Single-point Model Research Articles

A Multi-Point Correlation Model to Predict and Impute Earth-Rock Dam Displacement Data for Deformation Monitoring

Deformation is a critical indicator of structural integrity, and monitoring deformation is essential for ensuring the long-term safety of dams. However, characterizing the spatial correlations among dam deformation sequences and the similarity between displacements at various measurement points poses significant challenges when using single-point measurement models. Considering the limitations inherent in conventional models for processing spatiotemporal data, this paper introduces a novel model for predicting and imputing multi-point displacement monitoring data from earth-rock dams. The model integrates a convolutional neural network (CNN) with a bidirectional long short-term memory neural network (BiLSTM) while also incorporating an attention mechanism (AM). The CNN captures the spatial features of the displacement data, while the BiLSTM extracts temporal features. The AM assigns varying weights to input features, thereby enhancing the predictive accuracy of the model. The proposed model was experimentally validated, demonstrating its robust capabilities in data prediction and the imputation of missing data. The model provides a new strategy for forecasting dam deformation and addressing issues related to incomplete data.

ResGRU: A deep learning approach for settlement prediction in CFRD based on the spatiotemporal feature fusion method

Settlement is one of the most critical safety evaluation indicators for concrete face rock-fill dams. Deformation measurement data exhibit spatiotemporal correlation due to similar environmental factors and material properties. However, the current research has focused mainly on single-point prediction models, which ignore the relationship between settlement values and various parts of dams. To address these issues, the spatiotemporal correlation of settlements across multiple measurement points is considered and a deep learning approach based on spatiotemporal feature fusion is proposed. First, a density peak clustering algorithm categorizes monitoring points characterized by analogous deformation patterns. Then, a deep learning model combining residual neural networks (ResNet) and gated recurrent unit neural networks (GRU), i.e. ResGRU, is established. In ResGRU, ResNet is used to map the input data’s features to more complex high-dimensional patterns through convolution and obtain deep nonlinear spatial features. GRU is used to capture the temporal dependencies of data to obtain temporal features. Finally, the spatiotemporal features are fused through fully connected layers to predict settlements. The model is validated and compared with deep learning models using monitoring data collected from an actual concrete face rock-fill dam. The proposed approach demonstrates superior performance with an average RMSE and MAE of 0.352 and 0.255, respectively, and an average R2 of 0.988. The results show that the ResGRU model generally outperforms the other models. This approach provides an efficient and convenient approach for the settlement analysis of concrete face rock-fill dams.

Defect detection of grouting sleeve connection with energy ratio change in frequency domain

Controlling the grouting density is the key to ensure the construction quality of precast structures. Since the grouting sleeve is embedded in concrete, it is difficult to detect the grouting compactness of the sleeve. In this paper, the frequency domain analysis technique is used for non-destructive testing of concrete components with grouting defects. The collected ultrasonic testing signals are calibrated-denoised-decomposed-reconstructed. The damage degree of the grouting signal is evaluated and analyzed by quantifying the energy value of different frequency signals with two norms. Considering the effect of the random distribution of aggregates on the propagation of ultrasonic stress waves, a three-dimensional multi-point detection model and a two-dimensional plane single-point model based on piezoelectric effect are set up respectively. Results show that frequency energy distribution of the grout-deficient signal is concentrated to low-frequency range than that of grout-compact signal. When grouting defect accumulates to a certain extent, the main frequency may move from high-frequency band to lower-frequency band. Although the aggregates in different concrete components are randomly distributed, the test results of the two-dimensional plane model show that the validity of the defect quantitative index is 72%. The experimental tests results show that the method of using frequency energy transfer to lower-frequency band to determine grouting defects is effective, with a detection validity of 66.7%. Therefore, the non-destructive testing method for grouting sleeve compactness proposed in this paper can provide reference for engineering testing application.

