STUDY ON DAMAGE DETECTION AND QUANTIFICATION OF ANCIENT BUILDING TIMBER STRUCTURES BASED ON LAMINATION THEORY AND BP NEURAL NETWORKS

To quantify damage of ancient timber structures under ambient excitation, according to the lamination theory, a method was proposed to divide wood into damaged surface layers and undamaged middle layers by layered solid elements of the finite element method. The damage depth was taken as a new variable, combined with the total energy-change rates of wavelets as feature parameters and input into an improved BP neural network, and the reduction ratios of elastic moduli were considered as output parameters. Appling the method respectively to multi-scale models of a timber frame and Xi'an Bell Tower, the result showed that the maximum error of identification results was less than 2%, which meant it feasible to simulate wood damage by layered solid elements and adopt the damage depth as a parameter to quantify the damage of wood beams, which could provide reference for the study of damage early warning and local damage analysis of complex structures under ambient excitation