## 1 Introduction
Having a high heat tolerance, high irradiation tolerance, good corrosion resistance, good thermal fatigue resistance and desirable toughness, SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) materials stand as a potential replacement to the currently used materials in nuclear fuel cladding such as Zircaloys [8, 9, 10]. SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) is a kind of ceramic materials composed of three main components: Silicon carbide fibers, fiber-matrix interphase material and matrix of silicon carbide [11]. In the past, a series of researches have focused on the damage monitoring of the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) components. In their experiments, [PERSON] et al. [11] used a non-destructive monitoring technique: the impulse excitation, to investigate the association between signals and the degradation of either one of the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) components. [PERSON] et al. [10] applied multiple tests on tubes of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) materials using the emitted acoustic emission (AE). They showed that the tubes' degradation undergoes stages, which correspond to the individual degradation of each of the three components. As a materials characterization technique, AE is a well-known non-destructive method that is used in various damage studies of composite materials. During material deterioration, acoustic events in the forms of transient strain waves are emitted [12]. For instance, [PERSON] et al. [13] studied the acoustic emission signals to propose a new method to detect and localize damage in continuous ceramic fiber reinforced ceramic matrix composites. Furthermore, [PERSON] et al. [14] used AE as one of their non-destructive evaluation methods to assess, quantify damage undergone by ceramic matrix composites. In our previous studies [15, 16, 17], an open-end burst test was carried on nine specimens of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) assembled as tubes. During each burst test, as illustrated in Figure 1, pressure is continuously applied to a tube of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) to simulate stress experimented by the cladding in an accident. As pressure is applied, the cladding keeps getting degraded until it abruptly ruptures. Since the sample is porous and heterogeneous, its failure is a complex accumulation of a great number of microcracking events that involves the cracking of various phases and interface at different geometric scales. In this aspect, the failure process is fundamentally different than the single cracking events in the scratching test as described in [18]. Unlike in homogenous material, the acoustic wave propagation in heterogeneous material with a large number of cracks will be heavily distorted, reflected and attenuated, and are not amenable to analytical treatment. The application of the brutal force machine learning method is thus the better choice here than analytical models. With these said, the most informational characteristics are recorded AE amplitude (event energy), AE duration and hit frequencies. To closely monitor the degradation process, sensors were used to capture the mechanical strain and the emitted AE signals. Additionally, the amount of applied pressure (stress) also gets recorded. Taking our burst tests into consideration, we defined the remaining useful strength (RUS) of the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) as the pressure left to be applied before the tube ruptures. Following the monitoring process of the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\), more than 40,000 generated AE events were obtained. In light of this amount of AE, we propose three data-driven RUS prediction approaches under the supervised learning framework. Using two machine learning models including random-forest (RF) and convolutional neural networks (CNNs), we build models that learn the correlation between the AE features and the RUS of the material.