Digital Particle Analysis

Abstract

A computer-implemented method for characterizing solid particles, in particular sand particles, comprises the steps of: a) providing a sample of the solid particles to be analyzed within a predetermined sample area; b) taking at least one digital image of the sample of the solid particles with a camera of a mobile computing device; c) performing image particle analysis of the at least one digital image to extract at least one particle size parameter and / or at least one grain shape parameter of a particle population identified in the at least one digital image; and d) providing the at least one particle size parameter and / or the at least one grain shape parameter via a user interface, via a machine interface and / or on a data storage medium.

Description

[Technical field]

[0001] The present invention relates to a computer-implemented method for characterizing solid particles, particularly sand particles, as well as a system comprising means for carrying out the method and a computer readable medium comprising instructions for carrying out the method. Other aspects of the present invention relate to a method for providing a formulation for a hardenable composition and a method for producing a hardenable composition. [Background technology]

[0002] In the construction industry, hardenable compositions are widely used for various applications. Examples of such compositions are mortar, concrete, grout or screed compositions. These compositions typically contain a binder together with solid aggregates.

[0003] The binder may be selected from mineral binders, such as hydraulic binders, as well as organic binders, such as hardenable polymer compositions. The aggregate is selected depending on the desired properties of the hardenable composition. Typically, aggregates include sand, gravel, rock dust, and / or polymer particles.

[0004] For example, with regard to sand, up until now, high-quality river sand has usually been used. However, in the future, high-quality sand will become scarce and difficult to obtain. Therefore, sand of lower quality will have to be used more and more, either from natural resources such as low-grade river sand or from special manufacturing processes such as crushing. This will have a major impact on the preparation of hardenable compositions, in particular concrete and mortar compositions, since the specific properties of the sand have a significant effect on the properties of the hardenable compositions. This point must be duly taken into account, because even if the quality of the sand decreases, the technical requirements for the curing compositions, for example hardened concrete or mortar, will remain high or even become higher.

[0005] Generally, in order to produce in a targeted manner a hardenable composition having the desired properties, it is important to take into account the specific properties of the aggregates. For example, with low quality sands, it is particularly important to check whether for said low quality sands (i) the nature of the fines is important, (ii) the grain shape is important, and / or (iii) the fines content is sufficient.

[0006] Specifically, the behavior of sand in hardenable compositions, particularly concrete and mortar compositions, depends on, among other things, the crushing method, grain shape, surface texture, fines content, mineral contamination, and grain size.

[0007] Grading of sand and aggregates is a key factor in calculating the grading for concrete mix designs to ensure close packing of the sand and aggregates, whereby the grain shape in particular (which itself depends on the manufacturing method or source of the sand and / or aggregate) typically influences the required grading for concrete mix designs.

[0008] In the case of artificial sands, points (ii) and (iii) are particularly relevant. The fines content can be determined by sieving, which is a standard procedure but is very time consuming. Sieving will show whether the grain size is as desired or whether it has to be adjusted by blending with other sand fractions.

[0009] In contrast, grain shape, i.e. item (ii) above, is rarely analyzed in the routine work for optimizing the gradation of hardenable compositions. Usually, this is only done when necessary to control whether the aggregate meets a certain standard. This is because the analysis of the grain shape of a specific sand is very time-consuming and requires special equipment, as defined for example in the standard EN 933-1:2012-7:2012.

[0010] Therefore, there remains a need to provide an improved solution that allows for comprehensive characterization of aggregates. Summary of the Invention [Problem to be solved by the invention]

[0011] The object of the present invention is to provide an improved solution for characterizing aggregates, in particular including sand. Preferably, the solution should thereby be able to provide a fast and reliable characterization process with as few tools as possible. Particularly preferably, the solution should allow a complete characterization of aggregates, in particular with regard to their grain size and their grain shape parameters, to be carried out in a routine manner. Preferably, the characterized aggregates can be used to produce formulations of hardenable compositions with desired properties in a targeted manner. [Means for solving the problem]

[0012] Surprisingly, it has been found that the features of patent claim 1 make it possible to achieve this object. The core of the invention therefore relates to a computer-implemented method for characterizing solid particles, in particular sand particles, which comprises: a) providing a sample of solid particles to be analysed within a defined sample area, in particular a two-dimensional sample area; b) taking at least one digital image of the sample of solid particles with a camera of the mobile computing device or a camera connected to the mobile device; c) performing an image particle analysis of the at least one digital image to extract at least one particle size parameter and / or at least one particle shape parameter, preferably at least one particle size parameter and at least one particle shape parameter, of the particle population in the at least one digital image; d) providing at least one particle size parameter and / or at least one particle shape parameter, preferably at least one particle size parameter and at least one particle shape parameter, via a user interface, via a machine interface and / or on a data storage medium; Includes.

