Illustrates the effectiveness of anomaly detection for features S1-4 across various algorithms, (a) Iterative Rolling Difference-Z-score, (b) Isolation Forest, (c) One-Class SVM, (d) DBSCAN, (e) LOF, (f) K-Means, (g) Gaussian Mixture Model.

<p>Illustrates the effectiveness of anomaly detection for features S1-4 across various algorithms, (a) Iterative Rolling Difference-Z-score, (b) Isolation Forest, (c) One-Class SVM, (d) DBSCAN, (e) LOF, (f) K-Means, (g) Gaussian Mixture Model.</p>

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Bibliographic Details
Main Author:	Renjie Li (1863280) (author)
Other Authors:	Xiangxing Lu (17012468) (author), Jizhang Zhao (22190529) (author), Weibing Chen (4216441) (author), Huanwei Wei (22190532) (author), Cong Liu (66219) (author)
Published:	2025
Subjects:	Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified term structural reliability iterative rolling difference experimental results show conducting precise analysis assessing structural condition 60 &# 8211 200 points ), 14 &# 8211 0214 &# 8211 weld nail strain machine learning imputation machine learning data imputation concrete strain xlink "> site data shandong province robust solution research provides reliable reconstruction equipment instability enhancing long data loss data anomalies conducted within clustered anomalies capturing real anomaly detection
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Summary:	<p>Illustrates the effectiveness of anomaly detection for features S1-4 across various algorithms, (a) Iterative Rolling Difference-Z-score, (b) Isolation Forest, (c) One-Class SVM, (d) DBSCAN, (e) LOF, (f) K-Means, (g) Gaussian Mixture Model.</p>

Illustrates the effectiveness of anomaly detection for features S1-4 across various algorithms, (a) Iterative Rolling Difference-Z-score, (b) Isolation Forest, (c) One-Class SVM, (d) DBSCAN, (e) LOF, (f) K-Means, (g) Gaussian Mixture Model.

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