Evolution Of Depth Research Articles

Machine learning-based prediction of scour depth evolution around spur dikes

ABSTRACT Spur dikes are pivotal elements in river training, serving to mitigate the dynamic alterations induced by river degradation and aggradation. Traditionally, scour prediction models have relied on regression techniques, but the advent of soft computing and machine learning has offered opportunities for enhanced accuracy. This study focuses on the development of hybrid machine-learning models, including eXtreme Gradient Boosting (XGBoost), random forest (RF), convolutional neural network–long short-term memory, and artificial neural network, optimized using genetic algorithms to predict both temporal scour depth variation and maximum scour depth around the initial spur dike in a series. The analysis reveals strong associations between scour depth and various parameters such as non-dimensional time, spacing, channel width, time-averaged velocity, and densimetric Froude number. The models are established through an iterative process involving four predictor combinations. Results demonstrate XGBoost as the top-performing model, consistently exhibiting superior performance with R2 of 0.99, root mean square error (RMSE) of 0.012, and mean absolute error of 0.008 during training, and R2 of 0.96, RMSE of 0.044, and Kling–Gupta efficiency of 0.98 during testing for predicting temporal scour depth. For non-dimensional maximum scour depth, it reached R2 of 0.99 and RMSE of 0.005 in training, with R2 > 0.91 across all combinations during testing. Although RF showcases commendable accuracy, it slightly lags in precision compared to XGBoost.

Comparison of hybrid deep learning models for estimation of the time-dependent scour depth downstream of river training structures

Submerged weirs, mainly positioned downstream of bridges, play a key role in safeguarding against floods and long-term scour damage. However, the structural stability of these structures could be threatened by local scour holes. This study evaluates five deep learning algorithms—Deep Neural Networks, Convolutional Neural Networks (CNNs), Convolutional Extreme Gradient Boosting (CXGB), convolutional extremely randomized trees regression, and Self-Attention-based Convolutional Neural Network (SA-CNN) in predicting the evolution of scour depth. Using Hyperband and Bayesian optimization, the models were fine-tuned for maximum accuracy. Additionally, this study investigates the impact of two data splitting methods, including random pointwise sampling and case-wise sampling on model performance. Results indicate that the hybrid CXGB and the SA-CNN models outperform other models in terms of accuracy of the estimation of the time-dependent scour depth with R2 = 0.997 in pointwise and R2 = 0.878 in case-wise split strategies, respectively. This not only demonstrates the effectiveness of these sophisticated algorithms in time-dependent scour estimation but also clarifies the effects of various data sampling techniques on model performance. Finally, the contribution of features in provided estimations is discussed utilizing SHapley Additive exPlanations values. Results indicated that the time (T) and the ratio of the flow velocity to critical velocity U0/Uc had the greatest effect on the model outputs, while side slopes indicated a negligible effect on model output compatible with the physics of the problem.

A Framework for Assessing Ocean Mixed Layer Depth Evolution

AbstractThe ocean surface mixed layer plays a crucial role as an entry or exit point for heat, salt, momentum, and nutrients from the surface to the deep ocean. In this study, we introduce a framework to assess the evolution of the mixed layer depth (MLD) for realistic forcings and preconditioning conditions. Our approach involves a physically‐based parameter space defined by three dimensionless numbers: λs representing the relative contribution of the buoyancy flux and the wind stress at the air‐sea interface, Rh the Richardson number which characterizes the stability of the water column relative to the wind shear, and f/Nh which characterizes the importance of the Earth's rotation (ratio of the Coriolis frequency f and the pycnocline stratification Nh). Four MLD evolution regimes (“restratification,” “stable,” “deepening,” and “strong deepening”) are defined based on the values of the normalized temporal evolution of the MLD. We evaluate the 3D parameter space in the context of 1D simulations and we find that considering only the two dimensions (λs, Rh) is the best choice of 2D projection of this 3D parameter space. We then demonstrate the utility of this two‐dimensional λs − Rh parameter space to compare 3D realistic ocean simulations: we discuss the impact of the horizontal resolution (1°, 1/12°, or 1/60°) and the Gent‐McWilliams parameterization on MLD evolution regimes. Finally, a proof of concept of using observational data as a truth indicates how the parameter space could be used for model calibration.

