工学博士,教授,博士生导师,重庆大学土木工程学院教师,国际著名工程类SCI期刊《Mechanical System and Signal Processing》、《Journal of Vibration and Control》、《Engineering Structures》等的审稿人。在2009年10月~2010年10月期间,作为访问学者,在日本庆应大学的Akira Mita实验室从事研究工作。主要研究方向为1. 钢结构原理、设计及结构优化;2. 基于人工智能的结构健康监测;3. 工程结构的损伤检测识别;4. 型钢混凝土结构的力学性能和抗震性能。发表SCI、EI期刊论文60多篇。损伤识别方面的科研成果获得了2012年重庆市科技进步二等奖和2018年重庆市自然科学奖一项。目前在研的国家级课题:1. 国家重点研发计划课题,“严寒山地地基处理及大面积高容量临时设施安全运维关键技术”(2021YFF0306302); 2. 国家自然科学基金重大项目课题,“结构服役性能多维表征指标及其智能评价理论与方法”(52192663)的子课题1项。
研究方向
研究方向:
1. 钢结构原理、设计及结构优化;
2. 基于人工智能的结构健康监测;
3. 工程结构的损伤检测识别;
4. 型钢混凝土结构的力学性能和抗震性能。
主讲课程
材料力学、工程力学、弹性力学、建筑力学
学术兼职
教育部学位中心评审专家、国家自然科学基金项目通讯评审专家、广西科技项目通讯评审专家、重庆市科技项目通讯评审专家。
主要成果
发表SCI、EI期刊论文60多篇,并主持了国家级、省部级或中央高校项目十多项,其中,主持国家自然科学基金面上项目1项,主持国家重点研发项目课题1项,主持国家自然科学基金重大项目的子课题1项,主持省部级科研项目共4项、主持其他项目多项。此外,还主研了其他国家级或省部级或横向课题项目十多项。钢结构原理、设计及结构优化方面的研究成果,发表于国际期刊SMO、国内《振动与冲击》、《北京工业大学学报》、《河海大学学报》等多个期刊上。基于人工智能的结构健康监测方面的研究成果发表于国际高水平人工智能SCI期刊EAAI、ASOC、RUENG等多个期刊。工程结构的损伤检测方面的研究成果,不仅发表于国际期刊MSSP、SHM、JVC、IJSSD等多个期刊,还分别获得了2012年重庆市科技进步二等奖和2018年的重庆市自然科学奖一项。型钢混凝土结构的力学性能和抗震性能的研究方面,获得了重庆市建委科技项目的支持。目前在研国家重点研发项目课题和国家自然科学基金重大项目的子课题各1项。
作为通讯作者的部分代表性论文:
[1] Liu Chen, Guo Huiyong*, Di Jin, et al.. Structural health performance assessment using hysteretic-driven neural network with physical self-correction mechanism and its experimental study[J]. Applied Soft Computing, 2026, 190: 114580.
[2] Liu Chen, Guo Huiyong*, Di Jin, et al. Quantitative method for structural health evaluation under multiple performance metrics via multi-physics guided neural network[J]. Engineering Applications of Artificial Intelligence, 2025, 147: 110383.
[3] Zheng Kaixuan, Guo Huiyong*, Di Jin, et al. Quantitative health evaluation of frame structures using multi-physics enhanced neural networks with boundary constraints and damage consistency and its experimental study[J]. Structures, 2025, 72: 108281.
[4] Zheng Kaixuan, Guo Huiyong*, Di Jin, et al. Health evaluation of frame structures based on the gray cloud network model: proposal and experimental study[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2025, 11(1): 04024080.
[5] Liu Chen, Guo Huiyong*, Di Jin, et al. Intelligent hysteresis modeling for capturing structural features via multi-physics guided neural network and its earthquake resistant test[J]. Journal of Earthquake Engineering, 2024, 28(14): 4119–4145.
[6] Zuo Heng, Guo Huiyong*. Structural nonlinear damage identification based on the information distance of GNPAX/GARCH model and its experimental study[J]. Structural Health Monitoring, 2024, 23(2), 991-1012.
[7] Zuo Heng, Guo Huiyong*. Nonlinear damage identification method of transmission tower structure based on general expression for linear and nonlinear autoregressive model and Itakura distance[J]. Structural Health Monitoring, 2023, 22(1): 19-38.
