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Ulrike Dackermann
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A systematic review of advanced sensor technologies for non-destructive testing and structural health monitoring
S Hassani, U Dackermann
Sensors 23 (4), 2204, 2023
1882023
In situ assessment of structural timber using stress-wave measurements
U Dackermann, K Crews, B Kasal, J Li, M Riggio, F Rinn, T Tannert
Materials and structures 47, 787-803, 2014
1502014
Damage identification in civil engineering structures utilizing PCA‐compressed residual frequency response functions and neural network ensembles
J Li, U Dackermann, YL Xu, B Samali
Structural Control and Health Monitoring 18 (2), 207-226, 2011
1452011
Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks
U Dackermann, WA Smith, RB Randall
Structural health monitoring 13 (4), 430-444, 2014
1002014
Dynamic-Based Damage Identification Using Neural Network Ensembles and Damage Index Method
U Dackermann, J Li, B Samali
Advances in Structural Engineering 13 (6), 1001-1016, 2010
772010
A systematic review of optimization algorithms for structural health monitoring and optimal sensor placement
S Hassani, U Dackermann
Sensors 23 (6), 3293, 2023
742023
A comparative study of using static and ultrasonic material testing methods to determine the anisotropic material properties of wood
U Dackermann, R Elsener, J Li, K Crews
Construction and Building Materials 102, 963-976, 2016
672016
Identification of member connectivity and mass changes on a two-storey framed structure using frequency response functions and artificial neural networks
U Dackermann, J Li, B Samali
Journal of Sound and Vibration 332 (16), 3636-3653, 2013
672013
Elastic wave modes for the assessment of structural timber: ultrasonic echo for building elements and guided waves for pole and pile structures
M Krause, U Dackermann, J Li
Journal of Civil Structural Health Monitoring 5, 221-249, 2015
582015
Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory
Y Yu, U Dackermann, J Li, E Niederleithinger
Structural Health Monitoring 18 (1), 123-142, 2019
572019
A systematic review of data fusion techniques for optimized structural health monitoring
S Hassani, U Dackermann, M Mousavi, J Li
Information Fusion 103, 102136, 2024
542024
Vibration behaviour of steel-timber composite floors, part (1): Experimental & numerical investigation
AA Chiniforush, MM Alamdari, U Dackermann, HR Valipour, ...
Journal of Constructional Steel Research 161, 244-257, 2019
542019
Guided wave–based condition assessment of in situ timber utility poles using machine learning algorithms
U Dackermann, B Skinner, J Li
Structural Health Monitoring 13 (4), 374-388, 2014
482014
Location and severity identification of notch-type damage in a two-storey steel framed structure utilising frequency response functions and artificial neural network
B Samali, U Dackermann, J Li
Advances in Structural Engineering 15 (5), 743-757, 2012
472012
Effects of environmental and operational conditions on structural health monitoring and non-destructive testing: A systematic review
A Keshmiry, S Hassani, M Mousavi, U Dackermann
Buildings 13 (4), 918, 2023
412023
Vibration-based damage identification in civil engineering structures utilising artificial neural networks
U Dackermann, J Li, B Samali, FC Choi, K Crews
Proceedings of the 12th International Conference on Structural Faults + …, 2008
39*2008
Vibration-based damage identification methods for civil engineering structures using artificial neural networks
U Dackermann
University of Technology Sydney, 2010
372010
Condition assessment of foundation piles and utility poles based on guided wave propagation using a network of tactile transducers and support vector machines
U Dackermann, Y Yu, E Niederleithinger, J Li, H Wiggenhauser
Sensors 17 (12), 2938, 2017
312017
Cepstrum-based damage identification in structures with progressive damage
U Dackermann, WA Smith, MM Alamdari, J Li, RB Randall
Structural Health Monitoring 18 (1), 87-102, 2019
292019
Damage identification of a concrete arch beam based on frequency response functions and artificial neural networks
VV Nguyen, U Dackermann, J Li, MM Alamdari, SA Mustapha, P Runcie, ...
Department of Civil and Environmental Engineering, 2015
262015
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