This study presents a nondestructive evaluation method based on guided ultrasonic waves (GUW) to estimate corrosion in steel strands. Steel strands are one of the main components in constructing prestressed structures. Hidden corrosion in these structures has become a concern for designers, owners and regulators as it can eventuate in disastrous failure. In this study, a reference-free algorithm is proposed to quantify the extent of corrosion through estimating the cross section loss using dispersion curves and the velocity of certain frequency components in the waveform. Experimental test setups were designed to accelerate corrosion on two similarly loaded steel strands. One strand was embedded in concrete (to simulate a prestressed concrete beam) and the other was free (to resemble a prestressed cable). Visual inspection, halfcell potential, and mass loss measurements were employed as supporting evidences for the state of corrosion. An uncertainty analysis was also carried out to investigate how close this method can estimate the diameter of wires in a strand. The method could reasonably estimate the diameter of the wires without a reference baseline.
The most common assessment technique for reinforced concrete shear walls (RCSW) is Visual Inspection (VI). The current
practice suffers from subjective and labor intensive nature as it highly relies on judgment and expertise of the inspectors.
In post-earthquake events where urgent and objective decisions are crucial, failure of the conventional VI could be
catastrophic. Conventional VI is mainly based on width of residual cracks. Given that cracks could close partially (e.g.,
due to weight of the structure, behavior of adjacent elastic members, earthquake displacement spectrum, etc.), methods
based on crack width may lead to underestimating the state of damage and eventually an erroneous decision. This paper
proposes a novel method to circumvent the aforementioned limitations by utilizing the information hidden in crack
patterns. Crack patterns from images of the surface cracks on RCSW are extracted automatically, and Multifractal Analysis
(MFA) are applied on them. Images were taken from two large scale low aspect ratio RCSW under quasi-static cyclic
loading, and MFA showed clear correlation with tri-linear shear controlled behavior of walls which was observed in their
backbone curves.
Damage detection of pipeline systems is a tedious and time consuming job due to digging requirement, accessibility, interference with other facilities, and being extremely wide spread in metropolitans. Therefore, a real-time and automated monitoring system can pervasively reduce labor work, time, and expenditures. This paper presents the results of an experimental study aimed at monitoring the performance of full scale pipe lining systems, subjected to static and dynamic (seismic) loading, using Acoustic Emission (AE) technique and Guided Ultrasonic Waves (GUWs). Particularly, two damage mechanisms are investigated: 1) delamination between pipeline and liner as the early indicator of damage, and 2) onset of nonlinearity and incipient failure of the liner as critical damage state.
The most common damage assessment technique for concrete structures is visual inspection (VI). Condition assessed by
VI is subjective in nature, meaning it depends on the experience, knowledge, expertise, measurement accuracy, mental
attention, and judgment of the inspector carrying out the assessment. In many post-event assessments, cracks data
including width and pattern provide the most indicative information about the health or damage state of the structure.
Residual cracks are sometimes the only available data for VI. However, due to adjacent elastic members, earthquake
displacement spectrum, or re-centering systems, these measurements may lead to erroneous decisions. To overcome this
problem, this paper proposes a novel damage index based upon Fractal Dimension (FD) analysis of residual cracks as a
complementary method for VI. FD can quantify crack patterns and enhance the routine inspection procedure by
establishing a crack pattern recognition system. This algorithm was validated through an experimental study on a large
scale reinforced concrete shear wall (RCSW). The results demonstrate the novel technique as a quite accurate estimator
for damage grades and stiffness loss of the wall.
KEYWORDS: Structural health monitoring, Acoustic emission, Sensors, Expectation maximization algorithms, Atrial fibrillation, Systems modeling, Optical inspection, Picture Archiving and Communication System, Smart structures, Analytical research
Reinforced Concrete (RC) has been widely used in construction of infrastructures for many decades. The cracking behavior in concrete is crucial due to the harmful effects on structural performance such as serviceability and durability requirements. In general, in loading such structures until failure, tensile cracks develop at the initial stages of loading, while shear cracks dominate later. Therefore, monitoring the cracking modes is of paramount importance as it can lead to the prediction of the structural performance. In the past two decades, significant efforts have been made toward the development of automated structural health monitoring (SHM) systems. Among them, a technique that shows promises for monitoring RC structures is the acoustic emission (AE). This paper introduces a novel probabilistic approach based on Gaussian Mixture Modeling (GMM) to classify AE signals related to each crack mode. The system provides an early warning by recognizing nucleation of numerous critical shear cracks. The algorithm is validated through an experimental study on a full-scale reinforced concrete shear wall subjected to a reversed cyclic loading. A modified conventional classification scheme and a new criterion for crack classification are also proposed.
This paper proposes an adaptive Unscented Kalman Filter (UKF) algorithm for Acoustic Emission (AE) source
localization in plate-like structures in noisy environments. Overall, the proposed approach consists of four main stages:
1) feature extraction, 2) sensor selection based on a binary hypothesis testing, 3) sensor weighting based on a well-defined
weighting function, and 4) estimation of the AE source based on the UKF. The performance of the proposed
algorithm is validated through pencil lead breaks performed on an aluminum plate instrumented with a sparse array of
piezoelectric sensors. To simulate highly noisy environment, two piezoelectric transducers have been used to continually
generating high power white noise during testing.
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