SHM under varying environmental conditions: an approach based on model order reduction and deep learning

Torzoni, Matteo; Rosafalco, Luca; Manzoni, Andrea; Mariani, Stefano; Corigliano, Alberto

doi:10.1016/j.compstruc.2022.106790

Cited by 34 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They reported that this technique could deal with non-increasing characteristics (stiffness) and non-decreasing characteristics (the damage index after introducing damage) without directly measuring ambient factors. Torzoni et al [34] reported an effective damage localization strategy using vibration and temperature data to consider the influence of temperature fluctuation on the structural response. By allowing a limited number of predefined damage scenarios, temperature data were used as a condition, and deep learning technology was used as a supervised classification to deal with damage localization tasks.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

2022

View full text Add to dashboard Cite

In civil engineering structures, modal changes produced by environmental conditions, especially temperature, can be equivalent to or greater than the ones produced by damage. Therefore, it is necessary to distinguish the variations in structural properties caused by environmental changes from those caused by structural damages. In this paper, we present a review of the technical literature concerning variations in the vibration properties of civil structures under varying temperature conditions and damage identification methods for bridge structures. First, the literature on the effect of temperature on vibration properties is roughly divided into experimental and theoretical studies. According to the classification of theoretical research methods, the progress in research on the probability analysis method, the artificial intelligence method, and the optimization algorithm method in this field is reviewed. Based on the different methods of experimental research employed in this field, the experimental research is reviewed according to qualitative and quantitative analyses. Then, damage identification methods for bridge structures are reviewed, considering data-based and model-based methods. Finally, different research methods are summarized.

show abstract

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

2022

View full text Add to dashboard Cite

show abstract

“…In the literature of SHM, different approaches have been proposed to face the problems related to environmental and operational variations. A family of approaches is that of input-output models, which require measurements of both the environmental variables (the input) and the structural response (the output) to filter out the environmental effects through the adoption of, e.g., linear correlation models [ 19 , 20 , 21 , 22 ], neural networks [ 23 , 24 , 25 ] or support vector machines [ 26 , 27 ]. However, often not all the relevant environmental and operational variables are measured or known.…”

Section: Introductionmentioning

confidence: 99%

Short-Training Damage Detection Method for Axially Loaded Beams Subject to Seasonal Thermal Variations

Berardengo

Lucà

Vanali

et al. 2023

Sensors

View full text Add to dashboard Cite

Vibration-based damage features are widely adopted in the field of structural health monitoring (SHM), and particularly in the monitoring of axially loaded beams, due to their high sensitivity to damage-related changes in structural properties. However, changes in environmental and operating conditions often cause damage feature variations which can mask any possible change due to damage, thus strongly affecting the effectiveness of the monitoring strategy. Most of the approaches proposed to tackle this problem rely on the availability of a wide training dataset, accounting for the most part of the damage feature variability due to environmental and operating conditions. These approaches are reliable when a complete training set is available, and this represents a significant limitation in applications where only a short training set can be used. This often occurs when SHM systems aim at monitoring the health state of an already existing and possibly already damaged structure (e.g., tie-rods in historical buildings), or for systems which can undergo rapid deterioration. To overcome this limit, this work proposes a new damage index not affected by environmental conditions and able to properly detect system damages, even in case of short training set. The proposed index is based on the principal component analysis (PCA) of vibration-based damage features. PCA is shown to allow for a simple filtering procedure of the operating and environmental effects on the damage feature, thus avoiding any dependence on the extent of the training set. The proposed index effectiveness is shown through both simulated and experimental case studies related to an axially loaded beam-like structure, and it is compared with a Mahalanobis square distance-based index, as a reference. The obtained results highlight the capability of the proposed index in filtering out the temperature effects on a multivariate damage feature composed of eigenfrequencies, in case of both short and long training set. Moreover, the proposed PCA-based strategy is shown to outperform the benchmark one, both in terms of temperature dependency and damage sensitivity.

show abstract

“…Recent advancements have explored the use of DNNs such as convolutional neural networks (CNNs) and deep recurrent neural networks (DRNNs) within the health monitoring research field. In particular, the use of the convolution operation and its associated information extraction method generally works with measured time series data by converting them into 2D graphical representations (spectrograms) [21] or by extracting damage-sensitive features from the measured vibration response [22,23]. However, as the collected data usually consist of sequential time series signals, the most efficient deep learning architecture to be applied for prediction and classification falls within the RNNs domain [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Damage Detection in Composite Space Structures via Deep Learning

Angeletti,

Gasbarri,

Panella

et al. 2023

Sensors

View full text Add to dashboard Cite

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples.

show abstract

SHM under varying environmental conditions: an approach based on model order reduction and deep learning

Cited by 34 publications

References 42 publications

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

Short-Training Damage Detection Method for Axially Loaded Beams Subject to Seasonal Thermal Variations

Multi-Damage Detection in Composite Space Structures via Deep Learning

Contact Info

Product

Resources

About