Structural damage identification based on autoencoder neural networks and deep learning

By Chathurdara Sri Nadith Pathirage, Jun Yu Li, Lingjun Li, Hong Hao, Wanquan Liu, Pinghe Ni. Engineering structures, 2018

346 citations recorded by the source corpus.

Abstract Artificial neural networks are computational approaches based on machine learning to learn and make predictions based on data, and have been applied successfully in diverse applications including structural health monitoring in civil engineering. It is difficult to optimize the weights in the neural networks that have multiple hidden layers due to the vanishing gradient issue. This paper proposes an autoencoder based framework for structural damage identification, which can support deep neural networks and be utilized to obtain optimal solutions for pattern recognition problems of highly non-linear nature, such as learning a mapping between the vibration characteristics and structural damage. Two main components are defined in the proposed framework, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the original input vector while preserving the required necessary information, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. Vibration characteristics, such as natural frequencies and mode shapes, are used as the input and the structural damage are considered as the output vector. A pre-training scheme is performed to train the hidden layers in the autoencoders layer by layer, and fine tuning is conducted to optimize the whole network. Numerical and experimental investigations on steel frame structures are conducted to demonstrate the accuracy and efficiency of the proposed framework, comparing with the traditional ANN methods.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/7d88fe942c66913d1b646ea77f42e9dcb246220c

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In Civil and Structural Engineering

Citations in the corpus

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, background): “Pathirage [63] Vibration-based damage detection and finding the stiffness reduction of elements Modal information of 10,300 damage cases that include the first seven frequencies (7 arrays) and the regarding mode shapes at 14 beam-column joints (98 arrays)”
Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, neutral): “However, the classification is not so strict because these unsupervised models trained by a fine-tuning process are actually semisupervised (Pathirage et al. 2018).”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, methodology): “[182], the authors introduced another DL method for SDD based on autoencoders highlighting the dimensionality reduction and relationship learning (Figure 39).”