SASW (Spectral Analysis of Surface Waves) is practical and relatively effective in characterizing subsurface ground
truth. According to the surface wave in the interesting range of frequency, some criteria for source-receiver configuration
are employed and limit the applications. Challenges emerge when SASW is applied to study the surface wave involving
multiple modes effect and when the source is near the receiver. In such cases, multiple modes effects and evanescent
wave fields are present in array sensing and might weaken the inversion accuracy of pavement subsurface profile. In this
work, these issues were investigated and a complex wave number estimation based method was proposed. The complex
wave number was estimated by iterative linear exponential fitting from wave field model to response measurements.
Evanescent wave for near field and multiple modes effects were focused in the proposed method. Finally, simulated
signals from FEA model were processed to demonstrate the algorithm and the results were discussed.
Spectral Analysis for Surface Wave (SASW) is a widely practiced NDT method due to its ability to identify the
shear velocity profile of subsurface layers. However, the SASW method is limited to point-to-point inspection
because all data has to go through an inversion process, which is iterative and manual. Some automated iteration
techniques were developed to improve the efficiency of inversion analysis. These attempts did not change the
situation much because they were still based on the guess-and-check procedure incorporated with a forward analysis.
In this paper, a new inversion analysis algorithm is proposed to estimate the shear velocity profile rapidly without
performing conventional forward analysis. Unlike conventionally determining the dispersion curve with a stiffness
matrix or something similar, the dispersion curve of a layered structure is assumed to be a weighted combination of
the shear velocity profile. The weighting factors are determined according to the variation of particle displacement
with depth for a specified wavelength of surface wave. Based on this assumption, a fast inversion algorithm is
established to estimate the shear velocity profile from a given dispersion curve. No prior knowledge of the test site
or personal expertise is needed because this method does not require the initial values of the layer depths and shear
velocities. This new method allows the SASW method to be a fully automatic or even real-time reporting method for
highway pavement detection. The accuracy of this fast inversion algorithm is verified by comparing the results to
those of the conventional algorithm.
A large scale finite element model with high mesh resolution is established to simulate the ground truth of regular
highway pavement structure with subsurface debonding defects. The simulation is motivated by non-destructive testing
methods that derive information from the acoustic radiation of the surface wave. These NDT (Non-Destructive Testing) signals come from solid elastic wave propagation beneath pavement surface, which then couple with acoustic wave in air above the pavement surface. In this article, 2 main debonding phenomena, which are conventionally hidden below the pavement surface, are modeled and also compared with a healthy (perfectly intact) pavement structure model. Both the impact-response transient analysis and frequency spectrum analysis have been given to show a new opportunity to detect the subsurface debonding in pavement non-destructively through acoustic signals from heights above the pavement surface which are incorporated with ground truth information.
The Zhanjiang Bay Bridge is a cable stayed bridge with a main span of 480m. Structural behavior due to thermal effects
is presented in this paper in according to data received from a health monitoring system (HMS) since 2006. Data
obtained from the analysis includes temperature gradients and time lags in the steel box girder, concrete tower, and
stayed cables. By comparing the measured and calculated thermal displacements, it was possible to estimate the
unmeasured thermal gradient on the surface of the towers as well as to determine that one of the expansion joints was likely constrained and contributing to the bridges asymmetrical displacement.
KEYWORDS: Bridges, Data modeling, Sensors, Finite element methods, Structural health monitoring, Global Positioning System, Binary data, Machine learning, Inspection, Data processing
This paper applies support vector machine (SVM) to the field of structural health monitoring. SVM is a data processing
technique that performs binary classification. The machine in its name indicates its association with machine learning, a
category of algorithms that are able to solve classification problems by learning from example data given in a training
process. This paper uses SVM for abnormality detection on data from a cable-stayed bridge's health monitoring system.
The goal is to investigate whether the east end expansion joint is constraining the longitudinal motion of the bridge's
main girder, which is suspected due to the results of a finite element updating procedure. Regarding the training process,
distinct examples of the normal and abnormal expansion joint are unavailable from the health monitoring system. For
this reason training examples are obtained from a finite element model. Accordingly, since SVM accuracy is highly
dependent on the similarity between the training data and data being classified, the finite element modeling is a primary
challenge of the paper's approach. The contributions of this paper include an application of SVM to an in-service
structure, as well as a discussion on its performance and some limitations that affect its accuracy.
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