☆ 4.7 Article

Dam deformation early warning model based on cluster analysis and spatiotemporal data fusion

MEASUREMENT (2022)

Related references

Note: Only part of the references are listed.

Article Engineering, Biomedical

An independent central point OPTICS clustering algorithm for semi-supervised outlier detection of continuous glucose measurements

Yuhang Zhao et al.

Summary: In this paper, a semi-supervised outlier detection method based on a density-based clustering algorithm is proposed for anomaly detection of glucose concentration measurements. The method accurately detects outlier data, improves clustering performance using an optimization function, and shows higher effectiveness and superiority compared to existing methods.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Add to Collection

Article Engineering, Multidisciplinary

A comprehensive evaluation method for concrete dam health state combined with gray-analytic hierarchy-optimization theory

Hao Gu et al.

Summary: This article focuses on the comprehensive evaluation of the concrete dam health status, introducing an analysis method based on the fusion of different monitoring types and degree weight influence. By proposing an optimization division method using gray theory and analytic hierarchy process, a method based on multiple monitoring quantities is established to effectively evaluate the concrete dam health state. The validation of the established theory is demonstrated through a discussion of an existing concrete dam.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2022)

Add to Collection

Article Engineering, Civil

Delineation of isotopic and hydrochemical evolution of karstic aquifers with different cluster-based (HCA, KM, FCM and GKM) methods

Effat Eskandari et al.

Summary: In this study, a combination of isotopic and hydrogeochemical data was clustered using four different clustering analysis methods to improve protection, sustainability, and water resource management in a vulnerable karstic region. The results showed that the fuzzy C-mean and genetic K-means methods had the best performance, and five distinct categories of water resources were identified in the PV-JR region.

JOURNAL OF HYDROLOGY (2022)

Add to Collection

Article Engineering, Multidisciplinary

Deformation similarity characteristics-considered hybrid panel model for multi-point deformation monitoring of super-high arch dams in operating conditions

Guang Yang

Summary: This research develops a novel hybrid model for multi-point deformation monitoring of super-high arch dams in operating conditions. Observation point groups with similar deformation regularities can be produced using weighted distances and hierarchical clustering. The HHST panel model is proposed and optimized using the improved AFS algorithm, and confidence ellipsoid criteria are established for identifying abnormal deformations.

MEASUREMENT (2022)

Add to Collection

Article Construction & Building Technology

Spatio-temporal data mining method for joint cracks in concrete dam based on association rules

Bo Chen et al.

Summary: Structural health monitoring is widely used in dam engineering to uncover hidden safety information and diagnose safety status. This paper introduces a new perspective by integrating data mining into SHM, focusing on revealing association rules in COD monitoring data. By systematically investigating association rules, the study quantifies the association relation with specific indexes and applies the methods to the Baishan concrete gravity-arch dam in northeastern China, showing consistent results with dam safety monitoring theories.

STRUCTURAL CONTROL & HEALTH MONITORING (2022)

Add to Collection

Article Engineering, Civil

Dynamic Stacking ensemble monitoring model of dam displacement based on the feature selection with PCA-RF

Wei Lei et al.

Summary: This paper proposes a method to improve the prediction accuracy of monitoring models, including feature selection, hyper-parameter optimization, and model ensemble. By eliminating the collinearity problem between features and optimizing model hyperparameters, the prediction accuracy of the monitoring model is improved, and dynamic monitoring indexes are realized using the confidence interval estimation method.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2022)

Add to Collection

Article Computer Science, Software Engineering

An improved agglomerative hierarchical clustering anomaly detection method for scientific data

Peng Shi et al.

Summary: The study proposed an improved hierarchical clustering method for anomaly detection, aiming to improve the effectiveness and stability of anomaly detection. By dynamically adjusting the clustering number and selecting the optimum clustering distance mode, manual effort is reduced and better performance is achieved.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2021)

Add to Collection

Article Computer Science, Information Systems

An agglomerative hierarchical clustering algorithm for linear ordinal rankings

Nana Liu et al.

Summary: This paper introduces a new method for clustering LOR information using the AHC algorithm, by extending existing distance measure methods and simplifying aggregation methods. A numerical case study is presented to illustrate the algorithm's usage, and discussions are made on the features of the algorithm.

INFORMATION SCIENCES (2021)

Add to Collection

Article Engineering, Multidisciplinary

Prediction of arch dam deformation via correlated multi-target stacking

Siyu Chen et al.