Employing automated electrical resistivity tomography for detecting short- and long-term changes in permafrost and active-layer dynamics in the maritime Antarctic

Abstract. Repeated electrical resistivity tomography (ERT) surveys can substantially advance the understanding of spatial and temporal freeze–thaw dynamics in remote regions, such as Antarctica, where the evolution of permafrost has been poorly investigated. To enable time-lapse ERT surveys in Antarctica, an automated ERT (A-ERT) system is required, as regular site visits are not feasible. In this context, we developed a robust A-ERT prototype and installed it at the Crater Lake CALM-S site on Deception Island, Antarctica, to collect quasi-continuous ERT measurements. We developed an automated data processing workflow to efficiently filter and invert the A-ERT datasets and extract the key information required for a detailed investigation of permafrost and active-layer dynamics. In this paper, we report on the results of two complete year-round A-ERT datasets collected in 2010 and 2019 at the Crater Lake CALM-S site and compare them with available climate and borehole data. The A-ERT profile has a length of 9.5 m with an electrode spacing of 0.5 m, enabling a maximum investigation depth of approximately 2 m. Our detailed investigation of the A-ERT data and inverted results shows that the A-ERT system can detect the active-layer freezing and thawing events with high temporal resolution. The resistivity of the permafrost zone in 2019 is very similar to the values found in 2010, suggesting the stability of the permafrost over almost 1 decade at this site. The evolution of thaw depth exhibits a similar pattern in both years, with the active-layer thickness fluctuating between 0.20–0.35 m. However, a slight thinning of the active layer is evident in early 2019, compared to the equivalent period in 2010. These findings show that A-ERT datasets, combined with the new processing workflow that we developed, are an effective tool for studying permafrost and active-layer dynamics with very high resolution and minimal environmental disturbance. The ability of the A-ERT setup to monitor the spatiotemporal progression of thaw depth in two dimensions, and potentially in three dimensions, and to detect brief surficial refreezing and thawing of the active layer reveals the significance of the automatic ERT monitoring system to record continuous resistivity changes. An A-ERT monitoring setup with a longer profile length can investigate greater depths, offering effective monitoring at sites where boreholes are costly and invasive techniques are unsuitable. This shows that the A-ERT setup described in this paper can be a significant addition to the Global Terrestrial Network for Permafrost (GTN-P) and the Circumpolar Active Layer Monitoring (CALM) networks to further investigate the impact of fast-changing climate and extreme meteorological events on the upper soil horizons and to work towards establishing an early warning system for the consequences of climate change.

Experimental investigation on the mechanism of local scour around a cylindrical coastal pile foundation considering sloping bed conditions

Sea-crossing bridges are often subjected to local scour, significantly impacting the bearing capacity of the foundation system and potentially leading to bridge failures. This study presents experimental investigations of local scour around a cylindrical pile under different sediment slope conditions in unidirectional currents. Unlike previous studies that focused on horizontal sediment beds, this research explores the influence of inclined sediment beds, which reflect coastal and offshore topographies, which have some angles of inclination. Utilizing flume tests, the research investigates the evolution of scour depth, scour range, and the morphology of the sediment bed across various angles of inclination. The study also evaluates the effectiveness of a scour countermeasure involving rip-rap material around the pile on sloping beds. Specifically, a steeper inclination angle induces a faster flow velocity, resulting in increased scour depth at the upstream side of the pile and a reduced scour range at the downstream side due to backfilling. The countermeasure scenario reveals a significant reduction in both scour depth and range. This research provides analysis of the effects of inclined sediment beds, which reflect coastal topographies than horizontal ones, thereby providing insights for the design and protection of bridge foundations in diverse coastal conditions.

Impact of single and two successive droplets on a liquid pool

The interaction between droplets and a liquid pool is a widely observed fluid phenomenon with significant relevance to various industrial applications. This study numerically investigates the impact of both a single droplet and two successive droplets on a liquid pool with a fixed thickness. Particular emphasis was focused on the evolution of cavity depth and width during the deformation process. For single droplet impacts, the cavity depth exhibits linear growth with time in the early stage, consistent with predictions based on energy balance. This growth is independent of the Weber number (We) within the explored range of 96<We<345. Similarly, the cavity width shows weak dependence on the Weber number during early development, deviating and reaching a maximum width at later times. The maximum cavity width follows a power-law relationship with the Weber number, with a 0.5 exponent. In the case of successive droplet impacts with small initial separation, cavity depth also evolves linearly with time in the early stage but over an extended period. This prolonged growth is attributed to droplet merging, resulting in an effectively larger merged droplet. However, for successive droplets with large separation, the two linear growth stages exhibit intermittent interruptions due to the second impact occurring at a later time. The variation in cavity width due to different initial spacings between two successive droplets still exhibits similarity until a larger spacing causes a change in the rate of cavity width development.

