Thoroughly and accurately identifying various defects on concrete surfaces is crucial to ensure structural safety and prolong service life. However, in actual engineering inspections, the varying shapes and complexities of concrete structural defects challenge the insufficient robustness and generalization of mainstream models, often leading to misdetections and under-detections, which ultimately jeopardize structural safety. To overcome the disadvantages above, an efficient concrete defect detection model called YOLOv11-EMC (efficient multi-category concrete defect detection) is proposed. Firstly, ordinary convolution is substituted with a modified deformable convolution to efficiently extract irregular defect features, and the model’s robustness and generalization are significantly enhanced. Then, the C3k2module is integrated with a revised dynamic convolution module, which reduces unnecessary computations while enhancing flexibility and feature representation. Experiments show that, compared with Yolov11, Yolov11-EMC has improved precision, recall, mAP50, and F1 by 8.3%, 2.1%, 4.3%, and 3% respectively. Results of drone field tests show that Yolov11-EMC successfully lowers false and under-detections while simultaneously increasing detection accuracy, providing a superior methodology to tasks that require identifying tangible flaws in practical engineering applications.

Defect inspection of existing buildings is receiving increasing attention for digitalization transfer in the construction industry. The development of drone technology and artificial intelligence has provided powerful tools for defect inspection of buildings. However, integrating defect inspection information detected from UAV images into semantically rich building information modeling (BIM) is still challenging work due to the low defect detection accuracy and the coordinate difference between UAV images and BIM models. In this paper, a deep learning-based method coupled with transfer learning is used to detect defects accurately; and a texture mapping-based defect parameter extraction method is proposed to achieve the mapping from the image U-V coordinate system to the BIM project coordinate system. The defects are projected onto the surface of the BIM model to enrich a surface defect-extended BIM (SDE-BIM). The proposed method was validated in a defect information modeling experiment involving the No. 36 teaching building of Nantong University. The results demonstrate that the methods are widely applicable to various building inspection tasks.

A comprehensive understanding of the heterogeneity of urbanization development at different levels and its influencing factors is crucial for promoting global urbanization and advancing China’s new urbanization. Using indicators related to urbanization development, a multidimensional index system was constructed based on five dimensions: population, economy, space, society, and ecology. Employing methods such as the Mann–Kendall test, Sen’s trend analysis, multiple linear regression, and spatial autocorrelation analysis, the spatiotemporal evolution characteristics of urbanization from 2000 to 2019 were analyzed comprehensively at national, economic zone, provincial, and prefectural city scales. The results indicate the following. (1) From 2000 to 2019, urbanization levels at all levels showed an overall upward trend, with the national urbanization rate increasing most rapidly at 5.39%. (2) Trend analysis reveals rapid and significant growth trends in urbanization at the national and economic zone scales, while urban-level changes exhibit greater diversity and spatiotemporal heterogeneity. (3) Spatial distribution patterns show that urbanization levels in the eastern coastal economic zones are significantly higher than those in the northeastern economic zones, highlighting pronounced regional disparities in development and agglomeration effects in economically advanced regions and provinces. (4) Regression analysis demonstrates that spatial urbanization significantly influences urbanization development in China, with urban infrastructure playing a crucial role across different levels.