Next Article in Journal
Improved Parameter Identification Method for Envelope Current Signals Based on Windowed Interpolation FFT and DE Algorithm
Next Article in Special Issue
The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals
Previous Article in Journal
A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
Previous Article in Special Issue
A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(8), 112; https://doi.org/10.3390/a11080112

A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring

1,* , 1
and
2
1
School of Electrical Engineering, Computing and Mathematics Science, Curtin University, Kent Street, Bentley, WA 6102, Australia
2
Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, Kent Street, Bentley, WA 6102, Australia
*
Author to whom correspondence should be addressed.
Received: 25 June 2018 / Revised: 20 July 2018 / Accepted: 24 July 2018 / Published: 27 July 2018
(This article belongs to the Special Issue Discrete Algorithms and Discrete Problems in Machine Intelligence)
Full-Text   |   PDF [1938 KB, uploaded 27 July 2018]   |  

Abstract

In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches. View Full-Text
Keywords: deep auto-encoders; parallel structure; structural damage identification deep auto-encoders; parallel structure; structural damage identification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, R.; Li, L.; Li, J. A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring. Algorithms 2018, 11, 112.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top