An Evaluation Model for Node Influence Based on Heuristic Spatiotemporal Features.

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks.

High Temperature Intergranular Oxidation of Nickel Based Superalloy Inconel 718

AbstractIntergranular oxidation (IGO) of the Ni-based superalloy Inconel 718 was studied at 650 °C, 700 °C and 900 °C. The oxidized samples were characterized by X-ray diffraction and scanning electron microscopy. For all the studied temperatures, the external scale was mainly composed of Cr2O3, while the oxides along the grain boundaries were rich in Al and, to a minor extent, Ti. This was consistent with thermodynamic computations. The time evolution of the maximum depth of IGO was found to be parabolic with an apparent activation energy of 164 kJ/mol. The results of this study confirm with three temperatures that IGO kinetics can be described using an extension of the Wagner’s theory of internal oxidation, as recently suggested in the literature at 850 °C. According to this description, the mechanisms controlling the IGO kinetics of Inconel 718 are the aluminum diffusion in the alloy matrix and the oxygen diffusion along the interface between the alloy matrix and the oxidized grain boundary.

Exploring the phylogeny and depth evolution of cusk eels and their relatives (Ophidiiformes: Ophidioidei)

With 289 known species in 51 genera, the ophidiiform family Ophidiidae together with their relatives from the Carapidae (36 species in eight genera) of the same suborder Ophidioidei dominate the deep sea, but some occur also in shallow water habitats. Despite their high species diversity in the deep sea and wide bathymetric distributions, their phylogenetic relationships and evolution remain unexplored due in part to sampling difficulties. Thanks to the biodiversity exploratory program entitled “Tropical Deep-Sea Benthos” and joint efforts between Taiwan and French teams for sampling from different localities across the Indo-West Pacific over the last two decades, we are able to compile comprehensive datasets for investigations. In this study, 59 samples representing 36 of 59 known ophidioid genera are selected and used to construct a multi-gene dataset to infer the phylogenetic relationships of ophidioid fishes and their relatives. Our results reveal that the Ophidiidae forms a paraphyletic group with respect to the Carapidae. The four main clades of Ophidioidei resolved are the (1) clade comprising species from the subfamily Brotulinae; (2) clade that includes species in the genera Acanthonus and Xyelacyba; (3) clade grouping Hypopleuron caninum with species from the family Carapidae; and (4) clade containing the species in the subfamily Brotulotaenilinae, Neobythitinae (in part), and Ophidiinae. Accordingly, we suggest the following new revisions based on our results and proposed morphological diagnoses. The subfamily Brotulinae should be elevated to the family level. The genera Xyelacyba and probably Tauredophidium (unsampled in this study) should be included in the newly established family Acanthonidae with Acanthonus. The families Carapidae and Ophidiidae are re-defined. Our time-calibrated phylogenetic and ancestral depth reconstructions enable us to clarify the evolutionary history of ophidiiform fishes and infer past patterns of species distributions at different depths. While Ophidiiformes is inferred to have originated in shallow waters around 96.25 million years ago (Mya), the common ancestor to the Ophidioidei is inferred to have invaded the deep sea around 90.22 Mya, the dates coinciding with the global anoxic event of the OAE2. The observed bathymetric distribution patterns in Ophidioidei most likely point to the mesopelagic zone as the center of origin and diversification. This was followed by multiple events of depth transitions or range expansions towards either shallower waters or greater depth zones, which were likely triggered by past climate changes during the Paleogene-Neogene.

