###### Abstract  
Structural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their propagation. Structural damages may arise from various factors, including natural phenomena and human activities. To address this, diverse applications have been developed to enable timely detection of such damages. Among these, vibration-based methods have received considerable attention in recent years. By leveraging advancements in computer processing capabilities, machine learning and deep learning algorithms have emerged as promising tools for enhancing the efficiency and accuracy of vibration-based SHM. This study focuses on the application of convolutional neural networks (CNNs) for the classification and detection of structural damage within a steel-aluminum building model. An experimental platform was devised and constructed to generate data representative of building damage scenarios induced by bolt loosening. Both the typical placement of sensors on each floor and the utilization of only one accelerometer were employed to understand the effect of scarcity of accelerometers. By subjecting the building model to controlled vibrations and environmental conditions, the response data from both sensor configurations were collected and analyzed to evaluate the effectiveness of the CNN approach in detecting structural damage under varying sensor deployment strategies. The findings demonstrate that the CNNs exhibited high accuracy in both damage classification and detection, even under scenarios with limited sensor coverage. Moreover, the proposed method proved effective in identifying structural damage within building structures.  
structural health monitoring vibration-based methods; damage detection; convolutional neural networks (CNNs) 202420242024202420242024202420242024202420242024202420242024202024202420242020 which can be impractical or costly to implement, especially for large or complex structures. To address this challenge, researchers have begun exploring alternative approaches to structural health monitoring that leverage advancements in artificial intelligence and machine learning. In particular, convolutional neural networks (CNNs) have emerged as a promising tool for analyzing sensor data and detecting structural damage. CNNs are well-suited for capturing complex spatial and temporal patterns in data, making them ideal for processing the multi-dimensional sensor data generated by SHM systems. This study focuses on investigating the efficacy of CNNs for structural damage detection in scenarios where sensor coverage is limited. By utilizing just one accelerometer placed on a structure, rather than multiple sensors distributed throughout, we aim to develop a cost-effective and practical solution for SHM. Through experimental testing on a three-story laboratory frame under different sensor configurations, we evaluate the performance of CNN-based damage detection algorithms. Additionally, we explore techniques such as data windowing to enhance the effectiveness of CNNs in scenarios with sparse sensor coverage.