The widespread use of reclaimed water has alleviated the water resource crisis worldwide, but long-term use of reclaimed water for irrigation, especially in agricultural countries, might threaten the soil environment and further affect groundwater quality. An in-situ experiment had been carried out in the North China Plain, which aimed to reveal the impact of long-term reclaimed water irrigation on soil properties and distribution of potentially toxic elements (As, Cd, Cr, Hg, Zn and Pb) in the soil profile as well as shallow groundwater. Four land plots were irrigated with different quantity of reclaimed water to represent 0, 13, 22 and 35 years’ irrigation duration. Pollution Load Index (PLI) values of each soil layer were calculated to further assess the pollution status of irrigated soils by potentially toxic elements (PTEs). Results showed that long-term reclaimed water irrigation caused appreciable increase of organic matter content, and might improve the soil quality. High soil organic matter concentrations conduced to high adsorption and retention capacity of the soils toward PTEs, which could reduce the risk of PTEs leaching into deep layers or shallow groundwater. Highest levels of Cr, Pb and Zn were observed at 200–240 cm and 460–500 cm horizons in plots. Longer irrigation time (35 years and 22 years) resulted in a decreasing trend of As, Cd, Hg, Pb and Zn in lower part of soil profiles (>540 cm) compared with that with 13-years’ irrigation years. Long-term reclaimed water irrigation still brought about increases in concentrations of some elements in deep soil layer although their content in soils and shallow groundwater was below the national standard. Totally speaking, proper management for reclaimed water irrigation, such as reduction of irrigation volume and rate of reclaimed water, was still needed when a very long irrigation period was performed.

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.

The development and utilization of urban underground space is an important way to solve the “great urban disease”. As one of the most important types of urban underground foundations, utility tunnels have become increasingly popular in municipal construction. The investigation of utility tunnels is a general task and three-dimensional laser scanning technology has played a significant role in surveying and data acquisition. However, three-dimensional laser scanning technology suffers from noise and occlusion in narrow congested utility tunnel spaces, and the acquired point clouds are imperfect; hence, errors and redundancies are introduced in the extracted geometric elements. The topology of reconstructed BIM objects cannot be ensured. Therefore, in this study, a hierarchical segmentation method for point clouds and a topology reconstruction method for building information model (BIM) objects in utility tunnels are proposed. The point cloud is segmented into facades, planes, and pipelines hierarchically. An improved mean-shift algorithm is proposed to extract wall line features and a local symmetry-based medial axis extraction algorithm is proposed to extract pipelines from point clouds. A topology reconstruction method that searches for the neighbor information of wall and pipeline centerlines and establishes collinear, perpendicular, and intersecting situations is used to reconstruct a topologically consistent 3D model of a utility tunnel. An experiment on the Guangzhou’s Nansha District dataset successfully reconstructed 24 BIM wall objects and 12 pipelines within the utility tunnel, verifying the efficiency of the method.