PSI - Issue 41

G. Agalianos et al. / Procedia Structural Integrity 41 (2022) 452–460 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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1. Introduction The mechanical response of materials and structures, under load levels approaching the critical limits of failure, is a major concern of the engineering community. In this context, with the aid of theoretical and experimental works sensing techniques have been developed aiming to estimate the remaining service life and the remaining load carrying capacity of a structure approaching failure. Several studies refer to experimental techniques, either destructive or Non Destructive (NDT), that are capable of providing useful information about the internal changes occurring in a material when severe mechanical load is applied (Murakami, 1988; Onat and Leckie, 1988). A widely used NDT technique is the Acoustic Emission (AE) one that originates from earthquake observation (Armstrong, 1969). AEs refers to transient elastic stress waves generated by the sudden energy released when structural changes occur in the bulk of a material. These waves are recorded using sensors which are appropriate attached on the material’s surface (Wevers, 1997; Mouritz, 2003). The AE technique has been standardized according to ASTM with the ASTM E610-82 standard (ASTM, 1982). It has been used for the assessment of the damage level on brittle materials such as concrete and rocks (Lockner, 1993; Stanchits et al., 2006; Aggelis et al. 2013), for the real time monitoring of microcracking development and, also, their classification according to the mechanisms responsible for their generation, i.e., due to normal or shear/mixed loading (Ohno and Ohtsu, 2010; Zhang et al. 2020). Taking into account that the ΑΕ signals have similarities with the seismic waves (Zang et al., 1996; Khan et al., 2018; Yang et al., 2020; Minakov and Yarushina, 2021), it is reasonable to assume that statistical tools used for the analysis of the seismic waves could be, also, used for the analysis of AEs. Shiotani et al (2001) proposed the improved b-value (Ib-value) concept based on the use of statistical parameters such as the mean value and the standard deviation of the AE amplitudes were used. The Ib-value parameter is calculated taking advantage of the statistical analysis of the AE amplitudes distribution and it is related to the imminent fracture (Shiotani et al., 2001). It is accepted that the temporal variation of the Ib- values provides indications concerning the proximity of the system to its “critical stage” (impending failure). The Ib-value is calculated according to the relationship: ( ) ( ) ( ) − − + = + 10 1 10 2 1 2 log log N N Ib          (1) Another sensing technique, also widely used to monitor the internal damage, is the Pressure Stimulated Currents (PSC) technique (Stavrakas et al., 2004). The PSC technique is based on the detection of low-level electrical signals, emitted when brittle materials are subjected to mechanical loading at levels generating damage of the internal structure (Enomoto and Hashimoto, 1990; Vallianatos et al., 2004). According to Varotsos et al. (2002) and Vallianatos et al. (2004) the formation of microcracks in a quasi-brittle nonmetallic material produces electric charges, i.e., a complicated charged system of electric dipoles. These electric dipoles produce an electric potential along the crack resulting to flow of current. The PSC technique has been applied in a wide range of rocks and artificial materials (Stavrakas et al., 2003; Stavrakas et al., 2004; Triantis et al., 2006; Triantis et al., 2012; Cartwright et al., 2014; Li et al., 2015; Archer et al., 2016; Triantis et al., 2020; Loukidis et al., 2020; Li et al., 2021). The aforementioned techniques (i.e., the AE and the PSC ones) are proven to be well correlated to each other (Kyriazopoulos, 2017; Pasiou and Triantis, 2017; Triantis et al., 2017; Mastrogiannis et al., 2020; Zhang et al., 2021), at least qualitatively. In the present study, the data obtained by both the AE- and the PSC-techniques while Alfas-stone specimens were submitted to three-point bending are correlated to each other, paying special attention to the role of the specimens porosity. 2. Materials and experimental set up Alfas building stone is a natural homogeneous sedimentary porous limestone of layered structure, quarried near Alfas village in Crete, Greece. It is composed of calcite (about 91%) and contains, also, 2% quartz and 7% aragonite (Kaklis et al., 2017). Alfas’ color varies from white-gray to grayish-beige and rarely light yellow. Its modulus of where σ is the standard deviation, μ is the mean value of the AE amplitude distribution, α 1 is a coefficient related to the smaller amplitude and α 2 is a coefficient related to the fracture level.