个人简介

性别：男                                                                                       籍贯：河南信阳

职称：副教授、博导                                                                   Email:luohuan@ctgu.edu.cn

教育背景

2017/09－2020/12，Texas A&M University，College Station，土木工程，博士

2014/09－2017/07，中国地震局工程力学研究所，结构工程，硕士

2010/09－2014/07，黄河科技学院，土木工程，学士

工作履历

2022/01–至今，三峡大学，土木与建筑学院，副教授

2021/01–2021/12，三峡大学，土木与建筑学院，校聘副教授

主要研究方向

[1]     钢筋混凝土结构非线性地震分析

[2]     数据驱动计算

[3]     人工智能和机器学习

[4]     风险量化

[5]     优化计算

学术兼职

[1]    美国土木工程师协会（ASCE）结构工程专业委员会委员

学术成果

代表性学术论文：

[1]    Luo, H., & Paal, S. G. (2023). A novel outlier-insensitive local support vector machine for robust data-driven forecasting in engineering. Engineering with Computers, Springer, 1-19.

[2]    Luo, H., & Paal, S. G. (2023). A data‐free, support vector machine‐based physics‐driven estimator for dynamic response computation. Computer‐Aided Civil and Infrastructure Engineering, Wiley,38(1), 26-48.

[3]    Luo, H., & Paal, S. G. (2022). Data-driven seismic response prediction of structural components. Earthquake Spectra, 38(2), 1382-1416.

[4]    Luo, H., & Paal, S. G. (2022). Artificial intelligence-enhanced seismic response prediction of reinforced concrete frames. Advanced Engineering Informatics, 52, 101568.

[5]    Luo, H., & Paal, S. G. (2021). Reducing the effect of sample bias for small data sets with double‐weighted support vector transfer regression. Computer‐Aided Civil and Infrastructure Engineering, Wiley, 36(3), 248-263.

[6]    Luo, H.,& Paal, S. G. (2021). Advancing post-earthquake structural evaluations via sequential regression-based predictive mean matching for enhanced forecasting in the context of missing data. Advanced Engineering Informatics, Elsevier, 47, 101202.

[7]    Luo, H., & Paal, S. G. (2021). Metaheuristic least squares support vector machine-based lateral strength modelling of reinforced concrete columns subjected to earthquake loads. Structures, Elsevier,33, 748-758.

[8]    Luo, H., & Paal, S. G. (2019). A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments. Computer‐Aided Civil and Infrastructure Engineering, Wiley, 34(11), 935-950.

[9]    Luo, H., & Paal, S. G. (2018). Machine learning–based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals. Journal of Computing in Civil Engineering,ASCE, 32(5), 04018042.

[10] 骆欢,杜轲,孙景江,丁宝荣. (2018).小跨高比钢筋混凝土连梁非线性剪切滞回和分析模型研究[J]. 工程力学, 35(9): 161-169,179.

[11] 骆欢,杜轲,孙景江,丁宝荣. (2017). 联肢剪力墙非线性分析模型研究及数值模拟验证[J]. 工程力学,34(4):140-149+159.

[12] 骆欢,杜轲,孙景江,许卫晓,丁宝荣. (2017). 地震作用下钢筋混凝土框架结构倒塌全过程振动台试验研究[J]. 建筑结构学报, 38(12):49-56.