Abstract:To explore the feasibility of introducing remote sensing soil moisture data to assist in distributed hydrological model parameter calibration in semi-arid small watersheds, a joint parameter calibration method that integrated hard data (flow rate) and soft data (remote sensing soil moisture) was proposed. CLDAS satellite remote sensing soil moisture data were applied to the Grid-Multi-GA model. A multi-objective optimization framework was adopted, and the Nash efficiency coefficient of flow rate simulation and the Spearman correlation coefficient of soil moisture’s spatiotemporal distribution were used as dual evaluation indicators. By adjusting the weights of the system to dynamically balance two types of indicators, the optimal weight and its corresponding runoff generation and flow routing parameter combination were ultimately determined. To validate the feasibility of the joint calibration method, three model scenarios were established, namely uncalibrated parameters, calibration using only discharge, and joint calibration of discharge and soil moisture. The case study results in the Ningxia Yuanzhou Watershed demonstrate that the Grid-Multi-GA model jointly calibrated by flow rate and soil moisture achieves a Nash efficiency coefficient greater than 0.7 and a Spearman correlation coefficient of 0.84 in flood simulation for small watersheds, significantly outperforming both the uncalibrated model and the model calibrated only with flow rate.

Abstract:Through a systematic review of 94 pieces of literature related to the impact of floating photovoltaic power stations on aquatic ecological environments, the effects of such power stations on abiotic factors in water bodies (water temperature, dissolved oxygen concentration, nutrient concentration, and pollutant distribution) and aquatic organisms (phytoplankton, aquatic plants, zooplankton, fishes, and birds) were analyzed. It concludes that the photovoltaic panel coverage ratio is the core factor driving ecological effects. Floating photovoltaic power stations reshape phytoplankton community structures through light-thermal coupled stress, inhibit the photosynthesis of submerged plants, alter fish metabolic rhythms and birds’ migration behaviors, and trigger cross-trophic-level ecological cascade effects. By combining zonal layout and material optimization while balancing power generation benefits and ecological protection, a dynamic regulatory strategy for the floating photovoltaic coverage ratio is used: Coverage ratio in core water areas should be less than 30%, while that in ecological buffer zones should be set between 30% and 60%. It also points out the need for future research to further analyze the superimposed response mechanisms of photothermal effects and climate change, construct hydrodynamic-ecological coupling models, and promote the development of floating photovoltaic power generation toward a sustainable model featuring multi-energy complementarity and ecological synergy.

Abstract:To address the low accuracy and poor stability of water level recognition in complex natural environments, which result from illumination changes, alternation between wet and dry conditions, and background interference, an intelligent water level recognition method that integrated semantic segmentation with spatiotemporal information was proposed. An FCN-RCA model was constructed by combining residual connections, a convolutional attention mechanism, and an atrous convolution module; an image segmentation optimization method based on the flooding algorithm was introduced, and highly robust semantic segmentation and structural integrity optimization for the water body area were achieved. The segmentation results were mapped to the physical space of the slope domain via an affine transformation to build a virtual water gauge, enabling precise quantification of the water level. By leveraging the spatiotemporal continuity of video frames, temporal consistency optimization was applied to the water level boundary to enhance the stability and reliability of the recognition results. The case study at the Taleldesayi Station in Xinjiang demonstrates that the proposed method achieves a mean intersection over union (mIoU) exceeding 0.92 under various meteorological, hydrological, and illumination conditions, exhibiting excellent segmentation performance and accurate extraction of water level boundaries. The dynamic water level optimization method based on spatiotemporal information effectively ensures the stability and continuity of water level boundary recognition. Compared with the pre-optimization result, the absolute mean absolute error (MAE) and root mean square error (RMSE) reduce by 0.006 m and 0.004 m, respectively, while the Nash-Sutcliffe efficiency (NSE) increases by 0.024. In validation with long-term measured data, the MAE for water level recognition remains within 0.04 m, and the NSE reaches up to 0.997. These results confirm that the method can achieve centimeter-level water level recognition in complex environments, with good temporal consistency and engineering applicability.

