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The pore size distribution provides a suitable description of the pore space geometry that can be used to investigate the fractal nature of the pore space or to determine a fractal dimension. Fractal dimension describes the size of geometric objects as a function of resolution. It can be integrated into models of permeability prediction. We investigated the fractal dimension of the pore volume of 11 Eocene sandstone samples from China. This study describes an approach to use spectral induced polarisation spectra to estimate the pore size distribution and to determine the fractal dimension of the pore volume. Additionally, the fractal dimension was derived from data of the capillary pressure curves from mercury intrusion and the transversal relaxation time distribution of nuclear magnetic resonance. For samples with an effective pore radius larger than 1 μm, good agreement exists between the values of fractal dimension derived from the three different methods, which implies the identification of similar pore structures. Spectral induced polarisation can be a non-invasive laboratory technique for the estimation of the pore size distribution, but the application of the methodology for field measurements remains a challenging problem considering the limited frequency range.
We investigate the pore space of rock samples with respect to different petrophysical parameters using various methods, which provide data upon pore size distributions, including micro computed tomography (μ-CT), mercury intrusion porosimetry (MIP), nuclear magnetic resonance (NMR), and spectral induced polarization (SIP). The resulting cumulative distributions of pore volume as a function of pore size are compared. Considering that the methods differ with regard to their limits of resolution, a multiple length scale characterization of the pore space geometry is proposed, that is based on a combination of the results from all of these methods. The findings of this approach are compared and discussed by using Bentheimer sandstone. Additionally, we compare the potential of SIP to provide a pore size distribution with other commonly used methods (MIP, NMR). The limits of resolution of SIP depend on the usable frequency range (between 0.002 and 100 Hz). The methods with similar resolution show a similar behavior of the cumulative pore volume distribution in the overlapping pore size range. The methods μ-CT and NMR provide the pore body size while MIP and SIP characterize the pore throat size. Using this difference, the average pore body to throat ratio is determined to be about three for the Bentheimer sandstone.
Our study shows that a good agreement between the pore radii distributions can only be achieved if the curves are adjusted considering the resolution and pore volume in the relevant range of pore radii. The MIP curve with the widest range in resolution should be used as reference
We investigate the pore space of rock samples with respect to different petrophysical parameters using various methods, which provide data on pore size distributions, including micro computed tomography (µ-CT), mercury Intrusion porosimetry (MIP), nuclear magnetic resonance (NMR), and spectral-induced polarization (SIP). The resulting cumulative distributions of pore volume as a function of pore size are compared. Considering that the methods differ with
regard to their limits of resolution, a multiple-length-scale characterization of the pore space is proposed, that is based on a combination of the results from all of these methods.
The approach is demonstrated using samples of Bentheimer and Röttbacher sandstone. Additionally, we compare the potential of SIP to provide a pore size distribution with other commonly used methods (MIP, NMR). The limits of Resolution of SIP depend on the usable frequency range (between 0.002 and 100 Hz). The methods with similar Resolution show a similar behavior of the cumulative pore volume distribution in the verlapping pore size range. We assume that µ-CT and NMR provide the pore body size while MIP and SIP characterize the pore throat size. Our study Shows that a good agreement between the pore radius distributions can only be achieved if the curves are adjusted considering the resolution and pore volume in the relevant range of pore radii. The MIP curve with the widest range in Resolution should be used as reference.
Laser Induced Breakdown Spectroscopy – A Tool for Imaging the Chemical Composition of Concrete
(2022)
One of the most common causes of damage is the ingress of harmful ions into the concrete, which can lead to deterioration processes and affect structural performance. Therefore, the increasingly aging infrastructure is regularly inspected to assess durability. Regular chemical analysis can be useful to determine the extent and evolution of ion ingress and to intervene in a timely manner. This could prove more economical than extensive repairs for major damage, particularly for critical infrastructure. In addition to already established elemental analysis techniques in civil engineering such as potentiometric titration or X-ray fluorescence analysis, laser-induced breakdown spectroscopy (LIBS) can provide further important complementary information and benefits. The possibilities of LIBS are demonstrated using the example of a drill core taken from a parking garage.
Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic method for the analysis of the chemical composition of sample materials. Generally, the measurement of all elements of the periodic table is possible. In particular, light elements such as H, Li, Be, S, C, O, N and halogens can be measured. Calibration with matrix-matching standards allows the quantification of element concentrations. In combination with scanner systems, the two-dimensional element distribution can be determined. Even rough surfaces can be measured by online adjustment of the laser focus. LIBS can also be used on-site with mobile systems. Hand-held systems are available for point measurements.
Common applications include the investigation of material deterioration due to the ingress of harmful ions and their interaction in porous building materials. Due to the high spatial resolution of LIBS and the consideration of the heterogeneity of concrete, the determination of precise input parameters for simulation and modelling of the remaining lifetime of a structure is possible. In addition to the identification of materials, it is also possible to assess the composition for example of hardened concrete, which involves the cement or aggregate type used. Other important fields of application are the detection of environmentally hazardous elements or the material classification for sorting heterogeneous material waste streams during dismantling. Non-contact NDT for “difficult to assess” structures as an example application through safety glass or in combination with robotics and automation are also possible.
In this work, an overview of LIBS investigations on concrete is given based on exemplary laboratory and on-site applications.
The determination of water and salt distribution in brickwork and stonework is a frequent problem in cultural heritage protection. Conventional but reliable methods are usually based on the investigation of core samples and provide only punctual information. To avoid the destruction of valuable building fabric, a non-destructive approach using a combination of investigation methods is needed. A case study, demonstrating the possibilities of almost non-destructive testing methods and their combination is presented in this article.
Permeability estimation from spectral induced
polarization (SIP) measurements is based on a
fundamental premise that the characteristic relaxation
time (t) is related to the effective hydraulic radius (reff)
controlling fluid flow. The approach requires a reliable
estimate of the diffusion coefficient of the ions in the
electrical double layer. Others have assumed a value for
the diffusion coefficient, or postulated different values for
clay versus clay-free rocks. We examine the link between
t and reff for an extensive database of sandstone samples
where mercury porosimetry data confirm that reff is
reliably determined from a modification of the Hagen-
Poiseuille equation assuming that the electrical tortuosity
is equal to the hydraulic tortuosity. Our database does not
support the existence of 1 or 2 distinct representative
diffusion coefficients but instead demonstrates strong
evidence for 6 orders of magnitude of variation in an
apparent diffusion coefficient that is well correlated with
both reff and the specific surface area per unit pore
volume (Spor). Two scenarios can explain our findings:
(1) the length-scale defined by t is not equal to reff and is
likely much longer due to the control of pore surface
roughness; (2) the range of diffusion coefficients is large
and likely determined by the relative proportions of the
different minerals (e.g. silica, clays) making up the rock.
In either case, the estimation of reff (and hence
permeability) is inherently uncertain from SIP relaxation
time.
Permeability estimation from induced polarization (IP) measurements is based on a fundamental premise that the characteristic relaxation time τ is related to the effective hydraulic radius reff controlling fluid flow. The approach requires a reliable estimate of the diffusion coefficient of the ions in the electrical double layer. Others have assumed a value for the diffusion coefficient, or postulated different values for clay versus clay-free rocks. We have examined the link between a widely used single estimate of τ and reff for an extensive database of sandstone samples, in which mercury porosimetry data confirm that reff is reliably determined from a modification of the Hagen-Poiseuille equation assuming that the electrical tortuosity is equal to the hydraulic tortuosity. Our database does not support the existence of one or two distinct representative diffusion coefficients but instead demonstrates strong evidence for six orders of magnitude of variation in an apparent Diffusion coefficient that is well-correlated with reff and the specific surface area per unit pore volume Spor. Two scenarios can explain our findings:
(1) the length scale defined by τ is not equal to reff and is likely much longer due to the control of pore-surface roughness or
(2) the range of diffusion coefficients is large and likely determined by the relative proportions of the different minerals (e.g., silica and clays) making up the rock. In either case, the estimation of reff (and hence permeability) is inherently uncertain from a single characteristic IP relaxation time as considered in this study.
In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design.
This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development.