Creating geometric digital twins (gDTs) for as-built roads still has many limitations, such as low automation level and accuracy, limited asset types and shapes, and reliance on engineering experience. A novel scan-to-building information modeling (scan-to-BIM) framework is proposed for automatic road gDT creation based on semantically labeled point cloud data (PCD), which considers six asset types: road surface, road side (slope), road lane (marking), road/traffic sign, road/street light, and guardrail. The framework first segments the semantic PCD into spatially independent instances or parts, and then extracts the sectional polygon contours as their representative geometric information, stored in JavaScript Object Notation (JSON) files using a new data structure. Primitive gDTs are finally created from the JSON files using the corresponding conversion algorithms. The proposed method achieves an average distance error of 1.46 cm and a processing speed of 6.29 m/s on six real-world road segments with a total length of 1200 m.

To address the problems of missing detection and low detection accuracy of the existing helmet wearing detection algorithms for small and crowded targets detection, this paper proposes a helmet wearing detection method based on improved YOLOv5 and transfer learning. First, different from the default priori frame that is not suitable for the task, we use the K-means algorithm to cluster the suitable priori frame size for the detection task. Then, in the back of the feature extraction network, we introduce a spatial channel mixed attention module to strengthen the learning of relevant weights and suppress the weights of irrelevant backgrounds, respectively. Further, we improve the judgment metric of the non-maximum-suppression (NMS) algorithm in the post-processing stage of YOLOv5 to reduce the phenomenon of false deletion and missing of prediction boxes. After that, the proposed network is trained based on the strategy of transfer learning, which can overcome the scarcity of limited existing data sets and improve the generalization ability of the model. Finally, we build a cascade judgment framework for helmet wearing deployed in visual sensor networks. The experimental results show that our proposed method improves the average accuracy (IOU=0.5) to 93.6%, which is 5% higher than the original model in the helmet wearing data set. The proposed model also outperforms other state-of-the-art algorithms by obviously improving the accuracy of helmet wearing detection in the construction scenarios.

Accurate building energy simulation (BES) is essential for developing effective energy conservation strategies and implementing evidence-based policy interventions in the built environment. However, BES accuracy is often undermined by unrealistic weather data, as conventional Typical Meteorological Year (TMY) files fail to adequately capture urban microclimate variations. This study proposes a deep learning model that integrates wind-driven building morphology maps for high-resolution temporal microclimate prediction. By combining macro-scale wind dynamics with urban morphological features, encoded as frontal area maps, the model captures seasonal microclimate variations influenced by prevailing wind conditions. Validation conducted on a university campus demonstrates that the proposed model outperforms benchmark approaches in predicting air temperature and relative humidity (RH). The ground truth for validation is the real-time microclimate data collected by weather stations installed across the campus. Compared to TMY files, a standard deep learning model, and a deep learning model with wind directions, the proposed model reduces the root mean squared error (RMSE) in air temperature by 36.3%, 14.2%, and 14.0%, and RMSE in RH by 30.5%, 17.3%, and 17.3%, respectively. When integrated into BES for three test buildings, the model’s weather data enabled cooling energy prediction with less than 2% error, significantly outperforming alternative methods. Overall, the proposed model allows cross-building temporal microclimate prediction without requiring long-term weather data collection at the target building.

The transition toward battery electric vehicles (BEVs) is a critical element in the global shift toward sustainable transportation. This meta-analysis delves into the multifaceted factors influencing BEV performance, including environmental, technological, behavioral, and political-economic determinants. The purpose of this review is to systematically organize and assess how these factors impact BEV efficiency and sustainability across various operational scenarios, such as driving, charging, and decommissioning. By examining a wide range of literature, this study constructs a comprehensive framework that categorizes the primary components and performance metrics, revealing complex relationships and potential causal connections. The findings highlight that although technological advancements and regulatory frameworks are the predominant drivers of BEV performance, environmental conditions and user behaviors also play significant roles. The key emerging topics identified suggest further research avenues, particularly in optimizing battery technology and expanding policy support. Additionally, the analysis provides new and systematic insights compared with previous reviews, offering a clearer understanding of the determinants, their impacts, and the interactions between them. These insights are crucial for developing a transparent evaluation system for future research and policy formulation. This comprehensive synthesis not only aids in understanding the current landscape but also in directing future scholarly and practical endeavors in electric vehicle research.