A Grain Size Profile Prediction Method Based on Combined Model of Extreme Gradient Boosting and Artificial Neural Network and Its Application in Sand Control Design

SummaryThe grain size distribution along the well depth is of great significance for the prediction of the physical properties and the staged sand control design of the unconsolidated or weakly consolidated sandstone reservoir. In this paper, a new method for predicting the formation median grain size profile based on the combination model of extreme gradient boosting (XGBoost) and artificial neural network (ANN) is proposed. The machine learning algorithm and weighted combination model are applied to the prediction and analysis of reservoir grain size. The prediction model is improved from two aspects: First, the feature engineering of the XGBoost-ANN model is constructed by using the data of multiple sampling points on the logging curve. Second, the prediction accuracy is improved by increasing the dimension of the prediction model, that is, the XGBoost and ANN single-prediction models are weighted by the error reciprocal method and a combined prediction model containing multidimensional information is established. The research results show that compared with the single-point mapping model, the prediction accuracy of the multipoint mapping model considering the vertical geological continuity of the reservoir is higher than that of the single-point prediction and the coefficient of determination in the testing set can be improved up to 14.5%. The influence of different weighting methods on prediction performance is studied, and the prediction performance of original XGBoost, ANN, and XGBoost-ANN combined models is compared. The combined prediction model has a higher prediction accuracy than the single XGBoost and ANN models with the same number of sampling points and the coefficient of determination can be improved by up to 16.5%. The prediction accuracy and generalization ability of the XGBoost-ANN combined model are evaluated comprehensively. The combined model is used to design layered sand control of a well in an adjacent block, and good results have been achieved in production practice. This study provides a new method with high accuracy and efficiency for the prediction of unconsolidated sand median grain size profile.

An assessment of recent thermal radiation model performance for propane gas fire experiments

In this article, the performance of five radiation models is investigated, including one conventional radiation model, that is, single-point source model, as well as four recently developed models, that is, a weighted multipoint source model, a cuboid flame model, a multicuboid flame model and a multicylinder flame model. The models are assessed in terms of the height of a target above flame base, the horizontal distance of target from flame source, heat release rate and size of burner. The estimations of the theoretical models are compared with propane-based experimental data with heat release rate ranging from 100 to 300 kW and assessed through numerical and graphical analysis. From the results obtained, the single-point source model outperformed the recently developed radiation models in estimating radiant heat received by an object located at a distance from the fire source, albeit it does exhibit some sensitivity to modifications in input parameters.

Lateral Trajectory Tracking of Self-Driving Vehicles Based on Sliding Mode and Fractional-Order Proportional-Integral-Derivative Control

The tracking accuracy and vehicle stability of self-driving trajectory tracking are particularly important. Due to the influence of high-frequency oscillation near the sliding mode surface and the modeling error of the single-point preview model itself when using sliding mode control (SMC) for the trajectory tracking lateral control of self-driving vehicles, the desired tracking effect of self-driving vehicles cannot be achieved. To address this problem, a combination of sliding mode control and fractional-order proportional-integral-derivative control (FOPID) is proposed for the application of a trajectory tracking lateral controller. In addition, in order to compare with the trajectory tracking controller built using the single-point preview model, 12 real drivers with different levels of proficiency were selected for operational data collection and comparison. The simulation results and hardware-in-the-loop results show that the designed SMC + FOPID controller has high tracking accuracy based on vehicle stability. The trajectory accuracy based on SMC + FOPID outperforms the real driver data, SMC controller, PID controller, and model prediction controller.

A Method for Point Cloud Accuracy Analysis Based on Intensity Information.

Three-dimensional laser scanning has emerged as a prevalent measurement method in numerous high-precision applications, and the precision of the obtained data is closely related to the intensity information. Comprehending the association between intensity and point cloud accuracy facilitates scanner performance assessment, optimization of data acquisition strategies, and evaluation of point cloud precision, thereby ensuring data reliability for high-precision applications. In this study, we investigated the correlation between point cloud accuracy and two distinct types of intensity information. In addition, we presented methods for assessing point cloud accuracy using these two forms of intensity information, along with their applicable scopes. By examining the percentage intensity, we analyzed the reflectance properties of the scanned object's surface employing the Lambertian model. Our findings indicate that the Lambertian circle fitting radius is inversely correlated with the scanner's ranging error at a constant scanning distance. Experimental outcomes substantiate that modifying the surface characteristics of the object enables the attainment of higher-precision point cloud data. By constructing a model associating the raw reflectance intensity with ranging errors, we developed a single-point error ellipsoid model to assess the accuracy of individual points within the point cloud. The experiments revealed that the ranging error model based on the raw intensity is solely applicable to point cloud data unaffected by specular reflectance properties. Moreover, the devised single-point error ellipsoid model accurately evaluates the measurement error of individual points. Both analytical methods can be utilized to evaluate the performance of the scanner as well as the accuracy of the acquired point cloud data, providing reliable data support for various high-precision applications.