[0013] The method of the present invention is a unique approach that can be implemented on conventional mobile devices, such as smartphones, and therefore does not require complex and expensive analytical instruments. The method further allows for extremely fast, flexible and accurate digital characterization of solid particles, both in terms of size and shape, thereby allowing solid particles of different nature and size to be easily characterized on one and the same hardware device.

[0014] A fast and easy method for characterizing solid particles allows routine optimization of mix designs for hardenable compositions, such as concrete compositions, with negligible additional effort.

[0015] In particular, the method can be implemented in various ways, for example in the form of a standalone application running on a mobile device, without requiring further resources, for example a server system, etc. This is particularly beneficial when access to communication networks is limited, for example in remote locations or underground in cities. However, the method can also be implemented in a distributed computing environment, for example including a combination of mobile devices as clients and dedicated servers and dedicated processing units as storage units.

[0016] The method of the present invention can also be flexibly implemented in known software architectures, such as native applications, Progressive Web Applications (PWA), or hybrid applications (combination of native and PWA). Thereby, such applications can include useful internet links to tutorials or support sites as well as sharing functionality (e.g., email, Bluetooth, Airdrop, or other communication means). The method of the present invention can also be implemented in one single application software, or it can be divided into two or more separate applications, using appropriate software interfaces for data exchange between the applications. Furthermore, the application(s) can be flexibly extended to have additional functionality.

[0017] The application can be implemented for any type of operating system, including iOS, Android, Microsoft Windows and / or Linux.

[0018] Applications can be readily made available to anyone through various distribution channels, such as public download centers (e.g., the Apple® Store or Google Play®), websites operated by private companies, and / or dedicated download sites.

[0019] Further aspects of the invention are the subject matter of the other independent claims. Particularly preferred embodiments are outlined in the description and the dependent claims.

[0020] How to carry out the invention A first aspect of the present invention relates to a computer-implemented method for characterizing solid particles, in particular sand particles, comprising: a) providing a sample of solid particles to be analyzed within a predetermined sample area; b) taking at least one digital image of the sample of solid particles with a camera of the mobile computing device or a camera connected to the mobile device; c) performing an image particle analysis of the at least one digital image to extract at least one particle size parameter and / or at least one particle shape parameter, preferably at least one particle size parameter and at least one particle shape parameter, of the particle population in the at least one digital image; d) providing at least one particle size parameter and / or at least one particle shape parameter, preferably at least one particle size parameter and at least one particle shape parameter, via a user interface, via a machine interface and / or on a data storage medium; Includes.

[0021] In this regard, a mobile computing device is specifically intended to be a handheld computer, ie, a computer small enough to be held and operated by a person in their hand.

[0022] In particular, the mobile computing device includes human interface devices, including inter alia an input device and a display, and preferably a communications interface, most preferably a wireless communications interface.

[0023] In particular, the mobile computing device is selected from a mobile phone, a mobile computer, or a portable computer. In particular, the mobile computing device is selected from a smartphone, a phablet, a tablet computer, a portable computer, a smartwatch, and / or a head mounted display with a camera. A mobile phone and / or a smartphone are highly preferred.

[0024] Mobile phones and smartphones typically include high-resolution cameras, input devices, and displays, whereby the input devices and displays are typically integrated into a touch-sensitive display. Mobile phones and smartphones therefore provide all of the hardware components necessary to carry out the method of the present invention. Furthermore, such types of devices can be held stably in the hand, which makes them very suitable for image capture. At the same time, mobile phones and smartphones typically have displays large enough to display complex data in a fully readable manner.

