{'fraction_non_alphanumeric': 0.014586709886547812, 'fraction_numerical': 0.0, 'mean_word_length': 6.180232558139535, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Enhanced statistical damage identification using frequency change information with tunable piezoelectric circuitry

The frequency-shift based damage detection method entertains advantages such as the global detection capability and the easy implementation, but also suffers from drawbacks that include low detection sensitivity and the difficulty in identifying damage using a small number of measurable frequencies. Meanwhile, the damage detection performance is inevitably affected by the uncertainty/variations in the baseline model. In this research, we investigate an enhanced statistical damage identification method using the tunable piezoelectric circuitry. The tunable piezoelectric circuitry can lead to much enriched information of frequency-shift (before and after damage occurrence). The circuitry elements, meanwhile, can be directly and accurately measured and thus can be considered uncertainty-free. A statistical damage identification algorithm is formulated which can provide both the mean and variance of the elemental property changes. Our analysis indicates that the integration of the tunable piezoelectric circuitry can significantly enhance the robustness in damage identification under uncertainty and noise.