Updated Global Navigation Satellite System Observations and Attention-Based Convolutional Neural Network–Long Short-Term Memory Network Deep Learning Algorithms to Predict Landslide Spatiotemporal Displacement

Landslide displacement prediction has garnered significant recognition as a pivotal component in realizing successful early warnings and implementing effective control measures. This task remains challenging as landslide deformation involves not only temporal dependency within time series data but also spatial dependence across various regions within landslides. The present study proposes a landslide spatiotemporal displacement forecasting model by introducing attention-based deep learning algorithms based on spatiotemporal analysis. The Maximal Information Coefficient (MIC) approach is employed to quantify the spatial and temporal correlations within the daily data of Global Navigation Satellite System (GNSS) observations. Based on the quantitative spatiotemporal analysis, the proposed prediction model combines a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture spatial and temporal dependencies individually. Spatial–temporal attention mechanisms are implemented to optimize the model. Additionally, we develop a single-point prediction model using LSTM and a multiple-point prediction model using the CNN-LSTM without an attention mechanism to compare the forecasting capabilities of the attention-based CNN-LSTM model. The Outang landslide in the Three Gorges Reservoir Area (TGRA), characterized by a large and active landslide equipped with an advanced monitoring system, is taken as a studied case. The temporal MIC results shed light on the response times of monitored daily displacement to external factors, showing a lagging duration of between 10 and 50 days. The spatial MIC results indicate mutual influence among different locations within the landslide, particularly in the case of nearby sites experiencing significant deformation. The attention-based CNN-LSTM model demonstrates an impressive predictive performance across six monitoring stations within the Outang landslide area. Notably, it achieves a remarkable maximum coefficient of determination (R2) value of 0.9989, accompanied by minimum values for root mean squared error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE), specifically, 1.18 mm, 0.99 mm, and 0.33%, respectively. The proposed model excels in predicting displacements at all six monitoring points, whereas other models demonstrate strong performance at specific individual stations but lack consistent performance across all stations. This study, involving quantitative deformation characteristics analysis and spatiotemporal displacement prediction, holds promising potential for a more profound understanding of landslide evolution and a significant contribution to reducing landslide risk.

Analysis of Winter Anomaly and Annual Anomaly Based on Regression Approach

Studying the temporal and spatial dependence of ionospheric anomalies using total electron content (TEC) can provide an important reference for developing empirical ionospheric models. In this study, winter anomaly, annual anomaly, and the contributions of winter anomaly to annual anomaly were investigated during solar cycle 24 (2008–2018) by using the global ionosphere maps of the Center for Orbit Determination in Europe during the geomagnetic activity quiet period (Kp ≤ 5) based on a regression approach. Our detailed analysis shows the following: (1) Winter anomaly is more significant at 11:00–13:00 local time (LT), and the region of winter anomaly extends from North America to the Far East with increasing solar activity levels. (2) The minimum level of solar activity corresponding to the occurrence of winter anomaly was calculated at each grid point, which can provide a reference for single-point ionospheric modeling. (3) The annual anomaly reaches its maximum at 12:00 LT when the TEC in December is 34.4% higher than in June. (4) At 12:00 LT, the winter anomaly contributes up to 32% to the annual anomaly (at this time, the winter hemisphere contributes 57% to the annual anomaly).

A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4).

A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes.

