High resolution bolt pre-load looseness monitoring using coda wave interferometry

This paper proposes a new concept, named the Detectable Resolution of Bolt Pre-load (DRBP), by using the coda wave interferometry (CWI) to quantitatively measure the pre-load looseness at a high resolution. Due to its characteristics of roughness, irregularity, and randomly distributed asperities, the contact surface of the bolted components can function as a natural interferometer to scatter the propagation waves. The multiply-scattered coda waves can amplify the slight changes in the travel path and show the visible perturbation in the time domain. By calculating the time-shifted correlation coefficient of coda waves before and after the slight pre-load looseness, the tiny pre-load changes can be clearly revealed. To evaluate the feasibility of the proposed method, a theoretical model considering the time shifts of coda waves and the variations of pre-load is established. Based on the acoustoelastic effect and the wave path summation theory of coda wave interferometry, the model shows that the time shifts of coda waves change linearly with the variations of pre-load. Verification experiments are conducted, and the results show that the R-square values of the fitting curves are larger than 0.9216. In addition, the proposed approach has the feature of high resolution. The ultimate pre-load resolution of the proposed approach is 0.331%, that is, when the variation of pre-load is larger than 0.331%, it can be detected. Therefore, theoretical analysis and experimental results prove that the CWI-based pre-load detection approach holds great potential for the detection of bolt pre-load looseness, especially during the initial stage.

Towards Data-Driven Evaluation of Site Effects From Coda of Strong Motion Timeseries Using Recurrent Neural Networks

It is known that coda of strong motion records are products of numerous scatterings of body and surface waves within the subsurface soil structure. Several studies have successfully simulated coda wave envelopes by modelling the energy decay. However, due to limitations of quantifying soil heterogeneities, deterministic simulation of scattered wavefields is much more challenging. Therefore, the reverse problem of estimating non statistical properties of subsurface structure from coda waves remains a theoretical potential. On the other hand, machine learning techniques have proven useful in dealing with problems of similar nature, where a theoretical solution is imaginable yet hard to achieve due to a great number of unknown variables. This study utilizes artificial neural networks to propose a new approach of evaluating site effects from coda waves, with the future prospect of obtaining the similar results from microtremor records. A Long Short-Term Memory recurrent neural network is designed using Tensorflow 2 library in Python language. The study utilizes a strong motion dataset consisting of about 60000 3-component records as well as borehole data at 464 stations of Kiban-Kyoshin Network across Japan. The prediction input is coda wave timeseries of strong motion records, defined based on a parametric energy criterion, and all 3 seismograph components EW, NS and UD are used as parallel sequential features. In the first step, the prediction target is a vector of 3 site effect proxies namely, time-averaged shear-wave velocities for the upper 30-m depth Vs30 and the upper 10-m depth Vs10 and predominant frequency f0. In this step, different model parameter combinations are tested to ensure the basic model’s ability in extracting site-specific information from the input coda waves. One of the combinations is then used in the second step, in which the prediction target is surface to downhole ratio of Fourier Amplitude Spectra. For each of the 3 components EW, NS and UD, 100 identical networks are trained to each predict the desired ratio at a certain target frequency. Accuracy of test sample predictions confirms applicability of the proposed approach as well as its potential for future works on microtremor timeseries instead of coda waves.

Monte Carlo simulations of coupled body- and Rayleigh-wave multiple scattering in elastic media

Summary Seismic coda waves are commonly used in estimation of subsurface Q values and monitoring subsurface changes. Coda waves mainly consist of multiply scattered body and surface waves. These two types of waves interact with each other in the multiple scattering process, which thus leads to a spatiotemporal evolution of the body- and surface-wave energies. One cannot characterize the evolution because one has not fully understood the multiple scattering of the two types of waves. Thus one commonly assumes only one type of waves exists or ignores their interaction while studying the coda waves. However, neglecting the interaction leads to an incorrect energy evolution of the two types of waves and consequently biases the Q estimation or interpretation of coda-wave changes for monitoring. To better understand the interaction between these waves during multiple scattering and to model the energy evolution correctly, we propose a Monte Carlo algorithm to model the multiple scattering process. We describe the physics of the scattering for the two types of waves and derive scattering properties like cross sections for perturbations in elastic properties (e.g. density, shear modulus and Lamé parameters). Our algorithm incorporates this knowledge and thus physically models the body- and surface-wave energy evolution in space and time. The energy partitioning ratios between surface and body waves provided by our algorithm match the theoretical prediction based on equipartition theory. In the equipartition state, our simulation results also match Lambert’s cosine law for body waves on the free surface. We discuss how the Rayleigh-to-body-wave scattering affects the energy partitioning ratios. Our algorithm provides a new tool to study multiple scattering and coda waves in elastic media with a free surface.

