Hongzhe Yue

Citizen-centric smart city construction (SCC) has been the crucial mode for enhancing citizens’ well-being with rapid urbanization. While smart cities are constructed to improve urban operational safety, the concomitant low resilience of infrastructure, data breaches, and other issues also lead to physical, financial, and legal consequences, which therefore have the complicated the impact on citizens’ security perception of smart city construction (CSPSCC). To achieve sustainable smart city construction, it is important to clarify the influencing factors on CSPSCC. Although the enhancement of CSPSCC needs the joint efforts of citizens, government, and social organizations, the previous studies mostly focus on influencing factors from the single stakeholder. To address this gap, the theory of planned behavior was expanded to examine factors influencing CSPSCC from the perspective of participatory governance. Taking Nanjing as a case, hypotheses testing, mediating testing, and heterogeneity analysis were carried out for this theoretical model. The results show that the security governance of citizens, the government, and social organizations all had a positive impact on CSPSCC, with citizens’ behavioral intention being the most significant influencing factor. In addition, CSPSCC is also significantly affected by the citizens’ age, educational level, and usage frequency of smart city services.

The Automated Building Information Modeling (BIM) reconstruction of existing building curtain walls is crucial for promoting digital Operation and Maintenance (O&M). However, existing 3D reconstruction technologies are mainly designed for general architectural scenes, and there is currently a lack of research specifically focused on the BIM reconstruction of curtain walls. This study proposes a BIM reconstruction method from unmanned aerial vehicle (UAV) images based on neural radiance field (NeRF) and deep learning-based semantic segmentation. The proposed method compensates for the lack of semantic information in traditional NeRF methods and could fill the gap in the automatic reconstruction of semantic models for curtain walls. A comprehensive high-rise building is selected as a case study to validate the proposed method. The results show that the overall accuracy (OA) for semantic segmentation of curtain wall point clouds is 71.8%, and the overall dimensional error of the reconstructed BIM model is less than 0.1m, indicating high modeling accuracy. Additionally, this study compares the proposed method with photogrammetry-based reconstruction and traditional semantic segmentation methods to further validate its effectiveness.

With the increasing frequency of extreme weather events such as typhoons, the stability and resilience of the inter-city population mobility network is facing serious challenges. However, existing studies have failed to comprehensively reveal the dynamics of the overall regional network, especially the changes in network structure and drivers under disaster situations. Therefore, based on the Pressure-State-Response (PSR) framework and the complex network model, this paper constructs an assessment model for the characteristics of the population mobility network in the Yangtze River Delta (YRD) region under the influence of Typhoon Bebinca, in order to analyze the impact of the typhoon on the network structure and the changes in its driving factors. The study shows that the typhoon had a significant impact on the characteristics and patterns of the population mobility network in the YRD. First, during the typhoon, the network size and density decreased, long-distance mobility decreased, and mobility was concentrated among neighboring cities, showing the characteristics of “small world”. Second, after the typhoon landfall, the population inflow to the central city dropped significantly. Then, network connectivity declines during the typhoon, but modularity increases. Finally, in terms of drivers, differences in wind, air quality, residents' economic conditions, and the level of pollution control between cities significantly and negatively affect the network. In summary, this paper enriches the theoretical study of population mobility networks in disaster situations, and provides scientific basis and policy recommendations for enhancing emergency management capabilities and optimizing post-disaster recovery strategies in the YRD region.

Point clouds are increasingly leveraged for as-built model reconstruction of facilities, and these reconstructed models play a key role in applications for digital twins and sustainable building. However, point clouds of Mechanical, Electrical, and Plumbing (MEP) systems often experience extensive occlusions, which heavily affect the performance of model reconstruction. To tackle this problem, this study adopts deep learning (DL)-based point cloud completion algorithms to complete occluded MEP point clouds. To overcome the scarcity of datasets, this study utilizes parametric BIM modeling and occlusion simulations to generate point cloud datasets for MEP components. Based on generated datasets, the effectiveness of PoinTr DL algorithms and five distinct training strategies for point cloud completion are investigated. The results indicate that: 1) The PoinTr model with pre-train strategy achieved the best CD-L2 score of 0.330, demonstrating effective completion even with 75% missing of point clouds. 2) the completion of point clouds for real-world MEP components showed favorable results, underscoring the practical applicability of this approach.