Abstract: J. Kumar, C. Nerse, O. Sedehi, B. J. Halkon, U. Dackermann, J. C. Lai, S. M. Oberst (2025)

Proceedings of the International Congress on Sound and Vibration (ICSV31), 1–8p. (2025)

A framework for vibration-based termite detection in timber structures

Timber structures hold significant importance in the construction industry owing to their inherent sustainability, aesthetic appeal, and economic viability. However, in natural environments, timber pests such as termites pose a major threat to their structural integrity, leading to the deterioration of wood. Early detection of termite infestations is crucial for timber preservation. Here, we suggest a framework that measures various parameters across a timber structure, to aim for the development of a warning system for the early detection of wood-eating pests. Termites are known to use vibrations for foraging decisions and detecting competitors and predators, however, vibrations have not yet been successfully used for the identification and control of termites. Also, structures are exposed to different causes of damage and deterioration, with their condition able to be monitored in principle using vibrations. The suggested analysis framework consists of a sensor network to measure signals that are produced from different excitations and locations including biological signals, anthropogenic excitation, and environmental noise. Nonlinear time series analyses, such as recurrence plots, recurrence quantification analysis are employed and combined with machine learning methods, to extract signal features and patterns to classify different load cases that are beyond mere modal analysis. Core part of the network is the signal processing based on joint recurrence plots which fuses distributed sensor information of measured vibrations, humidity and temperature. The suggested framework will contribute to providing a scalable, economical, and practical solution to a critical worldwide problem in damage detection of timber and its structural health monitoring.

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