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Moisture ingress is one of the major deteriorating factors for building materials. Today, the only approved way to assess such damage is the gravimetric Darr method, which is essentially destructive. Substantial progress has been made using the geophysical complex-resistivity method, which can be applied non-destructively and provides spatial information along two-dimensional sections, rather than punctual along one borehole. Considerable advantages of complex resistivity are its sensitivity to textural properties, as well as the pore-fluid chemistry of wet, porous media. In a comprehensive laboratory study, and later in field scale experiments, it could be shown that complex resistivity may even be able to distinguish between salt content and saturation degree in a single measurement. A comparison with complementary nondestructive testing techniques points to the benefit and further research to be explored in multimethodical approaches.
Building stones are porous media and they can deteriorate through moisture ingress and secondary damage such as crystallization of soluble salts. Not only is this due to the increasing number of flood events in the past years but also structural damages of houses from activity such as leakage or rising moisture (groundwater) are the main causes. The potential benefit of several nondestructive testing methods to assess water damage in building stone has been studied in a field-scale experiment. Three testing walls made of fired clay brick, sandstone, and spongilite were flooded and their drying behavior monitored using infrared thermography, complex resistivity, ground penetrating radar, and ultrasonics. The results were compared to the average moisture content determined by gravimetric weighing of the specimens. Qualitatively, the results of the different nondestructive testing methods matched well. But in terms of quantitative data, some scatter was observed and the results should be viewed with care. Collecting time-consuming calibration data would help to overcome this problem, but especially when dealing with historic building structures, this is not always possible in practice.
Because of its mobility and ability to investigate exposed surfaces, single-sided (SiS) nuclear magnetic resonance (NMR) technology enables new application fields in geosciences. To test and assess its corresponding potential, we compare longitudinal (T1) and transverse (T2) data measured by SiS NMR with those of conventional geoscientific laboratory NMR. We use reference sandstone samples covering a broad range of pore sizes. Our study demonstrates that the lower signal-to-noise ratio of SiS NMR data generally tends to slightly overestimated widths of relaxation time distributions and consequently pore size distributions. While SiS and conventional NMR produce very similar T1 relaxation data, unbiased SiS NMR results for T2 measurements can only be expected for fine material, i.e. clayey or silty sediments and soils with main relaxation times below 0.05s. This limit is given by the diffusion relaxation rate due to the gradient in the primary magnetic field associated with the SiS NMR. Above that limit, i.e. for coarse material, the relaxation data is strongly attenuated. If considering the diffusion relaxation time of 0.2 s in the numerical data inversion process, the information content >0.2s is blurred over a range larger than that of conventional NMR. However, our results show that principle range and magnitudes of the relaxation time distributions are reconstructed to some extent. Regarding these findings, SiS NMR can be helpful to solve geoscientific issues, e.g. to assess the hydro-mechanical properties of the walls of underground facilities or to provide local soil moisture data sets for calibrating indirect remote techniques on the regional scale. The greatest opportunity provided by the SiS NMR technology is the acquisition of profile relaxation data for rocks with significant bedding structures at the µm scale. With this unique feature, SiS NMR can support the understanding and modeling of hydraulic and diffusional anisotropy behavior of sedimentary rocks.
Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability.
Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits.
Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study,
ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development.
An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering
(2021)
Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous intelligent systems such as material cyberinfrastructures or research databases. This contribution demonstrates the application of these technologies to the case of the 1H nuclear magnetic resonance relaxometry, which is commonly used to characterize water content and porosity distri-bution in solids. The methodology implemented for this purpose was developed specifically to be applied to materials science (MS) tests. The aim of this paper is to analyze such a methodology from the perspective of data interoperability using ontologies. Three benefits are expected from this ap-proach to the study of the implementation of interoperability in the NDT domain: First, expanding knowledge of how the intrinsic characteristics of the NDT domain determine the application of semantic technologies. Second, to determine which aspects of such an implementation can be improved and in what ways. Finally, the baselines of future research in the field of data integration for NDT are drawn.
Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.
To date, the destructive extraction and analysis of drilling cores is the main possibility to obtain depth information about damaging water ingress in building floors. The time- and costintensive procedure constitutes an additional burden for building insurances that already list piped water damage as their largest item. With its high sensitivity for water, a ground-penetrating radar (GPR) could provide important support to approach this problem in a non-destructive way. In this research, we study the influence of moisture damage on GPR signals at different floor constructions. For this purpose, a modular specimen with interchangeable layers is developed to vary the screed and insulation material, as well as the respective layer thickness. The obtained data set is then used to investigate suitable signal features to classify three scenarios: dry, damaged insulation, and damaged screed. It was found that analyzing statistical distributions of A-scan features inside one B-scan allows for accurate classification on unknown floor constructions. Combining the features with multivariate data analysis and machine learning was the key to achieve satisfying results. The developed method provides a basis for upcoming validations on real damage cases.
This paper studies the spatial and temporal spectral absorption of reflector signals of a 1.5 GHz ground penetrating radar (GPR) during a drying process of a brickwall from initial wet to later dry state. The non-stationary GPR signals were processed with short time-Fourier transform (STFT) and wavelet transform (WT) in a novel spatial-timefrequency (STF) domain. Spatial distribution of peak frequency at the direct wave (DW) across the antenna and a backwall reflection was studied to characterize the mechanism of spectral absorption of GPR wave. Results from WT were shown to be more preferred to those from STFT because the WT offers multiple resolutions to cope with both low and high frequency components in GPR wavelets but STFT does not. In addition to the traditional GPR signal interpretation in time-domain and our previous works on time–frequency domain, the analysis method operated in the STF domain provides another possibility of material characterization by GPR in large and field scale.
A number of recent investigations have highlighted the potential value of using relaxation times derived from electrical spectra to infer key physical properties of permeable rocks. To date, most studies have assumed a grain size or pore throat as a measure of the length scale of the ionic diffusive process, although this has been challenged in recent experimental investigations. We compare the electrical spectra of three sandstones, adopting a new approach in which the temperature of the rock samples is perturbed and the relaxation time measured as a function of temperature. Our results suggest that, for the sandstones tested here, the effective diffusion coefficient should be considered as a function of the electrical tortuosity. These findings may help explain the apparent long relaxation times observed in low-permeability rocks in recent experimental studies. We also highlight the need to account for temperature in related studies of electrical spectra.
The results from several laboratory studies of the relationships between electrical polarization and physical properties of porous media have prompted interest in the potential use of low-frequency electrical spectra to qualitatively or quantitatively map variation in hydrogeologic properties in the field. Compiling several published and unpublished data sets, supported by new measurements, we have examined the low-frequency electrical spectra of a range of natural and artificial porous media to assess the generality of proposed relationships between electrical and physical properties. Our work confirms a significant positive correlation between the magnitude of electrical polarization (quantified as imaginary conductivity at a specific frequency) and the surface-area/pore-volume ratio Spor. Analyzing the parameters of ageneralized Cole-Cole resistivity relaxation model fitted to many electrical spectra, we observe two apparent controls on the electrical relaxation. For samples with abundant relatively large pore throats, we observe a distinct increase in the time constant of the model with modal pore-throat size, in accordance with classical electrical relaxation models. However, for media with pore structures dominated by small pore throats, the diffusion-length scales do not appear to be controlled by modal pore-throat size. We conclude that for such media, the microstructure of the network of small pores leads to some connectivity of diffusion paths; thus, these samples exhibit relatively large time constants. There is potential value in addition to limitations when using electrical spectra to estimate physical properties of porous media, and we see the need for more appropriate generalized theories of electrical polarization in hydrogeologic media.