Structural Health Assessment of Timber Utility Poles Using Stress Wave Propagation and Artificial Neural Network Techniques

Timber utility poles represent a significant part of the power distribution and telecommunication infrastructure. Weathering, decay induced by fungus, and termite attacks deteriorate the condition of timber poles, causing a loss in their strength and toughness. Routine inspections are carried out to assess the condition of poles using conventional inspection techniques. However, the reliability of these techniques is in question. This paper proposes the stress wave propagation (SWP) technique for condition assessment of timber poles and the direct application of the artificial neural network (ANN) pattern recognition algorithm for signal classification. A Fourier-based signal decomposition method has been adopted and the frequency domain statistical features of the decomposed subcomponents were extracted to be used as the inputs for the ANN. Experiments were conducted using both intact and defective timber poles subjected to stress wave propagation. Different ANN models have been developed to classify the signals, and several controlling parameters were evaluated to obtain the best performance model. Further, the support vector machine (SVM) and k-means clustering algorithms were employed to classify the stress wave signals from intact and defective poles. Finally, the results of developed ANN models, SVM classifiers and k-means clustering models for pole classification were compared. The obtained success rates of the ANN model, the SVM classifier and the k-means clustering algorithm were 92%, 87% and 81%, respectively. Further, the trained ANN model was used to predict the health status of in-service poles, which were uprooted and subjected to full-scale bending tests after performing the stress wave propagation.