GTRF: A general deep learning framework for tuples recognition towards supervised, semi-supervised and unsupervised paradigms

Abstract

Tuple, featured as sequences of elements are regarded as one of the most predominant data forms in structural health monitoring (SHM). Activated by powerful capabilities of deep learning (DL) techniques, the DL-driven tuple recognition regime has facilitated plenty of problems settlement in SHM practice by mapping tuples with structural patterns, whereas the determined feature extraction strategies, designated network architectures and specified learning schemas (i.e., supervision paradigms) degrade severely to the model’s transferability and generalizability. Thereby, this study devises a novel General Tuple Recognition Framework (GTRF) towards supervised (SL), unsupervised (UL) and semi-supervised learning (SSL) paradigms. In the framework, pattern-sensitive features (PSFs) are quantitatively defined via a novel feature extractor intervened by deep autoencoder for downstream label propagation via an optimized fuzzy clustering algorithm in SL, UL and SSL paradigms. With sophisticated networks integrated and exquisite novelties embedded, the proposed GTRF is competent for various tuple recognition tasks in diverse learning paradigms regardless of the measurement types or lengths. Multi-level experimental tasks implementation representative in SHM scope were conducted for validations, varying in vibration SL-recognition of a prototype skyscraper, damage UL-detection of a laboratory RC beam and condition SSL-assessment of a full-scale building model. The results comprehensively confirmed the effectiveness and generality of proposed GTRF as well as comparable superiority in recognition accuracy and model adaptability. With flexible paradigm specialization, broad application and great space for optimization, the proposed GTRF framework can promisingly be a prototype for bridging the gap for DL algorithms fusion and models integration of different learning paradigms.