[0025] The expression "camera of a mobile computing device" is intended to mean an internal camera built into the mobile computing device. In contrast, "camera connected to a mobile computing device" means an external camera that is a separate device connected to the mobile computing device. For example, an external camera is a camera attached to the mobile computing device or a separate camera. The connection between the external camera and the mobile computing device can be a wired and / or wireless connection.

[0026] Preferably, the camera is a camera for taking images in the visible spectrum, in particular color images. A color image allows the color characteristics of the solid particles and / or the predetermined sample area to be taken into account in step c) of the method of the present invention. Therefore, preferably, at least one digital image taken in step b) is a color image. However, other cameras, such as monochrome cameras, can also be used if color information is not required. In this case, the image is a monochrome image, for example a black and white image.

[0027] It is also possible to use a camera to take images outside the visible spectrum, for example in the infrared and / or ultraviolet range of the electromagnetic spectrum. Such a camera can be used instead of or in addition to other cameras. In this case, step c) can take into account the properties of the solid particles in spectral ranges outside the visible spectrum.

[0028] Preferably, the resolution of the camera is at least 2 million pixels, in particular at least 5 million pixels, preferably at least 8 million pixels, in particular at least 12 million pixels, particularly preferably at least 20 million pixels, even more preferably at least 50 million pixels, or at least more than 100 million pixels. In particular, the resolution of the camera is at least 3,800 pixels by at least 2,100 pixels, i.e. a so-called 4K, preferably 8K, more preferably 12K, in particular 16K resolution. The higher the resolution, the smaller the solid particles can be identified in the sample area. However, in special embodiments, a camera with a lower resolution may also be suitable.

[0029] In particular, the mobile computing device is configured to automatically recognize the camera resolution.

[0030] In particular, the size of the optical sensor of the camera is at least 1 / 3'', in particular at least 1 / 2.5'', preferably at least 1 / 1.7'', preferably at least 2 / 3'', in particular at least 1 / 1.33'', or at least 1 / 1.2''. In particular, the size of the optical sensor of the camera is between 1 / 3'' and 1''.

[0031] In other words, the size by width×height of the optical sensor of the camera is preferably at least 4.8 mm×3.6 mm, in particular at least 5.7 mm×4.2 mm, preferably at least 7.6 mm×5.7 mm, preferably at least 8.8 mm×6.6 mm, in particular at least 9.6 mm×7.2 mm, or at least 10.6 mm×8.0 mm. In particular, the size by width×height of the optical sensor of the camera is 4.8 mm×3.6 mm to 13.2 mm×8.8 mm.

[0032] Generally, the larger the sensor size, the more light can be captured by the sensor, thereby improving image quality, although cameras with other sensor sizes may also be suitable.

[0033] In addition, supplemental lenses may be added to the camera, for example to increase or decrease the focal length of the camera.

[0034] If available, the mobile computing device may also have access to additional wide-angle and / or magnifying lenses, both optical and / or digital, as needed, to capture at least one digital image.

[0035] The predetermined sample area may be a two-dimensional sample area or a three-dimensional sample area.

[0036] The two-dimensional sample area is preferably a flat area, in particular a flat rectangular area, in particular the two-dimensional sample area is horizontally arranged and / or it is a horizontal sample area.

[0037] In a given sample area, particularly a two-dimensional sample area, the solid sample is preferably arranged in a monolayer and / or with essentially no particle overlap within the sample area.

[0038] However, according to another preferred embodiment, the solid samples within a given sample area, in particular within a three-dimensional sample area, are arranged in multiple layers and / or in a three-dimensional sample.

[0039] In this case, the method can for example be implemented in such a way that only the topmost surface layer particles and / or non-overlapping particles in the at least one digital image are taken into account and all other remaining particles are ignored in further processing, which can for example be achieved by means of suitable image processing algorithms in step c).

[0040] Alternatively, or in addition, step c) can use image processing algorithms designed to identify individual solid particles in particle agglomerates. These special techniques will prevent overlapping solid particles from biasing the analysis.

[0041] Preferably, the predefined sample area, in particular the two-dimensional sample area, comprises a reference scale and / or has a known size, which can be e.g. letters, geometric shapes, rulers and / or reference objects of known size within the sample area, thereby allowing an accurate determination of the size of the predefined sample area and an accurate extraction of the particle size parameter in step c).

