Structural Health Monitoring (SHM) systems increasingly require efficient and scalable methods for identifying structural damage under dynamic loading. Traditional learning-based SHM models often rely on high-dimensional features or deep architectures, which may be computationally intensive and difficult to deploy in real-time applications, especially in scenarios with limited resources or bandwidth constraints. In this work, we propose a lightweight classification framework based on Hyperdimensional Computing (HDC) to detect structural damage using vibration-induced features, aiming to reduce complexity while maintaining detection performance. The proposed method encodes a rich feature set, including time-domain, frequency-domain, and autoregressive (AR) model features into high-dimensional binary vectors through a sliding window approach, capturing temporal variations and local patterns within the signal. A supervised HDC classifier is trained to distinguish between healthy and damaged structural states using these compact encodings. The framework enables fast learning and low memory usage, making it particularly suitable for edge-level SHM applications where real-time processing is required. To evaluate the feasibility and effectiveness of the proposed method, experiments are conducted on vibration data collected from controlled lateral impact tests on a concrete-filled steel tubular structure. The results validate the method ability to detect the damage-induced variations in modal frequencies and highlight its potential as a compact, robust, and efficient solution for future SHM systems based on modal data.