Abstract:To improve the accuracy of flood forecasting in semi-humid and semi-arid regions, the runoff generation characteristics of these regions were analyzed, as well as indexes such as hydrometeorology, soil and topography, and vegetation landcover. The grid hierarchical-clustering runoff generation pattern identification method (abbreviated as GRGI) was established through the optimization of identification indexes, hierarchical weight evaluation, and rolling correction clustering. Case verification results of the Qianyang Watershed in the middle reaches of the Yellow River show that the GRGI method can accurately identify the spatial and temporal distribution of the saturation-excess and infiltration-excess runoff generation modes in the Qianyang Watershed, with the saturation-excess pattern grid dominating, accounting for more than 70% of the total. Compared with those of the Grid-XAJ and Grid-GA models, the qualified rates of runoff depth error, peak flow error, and peak time difference of the Grid-XAJ-SIDE model improved based on runoff generation identification rules are increased by about 12%, 35%, and 12%, respectively.

Abstract:To accurately identify key information such as the groundwater contaminant source location and contaminant release process, the simulation-optimization theoretical framework was used. The groundwater inversion problem that requires simultaneous identification of information from multiple contaminant sources was generalized as a mixed-variable optimization problem involving discrete and continuous variables. A two-stage combinatorial optimization algorithm based on tabu search and particle swarm optimization (TS-PSO) algorithm was proposed. The algorithm applied the tabu search method to locate contaminant sources and then used the particle swarm optimization algorithm to determine the contaminant release intensity and process. The verification results of the numerical examples show that compared with traditional evolutionary algorithms(GA and PSO algorithm), TS-PSO algorithm has higher solution efficiency, more reliable calculation results, and higher calculation accuracy. For the inversion problem of multiple contaminant sources, TS-PSO algorithm can quickly and effectively identify the location of contaminant sources, as well as the release intensity and release process of contaminants.

Abstract:To address the challenge of leak detection and localization in water pipelines for long-distance water supply projects, a test system for water hammer and leak was designed and constructed. Moreover, a numerical model of water hammer and leak coupling considering dynamic friction and viscoelastic effects was established to achieve accurate transient pressure simulation. An analytical model for leak reflection and transmission coefficients was proposed to investigate the influencing parameters of these coefficients, and an experimental system was designed to verify the model’s accuracy. The results of experiments and model analyses indicate that the leak location is positively correlated with the occurrence time of the pressure drop peak; as the number of leak holes increases, the pressure drop shows a “stepwise” increase, and the transient pressure attenuation rate accelerates; the energy dissipation of water hammer waves caused by dynamic friction reduces the reflection coefficient compared to the frictionless condition; the actual wave velocity of viscoelastic pipelines is lower than the theoretical calculation value, leading to an overestimation of the reflection coefficient.

Abstract:To accurately locate the levee seepage inlet, laboratory model tests and Fluent numerical simulations based on the tracer method were employed, and the effects of tracer dosage, seepage scale, and tracer injection point location on tracer transport pattern were analyzed. The results indicate that increasing the tracer dosage significantly raises the peak tracer mass fraction on the wall, shortens the penetration time, and accelerates tracer transport velocity. Enlarging the seepage scale accelerates the tracer loss rate, reduces the peak tracer mass fraction on the wall, and transforms the tracer distribution shape from circular to elliptical, with its major axis aligning along the line connecting the tracer injection point and the seepage inlet. This alignment can be used to infer the direction of the seepage inlet. Reducing the distance between the tracer injection point and the seepage inlet decreases the peak tracer mass fraction on the wall, prolongs the penetration time, and significantly expands the tracer distribution area.

Abstract:The fundamental principles of Beidou navigation satellite system (BDS)/global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) technology were outlined, with a focused review of their theoretical developments since the 21st Ccentury. The latest research advances in their integrated application for deformation monitoring were analyzed in depth. Core issues and potential challenges currently faced by the combined BDS/GNSS and InSAR technology in monitoring engineering deformation and ensuring safety prevention for high-steep slopes were summarized. It is suggested that this integrated approach can achieve centimeter-level accuracy while enabling full-area coverage, significantly enhancing the capability for high-steep slope deformation monitoring. Furthermore, it is pointed out that future efforts should focus on deepening the integration of BDS and domestic SAR satellite data based on an integrated space-air-ground monitoring network, alongside the development of industry-specific large language models tailored for high-steep slope monitoring.

