Guided wave-based condition assessment of in situ timber utility poles using machine learning algorithms

This paper presents a machine-learning-based approach for the structural health monitoring (SHM) of in-situ timber utility poles based on guided wave (GW) propagation. The proposed non-destructive testing method combines a new multi-sensor testing system with advanced statistical signal processing techniques and state-of-the-art machine learning algorithms for the condition assessment of timber utility poles. Currently used pole inspection techniques have critical limitations including the inability to assess the underground section. GW methods, on the other hand, are techniques potentially capable of evaluating non-accessible areas and of detecting internal damage. However, due to the lack of solid understanding on the GW propagation in timber poles, most methods fail to fully interpret wave patterns from field measurements. The proposed method utilises an innovative multi-sensor testing system that captures wave signals along a sensor array and it applies machine learning algorithms to evaluate the soundness of a pole. To validate the new method, it was tested on eight in-situ timber poles. After the testing, the poles were dismembered to determine their actual health states. Various state-of-the-art machine learning algorithms with advanced data pre-processing were applied to classify the poles based on the wave measurements. It was found that using a support vector machine classifier, with the GW signals transformed into autoregressive coefficients, achieved a very promising maximum classification accuracy of 95.7±3.1% using 10-fold cross validation on multiple training and testing instances. Using leave-one-out cross validation, a classification accuracy of 93.3±6.0% for bending wave and 85.7±10.8% for longitudinal wave excitation was achieved. © The Author(s) 2014

Elastic wave modes for the assessment of structural timber: ultrasonic echo for building elements and guided waves for pole and pile structures

© 2014, Springer-Verlag Berlin Heidelberg. This paper presents the state-of-the-art of using non-destructive testing (NDT) methods based on elastic waves for the condition assessment of structural timber. Two very promising approaches based on the propagation and reflections of elastic waves are described. While the first approach uses ultrasonic echoes for the testing of wooden building elements, the second approach uses guided waves (GW) for the testing of timber pole and pile structures. The basic principle behind both approaches is that elastic waves induced in a timber structure will propagate through its material until they encounter a change in stiffness, cross-sectional area or density, at which point they will reflect back. By measuring the wave echoes, it is possible to determine geometric properties of the tested structures such as the back wall of timber elements or the underground length of timber poles or piles. In addition, the internal state of the tested structures can be assessed since damage and defects such as rot, fungi or termite attacks will cause early reflections of the elastic waves as well as it can result in changes in wave velocity, wave attenuation and wave mode conversion. In the paper, the principles and theory of using elastic wave propagation for the assessment of wooden building elements and timber pole/pile structures are described. The state-of-the-art in testing equipment and procedures is presented and detailed examples are given on the practical application of both testing approaches. Recent encouraging developments of cutting edge research are presented along with challenges for future research

Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks

This article presents a response-only structural health monitoring technique that utilises cepstrum analysis and artificial neural networks for the identification of damage in civil engineering structures. The method begins by applying cepstrum-based operational modal analysis, which separates source and transmission path effects to determine the structure's frequency response functions from response measurements only. Principal component analysis is applied to the obtained frequency response functions to reduce the data size, and structural damage is then detected using a two-stage ensemble of artificial neural networks. The proposed method is verified both experimentally and numerically using a laboratory two-storey framed structure and a finite element representation, both subjected to a single excitation. The laboratory structure is tested on a large-scale shake table generating ambient loading of Gaussian distribution. In the numerical investigation, the same input is applied to the finite model, but the obtained responses are polluted with different levels of white Gaussian noise to better replicate real-life conditions. The damage is simulated in the experimental and numerical investigations by changing the condition of individual joint elements from fixed to pinned. In total, four single joint changes are investigated. The results of the investigation show that the proposed method is effective in identifying joint damage in a multi-storey structure based on response-only measurements in the presence of a single input. Because the technique does not require a precise knowledge of the excitation, it has the potential for use in online structural health monitoring. Recommendations are given as to how the method could be applied to the more general multiple-input case. © The Author(s) 2014

Structural damage identification utilising PCA-compressed frequency response functions and neural network ensembles

