ORCID Profile
0000-0001-6587-7493
Current Organisation
University of New South Wales
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Structural Engineering | Geospatial Information Systems | Technology not elsewhere classified | Dynamics, Vibration and Vibration Control | Geomechanics and Resources Geotechnical Engineering | Resources Engineering and Extractive Metallurgy | Mechanical Engineering |
Management and Productivity not elsewhere classified | Road Infrastructure and Networks | Mining and Extraction of Energy Resources not elsewhere classified | Civil Construction Planning
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 27-02-2016
Publisher: Informa UK Limited
Date: 08-05-2019
Publisher: SAGE Publications
Date: 07-03-2017
Abstract: This study addresses the detection and localization of barely visible indentation damage in composite sandwich structures using ultrasonic guided waves. A quasi-static loading was gradually applied on a specimen of carbon fiber reinforced epoxy with honeycomb core, with the resulting dent size varying between 0.2 and 2.7 mm. The fundamental symmetric (S 0 ) Lamb wave mode was excited to interrogate the structure. An anomaly measure was established based on symbolic time series analysis it was defined as the ratio between the norms of probability vectors obtained from the symbol sequence vectors before and after damage has occurred. The symbolic time series analysis method transforms time series data into symbol sequences according to a pre-constructed symbol space using a set number of partitions. The number of partitions selected was determined based on the maximum Shannon’s entropy approach. An imaging algorithm was adopted in order to localize the damage. The effects of the excitation frequency and the number of partitions on the precision of prediction were investigated. The adopted approach showed high sensitivity to a very small change of 0.2 mm on the surface panel after a quasi-static loading of 2-mm indentation. Furthermore, the ability of the method to detect progressive damage was demonstrated. The results obtained demonstrate that symbolic time series analysis has excellent potential for use in detecting small defects such as barely visible indentation damage.
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2016
Publisher: Trans Tech Publications, Ltd.
Date: 05-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.553.713
Abstract: This paper presents a novel damage localization method based on the measured Frequency Response Functions (FRFs) without demanding any previous data records of the structure in its healthy state. The main innovation of this study starts with reconstruction of FRFs curvature to develop spatial shape functions. It is demonstrated that reconstructed data significantly magnifies the influence of low-frequency spectra in damage detection procedure which is considered the milestone of this approach as excitation of the higher frequencies is not easy to obtain in most practical applications. The modified curvature data in all measured frequencies and locations is interpreted as a two dimensional image and then processed by employing 2-D discrete wavelet transform to detect any abrupt variation at damage site. Level one wavelet decomposition is utilised to provide the finest detail coefficients. It is illustrated that this approach presents a more recognizable pattern at damage site in all measured frequencies. The pattern can be described by a horizontal line parallel to the frequency spectra in 2-D image. Hence, the horizontal detail coefficients are utilised to detect this pattern as they are more sensitive to perturbation with orientation parallel to horizontal axis in the image. The main contribution of this approach lies in the fact that the proposed technique is able to detect the structural damage in all measured frequencies and the effectiveness of the method is independent of the excitation location. Moreover, the results provide a better visualisation at damage site which other FRF-based damage detection methods could not obtain. Applying broadband FRF data in this approach and the fact that there is no need for data from the healthy state of the structure are other advantages accompanying this method. The robustness of the proposed damage identification method was examined with various damage conditions in both single and multiple states. Moreover, the feasibility of the method was verified in presence of practical uncertainties such as noise using extensive numerical simulations. It was demonstrated that the proposed method is particularly attractive for practical applications as it opens an opportunity for online monitoring of the structural integrity without demanding any previous data records of the structure.
