2025-03-21

A Multi-Factor-Fusion Framework for Efficient Prediction of Pedestrian-level Wind Environment Based on Deep Learning Published in IEEE Access.

Efficient and accurate assessment of the Pedestrian-Level Wind Environment is essential to maintain a healthy and safe urban living environment. Numerical simulations, such as computational fluid dynamics and multi-scale modeling techniques, are commonly used for wind environment analysis. However, they are computationally intensive and time-consuming, particularly when dealing with the complexities of urban landscapes. This study proposes a novel Multi-Factor-Fusion (MFF) framework that leverages deep learning techniques. This framework integrates Graph Convolutional Networks and Long Short-Term Memory networks to extract and fuse multiple factors and create an end-to-end neural network model capable of directly predicting wind fields. By avoiding the need for grid division and iterative calculations, the framework significantly enhances the efficiency of wind environment analysis. Furthermore, multi-scale simulation data is used to train the model and correct the predictive results, ensuring the accuracy of the final results. This innovative approach has the potential to revolutionize the Pedestrian-Level Wind Environment prediction by achieving a trade-off between efficiency and accuracy.

Note: IEEE Access is an important SCI journal in the field of engineering technology. I am the first author and corresponding author of the paper. The research results were supported by the National Key Research and Development Program of China, and the Guangdong Basic and Applied Basic Research Foundation and the National Natural Science Foundation of China. 

2025-03-18

The paper Intelligent BIM Searching via Deep Embedding of Geometric, Semantic, and Topological Features has been published in the journal Buildings.

As a digital representation of buildings, building information models (BIMs) encapsulate geometric, semantic, and topological features (GSTFs), to express the visual and functional characteristics of building components and their connections to create building systems. However, searching for BIMs pays much attention to semantic features, while overlooking geometric and topological features, making it difficult to find and reuse rich knowledge in BIMs. Thus, this study proposes a novel approach to intelligent BIM searching by embedding GSTFs via deep learning (DL). First, algorithms for extracting GSTFs from BIMs and identifying required GSTFs from search queries are developed. Then, different GSTFs are embedded via DL models, creating vector-based representations of BIMs or search queries. Finally, similarity-based ranking is adopted to find BIMs highly related to the queries. Experiments show that the proposed approach demonstrates an efficiency of 780 times greater than manual retrieval methods and 4–6% more efficient than traditional methods. This study advances the field of BIM searching by providing a more comprehensive, accurate, and efficient method for finding and reusing rich knowledge in BIMs, ultimately contributing to better building design and knowledge management.

Note: Buildings, a journal in the field of Engineering and technology, is a Q2 SCI journal with an impact factor of 3.1 for 2024. The first author of the paper is Huang Pinhao, a master's student in the Department of Civil Engineering of Tsinghua University, and the corresponding author is Lin Jiarui, a civil engineering student of Tsinghua University. The research results have been funded by the financial support received from the National Key R&D Program of China and the National Natural Science Foundation of China.

2025-03-09

The application and development of digital twin in the marine domain has been published in the journal Ocean.

With the gradual shift of marine resource development towards large-scale and deep-sea exploration, traditional analytical and simulation techniques are no longer able to meet the growing demands of users. Digital twin (DT) technology offers a potential solution to address these challenges in the marine domain. This study focuses on DT technology for marine applications,  providing  a  detailed  discussion  on  the  concept,  application  framework,  key  technologies,  and  future developments in the marine DT (MDT) field. A systematic review of relevant literature on MDT was conducted using the Web of Science database, analyzing the research focuses within this field. This study proposes a definition for MDT and presents a five-layer application framework, including the perception layer, data layer, model layer, fusion layer, and application layer. Key technologies for MDT are summarized, with a particular emphasis on data collection and transmission, data storage and management, modeling and simulation, and monitoring analysis and evaluation techniques, along with their applications in the marine  domain.  The  future  prospects  of  MDT  are  discussed,  and  the  construction  and  application  of  a  DT  platform  are demonstrated using marine engineering as an example. Furthermore, potential challenges in the development of MDT are analyzed, and possible solutions are proposed.

Note: I am the first author of the paper, Professor Zhang Jianmin is the corresponding author, and the authors also include doctoral student Liu Yi. The research results were supported by Guangdong Basic and Applied Basic Research Foundation.

2025-02-12

The paper "Fukushima Contaminated Water Risk Factor: Global Implications" has been published in the international journal Environmental Science & Technology. 

The discharge of contaminated water from Fukushima poses a comprehensive threat to the global marine ecosystem and human health. Currently, discussions on the risks of discharging the contaminated water from Fukushima mostly lack quantitative indicators and fail to comprehensively consider various factors. To address these deficiencies, we innovatively proposed the Fukushima Contaminated Water Risk Factor (FCWRF), which integrates the risks of radionuclide diffusion, bioaccumulation, and global seafood trade to quantitatively assess the risks of discharging the contaminated water into the sea. The results show that the measurable comprehensive risk will spread to six continents around the world, and the diffusion of this comprehensive risk will be six times faster than that driven by ocean currents. FCWRF bridges the gap between different fields of radioactive nuclide risk assessment and provides support for timely and effective global response measures. Additionally, we have developed a visualization website for the dynamic database of the spatiotemporal distribution of FCWRF, named "Fukushima Risk", and made it available globally. This website visually presents the spatiotemporal evolution process of the global distribution of FCWRF and provides users with channels to select, view, and download the data they are concerned about. 

The Environmental Science & Technology Journal is a top-tier publication in both the JCR and the Chinese Academy of Sciences' Engineering Technology category 1, with an impact factor of 10.8. Professor Zhang Jianmin and I are the corresponding authors of the paper, while the first author is doctoral student Liu Yi, with Li Yilin and Min Yantao as co-authors. The research was funded by the Guangdong Provincial Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, and the Liao Shan Laboratory.

2025-01-23

Recently, I was honored to be awarded the title of "Outstanding Reviewer of 2024". I am grateful to the "Construction Technology (Chinese and English)" magazine for recognizing my work achievements. At the same time, I am deeply aware of the great responsibility and will continue to contribute to the development of the construction technology field in our country. 

Construction Technology  is a national-level professional scientific and technological journal in China, supervised by China Construction Technology Group Co., Ltd., and jointly hosted by Asia-Pacific Construction Technology Information Research Institute Co., Ltd., China State Construction Engineering Corporation Limited, and the China Civil Engineering Society. In 2023, it was selected as a high-quality scientific and technological journal in the field of architecture by the China Association for Science and Technology.