Summary: The study introduces a method for multi-target prediction that utilizes the internal relationships between target variables to provide reliable predictions simultaneously. Using Jinping-I arch dam as a case study, the proposed method demonstrates higher accuracy and anomaly detection capability compared to existing models.

APPLIED MATHEMATICAL MODELLING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Dynamic early-warning model of dam deformation based on deep learning and fusion of spatiotemporal features

Wenlong Chen et al.

Summary: The study proposes a novel dynamic early-warning model for dam deformation, utilizing deep learning and spatiotemporal features fusion. The model integrates POD and improved DKELM to extract spatial principal components and enhance dynamic updating ability through robust deep nonlinear mapping, while also formulating and updating deformation early-warning indicators considering randomness and fuzziness with the help of the cloud model's concept transformation and fusion rule.

KNOWLEDGE-BASED SYSTEMS (2021)

Add to Collection

Article Engineering, Civil

Spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series

Bowen Wei et al.

Summary: A spatiotemporal hybrid model for concrete arch dam deformation monitoring has been proposed, which introduces space coordinate and utilizes PSO-SVM algorithm to analyze and predict residual series, achieving comprehensive reflection of multi-point deformation monitoring.

ENGINEERING STRUCTURES (2021)

Add to Collection

Article Construction & Building Technology

Risk probabilistic assessment of ultrahigh arch dams through regression panel modeling on deformation behavior

Erfeng Zhao et al.

Summary: The study proposes a method for monitoring the deformation of arch dams based on a regression panel model, establishing an innovative functional relationship between measured values and real-time risk probabilistic function, providing a new approach to arch dam safety assessment.

STRUCTURAL CONTROL & HEALTH MONITORING (2021)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Dendrograms-based disclosure method for evaluating cluster analysis in the IoT domain

Roman Kaminskyy et al.

Summary: This paper presents a dendrograms-based method for 3D visualization of hierarchical clustering of multidimensional data collected from IoT devices and open databases. The method, built on a simple and efficient hierarchical clustering algorithm, significantly improves the quality of visualization and evaluation of cluster analysis results through rule definitions and quantitative indicators.

COMPUTERS & INDUSTRIAL ENGINEERING (2021)

Add to Collection

Review Environmental Sciences

The application of hierarchical clustering to analyzing ashes from the combustion of wood pellets mixed with waste materials

Jacek Grabowski et al.

Summary: Air pollution is the greatest environmental threat to human health in the European Union. In Poland, coal boiler plants and residential heating fuel combustion are significant sources of air pollution, with further research needed on combustion of waste materials.

ENVIRONMENTAL POLLUTION (2021)

Add to Collection

Article Engineering, Civil

Comparison of hierarchical clustering based deformation prediction models for high arch dams during the initial operation period

Jiang Hu et al.

Summary: This study compares the prediction performances of different data mining models for high arch dam deformation during initial operation period. Improved models like SBRT and modified HST show good performance in predicting the nonlinear deformation of high arch dams. Factors such as reservoir thermal stratification, concrete temperature rise, valley contraction, and non-monotonic time-dependent effect were found to affect dam deformation.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2021)

Add to Collection

Article Thermodynamics

Multi-chronological hierarchical clustering to solve capacity expansion problems with renewable sources

R. Dominguez et al.

Summary: Renewable sources are increasingly integrated into power systems for decarbonization, but their variability introduces uncertainty, requiring fast generating and storage units. Adequate representation of renewable variability in capacity expansion problems is crucial, often selected through clustering techniques. Trade-offs exist in current clustering algorithms, highlighting the need for innovative approaches to accurately capture inter-temporal correlation and representativeness with minimal clusters.

ENERGY (2021)

Add to Collection

Article Engineering, Civil

Prediction of the failure point settlement in rockfill dams based on spatial-temporal data and multiple-monitoring-point models

Yanlong Li et al.

Summary: This study established a multiple-monitoring-point (MMP) model to predict long-term missing data in malfunctioning settlement sensors by integrating spatiotemporal information. Through spatiotemporal data clustering analysis, suitable data was screened to significantly improve model prediction accuracy.

ENGINEERING STRUCTURES (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Application of the novel harmony search optimization algorithm for DBSCAN clustering

Qidan Zhu et al.

