**1. Introduction**

Alkali-silica reaction (ASR) is a chemical processes that has caused damage in concrete structures such as bridges [1–4], nuclear power plants [5–9], and concrete dams [5,10]. This reaction usually occurs between alkali hydroxides in the pore solution and the reactive silica in some aggregates. The result is an alkali-silica gel, which is hygroscopic and imbibes water and humidity. The gel expands in humidity exceeding 80% [6]. This expansion induces pressure on the concrete matrix and aggregates and causes micro-cracks and cracks when the pressure exceeds the tensile strength of the concrete [11,12]. Several traditional methods such as visual inspection, coring, and petrographic analysis have been utilized for monitoring and identifying the behavior of damage caused by ASR. Visual inspection is very subjective and depends on experience [13]. In addition, damage can only be detected by using this method, once it has reached the surface. Coring is a destructive method, which is discouraged in sensitive structures like nuclear power plants. Moreover, it is difficult to generalize the global condition of structures through evaluation of a few cores. Petrographic analysis is also

mostly dependent on the experience of the examiner, is likewise a destructive method, and is costly and time-consuming. Linking the micro-state of the material examined to the condition of the entire structure using petrographic analysis is difficult [11], although this is an active research area (RILEM TC-259). Therefore, to monitor structures affected by ASR, non-destructive and global/semi-global methods are attractive. Acoustic emission (AE) is a health monitoring approach which acts as a passive receiver to record internal activities in structures. This method is capable of continuous monitoring which is not the case with most traditional methods. In addition, AE sensors are very sensitive and can capture signals due to micro-scale defect formations coming from the internal regions of structures rather than only those at the surface [14,15]. Several studies have been conducted to evaluate the use of AE in detecting the damage in concrete caused by ASR in the laboratory [11,16–19].

Lokajícek et al. [ ˇ 16] employed ultrasonic sounding and AE to monitor the microstructural changes in mortar bars with different levels of aggregate reactivity. The P-wave velocity increased in the first days of the experiments and then declined abruptly for the specimens with the reactive aggregates. Furthermore, AE cumulative energy indicated the clear difference between the specimens with the reactive aggregates compared to the specimens without any ASR reactivity. Farnam et al. [17] characterized the AE signature for crack formations in aggregates, cement matrix, and interfacial transition zones (ITZ) during alkali-silica reaction using peak frequency and frequency of centroid of the AE signals. The high-frequency signals were assigned to the crack formation in the aggregate, while low-frequency signals were attributed to the crack formation in ITZ and cement matrix. Abdelrahman et al. [11], conducted an accelerated ASR test on concrete prism specimens. A correlation between cumulative signal strength and length change of the prism specimens was identified, and an AE based intensity analysis chart for the classification of ASR damage conditions in correlation with petrographic damage rating indices was developed.

All previously mentioned, research has been focused on ASR-induced damage detection using AE data in small-scale specimens (laboratory scale), which is far from the conditions found in real structures in the field. Furthermore, the stress boundary condition has generally not been taken into consideration in previous studies, while the stress boundary condition has proven to have a large effect on damage distribution in concrete structures affected by ASR [20,21].

In this study, acoustic emission is utilized to monitor the effects of ASR on large-scale, reinforced concrete structures while considering stress induced boundary conditions in completely plane-confined and partially plane-confined specimens. There are several differences between damage detection of small-scale and large scale structures using acoustic emission. The main challenge with monitoring of small-scale structures using AE is reflections. An example of an ASR study using AE on small-scale specimens was conducted by Abdelrahman et al. [11]. For large-scale structures, attenuation and dispersion of waves are of primary concern. Many sensors with an appropriate layout are needed for damage detection and source localization.

The test specimens were assembled, cured, and monitored at the University of Tennessee, Knoxville, and are part of a test program sponsored by the US DOE Light Water Reactor Sustainability Program. The confined specimen has a complete confinement provided by a rigid steel frame and steel reinforcement meshes. The unconfined specimen has partial confinement provided by steel reinforcement meshes. The specimens without transverse reinforcement resemble construction reminiscent of nuclear power plant containments. An unsupervised pattern recognition algorithm was employed to classify the AE signals based on frequency-energy based features. Different damage mechanisms for the confined and unconfined specimens were identified using AE data.
