling-assisted evolutionary/metaheuristic framework for expensive reliability-based design optimization problems [J]. Expert systems with applications, 2022, 205: 117640.

12Meng Z, Luo X, Zhou H. Lightweight design of arcuately stiffened cylindrical shells based on smeared stiffener method and active learning strategy [J]. Thin-Walled Structures, 2022, 174: 109167.

13Meng Z, Pang Y, Wu Z, et al. A novel maximum volume sampling model for reliability analysis [J]. Applied Mathematical Modelling, 2022, 102: 797-810.

14Meng Z, Guo L, Wang X. A general fidelity transformation framework for reliability-based design optimization with arbitrary precision [J]. Structural and Multidisciplinary Optimization, 2022, 65(1): 14.

15Meng Z, Guo L, Hao P, et al. On the use of probabilistic and non-probabilistic super parametric hybrid models for time-variant reliability analysis [J]. Computer Methods in Applied Mechanics and Engineering, 2021, 386: 114113.

16Meng Z, Pang Y, Zhou H. An augmented weighted simulation method for high-dimensional reliability analysis [J]. Structural Safety, 2021, 93: 102117.

17Meng Z, Zhao J, Jiang C. An efficient semi-analytical extreme value method for time-variant reliability analysis [J]. Structural and Multidisciplinary Optimization, 2021, 64(3): 1469-1480.

18Meng Z, Li G, Wang X, et al. A comparative study of metaheuristic algorithms for reliability-based design optimization problems [J]. Archives of Computational Methods in Engineering, 2021, 28: 1853-1869.

19Meng Z, Wu Y, Wang X, et al. Robust topology optimization methodology for continuum structures under probabilistic and fuzzy uncertainties [J]. International Journal for Numerical Methods in Engineering, 2021, 122(8): 2095-2111.

20Meng Z, Ren S, Wang X, et al. System reliability-based design optimization with interval parameters by sequential moving asymptote method [J]. Structural and Multidisciplinary Optimization, 2021, 63(4): 1767-1788.

21Meng Z, Zhang Z, Zhou H, et al. Robust design optimization of imperfect stiffened shells using an active learning method and a hybrid surrogate model [J]. Engineering Optimization, 2020, 52(12): 2044-2061.

22Meng Z, Pang Y, Pu Y, et al. New hybrid reliability-based topology optimization method combining fuzzy and probabilistic models for handling epistemic and aleatory uncertainties [J]. Computer Methods in Applied Mechanics and Engineering, 2020, 363: 112886.

23Meng Z, Zhang Z, Li G, et al. An active weight learning method for efficient reliability assessment with small failure probability [J]. Structural and Multidisciplinary Optimization, 2020, 61: 1157-1170.

24Meng Z, Zhang Z, Zhou H. A novel experimental data-driven exponential convex model for reliability assessment with uncertain-but-bounded parameters [J]. Applied Mathematical Modelling, 2020, 77: 773-787.

25Meng Z, Zhang Z, Zhang D, et al. An active learning method combining Kriging and accelerated chaotic single loop approach (AK-ACSLA) for reliability-based design optimization [J]. Computer Methods in Applied Mechanics and Engineering, 2019, 357: 112570.

26Meng Z, Zhang D, Li G, et al. An importance learning method for non-probabilistic reliability analysis and optimization [J]. Structural and Multidisciplinary Optimization, 2019, 59: 1255-1271.

27Meng Z, Zhang D, Liu Z, et al. An adaptive directional boundary sampling method for efficient reliability-based design optimization [J]. Journal of Mechanical Design, 2018, 140(12): 121406.

28Meng Z, Zhou H, Hu H, et al. Enhanced sequential approximate programming using second order reliability method for accurate and efficient structural reliability-based design optimization [J]. Applied Mathematical Modelling, 2018, 62: 562-579.

29Meng Z, Zhou H. New target performance approach for a super parametric convex model of non-probabilistic reliability-based design optimization [J]. Computer Methods in Applied Mechanics and Engineering, 2018, 339(9): 644-62.

30Meng Z, Hu H, Zhou H. Super parametric convex model and its application for non-probabilistic reliability-based design optimization [J]. Applied Mathematical Modelling, 2018, 55(3): 354-70.

31Meng Z, Zhou H, Li G, et al. A hybrid sequential approximate programming method for second-order reliability-based design optimization approach [J]. Acta Mechanica, 2017, 228(5): 1965-78.

32Meng Z, Pu Y, Zhou H. Adaptive stability transformation method of chaos control for first order reliability method [J]. Engineering with Computers, 2017, DOI: 10.1007/s00366-017-0566-2

33Meng Z, Yang D, Zhou H, et al. An accurate and efficient reliability-based design optimization using the second order reliability method and improved stability transformation method [J]. Engineering Optimization, 2018, 50(5): 749-65.

34Meng Z, Yang D, Zhou H, et al. Convergence control of single loop approach for reliability-based design optimization [J]. Struct Multidisc Optim, 2018, 57(3): 1079-91.

35Meng Z, Zhou HL, Li G, et al. A decoupled approach for non-probabilistic reliability-based design optimization [J]. Computers & Structures, 2016, 175(10): 65-73.36Meng Z, Li G, Yang DX, et al. A new directional stability transformation method of chaos control for first order reliability analysis [J]. Structural and Multidisciplinary Optimization, 2016, 1-12. DOI: 10.1007/s00158-016-1525-z

37Li G, Meng Z, Hao P. A Reliability-Based Design Optimization Approach with Adaptive Chaos Control using Kriging Model [J]. International Journal of Computational Methods, 2016, 13(1): 1650006.

38Meng Z, Li G, Wang B P, et al. A hybrid chaos control approach of the performance measure functions for reliability-based design optimization [J]. Computers & Structures, 2015, 146 (1): 32-43.

39Meng Z, Hao P, Li G, et al. Non-probabilistic reliability-based design optimization of stiffened shells under buckling constraint [J]. Thin-Walled Structures, 2015, 94(9): 325-33.