Acoustic emission (AE) and vibration signal are significant criteria of damage identification in structural health monitoring (SHM) engineering. Multi-disciplinary knowledge and synergistic parameter effects are technical challenges for damage assessment modelling. This study proposes a structural damage cause-and-effect analysis method based on parameter information entropy. Monitoring data is used to form a time-domain feature wave (TFW). The structural strength degradation factor (DF) would be used to define structural damage information entropy (SDIE) vector. The structural damage cause and effect model is developed in a probability sense. A fatigue index is adopted for damage assessment, and a causal strength index is proposed to locate the most likely damage cause. A sandstone-truss structure experiment was conducted to show that the proposed method is effective for damage evaluation and the experimental results provide strong support. This is a statistical damage identification method based on causal logic uncertainty, meaning a complicated mechanics calculation can be avoided.

Moisture is a vital factor in the structural stability of sandstone, which is the main component of low-permeability reservoir rocks. Hence, studies into moisture identification are crucial. Diverse information about rock, such as its structural and mechanical parameters, can be obtained from the acoustic emission (AE) signal. However, the types of AE parameters are varied, and the rock information that is represented by them is different. Traditional methods of parameter selection are mostly based on the correlation between parameters and the experience of researchers, which are not accurate when the correlation between parameters is fuzzy and does not meet automation requirements. In this study, a method of signal feature selection based on a data fluctuation rule and clustering analysis is proposed. This method takes the fluctuation law of the signal itself and the correlation degree of cluster labels as the basis, and the selection step is divided into two steps. An experimental platform is established, and uniaxial compression on sandstones with different moisture contents is carried out to verify the efficiency of this method. The selected feature parameters are used for moisture classification combined with a support vector machine (SVM) classifier, and the identification results verify the efficiency of energy security monitoring in low-permeability rocks.

Long-term SHM systems need to control the scale of monitoring data storage and reduce the maintenance cost of sensor failures. Inspired by human memory, an intelligent structural state retrieval model is presented. First, the mental model of human memory is introduced and then the structural state recognition model and recall model are established. Methods of calculating familiarity values for recognition and similarity values for recall are discussed for the model. An experiment on strain monitoring was carried out to show the implementation process while demonstrating the effectiveness of the model. It is shown that the structural state recognition model can discard a large amount of redundant information and the structural state recall model can restore the missing data of failed sensors.

Studies on the moisture and damage state of rocks generally focus on the qualitative description and mechanical information of rocks. This method is n

Structural health monitoring (SHM) is challenged by massive data storage pressure and structural fault location. In response to these issues, a bio-inspired memory model that is embedded with a causality reasoning function is proposed for fault location. First, the SHM data for processing are divided into three temporal memory areas to control data volume reasonably. Second, the inherent potential of the causal relationships in structural state monitoring is mined. Causality and dependence indices are also proposed to establish the mechanism of quantitative description of the reason and result events. Third, a mechanism of causality reasoning is developed for the reason and result events to locate faults in a SHM system. Finally, a deformation experiment conducted on a steel spring plate demonstrates that the proposed model can be applied to real-time acquisition, compact data storage, and system fault location in a SHM system. Moreover, the model is compared with some typical methods based on an experimental benchmark dataset.