Abstract:The long-term safety of geological disposal for high-level radioactive waste relies critically on the effective retardation of radionuclide migration by geological barriers. Given the characteristics of radionuclide migration processes at large spatiotemporal scales, hypergravity experiments and multi-scale numerical simulations have become essential tools for investigating long-term seepage and solute transport behaviors in fractured media. Research progress in hypergravity testing techniques for seepage and solute migration in fractured rock masses, similarity theories for hypergravity tests of fractured rock masses, and long-term safety evaluations of geological barriers was reviewed. To address current challenges such as the integrated preparation of micron-scale matrix and fracture systems, similarity theories for complex rough fractures, and coupled thermal-hydrological-mechanical-chemical (THMC) processes, future research should focus on the integrated printing of micro-fracture and pore structures, similarity theories for natural rough fracture structures in hypergravity experiments, multi-process response mechanisms, and full-process simulations spanning ten-thousand-year timescales.

Abstract:To address the limitations of earth pressure theory-based design for geosynthetic-reinforced soil (GRS) segmental walls, which is often relatively conservative and unable to simultaneously optimize lateral deformation and carbon emissions, an analytical method for predicting lateral deformation of GRS segmental walls with a life cycle assessment (LCA) framework was proposed. The analytical method considered the complex interactions among facing blocks, geosynthetic reinforcements, and backfill soil. By using multi-objective optimization techniques, an optimized design methodology for GRS segmental walls was developed to achieve a balanced trade-off between lateral deformation and carbon emissions. Validation using practical engineering case demonstrates that the optimized design methodology enables rapid calculation and is suitable for practical application. Lateral deformation can be reduced while maintaining equivalent carbon emissions by optimizing reinforcement length and spacing. The optimal reinforcement length increases with wall height: For walls lower than 5 m, a reinforcement length equal to 0.6 times the wall height is recommended, whereas for taller walls, a length of 0.7 times the wall height is advised.

Abstract:To investigate the bearing characteristics of pile foundations on clay slopes under horizontal loads and clarify the influence of a key factor, namely slope angle, on the performance of the pile foundations, model tests were conducted on horizontally loaded piles on clay slopes. The impact of slope angle on the horizontal displacement and bending moment of the pile foundations was explored, thereby revealing the horizontal bearing characteristics of the single pile under horizontal load on the slope. The numerical simulation method was used to analyze the influence of slope angle on the deformation mode and p-y curve of pile foundations. The results show that as the slope angle increases, the influence of the slope effect becomes gradually prominent, and the depth of the maximum bending moment point of the pile foundation has shifted upward. The horizontal displacement of the pile foundation increases. Compared with that in the flat site, when the horizontal load is 40 N, the horizontal displacement of the single pile on a 30° slope increases by 40%. Additionally, the initial stiffness and ultimate soil resistance of the p-y curve of pile foundations both decrease with the increase of the slope angle.

Abstract:To systematically investigate the influence of different pile types on the failure modes and overall stability of embankment supported by composite foundations, a three-dimensional finite element analysis method was employed to study pile failure characteristics and the evolution patterns of shear strain in foundation soils. A full lifecycle cost and benefit analysis framework was applied to evaluate the economic performance of various composite foundation configurations, thereby identifying the optimal design strategy with the highest cost efficiency. The results demonstrate that under embankment loading conditions, pile type significantly alters the failure mode of embankment supported by composite foundations. As pile stiffness increases, the failure mode transitions from shear failure to flexural failure, and the failure development pattern shifts from simultaneous failure to progressive failure. Specifically, in gravel pile composite foundations, the plastic shear strain in soils exhibits progressive development along the embankment centerline. In contrast, the shear strain evolution in plain concrete pile composite foundations displays distinct forward propagation at the embankment center and backward propagation at the slope toe region, ultimately forming a continuous critical sliding surface. Compared to strengthening piles across the entire area, installing high-strength piles only beneath the embankment slope toe to the shoulder of the embankment can significantly enhance the overall stability of the embankment, demonstrating superior return on investment and cost-effectiveness.

