性别：男学位：博士职务：所属学院：土木与水利工程学院社会兼职：E-mail：mengz@hfut.edu.cn
个人简介孟增，男，汉族，祖籍甘肃省陇南市，博士研究生导师。2015年10月获得大连理工大学工程力学博士学位。研究方向为不确定性结构优化、土木和航天结构分析及优化设计、结构拓扑优化等等。发表国际期刊论文40余篇，谷歌被引900余次，高被引论文3篇。多篇成果发表于计算力学、结构优化领域的顶级期刊和权威期刊。研究解决了可靠性优化的不稳定及低效率问题。提出了Super parametric convex model; Target performance approach; Modified chaos control method, Directional stability transformation method; Decoupled non-probabilistic reliability optimization method等新的不确定性模型和可靠性优化方法，并将其应用于先进航天结构制造。

联系方式：
Email：mengz@hfut.edu.cn
地址：合肥工业大学土木与水利工程学院纬地楼619室
个人网址：http://em.hfut.edu.cn/2016/0811/c500a7190/page.htm
课题组动态：
1. 2020年11月，2018届级硕士研究生庞永胜获得2020年度研究生国家奖学金与研究生一等奖学金！祝贺 ！
2. 2020年3月，2018届级硕士研究生庞永胜论文“New hybrid reliability-based topology optimization method combining fuzzy and probabilistic models for handling epistemic and aleatory uncertainties”被刊物《Computer Methods in Applied Mechanics and Engineering》(中科院小区1区，影响因子：4.821)接收！祝贺！
3.2019年8月，2017届级硕士研究生张卓辉论文“An active learning method combining Kriging and accelerated chaotic single loop approach (AK-ACSLA) for reliability-based design optimization”被刊物《Computer Methods in Applied Mechanics and Engineering》(中科院小区1区，影响因子：4.821)接收！祝贺！
4.2019年9月，2017届级硕士研究生张卓辉论文“A novel experimental data-driven exponential convex model for reliability assessment with uncertain-but-bounded parameters”被刊物《Applied Mathematical Modelling》(中科院小区2区，影响因子：2.841)接收！祝贺！
5.2019年10月，2017届级硕士研究生张卓辉获得2019年度研究生国家奖学金与研究生一等奖学金！祝贺 ！
6．2019年11月，2017届级硕士研究生张卓辉论文“An active weight learning method for efficient reliability assessment with small failure probability”被期刊《Structural and Multidisciplinary Optimization》(中科院小区2区，影响因子：3.925)接收！祝贺！
社会任职：
担任Computer Methods in Applied Mechanics and Engineering、 Applied Mathematical Modelling、 International Journal for Numerical Methods in Engineering、Reliability Engineering & System Safety、Structural and Multidisciplinary Optimization、Probabilistic Engineering Mechanics、Advances in Structural Engineering等20余个国际期刊审稿人
招人计划：
欢迎力学相关专业的本科生报考
主要研究方向：结构优化设计，可靠性分析，拓扑优化设计，计算力学
招收人数：2-3人
研究方向不确定性优化设计；结构可靠性分析；航天结构优化设计；拓扑优化设计等
主持科研项目(1)主持国家自然科学面上科学基金；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；2020年1月-2023年12月
(2)主持优秀青年人才培育计划A项目；高置信度结构可靠性拓扑优化算法研究；2020年1月-2022年12月
(3)主持国家自然科学青年科学基金；含缺陷加筋薄壁结构的全局可靠性优化设计方法研究；2017年1月-2019年12月
(4)主持安徽省自然科学青年科学基金；加筋筒壳结构后验可靠性优化设计方法研究；2017年；1月-2019年12月
(5)主持国家重点实验室开放课题项目；基于二次二阶矩方法的高效可靠度优化算法研究；2018年1月-2020年12月
(6)主持 学术新人提升计划B项目；基于超参数凸模型的高效可靠性优化算法研究；2018年5月-2020年12月

参加科研项目
获得奖励情况
发表科研论文[1]Meng 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.
[2]Meng 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.
[3]Meng 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.
[4]Meng 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
[5]Meng 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.
[6]Meng 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.
[7]Meng 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.
[8]Meng 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
[9]Li 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): **.
[10]Meng 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.
[11]Meng 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.
[12]Li G, Meng Z, Hu H. An adaptive hybrid approach for reliability-based design optimization [J]. Structural and Multidisciplinary Optimization, 2015, 51(5): 1051-1065.
[13]Hao P, Wang B, Li G, Meng Z, Wang L. Hybrid Framework for Reliability-Based Design Optimization of Imperfect Stiffened Shells [J]. AIAA Journal, 2015, 53 (10): 2878-2889.
[14]Hao P, Wang B, Li G, Meng Z, Tian K, Zeng D, Tang X. Worst Multiple Perturbation Load Approach of stiffened shells with and without cutouts for improved knockdown factors [J]. Thin-Walled Structures, 2014, 82 (9): 321-330.
[15]Hao P, Wang B, Li G, Meng Z, Tian K, Tang X. Hybrid optimization of hierarchical stiffened shells based on smeared stiffener method and finite element method [J]. Thin-Walled Structures, 2014, 82 (9): 46-54.
[16]Li G, Meng Z. A Modified Chaos Control Approach of the Performance Measure Functions for Reliability-based Design Optimization [M]. 10th World Congress on Structural and Multidisciplinary Optimization. Orlando, Florida, USA. 2013.
[17]Meng Z, Li G. Reliability-based design optimization combining the reliability index approach and the performance measure approach [M]. The 7th China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems. China. 2012.
[18]Li G, Meng Z. An adaptive reliability-based design optimization approach using modified chaos control method [M]. The Twelfth International Conference on Computational Structures Technology. 2014.
[19]孟增, 李刚. 基于修正混沌控制的一次二阶矩可靠度算法 [J]. 工程力学, 2015, 12:21-26.
[20]李刚, 孟增. 基于 RBF 神经网络模型的结构可靠度优化方法 [J]. 应用数学和力学, 2014, 35(11):1271-1279.