Dao-Yuan Tan

A deep understanding of rock wettability is essential for elucidating multiphase flow dynamics in porous rocks and for applications such as hydrocarbon reservoir prediction and the safety assessment of subsurface carbon or hydrogen storage. However, wettability alterations driven by the formation of electrical double layers (EDL) at rock–brine interfaces remain poorly understood, highlighting the need for a clear theoretical framework. In this study, we develop a simplified and explicit model for rock wettability based on EDL free‐energy, capturing the effects of interfacial electrostatics and brine compositions. Analysis of existing experimental data shows that the proposed model delivers superior accuracy, robustness, and practical applicability compared with the DLVO model. The model further reveals that nonpolar gas/brine/rock wettability depends on the square of the surface charge, surface potential, and pH, whereas oil/brine/rock contact angles are governed primarily by the absolute sum of the brine/oil and rock/brine zeta potentials. We also find that EDL compression plays a more dominant role in wettability alteration than interfacial tension under low‐salinity conditions. The non‐monotonic tendency of contact angle with salinity is divided into three regimes governed by the Jones‐Ray effect, EDL compression and changes in interfacial tension, respectively. By incorporating the Hofmeister effect, our framework further explains ion‐specific differences in wettability behavior. These findings provide a physics‐based understanding of rock wettability and offer new insights into multiphase flow through porous rocks, low‐salinity waterflooding, and CO ₂ /H ₂ geo‐storage.

Reinforcement corrosion is one of the major factors leading to premature deterioration and service-life reduction of reinforced concrete (RC) structures, particularly in chloride-rich environments. This study proposes a strain-based approach for quantitatively assessing the reinforcement corrosion using a quasi-distributed fiber Bragg grating (FBG) sensing system. A three-layer, four-direction FBG sensor array was embedded in a RC specimen to monitor corrosion-induced strain in real time. To establish quantitative relationships, seven companion specimens were subjected to accelerated corrosion in 5% NaCl solution under constant current. The corrosion degree was determined by mass losses and electrochemical corrosion potential using open-circuit potential (OCP) tests. Results show that the circumferential surface strain evolution follows three distinct stages: initial expansion, crack propagation and interfacial densification. Nonlinear calibration models were established to correlate strain with both mass loss and normalized corrosion potential, defining early-warning thresholds that distinguish initial corrosion from severe deterioration. Spatiotemporal analysis revealed a distinct non-uniform and localized evolution of surface strain, consistent with the heterogeneous corrosion morphology observed in specimens. The proposed dual-reference calibration framework enables distributed and real-time evaluation of reinforcement corrosion. It also provides an interpretable tool for durability assessment of RC structures.

Accurate characterization of deformation modulus of soils is essential for seismic hazard assessments and resilient geotechnical infrastructure design. Conventional techniques often yield discontinuous or biased modulus estimates due to sample disturbance and limited spatial coverage. For the first time, we introduce an in situ approach that exploits natural tidal forces coupled with distributed fiber‐optic sensing technology for continuous profiling of subsurface soil stiffness in a tidal river delta. Field measurements in the Yangtze Delta captured synchronous tidal‐induced pore pressure fluctuations and soil strain responses, demonstrating consistent linear stress–strain behavior from which depth‐resolved constrained moduli were derived. This tidal‐driven method effectively identifies subtle mechanical contrasts and meter‐scale interlayers with anomalously high or low stiffness, and enables continuous, long‐term monitoring of temporal and seasonal changes in soil stiffness. The proposed technique holds substantial promise for applications in sedimentological process studies and resilient infrastructure development within geologically dynamic coastal and deltaic environments.

Large-diameter gravity aqueducts are essential for water supply systems but face performance and safety risks from complex flow conditions. Effective flow-state monitoring is critical for hydraulic performance and infrastructure safety. However, conventional monitoring techniques like closed-circuit television (CCTV) inspection and ultrasonic sensing have limited real-time accuracy in distinguishing flow states. Here we show a real-time, distributed flow monitoring framework based on distributed acoustic sensing (DAS). A hierarchical clustering model, called DAS-Hydro HierarchyNet, was developed to analyze low-frequency acoustic signals and classify water flow states using a multi-level approach. The framework enables continuous flow monitoring along large aqueducts, overcoming point-based measurement limits. A 6 km case study in the Pearl River Delta demonstrates this approach’s feasibility and effectiveness. The results confirm that DAS combined with advanced AI classification enables accurate flow-state monitoring, water location detection, and flow velocity estimation, offering a scalable, intelligent solution for large-scale transmission monitoring.

Landslides cause significant economic losses and pose severe risks to human safety, making accurate predictions of landslide displacements essential for effective early warning systems. Many prediction models focus primarily on time series forecasting at individual monitoring points. Consequently, challenges are faced in capturing the spatial correlations of landslide displacements. In addition, the black‐box characteristics of the model limit the interpretability of the decision‐making process, which may make the prediction results difficult to use effectively for decision‐making. This paper proposes a deep learning model with interpretable features, which combines graph neural networks and gated recurrent units (GRUs) to capture the spatio‐temporal characteristics of landslide displacements. Using the Outang landslide in the Three Gorges Reservoir area as a case study, the effectiveness of the proposed model is validated through comparisons with long short‐term memory and GRU models. The results demonstrate that the spatio‐temporal graph neural network model provides accurate predictions of landslide displacement, exhibiting strong robustness and stability. The attention module enhances the interpretability of the model, revealing the influence of factors such as the groundwater table, reservoir water level, and temperature at different monitoring points. This work provides insights into advancing the understanding and forecasting of complex landslide behaviors.