Augmented deep neural network architecture for assessing damage severity in 3D concrete buildings under temperature fluctuations based on K-means optimization

This research paper introduces a novel approach for forecasting the severity of damage in column elements within a complex 3D concrete structure. The paper presents two innovative methods that have not been previously proposed. Firstly, a new equation is suggested to enhance the sensitivity of the dataset used for training and learning in machine learning techniques. This equation incorporates two crucial dynamic characteristics, namely frequencies and mode shapes. The second method involves a new technique for optimizing the architecture of a deep neural network (DNN) using K-means optimizer (KO), referred to KODNN. By employing KO, the optimal values for the DNN architecture and learning rate are determined. To assess the effectiveness of the proposed method, KODNN is employed to develop a model for predicting the compressive strength of concrete using benchmark datasets. A comparison is made between the obtained results from this example and those obtained using the original DNN model to demonstrate the performance improvement achieved. Finally, KODNN is utilized to predict the severity of damage in elements of a 3D concrete building under various temperature conditions, considering two specific damage cases. The results indicate that the proposed method achieves a high level of accuracy and reliability.