Very short-term solar ultraviolet-A radiation forecasting system with cloud cover images and a Bayesian optimized interpretable artificial intelligence model

High-dose single exposures of long-wavelength ultraviolet-A (UV-A) radiation may trigger severe biological and skin tissue damage in humans and animals, as well as photosynthetic damage in plants. In humans, the highly abundant UV-A is also linked to an increased risk of skin cancer. This paper develops a new forecasting framework tailored for very short-term UV-A predictions using a Bayesian optimized ensemble Neural Basis Expansion Analysis for Interpretable Time Series (B-E-NBEATS) method, integrated with cloud cover predictors and the solar zenith angle. The design phase of the proposed model entails feature selection using an efficient neighborhood component analysis algorithm and hyperparameter tuning with a Bayesian optimizer. To further enhance the predictive performance and reliability, the B-E-NBEATS model is calibrated via uncertainty quantification by adopting an ensemble approach with single-point models trained on three key loss functions. The prescribed model generates interpretable forecasts of 20-minute ahead UV-A by empowering the ensemble members to learn seasonal and trend components in UV-A datasets. With respect to the four benchmark models, the statistical metrics and visual infographics of predicted and observed UV-A reveal a superior forecasting capability of the proposed B-E-NBEATS model. The model yields a comparatively high correlation coefficient of 0.910, 0.869, 0.933, and 0.900 for the spring, summer, autumn, and winter testing phases, respectively. The superior performance by the newly designed hybrid ensemble model ascertains its potential utility in UV-A monitoring and mitigating subsequent harmful exposure risk for the general public, and animal and plant life.

Important effects of polypropylene on migration of ciprofloxacin in groundwater

To investigate the effect of microplastics on the transport and retention of antibiotics in subsurface environment, polypropylene (PP) and ciprofloxacin (CIP) are selected as typical microplastic and antibiotic contaminant, respectively. The key factors and main control mechanisms are systematically investigated using a combination of adsorption experiments, column experiments and numerical model. The adsorption experiments indicate the largest adsorption capacity of CIP on PP is 1.03 mg·g−1. Simultaneously, the results of column experiments and numerical model suggest that PP can significantly affect the migration behavior and retention of CIP in porous media. CIP mobility is positively correlated to flow velocity, initial concentration of CIP and ionic strength (Na+, Ca2+ and Ba2+). Moreover, compared to monovalent cation (Na+), divalent cations (Ca2+, Ba2+) have a stronger enhancement effect on CIP mobility and a stronger inhibition effect on CIP retention in saturated porous media. The transport of CIP in saturated porous media containing PP is simulated by a single-point kinetic model, and the equations of the transport kinetic parameters of CIP are obtained through regression analysis. The research could help to gain insight into understanding the environmental behavior of antibiotic in the groundwater system, and provide scientific basis for the accurate prediction and assessment of environmental risk caused by microplastic and antibiotic in the groundwater system.

A Data Factor Study for Machine Learning on Heterogenous Edge Computing

As plants and animals grow, there are many factors that influence the changes that will affect how plants grow and how botanical experts distinguish them. The use of the Internet of Things (IoT) for data collection is an important part of smart agriculture. Many related studies have shown that remote data management and cloud computing make it possible and practical to monitor the functionality of IoT devices. In automated agriculture, machine learning intelligence is more necessary to use to automatically determine whether the correlation between learning factors influences plant growth patterns. In this research experiment, the relevant data are automatically collected through a detection device, and data modeling and computation are performed in an edge computing environment. At the same time, the data model is transmitted via the communication protocol, and another node is available for verification of the modeling and calculation results. The experimental results show that the single-point data-trained model is able to accurately predict the growth trend of the plants. In the case of verification of the second measurement point at a different space, the data model must be trained with more than two layers in order to improve the training results and reduce errors.