Contextualizing Polarimetric Retrievals of Boundary Layer Height Using State-of-the-Art Boundary Layer Profiling

Abstract Knowledge about the depth of the planetary boundary layer (PBL) is crucial for a variety of applications, but direct observations of PBL depth are spatiotemporally sparse. Recent studies have proposed using operational dual-polarization weather radars to observe the evolution of PBL depth by capitalizing on unique differential reflectivity (ZDR) signatures of Bragg scatter at the top of the PBL. While this approach appears promising and cost-effective, uncertainties remain about the representativeness of these estimates and how its efficacy may vary by geography and climatology. To address these outstanding uncertainties, this study compares collocated observations collected from two WSR-88D radars and two state-of-the-art mobile boundary layer profiling systems and evaluates the proposed methodology over the full diurnal cycle. Results indicate good overall correspondence between the profiling- and radar-based PBL depth estimates, with an abrupt divergence during the early evening transition and large discrepancies overnight. Relatively large root-mean-square-deviations (RMSDs) coupled with small biases match expectations when comparing spatially averaged data with point observations during PBL growth, which capture frequent fluctuations. A qualitative examination of the radar data reveals signatures of elevated residual layers, clouds, and ground clutter, all of which can obfuscate the desired surface-based PBL signal but which may have their own utility. The prominence of the Bragg scatter signal is found to be correlated with the observed moisture gradient at the top of the PBL, reflecting climatological variability that should be considered. These findings motivate further work to improve the automated detection of Bragg scatter layers from polarimetric radar data. Significance Statement Knowledge of the height of the planetary boundary layer matters for weather forecasting, air quality, and renewable energy production. Currently, boundary layer height measurements are taken at select locations twice a day. However, a method to use the existing national network of polarimetric weather radars for this purpose has been proposed. This work evaluates this method against specialized boundary layer measurements. The results show that the method is generally reliable during the daytime and could be used for a variety of applications including climatologies and model evaluation. There remain a number of situational caveats, including residual turbulence, clouds/precipitation, ground clutter, and certain meteorological environments, that may require modification of the approach and need to be considered in future work.

Probabilistic deep learning prediction of natural carbonation of low-carbon concrete incorporating SCMs

This study develops a probabilistic neural network (PNN) based on 2165 laboratory data incorporating mixture constituents and environmental conditions parameters to estimate the natural carbonation depth of concrete. The high prediction accuracy and certainty level of the model, achieving a testing R2 of 0.95 and negative log-likelihood of 1.82, enabled performing numerical simulations of carbonation depth and rate evolution of 576 binary and ternary blends for 12 years of exposure under different climatic conditions of two cities, Toronto and Vancouver. Mixtures containing ordinary Portland cement (OPC), fly ash, ground granulated blast furnace slag (GGBFS), and limestone calcined clay with water-to-binder ratios of 0.35–0.55, OPC replacement levels of 0–0.6, and binder contents of 350–500 kg/m3 were studied. Results indicated that OPC-GGBFS binary binders and OPC-GGBFS-fly ash ternary binders showed the lowest natural carbonation rates of 1.03–4.78 and 1.10–3.76 mm/year0.5, respectively. The uncertainty quantification of model's predictions can facilitate informed and confident decision-making for designing durable and sustainable concrete mixtures.

Scour at the rear side of rubble mound revetments due to random wave overtopping: Laboratory experiments

This study presents the physical model experiments on the wave overtopping induced bed level changes at an unprotected rear side of a rubble mound revetment with a crown wall. In the experiments, the wave conditions, the backfill material size, and the backfill depth (vertical distance from the crest level to the rear side bed level) are varied. Two mobility parameters, namely the densimetric overtopping discharge and the relative fall velocity, are introduced to represent the initiation of motion and suspension of the backfill material, respectively. The temporal evolution of scour depth at the rear side is related to the mobility parameter, the number of waves and the backfill depth utilizing regression analyses. The effect of the backfill depth on the scour is found very limited for the available data set. However, the time scale of the scour is found to be related with the backfill depth only. For the present data set, it varied within a range of 1500–16000 number of waves approximately, with an average of 5600 waves. An equilibrium scour profile is reached 3 to 4 times the time scale. The geometry of the scour profile is expressed in terms of the scour depth, based on the equilibrium scour profile data.

Spatio-temporal characterisation of local scour around a circular bridge pier in cohesive soil with different clay/silt ratio

This paper presents new data on scour around a vertical pier in cohesive soils. It specifically focuses on the temporal evolution of local scour hole around the pier with increasing the clay/silt ratio content. Series of flume tests were performed using soil mixtures made with 85% fine sand and 15% fines. The fines fraction used in the soil mixtures includes silt, kaolinite clay, and various combinations of both with different proportions. The water velocity during the tests was kept constant and slightly above the erosion critical velocity of the fine sand used in the mixtures. A 3D laser scanner was employed to continuously record the geometry of the scour hole. Results show that increasing the Kaolinite clay in the fine fraction reduces significantly the scour. The mixture with a range around 7.5–10% of kaolinite clay fines content provides the threshold composition for cohesive soil behaviour effect in scouring process, recognized through scour pattern change. As the clay content increases, notable reductions are observed in the dimensions of the scour hole. Furthermore, the temporal evolution of maximum scour depth measured in flume was in agreement with the results from SRICOS-EFA (Scour Rate in COhesive Soil - Erosion Function Apparatus) method.