Abstract:Interferometric synthetic aperture radar (InSAR) was applied to Sentinel-1 satellite imagery from 2015 to 2024 to monitor long-term deformation of various cross-river bridge types in Jiangsu Province, China. The results demonstrate that InSAR effectively captures cross-river bridge deformation, with monitoring efficacy influenced by geometric structures and material properties of the bridge. Continuous steel truss structures and steel bridge towers exhibit strong backscattering characteristics, enabling dense monitoring point distributions that finely depict periodic deformation characteristics. For cable-stayed and suspension bridges, deformation concentrates at the mid-span and attenuates toward both ends, with notable differences in cumulative deformation magnitude. Integrated temperature data analysis reveals pronounced thermal expansion and temperature shrinkage effects in continuous steel truss bridges, characterized by periodic deformation amplitudes increasing from the mid-span center to both ends. Thermal effects in cable-stayed and suspension bridges are predominantly localized at steel bridge towers.

Abstract:An intelligent prediction model integrating convolutional neural networks (CNN), Transformer, and an adaptive residual gating (ARG) mechanism was proposed, so as to accurately predict the tunneling speed of double-shield tunnel boring machines (TBMs). The model utilized a dual-layer CNN to extract local features of tunneling parameters from different perspectives, employed a Transformer to capture global features among these parameters, and incorporated the ARG mechanism to dynamically weight the extracted local and global features. Based on the monitoring data from historical tunneling segments, the mean, maximum, and minimum tunneling speeds of future tunneling segments were predicted. The model was validated using 927 sets of tunneling data extracted from a mountain rail transit project in Sichuan Province. The results indicate that the model achieves a mean square error of 0.07, a mean absolute error of 0.21, a root mean square error of 0.26, and a coefficient of determination of 0.86, all outperforming the other three comparative models. By assigning weights to the multi-source features extracted by the model, key information is emphasized, thereby improving prediction accuracy. This validates the effectiveness of the ARG mechanism in multi-source models and provides insights for similar structured models.

Abstract:To explore the water stability and durability of fly ash and slag geopolymer-solidified feldspar powder roadbed filler, a method combining experiments and numerical simulation was adopted. Based on the analysis of the influence of the mass ratio of fly ash and slag and the modulus of activator on the unconfined compressive strength of fly ash and slag geopolymer mortar and fly ash and slag geopolymer-solidified feldspar powder mixture, the fly ash and slag geopolymer-solidified feldspar powder mixture was prepared with the optimized mix ratio. The softening coefficient and dry-wet cycle tests were carried out, and the finite element model was used to analyze the influence of different dry-wet cycle times on the settlement of the fly ash and slag geopolymer-solidified feldspar powder roadbed under vehicle load. The results show that the dosage of geopolymer significantly affects the water stability and durability of the fly ash and slag geopolymer-solidified feldspar powder mixture. The softening coefficient increases from 0.77 to 0.81 with the increase in the dosage of geopolymer. The loss rate of strength of the fly ash and slag geopolymer-solidified feldspar powder mixture after 30 dry-wet cycles is 3.8%. The maximum settlement of the roadbed shows a nonlinear growth trend with the increase of dry-wet cycle times, and the settlement gradually decreases with the increase in depth. Due to the water absorption and expansion of clay minerals, which destroy the cementation between particles, the dosage of red soil has a significant impact on the cementation performance of the fly ash and slag geopolymer-solidified feldspar powder mixture.

Abstract:The overall architecture, logical architecture, and application architecture of the basic platform for intelligent waterways in seaports were constructed. Key technologies were researched, such as full-element intelligent sensing, high-precision hydrodynamic sediment simulation, and automatic operation of the forecast system. On this basis, the basic platform for intelligent waterways was developed with Tianjin Port as a case study. The platform achieved real-time intelligent sensing of meteorological, hydrological, sedimentary, and vessel-related elements, along with simulated calculations of critical environmental elements such as water level, flow velocity, and water depth for the next 72 h. The platform operated automatically on the Tianhe supercomputer, achieving intelligent and dynamic forecasting of the navigation environment. Based on the normal operation of the basic platform for intelligent waterways in Tianjin Port for over one year, the experiences and lessons learned from the construction of the basic platform, as well as the directions for supporting waterway maintenance, navigation guarantee, and other aspects of intelligent production and operation management services of waterways, were discussed.

