Automatic identification of early strength of high-performance concrete based on micro-impedance sensing system and deep learning method

Abstract

Compressive strength is a vital measure for assessing the quality of high-performance concrete (HPC), and real-time monitoring of its early strength development is essential for guiding construction processes and ensuring structural safety. However, studies on early strength monitoring using the micro-impedance method are relatively scarce. This article introduces a novel approach for early strength monitoring, leveraging a multistacked piezoelectric impedance lightweight monitoring system combined with deep learning techniques. Three primary innovations are presented: (1) this study addresses the challenges of inadequate real-time performance, limited portability, and high cost in traditional structural health monitoring systems by proposing a hardware architecture that integrates a multilayer stacked piezoelectric sensor with the AD5933 impedance analyzer. By incorporating lightweight deep learning algorithms, the study develops a portable impedance monitoring system characterized by high sensitivity, high precision, and easy lightweight deployment. (2) The research innovatively introduces a lightweight deep learning model based on a multigrid architecture, comprising a feature extraction module and a metric learning module. The former achieves hierarchical feature capture through feature fusion of multiscale grid data, cross-modal analysis, and the use of multispecification convolutional kernels. The latter employs graph convolutional network layers to adaptively learn node parameters and optimizes feature parameters via similarity metric aggregation. (3) To address the issue of insufficient data during collection, this study utilizes a signal reconstruction and generation method based on an adaptive orthogonal matching pursuit algorithm. This approach enables the reconstruction of impedance data, mitigating the impact of limited data volume and providing a foundational data framework for subsequent deep learning databases. The proposed method shows promising potential for advancing intelligent early strength monitoring in HPC applications.