The Distribution and Evolution of Groundwater Level Depths and Groundwater Sustainability in the Hexi Corridor over the Last Five Years

Groundwater overexploitation for agricultural irrigation is prone to lead to numerous ecological concerns. This study delved into the present distribution and recent trend of groundwater levels in the plain areas of the Hexi Corridor in Northwest China according to the groundwater level depth (GWD) data from 264 monitoring wells in the Shiyang River Basin (SYB) and 107 in the Shule River Basin (SLB), recorded annually in April from 2019 to 2023. The key findings include the following: (1) Over the five-year span, the SYB’s GWD experienced change rates (CRs) ranging from −12.17 to 9.11 m/a (average: −0.13 m/a), with the number of monitoring wells showing increased and decreased GWDs accounting for 50% and 50%, respectively. By contrast, the SLB’s GWD exhibited CRs ranging from −1.87 to 2.06 m/a (average: 0.01 m/a), with the number of monitoring wells showing increased and decreased GWDs accounting for 52% and 48%, respectively; (2) the Wuwei (CR = 0.09 m/a) and Changning (0.58 m/a) basins in the SYB and the Yumen (0.06 m/a), Guazhou (0.05 m/a), and Huahai (0.03 m/a) basins in the SLB, witnessed rising groundwater levels. In contrast, the Minqin Basin (0.09 m/a) in the SYB and the southern Dunhuang Basin (0.04 m/a) in the SLB witnessed declines in the groundwater levels; (3) The groundwater sustainability assessment showed that the groundwater is still extremely unsustainable. This study’s insights are instrumental in targeted treatment, as well as the preparation and adjustment of sustainable groundwater protection strategies.

Effect of grain boundary on scratch behavior of polycrystalline copper

The anisotropy in the elastoplastic response of crystalline solids influences substantially their scratch resistance at the microscale. While grain boundaries typically act as obstacles to the motion of dislocations at the grain level, the scratch behavior in the vicinity of the grain boundary is influenced by other factors such as crystallographic orientation and pile-up topography. We performed a systematic set of nano-scratch simulations using the mechanism-based strain gradient crystal plasticity to study the evolution of scratch force and depth near grain boundaries. To validate our model, we conducted a nano-scratch test on a polycrystalline copper sample and compared the scratch depth profile for a specific grain boundary with the simulated result, where a good qualitative agreement was achieved. Our simulations showed that the scratch depth, force, and hardness vary between the corresponding values for the individual constitutive grains, and the variation decreases by reducing the grain size. Additionally, we analyzed the scratch response in terms of the pile-up topography and subsurface stresses and distinguished different regimes when the indenter approaches and scratches across the boundary. Our findings offer a new direction to optimize the scratch resistance of polycrystalline metals by tailoring the size and orientation of grains near the surface. Additionally, it introduces scratching as a robust material characterization method.

Experimental study on the optimal spur dike shape under downward seepage

ABSTRACT The successful stabilization of riverbanks and the construction of sustainable foundations, extensive knowledge of the scour process and accurate assessment of scour depth around spur dikes are required. This study focuses on optimizing the spur dike shape to manage flow turbulence effectively and minimize local scour while considering the impact of downward seepage. The investigation was conducted within an experimental flume featuring a mobile bed under a clear water regime and the provision for downward seepage. The investigation concentrated on three distinct spur dike shapes (T, L, and rectangular) under different seepage velocities (V S1 and V S2) and a no-seepage condition. The current study also illustrates changes in bed morphology, temporal evolution and longitudinal profile of scour depth with and without downward seepage. The results reveal that the downward seepage intensifies the motion of sediment particles, and more seepage leads to an escalated particle detachment, resulting in deeper scour depressions. The T-shaped spur dikes exhibited the lowest scour depths compared to the other shapes, both with and without seepage. The negative impact on velocity and RSS magnitude was observed at the near channels bed where maximum scour depth was achieved. This phenomenon is closely linked to horseshoe vortices’ formation and current generation. These factors play a pivotal role in the detachment of particles from the base of the structure.

