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In proportion to the immense construction of spatial structures is the emergence of catastrophes related to structural damages (e.g. loose connections), thus rendering personal injury and property loss. It is therefore essential to detect spatial bolt looseness. Current methods for detecting spatial bolt looseness mostly focus on contact-type measurement, which may not be practical in some cases. Thus, inspired by the sound-based human diagnostic approach, we develop a novel percussion method using the Mel-frequency cepstral coefficient and the memory-augmented neural network in this article. In comparison with current investigations, the main contribution of this article is the detection of multi-bolt looseness for the first time with higher accuracy than prior methods. In particular, in terms of new data obtained via similar joints, the memory-augmented neural network can help avoid inefficient relearn and assimilate new data to provide accurate prediction with only a few data samples, which effectively improves the robustness of detection. Furthermore, percussion was implemented with a robotic arm instead of manual operation, which preliminarily explores the potential of implementing automation applications in real industries. Finally, experimental results demonstrate the effectiveness of the proposed method, which can guide future development of cyber-physics systems for structural health detection.