Combined Effects of Polyamide Microplastics and Hydrochemical Factors on the Transport of Bisphenol A in Groundwater

Polyamide (PA) and bisphenol A (BPA) are selected as typical microplastic and endocrine-disrupting chemicals in this study. The adsorption of BPA on the surface of PA and the effect of PA on the transport behavior of BPA in groundwater are systematically investigated using a combination of batch experiments, column experiments and numerical models. The results of scanning electron microscope (SEM) and Fourier transform infrared spectra (FTIR) show that the surface of PA particles is changed significantly after adsorption of BPA. The isothermal adsorption process of BPA can be simulated by the Langmuir model and the Freundlich model. Kinetic adsorption, on the other hand, can be fitted by a quasi-first-order adsorption model, and the adsorption results indicate that the maximum adsorption of BPA on PA reaches 13 mg·g−1. The results of the column experiments suggest that the mass recovery rate of BPA decreases with PA content, and increases with flow velocity, while initial concentration has no apparent influence on BPA transport. In addition, due to the hydrolysis of BPA, the mass recovery rate of BPA does not change with pH under conditions of pH < 10.2 and increases substantially to 94% when pH > 10.2. Moreover, Ca2+ has a significant inhibitory effect on the transport of BPA, while Na+ has no apparent influence on the transport of BPA. The transport process of BPA in porous media is simulated using a single-point kinetic model, and the fitted mathematical relationships for the variation of kinetic parameters with environmental factors are obtained by regression analysis.

On the value of expert knowledge in estimation and forecasting of solar photovoltaic power generation

Reliable estimates and forecasts of Photovoltaic (PV) power output form a fundamental basis to support its large-scale integration. This is recognized in literature, where a growing amount of studies deal with the development of PV power estimation and forecasting models. In particular, machine learning techniques received significant attention in the past decade. Yet, the importance of predictor variables are consistently ignored in such developments and as a result those models fail to acknowledge the value of including physics-based models. In this study we quantify the value of predictor variables for PV power estimation and forecasting, assess deficiencies in estimation and forecasting models, and introduce a number of pre-processing steps to improve the overall estimation or forecasting performance. To this end, we use common physical models to create so-called expert variables and test their impact on the performance of single-point and probabilistic models. In addition, we investigate the optimal selection of predictor variables for PV power estimation and forecasting. By means of a sensitivity analysis, the paper shows how the value of expert variables is affected by the tilt angle of the PV system. To allow for a deeper insight into the importance of predictor variables, two case studies in different climate regions are considered in the numerical evaluation.

A Throughput Fast Measurement Method for Two-Antenna Equipped Wireless MIMO Terminals

According to the 3rd Generation Partnership Project specification, a period of 8-12.8 hours is required to evaluate the multiple-input-multiple-output (MIMO) performance of a wireless terminal for a single frequency point and channel model combination. The following paper proposes a semi-simulation, semi-measurement-based MIMO throughput modeling scheme which can reduce the 8-12.8 hour measurement time to 40-60 minutes, corresponding to more than a ten times improvement of the test efficiency, without loss of the test accuracy.

Spatiotemporal clustering analysis and zonal prediction model for deformation behavior of super-high arch dams

Super-high arch dams are affected by similar environmental factors, and there is some spatial and temporal correlation among the deformation measurement points, while the deformation law varies in different parts of the arch dam. Using a single-point model for the deformation points or a single spatiotemporal model for the whole arch dam may ignore the spatiotemporal correlation of the measurement points or not consider the spatiotemporal variability between regions. In this study, a novel analytical model for multi-point deformation monitoring of super-arch dams is proposed. By constructing deformation similarity characteristics to characterize the deformation similarity degree, then using the clustering by fast search and find of density peaks method (CFSFDP) to obtain the measurement point regions with similar deformation laws, the deformation laws of different deformation regions are analyzed. Then, based on the spatiotemporal integrated autoregressive moving average model (STARIMA), a spatiotemporal deformation analysis model is constructed for the deformation of super-high arch dams. Through example analysis, the fitting and prediction performance of the model is better than that of the single measurement point model and the spatiotemporal hybrid model, which provides an efficient and convenient new method for deformation monitoring and analysis of super-high arch dams.

Dry Deposition Methods Based on Turbulence Kinetic Energy: Part 1. Evaluation of Various Resistances and Sensitivity Studies Using a Single-Point Model.

