## 4 Discussion on Noise Effects, Model Selection, and Feature Sensitivity  
To investigate the effect of noise, the noise intensity level in the numerical simulation study was increased up to 10%. A total of 40 sets of noise-contaminated training data and 220 sets of test data were generated, with 180 corresponding to damage scenarios and 40 representing the healthy state for validation. It should be noted that the damage scenarios considered in this study represent light damage cases, with a maximum stiffness loss of 20%, corresponding to frequency shifts of up to 2.8%. Given the relatively small severity of these damage cases, further increase in the noise level would not be meaningful, as detecting very light damage under excessively high noise conditions is not a realistic expectation in practical SHM applications. The detection performance of the proposed methodology for all the damage cases, including the healthy state validation data at 10% noise level, is summarized in Table 4. The AE outperformed PCA even as the noise level increased. With the AE model, the critical Type II error--failing to issue an alert when damage is present--which was non-existent previously at lower noise levels, was calculated to be 10% at this noise level. Meanwhile, Type I error--false damage alarms--increased from 5% to 25%. As expected, lower noise levels yield more precise damage identification results. Hence, the proposed scheme is robust against moderate noise levels. Damage localization results, on the other hand, became unreliable for all damage cases except Damage Case 1 with increased noise level. This suggests that the pattern in the reconstruction error, which serves as a key indicator of damage location, is significantly affected by increased noise contamination for the light damage levels considered. Figure 12: (**a**) Damage detection features; (**b**) damage localization for Cases 1 and 2. To assess the effectiveness of OC-SVM as a classifier, a comparison was made with isolation forest (iForest), an alternative anomaly detection algorithm. iForest was chosen for its ability to identify outliers via recursive partitioning, which is particularly well-suited for structural damage detection, as it isolates anomalies directly without requiring prior labeling of the data. This makes it a more robust choice compared to clustering-based methods, which typically require assumptions about cluster shapes. Table 5 presents the comparative results of OC-SVM and iForest in detecting structural damage. The OC-SVM method utilizes a hyperplane-based decision boundary to classify data, while iForest adopts a tree-based approach, isolating outliers. In terms of error rates, iForest demonstrated an improvement in the Type I error (false positive rate), reducing it to 5%, compared to OC-SVM's higher rate of 25%. However, this improvement in Type I error came at the expense of an increase in Type II errors (false negative rate), which rose from 10%, the average for nine damage cases in OC-SVM, to 42.2% for iForest. While iForest effectively reduces false positives, it may be more conservative in detecting all damage cases, potentially leading to missed detection of actual damage, which is critical from a safety perspective in SHM. OC-SVM, on the other hand, maintains a more balanced performance. With a single hidden-layer AE architecture employed in this study, finding the optimum number of neurons proved to be critical, as the hidden layer directly serves as the latent space. The number of neurons determines the dimension of this space, affecting how well damage-sensitive features are captured. The results reported in Table 2 for the model with three neurons led to 5% Type I and 0% Type II errors. Increasing the number of neurons to 10 increased the false positives, impairing generalization and resulted in a Type I error of 30%, and a Type II error of 11%. Additionally, with increased neuron size, localization performance deteriorated and led to false identifications in half of the damage cases.