This paper presents a damage detection method that utilises FRF data to identify damage in beam structures. The proposed method uses artificial neural networks (ANNs) to map changes in FRFs to damage characteristics. To obtain suitable patterns for ANN inputs, the size of the FRFs is reduced adopting Principal Component Analysis (PCA) techniques. A hierarchy of neural network ensembles is created to take advantage of individual differences from sensor signals. To simulate field applications, the time history data are polluted with white Gaussian noise. The method involves finite element modelling of undamaged and damaged steel beams. By performing transient analysis with the numerical beams, the time histories are obtained and subsequently polluted with different levels of white Gaussian noise. FRFs are determined and compressed utilising PCA techniques. The PCA-reduced FRFs are then used as input patterns for training and testing of neural network ensembles giving the characteristics of the damage. © 2009 Taylor & Francis Group, London

Dynamic-based damage identification using neural network ensembles and damage index method

This paper presents a vibration-based damage identification method that utilises a "damage fingerprint" of a structure in combination with Principal Component Analysis (PCA) and neural network techniques to identify defects. The Damage Index (DI) method is used to extract unique damage patterns from a damaged beam structure with the undamaged structure as baseline. PCA is applied to reduce the effect of measurement noise and optimise neural network training. PCA-compressed DI values are, then, used as inputs for a hierarchy of neural network ensembles to estimate locations and severities of various damage cases. The developed method is verified by a laboratory structure and numerical simulations in which measurement noise is taken into account with different levels of white Gaussian noise added. The damage identification results obtained from the neural network ensembles show that the presented method is capable of overcoming problems inherent in the conventional DI method. Issues associated with field testing conditions are successfully dealt with for numerical and the experimental simulations. Moreover, it is shown that the neural network ensemble produces results that are more accurate than any of the outcomes of the individual neural networks

Transmissibility function analysis for boundary damage identification of a two-storey framed structure using artificial neural networks

This paper presents a damage identification technique that uses output-only scalar transmissibility measurements of a structure to identify boundary conditions. A damage index is formulated based on output-only acceleration response measurements from ambient floor vibration. The damage index is analysed by a system of artificial neural networks (ANNs) to predict boundary condition changes of the structure. Using the data compression and noise filtering capabilities of principal component analysis (PCA), the size of the damage index is reduced in order to obtain suitable patterns for ANN training. To test the proposed method, it is applied to different models of a numerical two-storey framed structure with varying boundary conditions. Boundary damage is simulated by changing the condition of individual joint elements of the structure from fixed to pinned. The results of the investigation show that the proposed method is effective in identifying boundary damage in structures based on output-only response measurements. © 2013 Taylor & Francis Group

A comparative study of using static and ultrasonic material testing methods to determine the anisotropic material properties of wood

© 2015 Elsevier Ltd. This paper presents a comparative study using static and ultrasonic testing for the determination of the full set of orthotropic material properties of wood. In the literature, material properties are typically only available in the longitudinal direction, and most international standards do not provide details on the testing of the other two secondary directions (radial and tangential). This work provides a comprehensive study and discussions on the determination of all twelve orthotropic material properties of two hardwood species using static testing and an alternative testing approach based on ultrasonic waves. Recommendations are given on the execution of the tests and the interpretation and calibration of the results

Vibration-based damage detection for timber structures in Australia

© 2014 by Nova Science Publishers, Inc. All rights reserved. The use of non-destructive assessment techniques for evaluating structural conditions of aging infrastructure, such as timber bridges, utility poles and buildings, for the past 20 years has faced increasing challenges as a result of poor maintenance and inadequate funding. Replacement of structures, such as an old bridge, is neither viable nor sustainable in many circumstances. Hence, there is an urgent need to develop and utilize state-of-the-art techniques to assess and evaluate the ?health state? of existing infrastructure and to be able to understand and quantify the effects of degradation with regard to public safety. This paper presents an overview of research work carried out by the authors in developing and implementing several vibration methods for evaluation of damage in timber bridges and utility poles. The technique of detecting damage involved the use of vibration methods, namely damage index method, which also incorporated artificial neural networks for timber bridges and time-based non-destructive evaluation (NDE) methods for timber utility poles. The projects involved successful numerical modeling and good experimental validation for the proposed vibration methods to detect damage for simple beams subjected to single and multiple damage scenarios and was then extended to a scaled timber bridge constructed under laboratory conditions. The time-based NDE methods also showed promising trends for detecting the embedded depth and condition of timber utility poles in early stages of that research

Condition assessment of timber utility poles based on a hierarchical data fusion model

© 2016 American Society of Civil Engineers. This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster-Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method