[0042] Preferably, the mobile computing device is configured to automatically determine the size of a given sample area, in particular a two-dimensional sample area, based on a reference scale.

[0043] Additionally or alternatively, the predefined sample area, in particular the two-dimensional sample area, has a predefined known size. In this case, the mobile computing device is preferably configured to set the predefined size, for example from a list of predefined sizes. In this case, no reference scale is required.

[0044] In particular, a thin sheet material, preferably a thin sheet material of a predefined size, is used as the predefined sample area, in particular a two-dimensional sample area. For example, paper, for example DIN A5, A4 or A3 paper, is used as the thin sheet material. Also, paper of other formats, for example tabloid, letter or statement format, can be used. In addition, a reference scale can be present on the thin sheet material. Preferably, the mobile computing device is configured to automatically determine the size of the predefined sample area, in particular the two-dimensional sample area.

[0045] Paper as a predefined sample area is readily available and fairly inexpensive.

[0046] In general, the size of a given sample area, especially a two-dimensional sample area, influences the smallest particle size that can be identified. The smaller the size of the sample area, the smaller the solid particles that can be identified in the given sample area, especially a two-dimensional sample area. Therefore, a small given sample area, especially a small two-dimensional sample area, is advantageous for characterizing small particles. Small particles are particularly those with a particle size D90 of not more than 0.5 mm, preferably not more than 0.1 mm, in particular not more than 0.063 mm.

[0047] In particular, certain sample areas, especially thin sheet materials, have a specific color that differs from the color of the solid particles. This is useful for identifying individual solid particles in step c) because it allows a better discrimination of the size and identity of particles close to the limit of the camera resolution. Therefore, preferably, the image particle analysis includes a step of subtracting a specific background color.

[0048] In particular, the specific color of the given sample area, especially the two-dimensional sample area, is white. This results in high contrast when characterizing solid particles, such as sand particles, typically used in hardenable compositions. However, for other particles, another specific color of the given sample area may be preferred.

[0049] Preferably, the given sample area, especially the two-dimensional sample area, especially the thin sheet material, is placed on a surface that is larger in all directions in space than the given sample area, especially the two-dimensional sample area, and has a different color than the given sample area. In particular, the color of the surface is selected to exhibit a high contrast with respect to the given sample area, especially the two-dimensional sample area. For example, if the color of the given sample area is white, the color of the surface is black.

[0050] In these cases, a given sample area, particularly a two-dimensional sample area, is entirely surrounded by a frame of a different color, which serves to identify the given sample area, particularly a two-dimensional sample area, within the digital image.

[0051] According to another preferred embodiment, a light-emitting surface is used as the predetermined sample area, in particular a two-dimensional sample area, whereby the light-emitting surface is in particular illuminated by the light source and emits light with a uniform light distribution over the entire surface.

[0052] In particular, a light table pad, in particular a light pad, is used as the predetermined sample area. This may be advantageously used to provide a light-emitting surface from which light is possibly emitted. The light table comprises a flat light-emitting surface that is oriented horizontally and illuminated from the backside by a light source. In particular, the light-emitting surface consists of a semi-transparent layer that is illuminated from the backside by a light source.

[0053] A light pad is in particular a thin light table having a thickness of less than 20% or 10% of the width and less than 20% or 10% of the length of the light emitting surface. Light tables and light pads are known, for example in the graphics field, and are commercially available.

[0054] When a light-emitting surface, in particular a light table, is used as the sample area, the solid particle sample is placed on the light-emitting surface, in particular on the front surface of the light-emitting surface.

[0055] Compared to other predefined sample areas, such as paper, a light emitting surface, in particular a light table or light pad, is particularly advantageous because it can eliminate shadows of sample particles, increase contrast, produce fewer artifacts, allow better particle recognition, especially for bright and / or small particles, improve the fitting of the calculated contours and thus more accurate determination of particle shape parameters and particle size, and overall improve the accuracy of the results.

[0056] In particular, the light-emitting surface is illuminated such that it emits a color different from the color of the solid particles, preferably the light-emitting surface is illuminated such that it emits white light.

