5 research outputs found
Intensity-Based Feature Selection for Near Real-Time Damage Diagnosis of Building Structures
Near real-time damage diagnosis of building structures after extreme events
(e.g., earthquakes) is of great importance in structural health monitoring.
Unlike conventional methods that are usually time-consuming and require human
expertise, pattern recognition algorithms have the potential to interpret
sensor recordings as soon as this information is available. This paper proposes
a robust framework to build a damage prediction model for building structures.
Support vector machines are used to predict the existence as well as the
probable location of the damage. The model is designed to consider
probabilistic approaches in determining hazard intensity given the existing
attenuation models in performance-based earthquake engineering. Performance of
the model regarding accurate and safe predictions is enhanced using Bayesian
optimization. The proposed framework is evaluated on a reinforced concrete
moment frame. Targeting a selected large earthquake scenario, 6,240 nonlinear
time history analyses are performed using OpenSees. Simulation results are
engineered to extract low-dimensional intensity-based features that can be used
as damage indicators. For the given case study, the proposed model achieves a
promising accuracy of 83.1% to identify damage location, demonstrating the
great potential of model capabilities
An Audio-Based Fault Diagnosis Method for Quadrotors Using Convolutional Neural Network and Transfer Learning
Quadrotor unmanned aerial vehicles (UAVs) have been developed and applied
into several types of workplaces, such as warehouses, which usually involve
human workers. The co-existence of human and UAVs brings new challenges to
UAVs: potential failure of UAVs may cause risk and danger to surrounding human.
Effective and efficient detection of such failure may provide early warning to
the surrounding human workers and reduce such risk to human beings as much as
possible. One of the commonest reasons that cause the failure of the UAV's
flight is the physical damage to the propellers. This paper presents a method
to detect the propellers' damage only based on the audio noise caused by the
UAV's flight. The diagnostic model is developed based on convolutional neural
network (CNN) and transfer learning techniques. The audio data is collected
from the UAVs in real time, transformed into the time-frequency spectrogram,
and used to train the CNN-based diagnostic model. The developed model is able
to detect the abnormal features of the spectrogram and thus the physical damage
of the propellers. To reduce the data dependence on the UAV's dynamic models
and enable the utilization of the training data from UAVs with different
dynamic models, the CNN-based diagnostic model is further augmented by transfer
learning. As such, the refinement of the well-trained diagnostic model ground
on other UAVs only requires a small amount of UAV's training data. Experimental
tests are conducted to validate the diagnostic model with an accuracy of higher
than 90%.Comment: ACC 2020 Final Versio
Deep Bayesian U-Nets for Efficient, Robust and Reliable Post-Disaster Damage Localization
Post-disaster inspections are critical to emergency management after
earthquakes. The availability of data on the condition of civil infrastructure
immediately after an earthquake is of great importance for emergency
management. Stakeholders require this information to take effective actions and
to better recover from the disaster. The data-driven SHM has shown great
promises to achieve this goal in near real-time. There have been several
proposals to automate the inspection process from different sources of input
using deep learning. The existing models in the literature only provide a final
prediction output, while the risks of utilizing such models for safety-critical
assessments should not be ignored. This paper is dedicated to developing deep
Bayesian U-Nets where the uncertainty of predictions is a second output of the
model, which is made possible through Monte Carlo dropout sampling in test
time. Based on a grid-like data structure, the concept of semantic damage
segmentation (SDS) is revisited. Compared to image segmentation, it is shown
that a much higher level of precision is necessary for damage diagnosis. To
validate and test the proposed framework, a benchmark dataset, 10,800 nonlinear
response history analyses on a 10-story-10-bay 2D reinforced concrete moment
frame, is utilized. Compared to the benchmark SDS model, Bayesian models
exhibit superior robustness with enhanced global and mean class accuracies.
Finally, the model's uncertainty output is studied by monitoring the softmax
class variance of different predictions. It is shown that class variance
correlates well with locations where the model makes mistakes. This output can
be used in combination with the prediction results to increase the reliability
of this data-driven framework in structural inspections
A Nonparametric Unsupervised Learning Approach for Structural Damage Detection
In a world of aging infrastructure, structural health monitoring (SHM)
emerges as a major step towards resilient and sustainable societies. The
current advancements in machine learning and sensor technology have made SHM a
more promising damage detection method than the traditional non-destructive
testing methods. SHM using unsupervised learning methods offers an attractive
alternative to the more commonly used supervised learning since it only
requires data of the structure in normal conditions for the training process.
The density-based novelty detection method provides a statistical element to
the damage detection process but it relies heavily on the accuracy of the
estimated probability density function (PDF). In this study, a novel
unsupervised learning approach for SHM is proposed. It is based on the Kernel
Density Maximum Entropy method by leveraging Bayesian optimization for
hyperparameter tuning and also by extending the method into the multivariate
space by the use of independent components analysis. The proposed approach is
evaluated on a numerically simulated three-story reinforced concrete moment
frame, where 94% of accuracy is achieved in structural damage detection
Model Uncertainty Quantification for Reliable Deep Vision Structural Health Monitoring
Computer vision leveraging deep learning has achieved significant success in
the last decade. Despite the promising performance of the existing deep models
in the recent literature, the extent of models' reliability remains unknown.
Structural health monitoring (SHM) is a crucial task for the safety and
sustainability of structures, and thus prediction mistakes can have fatal
outcomes. This paper proposes Bayesian inference for deep vision SHM models
where uncertainty can be quantified using the Monte Carlo dropout sampling.
Three independent case studies for cracks, local damage identification, and
bridge component detection are investigated using Bayesian inference. Aside
from better prediction results, mean class softmax variance and entropy, the
two uncertainty metrics, are shown to have good correlations with
misclassifications. While the uncertainty metrics can be used to trigger human
intervention and potentially improve prediction results, interpretation of
uncertainty masks can be challenging. Therefore, surrogate models are
introduced to take the uncertainty as input such that the performance can be
further boosted. The proposed methodology in this paper can be applied to
future deep vision SHM frameworks to incorporate model uncertainty in the
inspection processes