Coda Waves for Health Monitoring of Composites Under Low-Velocity Impact

Coda waves have been shown to be sensitive to lab-controlled defects such as very small holes in fibrous composite material. In the real world, damages are subtler and more irregular. The main objective of this work is to investigate coda wave capability to detect low-velocity impact damages. The emphasis is to detect the presence of barely visible impact damages using ultrasonic waves. Detection of incipient damage state is important as it will grow over the life of the structure. Differential features, previously used in similar work, have been utilized to detect realistic impact damages on carbon fiber composites. Quasi-isotropic composite laminates were subjected to low-velocity impact energy ranging from 2J to 4.5J. Two differential features reported could be used detect the presence of damage. It is also observed that ply orientation can be a deterministic factor for indicating damages. The size and shape of the impact damage has been characterized using ultrasonic C-scans. Results indicate that coda waves can be used for the detection of damage due to low-velocity impact.

Ultrasonic monitoring of stress and cracks of the 1/3 scale mock-up of nuclear reactor concrete containment structure

To evaluate the stress level and damage of a reinforced concrete containment wall (similar to those used in nuclear power plants) and its reaction to pressure variations, we conducted successive ultrasonic experiments on the exterior surface of the containment wall in the gusset area for three consecutive years (2017, 2018 and 2019). During each experiment, the pressure inside the containment wall increased gradually from 0 MPa to 0.43 MPa and then decreased back to 0 MPa. From the analysis of the ultrasonic coda waves obtained in the multiple scattering regime (80–220 kHz), we performed Coda Wave Interferometry to calculate the apparent velocity changes in the structure (denoted by dV/ V a) and Coda Wave Decorrelation (DC) measurements to produce 3D cartographies of stress and crack distribution. From three source–receiver pairs, located at the top, middle and bottom of the experimental region, we observe that coda waves dilate, shrink and remain almost unchanged, respectively. This corresponds to the decreasing, increasing and invariant pressure inside the concrete. The comparison of 3 years’ results demonstrates that the variation of dV/ V a and DC under the same pressure test increases through the years, which indicates the progressive deterioration and ageing of the concrete. From a large collection of source–receiver pairs at different times, the spatial–temporal variations of dV/ V a and DC are then used to produce a map of the structural velocity and scattering changes, respectively. We observe a decreasing velocity on the top part and an increasing in the middle one, which is in line with the dV/ V a analysis. The reconstructed scattering changes (or structural changes) highlight the active region during the inflation–deflation procedure, corresponding to the opening and closing (and sometimes the development) of cracks. The larger magnitude in 2019 than in 2017 indicates the increasing damage in the concrete.

Sensitivity kernels for static and dynamic tomography of scattering and absorbing media with elastic waves: a probabilistic approach

SUMMARY Scattered seismic coda waves are frequently used to characterize small scale medium heterogeneities, intrinsic attenuation or temporal changes of wave velocity. Spatial variability of these properties raises questions about the spatial sensitivity of seismic coda waves. Especially the continuous monitoring of medium perturbations using ambient seismic noise led to a demand for approaches to image perturbations observed with coda waves. An efficient approach to localize spatial and temporal variations of medium properties is to invert the observations from different source–receiver combinations and different lapse times in the coda for the location of the perturbations. For such an inversion, it is key to calculate the coda-wave sensitivity kernels which describe the connection between observations and the perturbation. Most discussions of sensitivity kernels use the acoustic approximation in a spatially uniform medium and often assume wave propagation in the diffusion regime. We model 2-D multiple non-isotropic scattering in a random elastic medium with spatially variable heterogeneity and attenuation using the radiative transfer equations which we solve with the Monte Carlo method. Recording of the specific energy density of the wavefield that contains the complete information about the energy density at a given position, time and propagation direction allows us to calculate sensitivity kernels according to rigorous theoretical derivations. The practical calculation of the kernels involves the solution of the adjoint radiative transport equations. We investigate sensitivity kernels that describe the relationships between changes of the model in P- and S-wave velocity, P- and S-wave attenuation and the strength of fluctuation on the one hand and seismogram envelope, traveltime changes and waveform decorrelation as observables on the other hand. These sensitivity kernels reflect the effect of the spatial variations of medium properties on the wavefield and constitute the first step in the development of a tomographic inversion approach for the distribution of small-scale heterogeneity based on scattered waves.