姓名：乔卫亮
职称：副教授
电子邮箱：xiaoqiao_fang@dlmu.edu.cn
工作经历
2012.07—2020.07：kaiyun开云官方网站助教、讲师
2014.08——2015.08：美国休斯敦大学，访问学者
2020.08—至今：kaiyun开云官方网站副教授
研究方向
主要从事船舶与海洋工程领域中安全可靠性、风险管控技术相关的基础理论与应用研究工作，研究方向包括：
（1）智能船舶（智能机舱）人机交互风险理论认知与管控技术；
（2）轮机智能感知与安全可靠性分析；
（3）轮机数字孪生技术。
主持的科研项目
1. 复杂水域中船舶智能航行人机交互系统的风险涌现与抑制方法研究，国家自然基金面上项目，49万，2026.01-2029.12，项目主持人；
2. 在航船舶安全风险防控效能评估与自适应优化技术研究，国家重点研发计划子课题，50万，2019.12-2022.11，项目主持人；
3. 俄乌冲突长期化冲击下北极海上运输系统韧性提升策略研究，教育部项目，8万，2023.09-2026.12，项目主持人；
4. 基于安全屏障理论的船舶火灾/爆炸风险管控机理研究，辽宁省自然基金项目，5万，2022.08-2025.07，项目主持人；
5. 基于安全屏障体系的船舶航行风险管控技术研究，大连市青年科技创新之星支持项目，10万，2021.01-2023.12，项目主持人；
6. 船岸交互联动过程中的风险管控分析技术，中国博士后基金项目，8万，项目主持人；
7. 基于安全边界识别的沿海智能航行船舶少人化营运新模式研究，中远海运集团科技项目，40万，2025.01-2026.08，项目主持人；
8. 高水平水上交通安全的底层逻辑与实践策略研究，海油基金会项目，50万元，2025.10-2025.12，项目主持人。
荣誉奖项
先后获得大连市“青年才俊”、学校优秀航海类青年教师等称号，获得的科技奖项有：
辽宁省科技进步二等奖（排名第1），2025年；
辽宁省技术发明二等奖（排名第4），2020年；
辽宁省哲学社会科学成果奖一等奖（排名第2），2023年；
中国造船工程学会科技进步一等奖（排名第8），2025年；
中国航海学会科技进步二等奖（排名第10），2024年；
中国商业联合会科技进步一等奖（排名第8），2022年。
学术兼职
交通运输部安委办交通运输重大风险分析研判专家组成员（水上交通）
中国应急管理学会交通运输应急管理工作委员会委员
代表性成果
2020年至今，出版学术著作5部，包括1部国家出版基金资助著作，以第一作者或通讯作者发表SCI检索论文42篇，其中JCR一区论文35篇，代表性论文主要包括：
[1] Zhao Y, Ma X, Qiao W*. Investigating spatial-temporal patterns of the important disruptions associated with China’s LNG shipping network. J Clean Prod 2026;538:147368.
[2] Zhao Y, Ma X, Qiao W*, Han B, zhang W. Exploring the dynamic characteristics of LNG shipping network resilience by integrating complex network and SIR simulation: A case of China. Ocean Engineering 2026;343:123431.
[3] Ma X, Zhang J, Deng W, Qiao W*. Strategic environment analysis for Arctic collaborative governance based on data-driven triple bottom line perspective. Ambio 2025;54:1549–58.
[4] Zhao Y, Ma X, Qiao W*, Han B. A methodology to evaluate the dynamic resilience of an LNG shipping network by a load redistribution strategy: A case in China. Energy 2025;334:137756.
[5] Zhao Y, Ma X, Qiao W*, Zhang J. Resilient maritime transportation system from the perspective of FRAM: conceptualization and assessment. Reliab Eng Syst Saf 2025;262:111155.
[6] Qiao W, Huang E, Zhang M, Ma X, Liu D. Risk influencing factors on the consequence of waterborne transportation accidents in China (2013–2023) based on data-driven machine learning. Reliab Eng Syst Saf 2025;257:110829.
[7] Deng W, Ma X, Qiao W*. Resilience-oriented safety barrier performance assessment in maritime operational risk management. Transp Res D Transp Environ 2025;139:104581.
[8] Wang X, Huang E, Qiao W*. Investigating Impacts of Risk Influence Factors on the Consequences of Marine Accidents in China by SE-CNN-GRU Algorithm. J Mar Sci Eng 2025;13:2169.
[9] Du Q, Ma X, Zhang R, Qiao W*. Analyzing the Causation of Collision Accidents Between Merchant and Fishing Vessels in China’s Coastal Waters by Integrating Association Rules and Complex Networks. J Mar Sci Eng 2025;13:1086.
[10] Feng W, Wang Z, Dai X, Dong S, Qiao W*, Ma X. Ship-To-Ship Liquefied Natural Gas Bunkering Risk Assessment by Integrating Fuzzy Failure Mode and Effect Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. J Mar Sci Eng 2025;13:710.