Summary: The study proposed the K-DBSCAN clustering method, utilizing the novel harmony search (novel-HS) optimization algorithm to improve the clustering parameters of DBSCAN, achieving better clustering results with K classifications. Experimental results demonstrated that this designed clustering method outperforms others and can be considered as a new clustering scheme for further research.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Add to Collection

Article Engineering, Multidisciplinary

A missing sensor measurement data reconstruction framework powered by multi-task Gaussian process regression for dam structural health monitoring systems

Yangtao Li et al.

Summary: In this study, a multi-task Gaussian process regression (mGPR) method is proposed to reconstruct missing data from faulty sensors by capturing the correlation among multiple sensors as a whole. The approach efficiently and accurately learns missing data from faulty sensors in dam SHM systems, showing significantly better performance compared to conventional multiple GPR methods.

MEASUREMENT (2021)

Add to Collection

Article Construction & Building Technology

Mixed-coefficient panel model for evaluating the overall deformation behavior of high arch dams using the spatial clustering

Shaowei Wang et al.

Summary: This study developed a mixed-coefficient panel model for dam displacements by grouping monitoring points and considering various causal factors. The model successfully represented the overall deformation behavior and structural integrity of a high arch dam, showing good performance in interpreting and predicting dam displacement and analyzing viscoelastic parameters of the dam body concrete.

STRUCTURAL CONTROL & HEALTH MONITORING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Piezometric level prediction based on data mining techniques

Joaquim Tinoco et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

The GH-EXIN neural network for hierarchical clustering

Giansalvo Cirrincione et al.

NEURAL NETWORKS (2020)

Add to Collection

Article Engineering, Multidisciplinary

An online anomaly recognition and early warning model for dam safety monitoring data

Xing Li et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2020)

Add to Collection

Article Engineering, Multidisciplinary

Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines

Fei Kang et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2020)

Add to Collection

Article Engineering, Multidisciplinary

Two spatial association-considered mathematical models for diagnosing the long-term balanced relationship and short-term fluctuation of the deformation behaviour of high concrete arch dams

Shaowei Wang et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2020)

Add to Collection

Article Construction & Building Technology

Displacement Model for Concrete Dam Safety Monitoring via Gaussian Process Regression Considering Extreme Air Temperature

Fei Kang et al.

JOURNAL OF STRUCTURAL ENGINEERING (2020)

Add to Collection

Article Construction & Building Technology

Coupling prediction model for long-term displacements of arch dams based on long short-term memory network

Wenju Liu et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

An optimized combination prediction model for concrete dam deformation considering quantitative evaluation and hysteresis correction

Qiubing Ren et al.

ADVANCED ENGINEERING INFORMATICS (2020)

Add to Collection

Article Construction & Building Technology

Cluster analysis-based anomaly detection in building automation systems

H. Burak Gunay et al.

ENERGY AND BUILDINGS (2020)

Add to Collection

Article Construction & Building Technology

Improved online sequential extreme learning machine for identifying crack behavior in concrete dam

Bo Dai et al.

ADVANCES IN STRUCTURAL ENGINEERING (2019)

Add to Collection

Article Engineering, Multidisciplinary

A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data

Bo Chen et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Gaussian process regression-based forecasting model of dam deformation

Chaoning Lin et al.

NEURAL COMPUTING & APPLICATIONS (2019)

Add to Collection

Article Water Resources

An intelligent singular value diagnostic method for concrete dam deformation monitoring

Jie Yang et al.

WATER SCIENCE AND ENGINEERING (2019)

Add to Collection

Article Construction & Building Technology

A novel model of dam displacement based on panel data

Chenfei Shao et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2018)

Add to Collection

Article Mechanics

Early-warning model of deformation safety for roller compacted concrete arch dam considering time-varying characteristics

Huaizhi Su et al.

COMPOSITE STRUCTURES (2018)

Add to Collection

Article Construction & Building Technology

Concrete dam deformation prediction model for health monitoring based on extreme learning machine

Fei Kang et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2017)

Add to Collection

Article Construction & Building Technology

Performance improvement method of support vector machine-based model monitoring dam safety

Huaizhi Su et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2016)

Add to Collection

Article Computer Science, Artificial Intelligence

Gene discretization based on EM clustering and adaptive sequential forward gene selection for molecular classification

Hung-Yi Lin

APPLIED SOFT COMPUTING (2016)

Add to Collection

Article Computer Science, Artificial Intelligence

Data and syntax centric anomaly detection for relational databases

Asmaa Sallam et al.

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2016)

Add to Collection

© Peeref 2019-2024. All rights reserved.