A data-driven structural damage identification approach using deep convolutional-attention-recurrent neural architecture under temperature variations. (1st February 2023)

• Record Type: Journal Article
• Authors: Fathnejat, Hamed; Ahmadi-Nedushan, Behrouz; Hosseininejad, Sahand; Noori, Mohammad; Altabey, Wael A.
• Is Part Of: Engineering structures. Volume 276(2023)
• Publication Date: 2023-02-01
• Subjects: Structural health monitoring (SHM) -- Deep Learning -- 1DCNN -- RNN variants -- Attention mechanism -- Environmental effects; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.115311
• Languages: English
• ISSNs: 0141-0296

Abstract

Graphical abstract: The Proposed Deep Learning based Architecture Highlights: Proposes a novel deep learning model that utilizes both 1DCNN and RNN variants. Uses the attention mechanism to improve the performance of the 1DCNN-RNN variant. Uses raw acceleration time-series type of sequential data as the input data. Compares the proposed model with equivalent dl -based model architectures. Moreover, examines the environmental variable which affects structural response. Abstract: In recent years, by emerging deep learning (DL) based algorithms, researchers have been exploring dl -based models to identify structural damage through data-driven approaches. dl -based data-driven techniques using autonomous feature extraction from raw sequential data are more robust under environmental variations. Extraction of robust features while considering the sequential dependencies will significantly improve the accuracy of damage identification by these techniques. In this regard, various architectures of dl -based models have been proposed. This study presents a novel dl -based model that utilizes both one-dimensional convolutional neural network (1DCNN) and recurrent neural network (RNN) variants using an attention mechanism. Attention mechanism improves the performance of the 1DCNN-RNN variants model precisely when its input data is raw acceleration time-history, as a kind of sequential data. The IASC-ASCE phase II and Qatar University grandstand simulator benchmarks are used to evaluate the proposed model by comparing its performance with dl -based neural network architectures that could be equivalent to this combination. Moreover, the environmental variable which affects structural response is also examined. Results demonstrate that the CNN-ATT-biGRU model architecture has the best accuracy and appropriate training time and model size among nine compared architectures.