[11] Qiao W, Guo H, Huang E, Chen H. Vibration-based multiphase flow identification by deep learning for the vertical section of subsea pipelines. Applied Ocean Research 2024;151:104167.
[12] Ma X, Zhang J, Du Q, Qiao W*. Multilateral collaborative governance of Arctic shipping safety: Examining the impact of the Arctic Council via collaborative network analysis. Ocean Coast Manag 2024;258:107363.
[13] Qiao W, Guo H, Deng W, Huang E, Lin G, Ma X, et al. Complex network-based risk analysis for maritime heavy casualties in China during 2012–2021. Ocean Engineering 2024;308:118258.
[14] Ma X, Du Q, Qiao W*, Liu Y. Collaboration network analysis for the Arctic issues based on a collection of international collaborative events. Polar Sci 2024;42:101091.
[15] Qiao W, Yang J, Zhao Y, Deng W, Ma X. On the determination of the maritime-specific EPC values in reducing human factors based on maritime foundering accidents in China. Ocean Engineering 2024;307:118192.
[16] Deng W, Qiao W*, Ma X, Han B. A novel unconstrained methodology for economic analysis of the level of repair with a case study of a multi-indenture and multi-echelon repair network. Comput Ind Eng 2024;192:110215.
[17] Xu M, Ma X, Qiao W*, Du Q. Characterizing collaborative networks for different arctic issues based on complex network analysis. Ocean Coast Manag 2024;255:107216.
[18] Deng W, Ma X, Qiao W. A novel methodology to quantify the impact of safety barriers on maritime operational risk based on a probabilistic network. Reliab Eng Syst Saf 2024;243:109884.
[19] Deng W, Qiao W*, Ma X, Han B. A novel methodology to evaluate criticality and sensitivity of safety barrier based on multi-agent interaction network. Expert Syst Appl 2024;244:123001.
[20] Qiao W, Huang E, Guo H, Li W, Chen H. Identification of two-phase flow patterns in Z-shaped offshore pipelines based on deep learning technologies. Ocean Engineering 2024;291:116422.
[21] Liu Y, Ma X, Qiao W*, Ma L, Han B. A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system. Reliab Eng Syst Saf 2024;241:109620.
[22] Deng W, Ma X, Qiao W*. A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy. Mathematics 2024;12:2570.
[23] Liu Y, Ma X, Qiao W*, Han B. A methodology to model the evolution of system resilience for Arctic shipping from the perspective of complexity. Maritime Policy & Management 2024;51:867–82.
[24] Ma X, Lan H, Qiao W*, Han B, He H. On the causation correlation of maritime accidents based on data mining techniques. Proc Inst Mech Eng O J Risk Reliab 2024;238:905–19.
[25] Xu M, Ma X, Zhao Y, Qiao W*. A Systematic Literature Review of Maritime Transportation Safety Management. J Mar Sci Eng 2023;11:2311.
[26] Qiao W, Guo H, Huang E, Su X, Li W, Chen H. Real-Time Detection of Slug Flow in Subsea Pipelines by Embedding a Yolo Object Detection Algorithm into Jetson Nano. J Mar Sci Eng 2023;11:1658.
[27] Ma X, Chen L, Wu W, Liu Y, Qiao W*, Ma L. Comparative Analysis of Arctic-Related Strategies at the National Level: Competition and Collaboration. Systems 2023;11:413.
[28] Qiao W, Guo H, Huang E, Chen H, Lian C. Two-Phase Flow Pattern Identification by Embedding Double Attention Mechanisms into a Convolutional Neural Network. J Mar Sci Eng 2023;11:793.
[29] Qiao W, Huang E, Guo H, Lian C, Chen H, Ma X. On the causation analysis for hazards involved in the engine room fire-fighting system by integrating STPA and BN. Ocean Engineering 2023;288:116073.
[30] Liu Y, Ma X, Qiao W*, Han B. On the determination and rank for the environmental risk aspects for ship navigating in the Arctic based on big Earth data. Risk Analysis 2023;43:2186–210.
[31] Qiao W, Ma X, Liu Y, Deng W. Resilience evaluation of maritime liquid cargo emergency response by integrating FRAM and a BN: A case study of a propylene leakage emergency scenario. Ocean Engineering 2022;247:110584.
[32] Ma X, Deng W, Qiao W*, Lan H. A methodology to quantify the risk propagation of hazardous events for ship grounding accidents based on directed CN. Reliab Eng Syst Saf 2022;221:108334.
[33] Ma X, Deng W, Qiao W*, Luo H. A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network. Risk Analysis 2022;42:2800–22.
[34] Ma X, Zhou Q, Liu Y, Liu Y, Qiao W*. Security of the Arctic route from the resilience perspective: the ideal state, influencing factors, and evaluation. Maritime Policy & Management 2021;48:846–59.
[35] Qiao W, Liu Y, Ma X, Liu Y. A methodology to evaluate human factors contributed to maritime accident by mapping fuzzy FT into ANN based on HFACS. Ocean Engineering 2020;197:106892.
[36] Qiao W, Liu Y, Ma X, Liu Y. Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network. Risk Analysis 2020;40:957–80.