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 10-01-2019
Publisher: Unpublished
Date: 2022
Publisher: Springer International Publishing
Date: 2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2018
Publisher: Springer International Publishing
Date: 15-05-2020
Publisher: Springer International Publishing
Date: 19-06-2023
Publisher: Elsevier BV
Date: 10-2019
Publisher: Elsevier BV
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: Elsevier BV
Date: 12-2023
Publisher: Elsevier BV
Date: 08-2016
Publisher: SAGE Publications
Date: 16-10-2019
Abstract: This article aims at developing a new framework to identify and assess progressive structural damage. The method relies solely on output measurements to establish the frequency response functions of a structure using cepstrum-based operational modal analysis. Two different damage indicative features are constructed using the established frequency response functions. The first damage feature takes the residual frequency response function, defined as the difference in frequency response function between evolving states of the structure, and then reduces its dimension using principle component analysis while in the second damage indicator, a new feature based on the area under the residual frequency response function curve is proposed. The rationale behind this feature lies in the fact that damage often affects a number of modes of the system, that is, it affects the frequency response function over a wide range of frequencies as a result, this quantity has higher sensitivity to any structural change by combining all contributions from different frequencies. The obtained feature vectors serve as inputs to a novel multi-stage neural network ensemble designed to assess the severity of damage in the structure. The proposed method is validated using extensive experimental data from a laboratory four-girder timber bridge structure subjected to gradually progressing damage at various locations with different severities. In total, 13 different states of the structure are considered, and it is demonstrated that the new damage feature outperforms the conventional principle component analysis–based feature. The contribution of the work is threefold: first, the application of cepstrum-based operational modal analysis in structural health monitoring is further validated, which has potential for real-life applications where only limited knowledge of the input is available second, a new damage feature is proposed and its superior performance is demonstrated and finally, the comprehensive test framework including extensive progressive damage cases validates the proposed technique.
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2014
Publisher: SAGE Publications
Date: 06-01-2017
Abstract: We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.
Publisher: World Scientific Pub Co Pte Ltd
Date: 08-2018
DOI: 10.1142/S0219455418400023
Abstract: This paper presents a novel method to localize and quantify damage in a jack arch structure by introducing a linkage modeling technique to overcome issues caused by having limited sensors. The main strategy in the proposed Frequency Response Function (FRF)-based sensitivity model updating approach is to ide the specimen into partitions. The Young’s modulus of each partition is then updated to detect stiffness reduction caused by damage. System Equivalent Reduction Expansion Process (SEREP) is used to reduce the full finite element (FE) model to a linkage model. The number of measured degrees of freedom (DOFs) is then expanded to the linkage model using the mass and stiffness matrices of the linkage model for the synthesis of interpolated FRFs. The FRF sensitivities are then formulated using the linkage model along with the interpolated FRFs to iteratively calculate the values of the updating parameters until convergence is achieved. The methodology and theory behind this procedure are discussed and verified using a numerical and experimental study. The successful implementation of this method has the potential to detect the location and severity of damage where sensor placement is limited.
Publisher: Elsevier BV
Date: 04-2023
Publisher: Springer International Publishing
Date: 2014
Publisher: MDPI AG
Date: 02-01-2018
DOI: 10.3390/S18010111
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2017
Publisher: Association for Computing Machinery (ACM)
Date: 13-11-2018
DOI: 10.1145/3230708
Abstract: One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article proposes a novel adaptive self-advised online OCSVM that incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior s les in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly improved the classification error rates, was able to assimilate the changes in the positive data distribution over time, and maintained a high damage detection accuracy in all case studies.
Publisher: IOP Publishing
Date: 11-01-2017
Publisher: Springer Singapore
Date: 04-09-2020
Publisher: Elsevier BV
Date: 03-2017
Publisher: ACM
Date: 24-10-2016
Publisher: Hindawi Limited
Date: 06-12-2019
DOI: 10.1002/STC.2299
Publisher: Elsevier BV
Date: 04-2019
Publisher: Unpublished
Date: 2022
Publisher: Elsevier BV
Date: 07-2014
Publisher: SAGE Publications
Date: 30-08-2019
Abstract: A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.
Publisher: Springer Science and Business Media LLC
Date: 05-2015
Publisher: Hindawi Limited
Date: 28-07-2015
DOI: 10.1002/STC.1683
Publisher: Elsevier BV
Date: 12-2023
Location: Iran (Islamic Republic of)
Start Date: 2018
End Date: End date not available
Funder: University of New South Wales
View Funded ActivityStart Date: 2017
End Date: End date not available
Funder: Data61
View Funded ActivityStart Date: 02-2021
End Date: 12-2025
Amount: $430,075.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2022
End Date: 07-2027
Amount: $4,980,000.00
Funder: Australian Research Council
View Funded Activity