Fig. 2: Strength sensing data collection and visualization for concrete structures.
From: Real-time concrete strength monitoring using piezoelectric sensors and deep learning
a Strength distribution across seven concrete slabs at various curing ages, representing different strength development patterns captured in the dataset. b Histogram of the overall dataset, featuring both internal temperature and compressive strength measurements. The temperature follows a normal distribution, influenced by external environmental factors and concrete’s exothermic hydration process. The strength data can be segmented into two categories: early-age concrete, which was densely sampled, and later-age concrete exhibiting a normal distribution, shaped by different mix designs and curing conditions. c EMI sensing performance from a single representative sensor, showing signal variations as the concrete ages, with clear changes over time. d Principal component (Principal component) analysis of real-time concrete sensing EMI signals only. Here, PC_1 captures the dominant trend of strength-related signal evolution, while PC_2 represents secondary variations in the EMI response. e Principal component analysis of both real-time EMI signals and baseline EMI signals, highlighting the high correlation between EMI signal patterns and concrete strength development.