[0057] In particular, the light source comprises a source of white light. Optionally, the light source additionally comprises a source of light of a color other than white. In particular, the color of the light source can be switched to a different color. The color other than white enhances the detection of light-colored whitish sand.

[0058] In particular, the light source is an LED light source, which generates minimal heat on the translucent surface or in the sample area, respectively, compared to other light sources, thereby reducing the risk of heat-induced changes to the sample.

[0059] LEDs can cause disturbances in temporal light modulation. Temporal light modulation is a change in the light emission or spectral distribution over time. Such modulation can lead to undesirable visual perceptions such as flicker, stroboscopic effects, and phantom array effects. Such effects are also known as temporal light artifacts. Such effects are described, for example, in “On the state of knowledge concerning the effects of temporal light modulation” by JAVeitch et al., 20 Lighting Res.Technol 2021, 53, 89-92. In the context of the present application, it is preferable to avoid such temporal light modulation and the effects caused thereby. Therefore, according to some embodiments, the light source is configured to avoid effects resulting from temporal light modulation.

[0060] Preferably, the light-emitting surface, in particular a light table or a light pad, is configured such that the light intensity of the light-emitting surface can be adjusted, in particular continuously or stepwise, for example, the light-emitting surface, in particular a light table or a light pad, is configured such that the light intensity can be switched between 2 to 5, in particular 3 to 4, different light intensities.

[0061] In another preferred embodiment, the light emitting surface, in particular a light table or light pad, that emits light is configured such that it emits polarized light. This can be achieved for example by using a light source that generates polarized light and / or a polarizing filter that is placed behind, on and / or in the emitting surface. The polarizing filter can be selected for example from a foil. The polarized light can be used to further improve the determination of particle parameters and shapes. The foil can also be a color foil.

[0062] In another preferred embodiment, the emitted light is such that it does not cause interference.

[0063] Furthermore, the light-emitting surface, in particular of the light table or the light pad, can be covered with a protective foil, for example to increase scratch resistance, the protective foil being preferably transparent with respect to the light emitted from the light-emitting surface. In particular, the protective foil is made of a synthetic material. In particular, the protective file is a replaceable foil.

[0064] In particular, the light emitting surface, in particular a light table or light pad, includes a frame surrounding the light emitting surface, the frame having a different colour than the light emitting surface, in particular a darker colour than the light emitting surface, in particular black, which serves to identify a given sample area, in particular a two-dimensional sample area, in a digital image.

[0065] For example, the size of the light-emitting surface, in particular the light table or light pad, is equal to the size of a DIN A5, A4 or A3 paper, or has the size of a tabloid, letter or statement format. Typically, the light pad is slightly larger in size than any of the above-mentioned formats to ensure that a paper of a certain format fits perfectly into a light pad of such format. Therefore, the actual size of the light-emitting surface, in particular the light table or light pad, may also be slightly larger than any of the above-mentioned formats. Preferably, the mobile computing device is configured to manually and / or automatically specify the size of the light-emitting surface.

[0066] Taking at least one digital image of the sample of solid particles with a camera is preferably performed under daylight conditions and / or using a light source, e.g., a flashlight, for illuminating the solid particles, whereby the light used to illuminate the solid particles is preferably sufficiently diffused to avoid shadows and light non-uniformities. The mobile computing device is preferably configured to automatically adjust the light conditions to achieve a balanced exposure.

[0067] In another preferred embodiment, the camera is aligned in a parallel plane, in particular horizontally, relative to a predefined sample area, in particular a two-dimensional sample area. Preferably, the mobile computing device is configured to automatically warn the user and / or not capture at least one image in the event of a non-parallel alignment. In this case, the mobile computing device preferably comprises at least one position sensor which can be evaluated when performing the method of the invention.

[0068] In particular, when taking at least one digital image of the solid particle sample in step b), the camera is aligned horizontally, in particular in a horizontal and parallel plane to the predefined sample area, whereby preferably the predefined sample area is a horizontal area and / or it is aligned horizontally. The horizontal alignment can be performed manually or by using assistance functions known to those skilled in the art.

[0069] In particular, a camera is aligned horizontally if its optical axis extends vertically - the optical axis is an imaginary line that defines the path that light follows as it propagates through the camera system.

