Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Monitoring indexes of concrete dam based on correlation and discreteness of multi-point displacements.

ABSTRACT: Monitoring indexes are significant for real-time monitoring of dam performance in ensuring safe and normal operation. Traditional methods for establishing monitoring indexes are mostly focused on single point displacements, and rational monitoring indexes based on multi-point displacements are rare. This study establishes monitoring indexes based on correlation and discreteness of multi-point displacements. The proposed method is applicable when several monitoring points show strong correlation. In this study, principal component analysis (PCA) was introduced for preprocessing the observations of multi-point displacements. Correlation and discreteness of multi-point displacements were extracted and constructed. The correlation and discreteness parts described the integral and local variance of the displacement field. On this basis, the annual maximum values of the correlation and discreteness parts were selected and their probability density functions (PDF) could be generated by employing the principle of maximum entropy. PDF was constructed using maximum entropy method and was least subjective because it barely provided the moment information of the observations. The multi-point monitoring indexes were then determined by the typical low probability method based on the obtained PDFs. Finally, the proposed method was analyzed using a practical engineering and was verified in terms of its feasibility.

SUBMITTER: Qin X

PROVIDER: S-EPMC6049955 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Influence of Demographic and Health Survey Point Displacements on Raster-Based Analyses.

Project description:With this paper we explore the sensitivity of study results to spatial displacements associated with Demographic and Health Survey (DHS) data in research that integrates ancillary raster data. Through simulation studies, we found that the impact of DHS point displacements on raster-based analyses can be moderated through the generation of covariates representing average values from neighborhood buffers. Additionally, raster surface characteristics (i.e., spatial smoothness) were found to affect the extent of bias introduced through point displacements. Although simple point extraction produced unbiased estimates in analyses involving smooth continuous surfaces, it is not recommended in analyses that involve categorical raster surfaces.

| S-EPMC5993452 | biostudies-literature

Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses.

Project description:We evaluate the impacts of random spatial displacements on analyses that involve distance measures from displaced Demographic and Health Survey (DHS) clusters to nearest ancillary point or line features, such as health resources or roads. We use simulation and case studies to address the effects of this introduced error, and propose use of regression calibration (RC) to reduce its impact. Results suggest that RC outperforms analyses involving naive distance-based covariate assignments by reducing the bias and MSE of the main estimator in most settings. Proposed guidelines also address the effect of the spatial density of destination features on observed bias.

| S-EPMC4946438 | biostudies-literature

Influence of Demographic and Health Survey Point Displacements on Point-in-Polygon Analyses.

Project description:We use Demographic and Health Survey (DHS) data to evaluate the impact of random spatial displacements on analyses that involve assigning covariate values from ancillary areal and point feature data. We introduce a method to determine the maximum probability covariate (MPC), and compare this to the naive covariate (NC) selection method with respect to obtaining the true covariate of interest. The MPC selection method outperforms the NC selection method by increasing the probability that the correct covariate is chosen. Proposed guidelines also address how characteristics of ancillary areal and point features contribute to uncertainty in covariate assignment.

| S-EPMC4950678 | biostudies-literature

Deep Learning-Based Diagnosing Structural Behavior in Dam Safety Monitoring System.

Project description:Collecting a myriad of prototype data through various types of monitoring sensors plays a virtual important role in many aspects of dam safety such as real-time grasp of safety state, exposure of hidden dangers, and inspection design and construction. However, the current methods of prediction are weak in the long-term sequence of nodes with missing and abnormal error value. Moreover, the limitation caused by the apparatus, environmental factors, and network transmission can lead to the deviation and inconsistency of diagnosis and evaluation of local region. In this paper, we consider the correlation of data on nodes in the entire monitoring network. To avoid the deviation caused by noise and missing value in the single-node data sequence, we calculate the correlation between the multiple sequences. A single-node assessment model based on multiple relevant sequence (SAM) is proposed to improve the accuracy of single node assessment. Given the different nodes of a local region have varying impacts on the evaluation results, a local region evaluation algorithm based on node credibility (LREA) is presented to model the credibility of nodes in order to alleviate inconsistent evaluation results in the local region of dam. LREA can assess the dam's operation state by considering the variations in credibility and multiple nodes coordination. The experimental results illustrate the LREA can reveal the trends of the monitoring values change in a timely and accurate way, which can elevate the accuracy of evaluation results of dam safety.

| S-EPMC7915524 | biostudies-literature

Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System.

