Current autoencoder-based unsupervised anomaly detection in Dynamic Data Driven Applications Systems (DDDAS) typically depends on a comprehensive collection of historical normal signals as training data. However, such unsupervised models struggle to perform effectively under dynamic conditions when there is a significant divergence between the environmental conditions of the evaluation data and those of the training data. To address this, our previous study introduces an unsupervised anomaly detection method trained solely on the current evaluation data, eliminating the dependency on historical data and thus more applicable in DDDAS. This method utilizes the inherent bias learning property of neural networks, which typically prioritizes learning from larger classes (normal signals) at the expense of smaller ones. However, a crucial limitation of this new autoencoder-based damage detection method is its performance degradation when the evaluation data includes an increasing number of abnormal signals. To enhance anomaly detection under these conditions, an optimal weight decay regularization strategy is provided in this study to limit the autoencoder’s ability to learn abnormal signals. The efficacy of the novel autoencoder-based anomaly detection method in DDDAS is validated using guided waves gathered from a structural health monitoring system under uncontrolled and dynamically varying environmental conditions.