“…The AE ringing count refers to the quantification of oscillations that surpass a specific threshold, providing an approximate indication of the frequency and intensity of the AE signal [58,59]. The slope of the cumulative ringing count in the acoustic emission count can offer useful insights into monitoring the advance of damage, evaluating the integrity of structures, and anticipating potential failure mechanisms in various materials and structural elements undergoing AE testing.…”
Ultra-high-performance concrete (UHPC) is widely used because of its exceptional properties, such as high compressive and flexural strength, low permeability, and resistance to abrasion and chemical attack. It is commonly employed for intricate constructions like skyscrapers, precast concrete components, and infrastructure. Nevertheless, the incorporation of appropriate fibers into UHPC is carried out in order to accomplish objectives such as augmenting strength, enhancing toughness, and regulating cracking. This study employed magnetite as an additive to a UHPC block in order to examine the mechanical characteristics of a newly cast UHPC block. Acoustic emission was employed to evaluate the damage to the UHPC block for tracking purposes. Acoustic emission is a non-invasive testing technique that does not cause harm to the specimen when it is exposed to a load. On the basis of this, many critical locations that indicated the propagation of cracks were analyzed, as well as various loading stages across the specimen. The b-value is a method that can evaluate the extent of damage by analyzing the amplitude distribution. Distinct paths of b-values were noted for each loading stage, indicating major damage scenarios based on their slopes.
“…The AE ringing count refers to the quantification of oscillations that surpass a specific threshold, providing an approximate indication of the frequency and intensity of the AE signal [58,59]. The slope of the cumulative ringing count in the acoustic emission count can offer useful insights into monitoring the advance of damage, evaluating the integrity of structures, and anticipating potential failure mechanisms in various materials and structural elements undergoing AE testing.…”
Ultra-high-performance concrete (UHPC) is widely used because of its exceptional properties, such as high compressive and flexural strength, low permeability, and resistance to abrasion and chemical attack. It is commonly employed for intricate constructions like skyscrapers, precast concrete components, and infrastructure. Nevertheless, the incorporation of appropriate fibers into UHPC is carried out in order to accomplish objectives such as augmenting strength, enhancing toughness, and regulating cracking. This study employed magnetite as an additive to a UHPC block in order to examine the mechanical characteristics of a newly cast UHPC block. Acoustic emission was employed to evaluate the damage to the UHPC block for tracking purposes. Acoustic emission is a non-invasive testing technique that does not cause harm to the specimen when it is exposed to a load. On the basis of this, many critical locations that indicated the propagation of cracks were analyzed, as well as various loading stages across the specimen. The b-value is a method that can evaluate the extent of damage by analyzing the amplitude distribution. Distinct paths of b-values were noted for each loading stage, indicating major damage scenarios based on their slopes.
“…Bert Peeters et al reviewed the status of cavitation-related AE [24]. Kensuke Kageyama et al found that the AE behaviors of Schefflera and Olive trees were strongly influenced by drought stress [25].…”
Water plays an important role in various physiological activities of living trees. Measuring trunk moisture content (MC) in real-time without damage has important guiding significance for transpiration research in forest ecosystems. However, existing standing tree MC detection methods are either too cumbersome to install or cause different degrees of damage. Here, we propose a novel Internet of Things (IoT) monitoring system that includes wireless acoustic emission sensor nodes (WASNs) and underground soil MC sensor nodes to efficiently detect and diagnose the MC level of living tree trunks. After the characteristic parameters were collected by the two sensors, a feature selection and multi-sensory global fusion method for MC diagnosis was designed and developed and several statistical parameters were selected as the input variables to predict the heartwood MC level with a support vector machine (SVM) model. Moreover, to achieve the highest prediction accuracy, an improved sparrow search algorithm (ISSA) is applied to ensure the most suitable parameter combinations in a two-objective optimization model. Extensive experiments result in a fusion of the environment, and AE signals show that the proposed mechanism has better diagnostic performance than state-of-the-art methods and is more adaptable to the fluctuation of working conditions.
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