Abstract:In order to reveal the influence of the geometric parameters of the curved channel on the navigable water flow conditions and ship motion characteristics, the typical curved channel section of the Yangtze-to-Huaihe River Water Diversion Project was taken as the research object. Moreover, the mathematical model of the water flow based on MIKE21 and the MMG ship manoeuvring model were established. The influence of the turning angle ( θ =60°, 90°, 120°), the bending radius ( R =540, 1 000,1 500 m), and the channel width ( B =60, 90 m) on the flow characteristics of the curved channel were analyzed in a systematic way, and the movement law of 1 000 t class cargo ship in the curved channel was quantified. The results show that the increase in the turning angle significantly aggravates the complexity of water flow; the backwater at the top of the bend at θ =120° rises by 0.012 m compared with that at 60°, and the maximum transverse gradient of the water surface shifts from the downstream 1/3 to the upstream 1/3; the main stream shifts to the convex bank. The increase of the bending radius improves the navigable conditions, and the difference in water level between the concave and convex banks at R =1 500 m decreases by 60% compared with that at R =540 m; the maximum flow velocity decreases from 0.55 m/s to 0.35 m/s. The reduction of the channel width significantly worsens the water flow conditions, and the maximum flow velocity and the water level difference between upstream and downstream increase significantly at B =60 m compared with B =90 m. As for the ship motion characteristics, the downstream ship is dominated by the water flow, and the traverse speed with the rudder angle and drift angle show a strong negative correlation; the upstream ship is dominated by its own power, and the correlation of each parameter is weak.

Abstract:Ship speed optimization is a key technical measure for reducing energy consumption and emissions, and considering the dynamic navigation environment is crucial for achieving precise optimization. The research progress in the ship speed optimization field was systematically reviewed from three perspectives: environmental factor impact mechanisms, environmental information perception technologies, and optimization models and algorithms. The models have evolved from static planning to dynamic optimal control methods capable of handling time-varying environments. Meanwhile, data-driven techniques have been widely applied to environmental forecasting and energy consumption modeling, enhancing optimization accuracy and efficiency. Despite significant progress in ship speed optimization research, challenges remain in the refined modeling of ship-environment coupling mechanisms, the robustness and efficiency of optimization algorithms, and multi-objective collaborative optimization. Future work should strengthen research on coupling mechanisms, develop intelligent optimization algorithms that integrate machine learning with traditional methods, and expand the optimization scope from single ships to fleet collaboration to promote the continuous development and application of ship speed optimization technology.

Abstract:In order to investigate the impact of three-dimensional effects on large-diameter monopiles used in offshore wind turbines, two-dimensional analytical model for pile-soil interaction was developed for large-diameter monopiles subjected to horizontal loading in various soil types, which considered only the radial and circumferential stress components distributed along the pile shaft. A comparative analysis was then conducted based on this model to investigate the effects of three-dimensional effects on the monopile’s lumped stiffness, the response of the pile shaft (horizontal displacement, rotation angle, shear force, and bending moment), and the distributed spring stiffness. The results indicate that three-dimensional effects significantly influence the force-controlled lumped stiffness, pile deformation, and shear response of monopiles, while their impact on position-controlled lumped stiffness and bending moment distribution is relatively minor. Neglecting three-dimensional effects yields larger pile shaft responses, leading to a more conservative design approach. The three-dimensional effect has little influence on the p-y spring; however, the m-φ spring, which accounts for three-dimensional effects, is the fundamental cause of differences in lumped stiffness and pile shaft response in monopiles. Therefore, the influence of the m-φ spring should be considered when designing large-diameter monopiles.

Abstract:In view of the poor robustness of feature extraction in underwater vision tasks such as image registration and 3D reconstruction caused by the decline in underwater optical image quality, an lightweight SuperPoint network was proposed. This network addressed the common challenges of detail degradation in underwater optical images, including color distortion and blurring. By leveraging an attention mechanism, it constructed a frequency-spatial dynamic attention fusion module that integrated feature information from both the frequency and spatial domains, thereby enhancing the network’s capability for feature extraction in underwater degraded images. A residual feature enhancement depthwise separable convolutional module was constructed to reduce model complexity and enhance the feature extraction ability of the network. Verification results demonstrate that, compared with the SuperPoint network, the network proposed in this paper achieves a 13.8% reduction in the number of parameters, an 8.0% decrease in computational complexity, and a 31.7% improvement in frame rate. Meanwhile, its repeatability rates under illumination variation and viewpoint variation are increased by 2.3% and 2.1%, respectively. In addition, the network exhibits excellent robustness in feature extraction in the performance evaluation of feature point detection and matching on the SQUID and FLSea datasets.