A Potential Outlier Detection Model for Structural Crack Variation Using Big Data-Based Periodic Analysis

Cracks in concrete structures, caused by aging, adjacent construction, and seismic activity, pose critical risks to structural integrity, durability, and serviceability. Traditional monitoring methods based solely on absolute thresholds are inadequate for detecting progressive crack growth at early stages. This study proposes a big data-driven anomaly detection model that combines absolute threshold evaluation with periodic trend analysis to enable both real-time monitoring and early anomaly identification. By incorporating relative comparisons, the model captures subtle variations within allowable limits, thereby enhancing sensitivity to incipient defects. Validation was conducted using approximately 2700 simulated datasets with an increase–hold–increase pattern and 470,000 real-world crack measurements. The model successfully detected four major anomalies, including abrupt shifts and cumulative deviations, and time series visualizations identified the exact onset of abnormal behavior. Through periodic fluctuation analysis and the Isolation Forest algorithm, the model effectively classified risk trends and supported proactive crack management. Rather than defining fixed labels or thresholds for the detected results, this study focused on verifying whether the analysis of detected crack data accurately reflected actual trends. To support interpretability and potential applicability, the detection outcomes were presented using quantitative descriptors such as anomaly count, anomaly score, and persistence. The proposed framework addresses the limitations of conventional digital monitoring by enabling early intervention below predefined thresholds. This data-driven approach contributes to structural health management by facilitating timely detection of potential risks and strengthening preventive maintenance strategies.