[0070] By taking at least one digital image of the sample of solid particles in step b) with a camera in a horizontal arrangement, in particular in a horizontal and parallel plane to the predetermined sample area, the image taking process is greatly simplified, in particular by adjusting the height of the camera above the predetermined sample area, the allocation of the sample area in the image can be easily maximized in a horizontal arrangement.

[0071] Also, a horizontal arrangement of predefined sample areas or a horizontal sample area is advantageous since the solid particles automatically remain stable in their position and do not move during the image acquisition process, whereby the predefined sample areas can be placed, for example, on a table or any other surface that is essentially horizontal.

[0072] In the case of a vertically aligned camera and a non-horizontal alignment of the sample area, this is less convenient and requires special means to hold the solid particles of the sample in place.

[0073] However, the method can also be performed with non-parallel planar arrays of cameras.

[0074] In particular, when at least one digital image of the sample of solid particles is taken in step b) by the camera of the mobile computing device or a camera connected to the mobile device, the solid particles of the sample remain motionless, which significantly improves the image quality and the image particle analysis in step c).

[0075] In another preferred embodiment, at least two successive digital images of the sample area are taken. In this case, step c) is preferably performed with at least two digital images. This may be beneficial for increasing the statistical count number and for determining more precisely the at least one particle size parameter and the at least one particle shape parameter. In particular, in step c) the at least two digital images are superimposed.

[0076] In particular, when taking the at least one image, the camera is aligned to maximize the allocation of sample area in the image. This can be achieved, for example, by providing alignment instructions to the user and / or by automatically adjusting at least one setting of the camera, for example the focal length of the camera. Therefore, preferably, the mobile computing device is configured accordingly.

[0077] Highly preferably, the minimum detectable particle size is calculated, preferably taking into account the resolution of the camera, the allocated area of ​​the sample area in the total area of ​​the image, and the actual size of the sample area. Preferably, the mobile computing device is configured accordingly.

[0078] In particular, if the minimum detectable particle size is less than a predetermined threshold, a warning may be provided to the user, alignment instructions may be provided to the user, and / or camera settings, e.g., focal length, may be adjusted. The predetermined threshold may, for example, be manually set. This helps to avoid taking images under unsuitable conditions.

[0079] The image grain analysis of the at least one digital image in step c) can be performed using known, readily available image analysis algorithms, for example by using software packages and / or libraries and / or artificial intelligence algorithms provided in Matlab (by MathWorks®), OpenCV (see https: / / opencv.org), ImageJ (by Wayne Rasband: see https: / / imagej.net).

[0080] The at least one particle size parameter extracted preferably includes at least one of the following parameters: - a particle size distribution of a particle population identified in at least one digital image; - average particle size, average diameter, and / or D, where x=1-100 x -value, e.g. D 10 , D 50 , D 85 , and D 100 Value, and / or deviations from predefined nominal values ​​and / or nominal distributions, e.g. from a specific type of Fula curve, a standard sieving curve, and / or -Coarse grain rate.

[0081] According to a particularly preferred embodiment, the at least one particle size parameter extracted comprises or is a particle size distribution of the particle population identified in the at least one digital image.

[0082] These are very relevant parameters when providing a formulation for a hardenable composition containing solid particles, however other parameters may also be provided.

[0083] The particle size distribution extracted thereby shall in particular correspond to the particle size distribution defined in standard EN933-1:2012.

[0084] Another possible method for determining the particle size distribution is laser light diffraction, as described in ISO 13320: 2009. The particle size distribution extracted shall therefore correspond in particular to the particle size distribution defined in the standard ISO 13320: 2009.

[0085] D x A value means that x% of a particle assembly has a particle size smaller than a certain value. Thus, for example, D 90 The value of is the value at which 90% of the particle assemblies meet the given D 90 The average particle size is therefore particularly defined as the particle size D 50 corresponds to a value (50% of the particles are smaller than a certain number and 50% are correspondingly larger).

[0086] In general, the at least one particle size parameter extracted preferably includes at least the particle size distribution of the particle population identified in the at least one digital image.

[0087] In particular, for particle sizes smaller than the minimum detectable particle size, the particle size distribution can be estimated based on the extracted particle size distribution, for example using polynomial extrapolation, particularly based on Lagrange interpolation, or using Newton's difference method to generate a Newton series that fits the extracted particle size distribution.

