158 research outputs found
HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis
Monitoring bridge health using vibrations of drive-by vehicles has various
benefits, such as no need for directly installing and maintaining sensors on
the bridge. However, many of the existing drive-by monitoring approaches are
based on supervised learning models that require labeled data from every bridge
of interest, which is expensive and time-consuming, if not impossible, to
obtain. To this end, we introduce a new framework that transfers the model
learned from one bridge to diagnose damage in another bridge without any labels
from the target bridge. Our framework trains a hierarchical neural network
model in an adversarial way to extract task-shared and task-specific features
that are informative to multiple diagnostic tasks and invariant across multiple
bridges. We evaluate our framework on experimental data collected from 2
bridges and 3 vehicles. We achieve accuracies of 95% for damage detection, 93%
for localization, and up to 72% for quantification, which are ~2 times
improvements from baseline methods
Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
Onsite disasters like earthquakes can trigger cascading hazards and impacts,
such as landslides and infrastructure damage, leading to catastrophic losses;
thus, rapid and accurate estimates are crucial for timely and effective
post-disaster responses. Interferometric Synthetic aperture radar (InSAR) data
is important in providing high-resolution onsite information for rapid hazard
estimation. Most recent methods using InSAR imagery signals predict a single
type of hazard and thus often suffer low accuracy due to noisy and complex
signals induced by co-located hazards, impacts, and irrelevant environmental
changes (e.g., vegetation changes, human activities). We introduce a novel
stochastic variational inference with normalizing flows derived to jointly
approximate posteriors of multiple unobserved hazards and impacts from noisy
InSAR imagery
Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing
Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic
cables into a large-scale strain sensing array has shown the potential to
revolutionize urban traffic monitoring by providing a fine-grained, scalable,
and low-maintenance monitoring solution. However, the real-world application of
DAS is hindered by challenges such as noise contamination and interference
among closely traveling cars. In response, we introduce a self-supervised U-Net
model that can suppress background noise and compress car-induced DAS signals
into high-resolution pulses through spatial deconvolution. Our work extends
recent research by introducing three key advancements. Firstly, we perform a
comprehensive resolution analysis of DAS-recorded traffic signals, laying a
theoretical foundation for our approach. Secondly, we incorporate space-domain
vehicle wavelets into our U-Net model, enabling consistent high-resolution
outputs regardless of vehicle speed variations. Finally, we employ L-2 norm
regularization in the loss function, enhancing our model's sensitivity to
weaker signals from vehicles in remote traffic lanes. We evaluate the
effectiveness and robustness of our method through field recordings under
different traffic conditions and various driving speeds. Our results show that
our method can enhance the spatial-temporal resolution and better resolve
closely traveling cars. The spatial deconvolution U-Net model also enables the
characterization of large-size vehicles to identify axle numbers and estimate
the vehicle length. Monitoring large-size vehicles also benefits imaging deep
earth by leveraging the surface waves induced by the dynamic vehicle-road
interaction.Comment: This preprint was re-submitted as a revised version to the IEEE
Transactions on Intelligent Transportation Systems on June 27, 202
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