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This article investigates the application of vibro-acoustic modulation testing for diagnosing damage in concrete structures. The vibro-acoustic modulation technique employs two excitation frequencies on a structure. The interaction of these excitations in the measured response indicates damage through the presence of sidebands in the frequency spectra. Past studies using this technique have mostly focused on metals and composites (thin plates or laminates). Our research focuses on concrete, which is a highly heterogeneous material susceptible to a variety of chemical, physical, and mechanical damage processes. In particular, this article investigates diagnosing cracking in concrete from an expansive gel produced by an alkali–silica reaction in the presence of moisture. Past studies have been limited to damage detection using vibro-acoustic modulation testing, whereas this article extends the technique to damage localization. A cement slab with pockets of reactive aggregate is used to investigate the diagnosis technique. The effects of different testing parameters, such as locations, magnitudes, and frequencies of the two excitations, are analyzed and incorporated in the damage localization methodology. A Bayesian probabilistic methodology is developed to fuse the information from multiple test configurations in order to construct damage probability maps for the test specimen. The results of vibro-acoustic modulation–based damage localization are validated by petrographic study of cores taken from the slab.

Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using “baseline” data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate “size” of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28-2015, LA-UR-15-28442, unclassified.)

This research is concerned with how to use available experimental data from tests of lower complexity to inform the prediction regarding a complicated system where no test data is available. Typically, simpler test configurations are used to infer the unknown parameters of an engineering system. Then the calibration results are propagated through the system model to predict the uncertainty in the system response. However, it is important to note that parameter estimation results are affected by the quality of the model used to represent the test configuration. Therefore, it is necessary that the model of the test configuration be also subjected to rigorous validation testing. Then the calibration and validation results for the test configurations need to be integrated to produce the distributions of the parameters to be used in the system-level prediction. Such a systematic roll-up methodology that integrates calibration and validation results at multiple levels of test configurations has been previously established (Sankararaman and Mahadevan, Reliab Eng Syst Saf 138:194–209, 2015).

This research is investigating a probabilistic framework for health diagnosis and prognosis of aging concrete structures in nuclear power plants. The current work focuses on degradation caused by ASR (alkali-silica reaction), which is a chemical reaction between the cement and certain aggregates containing amorphous silica – in the presence of water the ASR gel will expand causing damage to the concrete structure. Several different monitoring techniques — infrared thermography, digital image correlation (DIC), mechanical deformation measurements, nonlinear impact resonance acoustic spectroscopy (NIRAS), and vibro-acoustic modulation (VAM) —are being studied for ASR diagnosis of the specimen. This paper reports on the VAM study, which uses two simultaneous excitation frequencies; the interaction of these frequencies indicates damage through the presence of sidebands. Multiple resonant frequencies are considered along with their corresponding mode shapes. The possibility for damage localization is investigated.

This research is seeking to develop a probabilistic framework for health diagnosis and prognosis of aging concrete structures in nuclear power plants that are subjected to physical, chemical, environment, and mechanical degradation. The proposed framework consists of four elements: monitoring, data analytics, uncertainty quantification, and prognosis. The current work focuses on degradation caused by ASR (alkali-silica reaction). Controlled concrete specimens with reactive aggregate are prepared to develop accelerated ASR degradation. Different monitoring techniques — infrared thermography, digital image correlation (DIC), mechanical deformation measurements, nonlinear impact resonance acoustic spectroscopy (NIRAS), and vibro-acoustic modulation (VAM) — are studied for ASR diagnosis of the specimens. Both DIC and mechanical measurements record the specimen deformation caused by ASR gel expansion. Thermography is used to compare the thermal response of pristine and damaged concrete specimens and generate a 2-D map of the damage (i.e., ASR gel and cracked area), thus facilitating localization and quantification of damage. NIRAS and VAM are two separate vibration-based techniques that detect nonlinear changes in dynamic properties caused by the damage. The diagnosis results from multiple techniques are then fused using a Bayesian network, which also helps to quantify the uncertainty in the diagnosis. Prognosis of ASR degradation is then performed based on the current state of degradation obtained from diagnosis, by using a coupled thermo-hydro-mechanical-chemical (THMC) model for ASR degradation. This comprehensive approach of monitoring, data analytics, and uncertainty-quantified diagnosis and prognosis will facilitate the development of a quantitative, risk informed framework that will support continuous assessment and risk management of structural health and performance.This research is seeking to develop a probabilistic framework for health diagnosis and prognosis of aging concrete structures in nuclear power plants that are subjected to physical, chemical, environment, and mechanical degradation. The proposed framework consists of four elements: monitoring, data analytics, uncertainty quantification, and prognosis. The current work focuses on degradation caused by ASR (alkali-silica reaction). Controlled concrete specimens with reactive aggregate are prepared to develop accelerated ASR degradation. Different monitoring techniques — infrared thermography, digital image correlation (DIC), mechanical deformation measurements, nonlinear impact resonance acoustic spectroscopy (NIRAS), and vibro-acoustic modulation (VAM) — are studied for ASR diagnosis of the specimens. Both DIC and mechanical measurements record the specimen deformation caused by ASR gel expansion. Thermography is used to compare the thermal response of pristine and damaged concrete specimens and generate a...