Revealing the failure mechanism of high strength seawater coral aggregate reinforced concrete slabs based on acoustic emission technology: Parameter analysis and DBN-BPNN classification

Abstract

In this study, high strength seawater coral aggregate concrete (SCAC) with a compressive strength of 80 MPa was prepared, and seawater coral aggregate reinforced concrete slab (SCARCS) was designed with epoxy-coated steel bars. Combining with the AE technology, monotonic flexural static tests were conducted on SCARCS specimens with three varying reinforcement ratios (i.e., 0.85%, 1.13%, and 1.41%). Results showed that the increasing the reinforcement ratio led to an increase in peak bearing capacity and a decrease in center deflections at the peak. The loading process can be divided into four stages based on the varying magnitudes of the b-value and Ib-value. The primary mode of failure was tensile microcracks at various stages, and the ratio of shear microcracks reached its maximum value in the final stage of loading. Besides, based on the signal intensity analysis, it can be observed that the scale and complexity of cracks propagation inside the SCARCS specimen increased as the reinforcement ratio rose. In addition, back propagation neural network (BPNN) classification model optimized by deep belief network (DBN) algorithm was proposed to predict the damage degree of SCARCS during loading, and the average prediction accuracy of the proposed model was shown to be over 89%. The classification outcomes demonstrated that with an increase in the reinforcement ratio of SCARCS, the proportion of AE signals in Cluster 3 increased, while the proportion of AE signals in Cluster 2 dropped. Notably, both the average frequency and energy presented an ascending trend. The findings of this study provided valuable insights for designing and assessing the safety of SCAC structures for essential island and reef engineering infrastructure.