Acoustic emission monitoring (AEM) has emerged as an effective technique for detecting wire breaks resulting from, e.g., stress corrosion cracking, and its application on prestressed concrete bridges is increasing. The success of this monitoring measure depends crucially on a carefully designed sensor layout. For this, the attenuation of elastic waves within the structure’s material is ideally determined in situ through object-related measurements (ORMs) with a reproducible signal source, typically a rebound hammer. This assumes that the attenuation coefficients derived from rebound hammer tests are comparable to those from wire breaks, thus allowing their results to be directly applied to wire break detection without further adjustments. This study challenges this assumption by analysing attenuation behaviour through an extensive dataset. Employing time-domain and frequency analysis, the research generates attenuation profiles from laboratory experiments and in situ measurements across various girders and bridge structures, extracting the slope and residual standard deviation (RSD). While generally validating this approach, the findings highlight differences in attenuation behaviour from among wire break signals and rebound hammer impulses, whereby the latter potentially underestimates the relevant attenuation of wire breaks by approximately 20%. Consequently, a transfer factor is proposed to adjust ORM results obtained with the rebound hammer for wire break scenarios. It consists of a scaling factor of 1.2 to modify the average attenuation coefficient and a constant term of ±1.0 dB/m to cover a 95% confidence interval, and thus, account for sample scattering. Moreover, the anisotropic attenuation behaviour across different structures was studied, showing that transverse attenuation consistently exceeds the longitudinal, significantly influenced by structural features such as voids. In prefabricated concrete bridges with in situ-cast concrete slabs, transverse signal transmission remains unhindered across multiple elements. Finally, the results provide a valuable reference for the design of sensor layouts in bridge monitoring, particularly benefiting scenarios where direct in situ experiences are lacking.

Regular inspections of important civil infrastructures are mandatory to ensure structural safety and reliability. Until today, these inspections are primarily conducted manually, which has several deficiencies. In context of prestressed concrete structures, steel tendons can be susceptible to stress corrosion cracking, which may result in breakage of individual wires that is visually not observable. Recent research therefore suggests Acoustic Emission Monitoring for wire break detection in prestressed concrete structures. However, in noisy environments, such as wind turbines, conventional acoustic emission detection based on user-defined amplitude thresholds may not be suitable. Thus, we propose the use of matched filters for acoustic emission detection in noisy environments and apply the proposed method to the task of wire break detection in post-tensioned wind turbine towers. Based on manually conducted wire breaks and rebound hammer tests on a large-scale test frame, we employ a brute-force search for the most suitable query signal of a wire break event and a rebound hammer impact, respectively. Then, we evaluate the signal detection performance on more than 500 other wire break signals and approximately one week of continuous acoustic emission recordings in an operating wind turbine. For a signal-to-noise ratio of 0 dB, the matched filter approach shows an improvement in AUC by up to 0.78 for both, the wire break and the rebound hammer query signal, compared to state-of-the-art amplitude-based detection. Even for the unscaled wire break measurements originally recorded at the 12 m large laboratory test frame, the improvement in AUC still lies between 0.01 and 0.25 depending on the wind turbine noise recordings considered for evaluation. Matched filters may therefore be a promising alternative to amplitude-based detection algorithms and deserve particular consideration with regard to Acoustic Emission Monitoring, especially in noisy environments or when sparse senor networks are required.