你好, Hello, こんにちは, Bonjour, Привет, Hallo, مرحبا, नमस्ते, 안녕하세요, Ciao. I'm Liu Zenghui. I am currently completing my master's studies at the School of Future Technology, Tianjin University, primarily engaged in fire dynamics, building fire analysis, and intelligent fire protection. The supervisor of my master's program is Professor Gang Liu. Between October 2022 and July 2023, I conducted one-year research on the spatiotemporal prediction of infection risks in waiting rooms as a research assistant. In the future, I will devote myself to research on resilience, sustainability, and equity of complex urban systems. I am also a student member of the Architectural Society of China, the Fire Engineers Association, the International Society for Urban Information, and the International Building Performance Simulation Association. I also serve as a peer reviewer for journals such as Humanities and Social Sciences Communications and Journal of Safety Science and Resilience.Since April 2025, I have been on probation as a young editorial board member of Jandoo press Journal of Sustainable Built Environment (JSBE). In my spare time, I play basketball, table tennis, Guan Dan (a kind of card game), listen to music, and play the guitar (for indie music lovers).
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GANG Liu#,Zenghui Liu#,Guanhua Qu*,Lei Ren*.
Process Safety and Environmental Protection 2024
This study combines distributed fiber optic temperature sensing systems with deep learning algorithms to develop a dual-agent intelligent fire detection method for rapidly and accurately predicting key fire information in large commercial spaces, including the location of the fire source, the intensity of fire development, and the distribution of carbon monoxide on critical planes. This work not only improves the accuracy and response speed of fire alarms but also provides data support for emergency evacuation and rescue operations, reducing casualties and property loss caused by fires, and has significant practical importance for the safety protection systems of modern commercial buildings.
GANG Liu#,Zenghui Liu#,Guanhua Qu*,Lei Ren*.
Process Safety and Environmental Protection 2024
This study combines distributed fiber optic temperature sensing systems with deep learning algorithms to develop a dual-agent intelligent fire detection method for rapidly and accurately predicting key fire information in large commercial spaces, including the location of the fire source, the intensity of fire development, and the distribution of carbon monoxide on critical planes. This work not only improves the accuracy and response speed of fire alarms but also provides data support for emergency evacuation and rescue operations, reducing casualties and property loss caused by fires, and has significant practical importance for the safety protection systems of modern commercial buildings.
Guanhua Qu#,Zenghui Liu#,Lei Ren*,Gang Liu*.
Journal of Building Engineering 2024
The innovation of this study is to generate infection risk map by intelligent method and manage the system, so as to achieve accurate control of airborne infection risk in waiting room. The results of this study not only help to select hospital design scheme, but also optimize the operation control strategy of ventilation system, so as to strengthen the control of infectious diseases and reduce the threat of cross-infection of respiratory infectious diseases to public health safety during peak periods in hospitals..
Guanhua Qu#,Zenghui Liu#,Lei Ren*,Gang Liu*.
Journal of Building Engineering 2024
The innovation of this study is to generate infection risk map by intelligent method and manage the system, so as to achieve accurate control of airborne infection risk in waiting room. The results of this study not only help to select hospital design scheme, but also optimize the operation control strategy of ventilation system, so as to strengthen the control of infectious diseases and reduce the threat of cross-infection of respiratory infectious diseases to public health safety during peak periods in hospitals..
Zenghui Liu*,Yingnan Zhuang.
Journal of Building Engineering 2024
This study not only improves the emergency response strategies for urban residential fires, but also provides customized fire safety policies for different urban environments, effectively reducing fire risk. Through the introduction of resampler technology and interpretable machine learning, this study not only improves the prediction accuracy of the model, but also enhances the transparency and interpretability of the model, providing a more scientific and reliable tool for fire risk management..
Zenghui Liu*,Yingnan Zhuang.
Journal of Building Engineering 2024
This study not only improves the emergency response strategies for urban residential fires, but also provides customized fire safety policies for different urban environments, effectively reducing fire risk. Through the introduction of resampler technology and interpretable machine learning, this study not only improves the prediction accuracy of the model, but also enhances the transparency and interpretability of the model, providing a more scientific and reliable tool for fire risk management..