Using Moving Grids Method for Water Depth Evolution study in the Coupled System Saint Venant-Exner Equation

In this paper, the moving grids method based on the method of lines is used to simulate the Saint Venant-Exner coupled system where we focus on the water depth evolution. To illustrate the efficiency and accuracy property of the present method, we compare the computed solutions with a reference one obtained using a very fine mesh. The calculations give good agreement between reference solutions and numerical solutions.

Thermal stress coupling mechanism during nanosecond impulse delay for the synergistic study of continuous-nanosecond combined laser removal of the rubber layer on a concrete surface.

Based on the principle of laser ablation and elastic vibration effect, a model of continuous nanosecond combined laser removal of rubber marks on a concrete surface was established. The model can explain the evolution of temperature, stress, and removal depth on time and laser energy density during laser cleaning. The results show that the theoretical adsorption force between the rubber layer and the concrete base is approximately 3.88×10-9 N. The continuous laser cleaning threshold is 561.31J/c m 2. In the combined laser, the continuous laser is 534.41J/c m 2, and the nanosecond laser is 0.35J/c m 2. As the delay time between the 2ns laser beams increases, the maximum peak in the temperature curve gradually decreases. The optimal cleaning delay was obtained as Δ t=0.65S. The peak temperature at the characteristic position (0µm, 0µm) is 592.13K, which is lower than the vaporization temperature of the rubber layer. The thermal stress values generated at this characteristic position exceed the adsorption stress values, indicating that the elastic removal mechanism is the main removal mechanism at the junction between the rubber layer and the concrete substrate.

Strength characteristics and carbonation depth evolution of modified magnesium slag based solid waste storage backfill materials

CO2 storage is one of the effective ways to achieve China's 30·60 dual carbon goal. The study of the interaction between CO2 and storage backfill material lays a foundation for the long-term stability and gas tightness of CO2 storage. Based on the modified magnesium slag-based solid waste backfill material constructed by the storage, in order to explore the strength characteristics and carbonation depth evolution of the CO2 storage backfill material, the interaction between CO2 and the storage backfill material was analyzed from the perspectives of uniaxial compressive strength, carbonation depth test, pore structure distribution, microstructure and phase composition. The results show that carbonation can significantly improve the compressive strength of the storage backfill material, and the carbonation depth increases with the increase of carbonation time, and increases slowly after complete carbonation. The later the initial carbonation age, the faster the growth rate of compressive strength and carbonation depth, and the greater the growth rate. After high concentration CO2 curing, the diffusion rate and carbonation degree of the storage backfill material gradually decreased from the outside to the inside, and the calcium carbonate content also gradually decreased. The carbonation can significantly reduce the porosity of the sample, gradually transform from large pores to small pores, and the pore size distribution and pore structure are significantly improved. Therefore, the later the initial carbonation age is, the more beneficial the carbonation effect is for the long-term stability and air tightness development of the backfill material of the CO2 storage, which provides important theoretical guidance for the construction and storage method of the CO2 storage.

Remaining Useful Life Estimation of Hollow Worn Railway Vehicle Wheels via On-Board Random Vibration-Based Wheel Tread Depth Estimation.

The problem of remaining useful life estimation (RULE) of hollow worn railway vehicle wheels in terms of remaining mileage via wheel tread depth estimation using on-board vibration signals from a single accelerometer on the bogie frame is presently investigated. This is achieved based on the introduction of a statistical time series method that employs: (i) advanced data-driven stochastic Functionally Pooled models for the modeling of the vehicle dynamics under different wheel tread depths in a range of interest until a critical limit, as well as tread depth estimation through a proper optimization procedure, and (ii) a wheel tread depth evolution function with respect to the vehicle running mileage that interconnects the estimated hollow wear with the remaining useful mileage. The method's RULE performance is investigated via hundreds of Simpack-based Monte Carlo simulations with an Attiko Metro S.A. vehicle and many hollow worn wheels scenarios which are not used for the method's training. The obtained results indicate the accurate estimation of the wheels tread depth with a mean absolute error of ∼0.07 mm that leads to a corresponding small error of ∼3% with respect to the wheels remaining useful mileage. In addition, the comparison with a recently introduced Multiple Model (MM)-based multi-health state classification method for RULE, demonstrates the better performance of the postulated method that achieves 81.17% True Positive Rate (TPR) which is significantly higher than the 45.44% of the MM method.