Different functions are used to account for turbulence strength in the atmospheric boundary layer for different stability regimes. These functions are one of the sources for differences among different atmospheric models' predictions and associated biases. Also, turbulence strength is underrepresented in some of the resistance formulations. To address these issues with dry deposition, firstly we take advantage of three-dimensional (3-D) turbulence information in estimating resistances by proposing and validating a 3-D turbulence velocity scale that is relevant for different stability regimes of boundary layer. Secondly, we hypothesize and validate that friction velocity measured by 3-D sonic anemometer can be effectively replaced by the new turbulence velocity scale multiplied by the von Karman constant. Finally, we (1) present a set of resistance formulations for ozone (O3) based on the 3-D turbulence velocity scale; (2) intercompare estimations of such resistances with those obtained using existing formulations; and, (3) evaluate simulated O3 fluxes using a single-point dry deposition model against long-term observations of O3 fluxes at the Harvard Forest (MA) site. Results indicate that the new resistance formulations work very well in simulating surface latent heat and O3 fluxes when compared to respective existing formulations and measurements at a decadal time scale. Findings from this research may help to improve the capability of dry deposition schemes for better estimation of dry deposition fluxes and create opportunities for the development of a community dry deposition model for use in regional/global air quality models.

Defect-mode-induced energy localization/harvesting of a locally resonant phononic crystal plate: Analysis of line defects

Phononic crystals that are artificially engineered structures have recently been introduced for vibration energy harvesting and sensing applications due to their unique features of band gaps and wave propagation control. Conventional energy harvesters made of phononic crystals are mainly designed for acoustic energy harvesting at a high-frequency vibration source (i.e., kHz levels). In this work, a defect-mode-induced energy harvester is designed for low-frequency excitations in the range of 0–300 ​Hz. The entire system that is a locally resonant phononic crystal (LRPC) plate with line defect patterns is consisted of elastic-wrapped core scatterers periodically embedded in epoxy resin. A two-dimensional (2D) three-component unit cell structure is arranged on the plate and the band gap property is analyzed to optimize the geometric parameters. Defects are then introduced to the LRPC plate with a 7 ​× ​7 point array for analysis. In addition, numerical and experimental studies are conducted to investigate the performance of energy harvesting when attaching a piezoelectric patch on the defect points. The results demonstrate that the proposed LRPC vibration energy harvester having a line defect mode (with continuous or alternate points) shows good performance in energy harvesting, in which a peak power output of 42.72 ​mV can be achieved under 10 ​m/s2 and 252 ​Hz. The performance is almost 6 times more than that of the single-point defect model under the same excitation conditions. The present LRPC-type energy harvester with a line defect mode is more suitable for energy harvesting for low-frequency and broadband conditions.

Safety Assessment of Channel Seepage by Using Monitoring Data and Detection Information

Seepage analysis has always been the focus of channel safety and stability research. Establishing a diagnosis method based on osmotic pressure monitoring data and combining the detection information to achieve osmotic safety is also an effective way to ensure the safety and stability of osmotic engineering. In this paper, a high-fill channel section of a water diversion project is taken as an example, and the study of osmotic safety is carried out by analyzing the engineering characteristics of linear engineering. High-fill channel sections were selected to study the temporal and spatial characteristics of various monitoring data reflecting the osmotic behavior of linear engineering; that is, these data reflect the time-varying regularity characteristics of the osmotic pressure value and the changing regularity of environmental variables. A single-point multifactor model of the monitoring data was established by establishing an evaluation index system, combining the monitoring index value method and the cloud model theory method according to the distribution law of the measured data and considering the uncertainty of the osmotic pressure data. Additionally, this model was integrated with the set pair analysis method to determine the monitoring data evaluation level; channel detection data information was collected, the abnormal detection of detection information was realized, and the monitoring data results were used to verify the detection results. In this way, an adaptive evaluation method reflecting the working behavior of high-filled channel sections is established, and a diagnostic technology for the safe operation of high-filled channel sections of linear engineering is proposed. The application results show that this method is suitable for engineering an osmotic safety assessment.