[8] Liu Zhao, Guo Huiyong*. Experimental study on structural damage identification of multi-sensor separated channel network[J]. Measurement. 2023, 220: 113382
[9] Zuo Heng, Guo Huiyong*. Structural nonlinear damage identification method based on the Kullback–Leibler distance of time domain model residuals[J]. Remote Sensing, 2023, 15(4), 1135.
[10] 郭惠勇*, 李孟. 基于时域模型相对熵的塔架结构非线性损伤检测研究[J]. 仪器仪表学报, 2023, 44(01): 143-153.
[11] 魏佳恒,郭惠勇*. 基于贝叶斯优化BiLSTM模型的输电塔损伤识别[J]. 振动与冲击,2023,42(1): 238-248.
[12] Guo Huiyong*, Zuo Heng. Structural Nonlinear Damage Identication Using the Residual Deviation Distance Conversion Index of the Time-Domain Model, International Journal of Structural Stability and Dynamics, 2022, 22(3-4): 2240002-1-21
[13] Heng Zuo, Huiyong Guo*. Structural nonlinear damage identification based on Bayesian optimization GNAR/GARCH model and its experimental study[J]. Structures, 2022, 45, 867-885.
[14] Guo H Y*, Yuan H F, Huang Q.Structural damage identification based on gray cloud rule generator algorithm. Advancesin Mechanical Engineering, 2019, 11(1): 1-13.
[15] Guo H Y*. Structural multi-damage identification based on strain energy and micro-search artificial fish swarm algorithm. Journal of Vibroengineering,2017, 19(5): 3255-3270.
[16] Cheng J J, Guo H Y* , Wang Y S. Structural Nonlinear Damage Detection Method Using AR/ARCH Model, International Journal of Structural Stability and Dynamics, 2017, 17(8): 1750083-1-27.
[17] 郭惠勇,宋小辉,李正良. 基于改进人工鱼群算法的输电塔塔腿拓扑设计优化[J]. 振动与冲击,2017,36(04):52-58.
[18] 樊周正 ,郭惠勇(通讯作者). 内置型钢剪力架板柱节点抗冲切性能研究[J]. 重庆工商大学学报(自然科学版),2016 , 33 (4) :1-7.
[19] Guo H Y*, Li Z L. Structural damage identification based on evidence fusion and improved particle swarm optimization. Journal of Vibration and Control, 2014, 20(9): 1279-1292.
[20] Guo H Y*, Li Z L. Structural Multidamage Identification Based on Modal Strain Energy Equivalence Index Method. International Journal of Structural Stability and Dynamics, 2014, 14(7): 1450028-1-17.
[21] Guo H Y*, Li Z L. Structural damage identification based on Bayesian theory and improved immune genetic algorithm. Expert Systems with Applications, 2012, 39: 6426-6434.
[22] 郭惠勇, 罗乐,李正良,曾虹. 基于精细时程积分的输电塔风振响应下的拓扑优化.北京工业大学学报(自科版),2011,37(7):1012-1018.
[23] Guo H Y*, Li Z L. Two stage multidamage detection method based on energy balance equation. Journal of Nondestructive Evaluation, 2011, 30: 186-200.
[24] Guo H Y*, Li Z L. Structural topology optimization of high-voltage transmission tower with discrete variables. Structural and Multidisciplinary Optimization, 2011, 43(6):851-861.
[25] 郭惠勇,李小晶,李正良. 大跨越输电塔的结构优化分析研究. 河海大学学报(自科版), 2010,38(1): 93-97
[26] Guo H Y*. Li Z L. A two stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm. Mechanical System and Signal Processing, 2009, 23: 769-782.
[27] Guo H Y*, Zhang L. A Weighted Balance Evidence Theory for Structural Multiple Damage Localization. Computer Methods in Applied Mechanics and Engineering, 2006,195(44-47): 6225-6238.
[28] Guo H Y*. Structural Damage Detection using Information Fusion Technique. Mechanical System and Signal Processing, 2006,20:1173-1188.
获奖情况
2012年重庆市科技进步二等奖,题目名称为:“工程结构损伤的检测诊断与评估关键技术及应用”,个人排名第2名。
2018年重庆市自然科学奖一项,题目名称为:“工程结构振动灾害效应研究”。
研究生培养
共培养研究生52名,其中已毕业研究生44名。每年招收博士1名,硕士3名。
联系方式
邮箱:guohy@cqu.edu.cn
电话:13996118306