Project description:Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam's working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.

| S-EPMC7249191 | biostudies-literature

Monitoring Inflow Dynamics in a Multipurpose Dam Based on Travel-time Principle

Project description:Understanding inflow dynamics in a dam lake forms the basis for optimal dam operation and management practices. However, methods pertaining to adequately determining negative inflows and addressing them, as well as quantifying uncertainties in dam inflow, have been scarcely investigated. In this study, the inflow was observed using two pairs of fluvial acoustic tomography (FAT) systems placed diagonally in a dam lake, forming a crossed-shaped pattern. The “travel-time” principle is the primary approach for measuring the inflow by FAT. The novelty of this study is in discussing the inflow characteristics within a slow water-flow environment monitored by FAT. Based on the reciprocal sound transmission, we upgraded an equation to estimate the flow direction; this newly proposed generalized equation can be used in a fluctuating flow environment. We also discussed the sound propagation characteristics for slow flow velocities. Finally, we demonstrated that a small inaccuracy in the acoustic signal, even by a sub-millisecond, can cause significant errors in measurements. One of the novel findings of this study is the detection of internal waves using the improved flow direction equation and acoustic travel-time records. Overall, this study presents a promising approach for inflow measurements under extremely slow flow conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s11269-022-03161-w.

| S-EPMC9067559 | biostudies-literature

A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors.

Project description:Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly.

| S-EPMC9573161 | biostudies-literature

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming.

Project description:Rice husk ash (RHA) is a significant pollutant produced by agricultural sectors that cause a malignant outcome to the environment. To encourage the re-use of RHA, this work used multi expression programming (MEP) to construct an empirical model for forecasting the compressive nature of concrete made with RHA (CRHA) as a cement substitute. Thus, the compressive strength of CRHA was developed comprising of 192 findings from the broad and trustworthy database obtained from literature review. The most significant characteristics, namely the specimen's age, the percentage of RHA, the amount of cement, superplasticizer, aggregates, and the amount of water, were used as input for the modeling of CRHA. External validation, sensitivity analysis, statistical checks, and Shapley Additive Explanations (SHAP) analysis were used to evaluate the models' performance. It was discovered that the most significant factors impacting the compressive strength of CRHA are the age of the concrete sample (AS), the amount of cement (C) and the amount of aggregate (A). The findings of this study have the potential to increase the re-use of RHA in the production of green concrete, hence promoting environmental protection and financial gain.

| S-EPMC9181226 | biostudies-literature

Global sensitivity analysis of parameters based on sPCE: The case study of a concrete face rockfill dam in northwest China.

Project description:To effectively identify the key material parameters of different zones of concrete face rockfill dams and improve the efficiency of parameter optimization, a global sensitivity analysis method of parameters based on sparse polynomial chaotic expansion (sPCE) is proposed in this paper. The latin hypercube sampling method is used to select multiple groups of material parameters, and then finite element method is used to calculate the displacement of dam characteristic nodes in dam body. On this basis, the displacement is expanded by sPCE, and the polynomial basis function is reconstructed by orthogonal matching pursuit to improve the construction and analysis efficiency of the proxy model. According to the chaos coefficients, Sobol' indices are calculated to evaluate the influence of the material parameters and their interaction on different displacements of the dam. The results show that the sPCE model can accurately simulate dam displacement and its statistical characteristics with a relatively small sample size. The sensitivity of the same parameter has spatial variability, and under the influence of parameter levels and spatial distribution of different materials, the parameter sensitivity ranking of different zones has certain differences. The proposed method provides a new reference to sensitivity analysis and uncertainty analysis for practical engineering.

| S-EPMC10470912 | biostudies-literature

Sensitive real-time monitoring of refractive indexes using a novel graphene-based optical sensor.

Project description:Based on the polarization-sensitive absorption of graphene under conditions of total internal reflection, a novel optical sensor combining graphene and a microfluidic structure was constructed to achieve the sensitive real-time monitoring of refractive indexes. The atomic thickness and strong broadband absorption of graphene cause it to exhibit very different reflectivity for transverse electric and transverse magnetic modes in the context of a total internal reflection structure, which is sensitive to the media in contact with the graphene. A graphene refractive index sensor can quickly and sensitively monitor changes in the local refractive index with a fast response time and broad dynamic range. These results indicate that graphene, used in a simple and efficient total internal reflection structure and combined with microfluidic techniques, is an ideal material for fabricating refractive index sensors and biosensor devices, which are in high demand.

| S-EPMC3510467 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data