TY - JOUR
KW - Tree defect detection
KW - Single-device sensing
KW - Non-destructive testing
KW - Acoustic excitation
KW - Motion magnification
AU - Yishuang Zhang
AU - Mirosław Skibniewski
AU - Cheuk Chow
AU - Denvid Lau
AB - Structural failures caused by internal defects in trees pose major safety, operational, and financial risks to nearby buildings, transport corridors, and other constructed infrastructure. Routine inspection of internal or subsurface defects across widely distributed tree structures remains challenging due to the limitations of conventional non-destructive testing (NDT) methods, such as bulky equipment and poor field adaptability. To enable low-cost and field-adaptable detection of internal tree defects, this study proposes a single-device inspection system that uses a standard mobile phone for acoustic excitation and vibration sensing, evaluated through experiments on cedar specimens with prefabricated internal defects. Surface vibration responses exhibit strong correlation with internal structural characteristics, with indicative consistency between theoretical intrinsic frequencies and video-derived response characteristics. The results show that detection sensitivity is influenced by excitation frequency and decreases for smaller or deeper defects due to amplitude attenuation. Resonance-tuned excitation significantly amplifies vibration responses up to fourfold, enabling detection of subsurface defects with a minimum detectable size of approximately 1 mm under controlled conditions. Repeated experimental observations demonstrate consistent sensitivity to defect presence and size. Based on these characteristics, a defect-sensitive framework is developed to support structural condition assessment. These findings contribute to the advancement of portable NDT techniques for natural civil materials and support broader applications in infrastructure monitoring. The proposed approach offers potential for further investigation under more complex field conditions, with implications for improved robustness and practical applicability.
BT - Results in Engineering
DO - https://doi.org/10.1016/j.rineng.2026.111732
N2 - Structural failures caused by internal defects in trees pose major safety, operational, and financial risks to nearby buildings, transport corridors, and other constructed infrastructure. Routine inspection of internal or subsurface defects across widely distributed tree structures remains challenging due to the limitations of conventional non-destructive testing (NDT) methods, such as bulky equipment and poor field adaptability. To enable low-cost and field-adaptable detection of internal tree defects, this study proposes a single-device inspection system that uses a standard mobile phone for acoustic excitation and vibration sensing, evaluated through experiments on cedar specimens with prefabricated internal defects. Surface vibration responses exhibit strong correlation with internal structural characteristics, with indicative consistency between theoretical intrinsic frequencies and video-derived response characteristics. The results show that detection sensitivity is influenced by excitation frequency and decreases for smaller or deeper defects due to amplitude attenuation. Resonance-tuned excitation significantly amplifies vibration responses up to fourfold, enabling detection of subsurface defects with a minimum detectable size of approximately 1 mm under controlled conditions. Repeated experimental observations demonstrate consistent sensitivity to defect presence and size. Based on these characteristics, a defect-sensitive framework is developed to support structural condition assessment. These findings contribute to the advancement of portable NDT techniques for natural civil materials and support broader applications in infrastructure monitoring. The proposed approach offers potential for further investigation under more complex field conditions, with implications for improved robustness and practical applicability.
PY - 2026
EP - 111732
T2 - Results in Engineering
TI - Single-device on-site inspection system for non-destructive internal defect detection in trees near constructed facilities
UR - https://www.sciencedirect.com/science/article/pii/S2590123026027556
VL - 32
SN - 2590-1230
ER -