A novel deep learning-based method for damage identification of smart building structures

TLDR

Intelligent structural damage identification algorithms based on machine learning have attracted attention, yet their performance depends heavily on selected signatures and feature extraction is time‑consuming, limiting real‑time applicability and generalizability. This study introduces a deep convolutional neural network to identify and localise damages in smart building structures. The network automatically extracts low‑ and high‑level features from raw signals and fuses them across layers, and its performance was evaluated on a five‑level benchmark building with adaptive smart isolators under seismic loading. The method demonstrates superior generalisation and higher identification accuracy than conventional machine‑learning approaches, establishing it as an effective solution for smart structure damage detection.

Abstract

In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning–based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.