Abstract
Modal macro strain-based damage identification is a promising approach since it has the advantages of high sensitivity and effectiveness over other related methods. In this paper, a basalt fiber-reinforced polymer (BFRP) pipeline system is used for analysis by using long-gauge distributed fiber Bragg grating (FBG) sensors. Dynamic macro strain responses are extracted to form modal macro strain (MMS) vectors. Both longitudinal distribution and circumferential distribution plots of MMS are compared and analyzed. Results show these plots can reflect damage information of the pipeline based on the previous work carried out by the authors. However, these plots may not be good choices for accurate detection of damage information since the model is 3D and has different flexural and torsional effects. Therefore, by extracting MMS information in the circumferential distribution plots, a novel deep neural network is employed to train and test these images, which reflect the important and key information of modal variance in the pipe system. Results show that the proposed Deep Learning based approach is a promising way to inherently identify damage types, location of the excitation load and support locations, especially when the structural types are complicated and the ambient environment is changing.
Highlights
Building resilient, ecological and sustainable infrastructure systems is increasingly becoming important since these systems degrade with time and are prone to damage when they are subjected to natural hazards and other unexpected disturbances
This paper first presented the basic theory of modal macro strain-based long gauge distributed sensing technology, and deep learning theory
Both longitudinal distribution and circumferential distribution of modal macro strain were investigated and results indicated some characteristics of damage extent, load excitation location and support location
Summary
Ecological and sustainable infrastructure systems is increasingly becoming important since these systems degrade with time and are prone to damage when they are subjected to natural hazards and other unexpected disturbances. This paper extracts the section feature of modal macro strain of pipelines and applies deep learning algorithm to classify and identify different damage cases, and identify loading location and support locations This approach is proved as a promising way to identify structural damages especially for completed structural types or rugged environment. Modal macro strain theory is mainly used for inherently extracting sensitive information of structures, which is later regarded as MMS distribution plots These plots and images are fed into convolutional neural network for identifying damage severity, load location and support locations. Modal macro strain within the long gauge sensor can be measured from the peak value of the power spectral density (PSD) of macro strain signals from distributed dynamic response when the pipeline is subjected to ambient excitation. Interspersed with sub-sampling layers, convolutional layers are established to increase computation efficiency and further improve configural and spatial invariance
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