Alireza Farhidzadeh

Adaptive multisensor data fusion for acoustic emission source localization in noisy environment

This article proposes an adaptive multisensor fusion algorithm for acoustic emission source location in isotropic plate-like structures in noisy environments. Overall, the approach consists of the following four main stages: (a) feature extraction, (b) sensor selection based on a binary hypothesis testing, (c) sensor weighting based on a well-defined reliability function, and (d) estimation of the acoustic emission source location based on the extended Kalman filter. 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. Two experimental setups have been used to simulate a highly noisy environment. In the first setup, the experimental signals have been artificially corrupted with different levels of noise generated by a Monte Carlo simulation. In the second setup, two piezoelectric transducers have been used to continuously generate high-power white noise during testing. The results show the capability of the proposed algorithm for inflight structural health monitoring of metallic aircraft panels in highly noisy operational situation.

Reference-free corrosion damage diagnosis in steel strands using guided ultrasonic waves.

This study presents a nondestructive evaluation method based on guided ultrasonic waves (GUW) to quantify corrosion damage of prestressing steel strands. Specifically, a reference-free algorithm is proposed to estimate the strands cross-section loss by using dispersion curves, continuous wavelet transform, and wave velocity measurements. Accelerated corrosion tests are carried out to validate the proposed approach. Furthermore, the propagation of Heisenberg uncertainty to diameter measurement is also investigated. The method can reasonably estimate the wires diameter without any baseline as a reference.

Multifractal analysis of crack patterns in reinforced concrete shear walls

Conventionally, the assessment of reinforced concrete shear walls relies on manual visual assessment which is time-consuming and depends heavily on the skills of the inspectors. The development of automated assessment employing flying and crawling robots equipped with high-resolution cameras and wireless communications to acquire digital images and advance image processing to extract crack patterns has paved the path toward implementing an automated system which determines structural damage based on visual signals acquired from structures. Since there are few, if any, studies to correlate crack patterns to structural integrity, this article proposes to analyze crack patterns using a multifractal analysis. The approach is initially tested on synthetic crack patterns, and then it is applied to a set of experimental data collected during the testing of two large-scale reinforced concrete shear wall subjected to controlled reversed cyclic loading. The structural response data available for each specimen are used to link the multifractal parameters with the structural performance of the two specimens. A relationship between the multifractal parameters and the crack patterns’ evolution and mechanism is noted. The results show that as the crack patterns extend and grow, multifractal parameters move toward higher values. The parameters jump as the mechanical response shows severe stiffness loss. In this study, no attempt is made to automate the process of mapping cracks from images.

Multifractal analysis of two-dimensional images for damage assessment of reinforced concrete structures

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.

Adaptive unscented Kalman filter (UKF) for acoustic emission (AE) source localization in noisy environment

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.

Corrosion damage estimation in multi-wire steel strands using guided ultrasonic waves

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.

A vision-based technique for damage assessment of reinforced concrete structures

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.

Gaussian mixture modeling of acoustic emissions for structural health monitoring of reinforced concrete structures

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.

Bayesian decision and mixture models for AE monitoring of steel–concrete composite shear walls

This paper presents an approach based on an acoustic emission technique for the health monitoring of steel–concrete (SC) composite shear walls. SC composite walls consist of plain (unreinforced) concrete sandwiched between steel faceplates. Although the use of SC system construction has been studied extensively for nearly 20 years, little-to-no attention has been devoted to the development of structural health monitoring techniques for the inspection of damage of the concrete behind the steel plates. In this work an unsupervised pattern recognition algorithm based on probability theory is proposed to assess the soundness of the concrete infill, and eventually provide a diagnosis of the SC walls health. The approach is validated through an experimental study on a large-scale SC shear wall subjected to a displacement controlled reversed cyclic loading.

Structural health monitoring of pipelines rehabilitated with lining technology

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.