# Structural Health Monitoring of Large Structures Using Acoustic Emission-Case Histories
## 2 Acoustic Emission Applications  
With the goal given above, this special issue was initiated to collect latest research on relevant subjects. There were 28 papers submitted to this special issue, and 14 papers were accepted (i.e., a 50% acceptance rate). A key review paper, authored by [PERSON] and [PERSON] [7], covered AE applications for the SHM of mines and in various geological settings. Of these, underground repositories for nuclear waste are especially significant, and millimeter-size defects were located in a million cubic meter volume, demonstrating in situ AE monitoring is a useful tool to observe instabilities in rock long before any damage becomes visible. [PERSON] [8] presented the use of an AE method for determining the seismicity of a lake bottom following the massive 2011 Tohoku earthquake. Another review by [PERSON] et al. [9] discussed AE methods for evaluating the structural integrity of asphalt pavements located in cold regions. It amply shows that AE allows for relatively rapid and inexpensive characterization of pavement materials and can be used for enhancing pavement sustainability and resiliency to thermal loading. Three papers dealt with the SHM of industrial structures, bridges, and masonry buildings. [PERSON] et al. [10] presented a series of examples of AE monitoring of industrial facilities under load, both static and fatigue conditions. Some were one of a kind structures, forcing them to improvise various techniques. They successfully evaluated oil refinery tanks and towers, pipelines, rotary kilns, bridge structures, drag lines, etc. [PERSON] [11] also covered AE detection of active destructive processes that are in progress on various structures, including steel bridges, steel columns, and a large suspension bridge. The use of pattern recognition analysis was effective in identifying active damage progression. [PERSON] et al. [12] detected AEs from seismically induced cracking in an ancient building. Work by [PERSON] et al. [13] was directed at the evaluation of aerospace composite parts via AE monitoring. They developed a multi-dimensional parts assessment method by taking advantage of AE data recorded during structural testing. For this goal, they tested 16 composite fixed-wing-aircraft spars using a structural loading sequence designed around a manufacturer-specified design limit load (DLL). While loading, the Felicity ratio was calculated. Along with specific AE data from post-processing, they deduced spar test classification in terms of apparent damage behavior. In applications aimed toward the SHM of concrete structures, the [PERSON] group presented two studies [14; 15]. In the first one, [PERSON] et al. [14] applied remote AE monitoring on an actively corroding concrete structure for one year and showed the feasibility of using AE for corrosion damage detection and classification. Here, studies of decommissioned components and control concrete parts were examined in parallel and comparative evaluation led to the conclusion. In the second study, [PERSON] et al. [15] examined alkali-silicate reaction on large-scale concrete structures. They utilized an agglomerative hierarchical algorithm to classify the AE data based on energy-frequency based features. AE observations were correlated to confinement strains, allowing the assessment of structural degradation via AE signal features. In all AE applications, source location is essential and [PERSON] et al. [16] proposed a new scheme for dealing with outliers, by introducing a preconditioned closed-form solution based on weight estimation. While this produced improvements, some real effects like damage-induced velocity variation still needs additional study, as noted by the authors. [PERSON] et al. [17] provided another approach for improving source location accuracy. This is called multiple signal classification (MUSIC), which adds directional scanning ability and easy arrangement of the sensor array. This approach takes advantage of Lamb wave propagation behavior, focusing on identified center frequency.