Acoustic emission (AE) monitoring is a useful technique to monitor the health of a structure continuously, helping to prevent potential failure. AE are elastic waves produced and emitted during fracture processes inside a material and are recorded by sensors. Using quantitative geophysics-based methods, the recorded signals can be processed to monitor and describe the spatio-temporal growth of fracture in brittle materials such as concrete in real-time. Because of the complex nature of the recorded elastic signals and the non-homogeneous medium condition of concrete, data are usually processed manually. Combined with the high processing cost of the large datasets collected, which may exceed Terabytes, this approach has not found many real-world applications. Thus, an automated methodology is needed that can reduce costs, while maintaining high-precision, for implementation in a structural health monitoring (SHM) scheme. Here we discuss the application of a new automated and high-precision AE monitoring algorithm and software called SIMRGH [5,6] suitable for SHM of concrete structures. The core software has been developed for the laboratory-scale (in scale of centimeters) hydraulic fracture monitoring. It is up-scaled to the meters scale and works for heterogeneous media. The software works with various standard data formats and can handle trigger-based as well as continuous data. In this paper, we show some initial results of implementing the software for AE monitoring of two 4.88-meter-long concrete beams loaded in the laboratory and compare it with manually processed AE data. We were able to locate at least three up to 10 times more AE sources compared to when manual processing was used and with higher precision. If enough processing units are provided, the software can run in parallel and enable real-time SHM with excellent precision on crack geometry imaging. Future work will include implementing moment tensor inversion (MTI) to characterize AE source physics, providing valuable information for decision makers regarding the nature of the captured data.