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Wenli Liu

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4 papers

1 author row

EAAI Journal 2026 Journal Article

Vector attention-based point cloud network for semantic segmentation of sewer sonar data

Wenli Liu
Yueming Jiang
Hanlin Li
Lei Yang
Hanbin Luo

Sonar technology is unaffected by lighting or water conditions, making it ideal for inspecting water-filled sewers. Nonetheless, significant challenges remain in utilizing sonar point clouds effectively. This research introduces the Vector Attention-based Point Cloud Network (VAPCNet), a deep learning method for semantic segmentation of sewer sonar point clouds. It is based on a U-Net style encoder-decoder architecture and consists of the attention module, the contraction module, and the expansion module. Additionally, to mitigate data imbalance, a weighted focal loss was employed during training. VAPCNet demonstrates excellent performance on a sewer dataset collected by a sonar robot, achieving an overall accuracy of 95. 9 % and a mean Intersection over Union (mIoU) of 86. 4 %. It demonstrates robustness to point perturbations and supports a lightweight design by adjusting encoder stages without sacrificing accuracy. These advantages make VAPCNet an innovative solution for employing sonar technology in sewer detection, emphasizing its practical potential.

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EAAI Journal 2024 Journal Article

Geotechnical risk modeling using an explainable transfer learning model incorporating physical guidance

Fenghua Liu
Wenli Liu
Ang Li
Jack C.P. Cheng

While Artificial intelligence (AI) has been successfully applied in assessing geotechnical risk, such methods heavily rely on data quality to achieve satisfactory performance, and their results hardly can be interpreted due to their opaque design. With this in mind, this paper aims to address the following research gap: How can we accurately model geotechnical risks using limited data and domain knowledge, and efficiently explain the results of AI model? We develop a physics-guided transfer learning (PGTL) model to enhance the explainability and accuracy of geotechnical risk modeling. With the help of a physical model that simulates the tunnel excavation, a physics-guided dataset with 1000 samples is established and used to train a deep neural network. On these bases, transfer learning is adopted to fuse the features of physics mechanisms and monitoring data, constructing an explainable prediction model of geotechnical risk. To further support risk decision-making, feature relevance techniques are employed to assess the contribution of input parameters to risk. A shield tunnel construction in Wuhan is selected as a case to validate the effectiveness of the proposed method. The PGTL exhibits a more promising accuracy with R 2 of 0. 777 in contrast to three popular machine learning approaches, and provides insights into parameters that significantly induce risk, enhancing site managers’ understanding of tunnel construction and being conducive to tunnel safety.

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IS Journal 2014 Journal Article

Collaboration Pattern and Topic Analysis on Intelligence and Security Informatics Research

Wenli Liu
Xiaolong Zheng
Tao Wang
Hui Wang

In this article, researcher collaboration patterns and research topics on Intelligence and Security Informatics (ISI) are investigated using social network analysis approaches. The collaboration networks exhibit scale-free property and small-world effect. From these networks, the authors obtain the key researchers, institutions, and three important topics.

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IS Journal 2013 Journal Article

Traffic Congestion and Social Media in China

Ke Zeng
Wenli Liu
Xiao Wang
Songhang Chen

Research on social media has been applied to various academic fields. During this year's Chinese National Holiday traffic congestion event, online users showed great enthusiasm on social media, such as forums, Weibo, communities, and other platforms. This article describes the construction of a dynamic evolution network, analyzes the transformation of online users' concentration, and studies the geographic distribution of travelers by analyzing online users' attributes.

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