The sphere target played a vital role in terrestrial LiDAR applications, and solving its geometrical center based on point cloud was a widely concerned problem. In this study, we proposed a newly finite random search algorithm for sphere target fitting. Based on the point cloud data and the geometric characteristics of the sphere target, the algorithm realized the target sphere fitting from the perspective of probability and statistics with the help of parameter estimation. Firstly, an initial constraint space was constructed, and the initial center and radius were determined by finite random search. Then, the optimal spherical center and radius were determined gradually through continuous iterative optimization. We tested the algorithm with the simulated and realistic point cloud. Experimental results showed that the proposed algorithm could be effectively applied to all kinds of point cloud fitting. When the coverage rate was bigger than 30%, the fitting accuracy could reach within 0.01 mm for all kinds of point clouds. When the coverage rate was less than 20%, the fitting accuracy can reach ±1 mm, although it was reduced to a certain extent.

A laboratory flume simulating a riverbed sand dune containing a low-permeability clay lens was constructed to investigate its influence on the quality and quantity of hyporheic exchange. By varying the depths and spatial locations of the clay lens, 24 scenarios and one blank control experiment were created. Dye tracers were applied to visualize patterns of hyporheic exchange and the extent of the hyporheic zone, while NaCl tracers were used to calculate hyporheic fluxes. The results revealed that the clay lens reduces hyporheic exchange and that the reduction depends on its spatial location. In general, the effect was stronger when the lens was in the center of the sand dune. The effect weakened when the lens was moved near the boundary of the sand dune. A change in horizontal location had a stronger influence on the extent of the hyporheic zone compared with a change in depth. The size of the hyporheic zone changed with the depth and position of the clay lens. There was a maximum of hyporheic extent with the lens at a depth of 0.1 m caused by changes of water flow paths.

Aiming at the gross error and data missing in the monitoring sequence of concrete dam, the variational mode decomposition method and the gated recurrent unit depth learning algorithm are respectively used to extract the effective information of the monitoring sequence. On the basis of the research on the characteristics of the traditional concrete dam structural behavior characterization model, the paper explores the expression mode of the effect of cold wave, freeze-thaw, wintering layer and other influencing factors. In order to reflect the correlation between the monitoring measurement values, the space coordinate variable is introduced to establish the monitoring measurement change characterization model, so as to realize the characterization and analysis of the structural behavior changes of concrete dams in cold regions and the quantitative analysis of various influencing factors. Based on the research in this article, we can fully understand the operation status of the dam, identify hidden dangers, and carry out relevant risk investigation and reinforcement. It can reduce the risk of dam failure to a certain extent.

The Hechangzhou Anabranching Reach is a typical tidal anabranching segment of the Yangtze River with intense riverbed evolution driven by altered water-sediment regimes and channel regulation. Submerged dikes stabilize river regimes and navigation yet trigger differential local clear-water scour; most prior studies only adopt short-term bathymetric data and lack long-term quantitative analysis across diverse hydrological scenarios, so the synergistic geomorphic mechanism under the control of the submerged dike group remains unclear. This study integrates the 2002–2025 flow division ratio series and multi-period bathymetric data from 2018 to 2026 to quantify spatiotemporal deep-channel adjustments, scour-deposition patterns and cross-sectional deformation of the two anabranches, and evaluate morphological effects of river training structures and relevant potential scour risks. The submerged dike group regulates diversion patterns and keeps the left anabranch’s flow division ratio below 70%. The right anabranch deep channel undergoes persistent vertical incision and reverses its historical deposition trend, while the left anabranch features spatially uneven scour: intense scour zones migrate within the SD1–SD2 reach, and persistent landward erosion occurs between SD2 and SD3. The reach’s polarized evolution of intensified scour and attenuated deposition arises from the joint control of the submerged dike group, bank protection and reduced basin sediment supply. This study reveals the spatially differentiated riverbed evolution driven by engineering-induced hydrodynamic redistribution, offering scientific support for refined waterway management and scour prevention in analogous tidal anabranching rivers.

With the increasing development of underground spaces adjacent to urban levees, contact seepage frequently occurs at the interface between the soil and underground structures. However, traditional geophysical detection methods are often rendered ineffective in such environments due to spatial restrictions and detection blind spots. To address these challenges, this paper proposes a vertical-array lateral scanning detection method. This approach utilizes electrical resistivity tomography (ERT) with flat-base electrodes and ground-penetrating radar (GPR) to acquire data directly from vertical wall surfaces. The feasibility of this method is validated through numerical simulations and field data. The results indicate that the proposed method effectively overcomes the high-resistance shielding effect of hardened walls and clearly reveals the electrical structure of the soil behind the wall. Specifically, the contact seepage zone manifests as a layered low-resistivity feature immediately adjacent to the wall, while the penetrating leakage channel presents as a continuous low-resistivity anomaly extending from the contact interface deep into the levee body. These findings confirm the applicability of this technology for the qualitative identification and effective detection of hazards in complex, space-restricted urban environments.

The study area is located in the Houzhai Basin, Guizhou Province, in southwest China. The aquifer is a highly heterogeneous triple porosity karst aquifer with diverse hydraulic properties. The Pingshan Sinkhole serves as a perfectly placed well whose water level records the water level in the aquifer. The rainfall and water level time series from 1990 to 1995 have been analyzed and the results indicate that the observed water level variation can be divided into three stages, i.e. low water level stages, water level decreasing-rising stages and high water level stages. A Pingshan Sinkhole water level prediction model has been developed using partial least squares (PLS) regressions and artificial neural network (ANN) methods. The rainfall in the previous 12 hours and the antecedent water level are the main factors influencing the sinkhole water level in the rainy season. The predicted results obtained by the ANN or PLS regression combined with the ANN method are better than the results of PLS regression method alone. The study of the variation and prediction of water level in the Pingshan Sinkhole could benefit the understanding of the hydrological cycle and the hydrogeological conditions in this highly heterogeneous triple porosity karst aquifer.