  Structural Health Monitoring (SHM) is vital for evaluating structural
condition, aiming to detect damage through sensor data analysis. It aligns with
predictive maintenance in modern industry, minimizing downtime and costs by
addressing potential structural issues. Various machine learning techniques
have been used to extract valuable information from vibration data, often
relying on prior structural knowledge. This research introduces an innovative
approach to structural damage detection, utilizing a new Convolutional Neural
Network (CNN) algorithm. In order to extract deep spatial features from time
series data, CNNs are taught to recognize long-term temporal connections. This
methodology combines spatial and temporal features, enhancing discrimination
capabilities when compared to methods solely reliant on deep spatial features.
Time series data are divided into two categories using the proposed neural
network: undamaged and damaged. To validate its efficacy, the method's accuracy
was tested using a benchmark dataset derived from a three-floor structure at
Los Alamos National Laboratory (LANL). The outcomes show that the new CNN
algorithm is very accurate in spotting structural degradation in the examined
structure.
