ArticleSensors (Basel, Switzerland)2020
Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Smart Sensors for Structural Health Monitoring and Nondestructive Evaluation: 2nd Edition.Sensors (Basel, Switzerland) · 2026Article
- Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.Sensors (Basel, Switzerland) · 2024Article
- Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review.Sensors (Basel, Switzerland) · 2023Review
- Short-Training Damage Detection Method for Axially Loaded Beams Subject to Seasonal Thermal Variations.Sensors (Basel, Switzerland) · 2023Article
- Prediction of the Physical Properties of a Structural Member by the Impact Hammer Test.Sensors (Basel, Switzerland) · 2022Article
- Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images.Sensors (Basel, Switzerland) · 2022Article
- Structural Responses Estimation of Cable-Stayed Bridge from Limited Number of Multi-Response Data.Sensors (Basel, Switzerland) · 2022Article
- Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology.Sensors (Basel, Switzerland) · 2022Article
- Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions.Sensors (Basel, Switzerland) · 2021Article
- An Autoencoder-Based Deep Learning Approach for Load Identification in Structural Dynamics.Sensors (Basel, Switzerland) · 2021Article
- Accurate Correlation Modeling between Wind Speed and Bridge Girder Displacement Based on a Multi-Rate Fusion Method.Sensors (Basel, Switzerland) · 2021Article
- Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling.Sensors (Basel, Switzerland) · 2021Article
- A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders.Sensors (Basel, Switzerland) · 2021Article
- Evaluation of Teachers' Educational Technology Ability Based on Fuzzy Clustering Generalized Regression Neural Network.Computational intelligence and neuroscience · 2021Article
- Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network.Computational intelligence and neuroscience · 2021Article
- The Online Teaching Mode of College English in the Context of Gaussian Variant Genetic Algorithm.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.