A Comparative Study of Damage-Sensitive Features for Rapid Data-Driven Seismic Structural Health Monitoring

Abstract

Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection-based condition assessment that is slow and potentially subjective. The large variety of sensing solutions that has become available at affordable cost in recent years allows the engineering community to envision permanent-monitoring applications even in conventional low-to-mid-rise buildings. When combined with adequate structural health monitoring (SHM) techniques, sensor data recorded during earthquakes have the potential to provide automated near-real-time identification of earthquake damage. Near-real time building assessment relies on the tracking of damage-sensitive features (DSFs) that can be directly and rapidly derived from dynamic monitoring data and scaled with damage. We here offer a comprehensive review of such damage-sensitive features in an effort to formally assess the capacity of such data-driven indicators to detect, localize and quantify the presence of nonlinearity in seismic-induced structural response. We employ both a parametric analysis on a simulated model and real data from shake-table tests to investigate the strengths and limitations of purely data-driven approaches, which typically involve a comparison against a healthy reference state. We present an array of damage-sensitive features which are found to be robust with respect to noise, to reliably detect and scale with nonlinearity, and to carry potential to localize the occurrence of nonlinear behavior in conventional structures undergoing earthquakes.

A full‐scale case study of vibration‐based structural health monitoring of bridges: prospects and open challenges

“…The stiffness reduction provoked by damage in turn provokes a reduction in the frequency of peaks in the transmissibility. While the transmissibility depends on the general vibration modes and is thus a global indicator, it may also contain information about damage location in the substructure defined by the input-output sensor pair, for which transmissibility is defined [18]. The transmissibility, shown in Figure 8 for one sensor pair, tracks the evolution between three damage states (DS2: exposure of rebars in 2 positions; DS3: exposure of rebars in 3 positions; DS6: drilling of holes into the bridge girder in one position and cutting of prestressed cable).…”

“…where uncertainty and variability are intrinsically stemming from measurements noise and environmental effects, the inclusion of a more robust probabilistic perspective is of crucial importance. For instance, when using the transmissibility assurance criterion (TAC) [18], defined in Eq. 1, the change in the transmissibility of a specific frequency range of interest can be evaluated in a more quantitative and robust manner.…”

“…Changes in transmissibility [17,18] are one such feature that may reveal the presence of damage. The stiffness reduction provoked by damage in turn provokes a reduction in the frequency of peaks in the transmissibility.…”

The “M and P” Technique for Damage Identification in Reinforced Concrete Bridges

“…Using the "modal time-histories method", eigenfrequencies, mode shapes, and modal damping ratios have been calculated within the linear domain for a variety structures [11]; (d) the "minimum rank perturbation theory" (MRPT), as proposed by Zimmerman and Kaouk [12,13], interprets a non-zero entry in the damage vector as an indicator of the damage location; (e) a technique developed by Domaneschi et al [14,15], which involves utilizing the discontinuity of mode shape forms; (f) the concept of the damage stiffness matrix is explored in notable works, including those by Peeters [3], Amani et al [16], and Zhang et al [17]; (g) techniques that integrate structural health monitoring with pushover analysis are employed for the detection of damage in both individual structural elements [18] and frame structures [19]; (h) several artificial neural network techniques that were developed by Nazari and Baghalian [20] for simple symmetric beams. It is noteworthy to mention the recent research contributions of Reuland et al [21], who conducted an extensive review of data-driven damage indicators for rapid seismic structural health monitoring. Additionally, Martakis et al [22] explored the integration of traditional structural health monitoring techniques with innovative machine learning tools, offering a comprehensive perspective.…”

Identification of Damage in Planar Multistory Reinforced Concrete Frames Developing a Beam-Sway Plastic Mechanism Using the “M and P” Technique

“…Moreover, the idea of the damage stiffness matrix is presented in interesting works, such those of Peeters [3], Amani et al [17], and Zhang et al [18]. It is also worth mentioning the recent research efforts by Reuland et al [19], which led to a comprehensive review of data-driven damage indicators for rapid seismic structural health monitoring, as well as those by Martakis et al [20], which considered a combination of traditional structural health monitoring techniques with novel machine learning tools. With regard to the rapid spread and application of machine learning (ML), such as artificial neural networks (ANN), in structural engineering, some recent research works in this field are mentioned that consider different types of loading on structures [21][22][23][24][25].…”