[0088] In particular, the at least one grain shape parameter extracted includes at least one of the following parameters: - Roundness, - Sphericity, -Aspect ratio, - roughness, -area envelopment, - flake index, -shape index, - the percentage of fractured and destroyed surfaces, and / or -Slope.

[0089] These are parameters that have so far been difficult to analyze in routine work, but they have a large impact on optimizing the particle size of a curable composition.

[0090] In particular, the grain shape parameters shall correspond to the shape parameters defined in standard EN 933-1:2012-7:2012.

[0091] In particular, grain shape parameters include the aspect ratio expressed as maximum Feret diameter to minimum Feret diameter.

[0092] Other shape parameters that can be extracted are described in Blot and Pye, Sedimentology (2008), 55.31-63.

[0093] In particular, at least one particle shape parameter is extracted only for particles of a certain size, in particular for particles larger than a certain threshold size. For smaller particles, the particle shape of the solid particles has less effect on the properties of the hardenable composition. For example, at least one particle shape parameter is extracted for particles >0.5 mm, in particular >1 mm.

[0094] However, it is possible to analyze the shape parameters of all solid particles if desired.

[0095] Furthermore, it is possible to extract at least one grain shape parameter for particles larger than a lower threshold size and for particles smaller than an upper threshold size, whereby in particular at least two, at least three or more different grain shape parameters can be simultaneously extracted within a range between a lower threshold and an upper threshold.

[0096] This allows one to identify shape parameters of particles of particular sizes that are known to affect a particular property of interest of the curable composition, such as, for example, the rheology of the curable composition.

[0097] According to another preferred embodiment, at least one individual particle shape parameter is extracted for each of at least two or more predetermined particle groups of particles, for example sieved particle groups, whereby in particular at least one individual average value of the at least one particle shape parameter is extracted for each particle group, which can provide detailed information about the size-dependent distribution of the particle shape parameter.

[0098] For example, the average value of at least one particle shape parameter is provided separately for at least two or more predetermined particle groups, e.g., sieved particle groups. Optionally, the particle counts for each particle group can be normalized by the volume of the particles under consideration to obtain data comparable to, for example, a particle size distribution. In addition, in this case, at least two, at least three, or more different particle shape parameters can be provided simultaneously.

[0099] Such data can be represented, for example, in a bar plot in the form of a bar showing a group of particles of a given particle size as a category and the corresponding average value of at least one particle shape parameter, the height or length of which is proportional to the numerical value it represents.

[0100] Additionally, an average value of at least one particle shape parameter for particles of several selected particle populations can be provided, e.g., particles in particle populations that are greater than and / or less than a certain particle population threshold. As before, this can, for example, identify shape parameters of particles in a particle population that are known to affect a particular property of interest of the hardenable composition.

[0101] In another preferred embodiment, for each of the at least one digital image, a contour image is generated. The contour image is also called a contour image and includes the contours of all particles identified in the digital image. The contour image can be provided in step d) via a user interface, via a machine interface and / or on a data storage medium. For example, the contour image can be displayed within the application and / or stored on an external server. The contour image can serve as a tool for evaluating the quality of the digital image and / or the analysis performed.

[0102] In particular, in step b) at least two, preferably at least three, in particular at least five or at least ten digital images are taken, and in step c) a particle analysis is performed for each image, and a deviation, in particular a standard deviation, of the at least one particle size parameter and / or at least one grain shape parameter is determined by considering each of the at least one particle size parameter and / or at least one grain shape parameter extracted individually from the at least two images. The deviation can serve as a tool for assessing the quality of the digital images and / or the analysis performed.

[0103] In particular, if the deviation is greater than a predetermined threshold, a warning may be provided to the user, and / or if the deviation is greater than a predetermined threshold, the digital image and / or contour image in which the parameter difference occurs may be identified and / or indicated.

[0104] Preferably, the method of the invention further comprises the step of assigning at least one attribute of the sample of solid particles. In particular, the attribute is: - a unique identifier for the solid particle sample; the chemical and / or physical properties of the sample of solid particles, in particular the nature of the solid particles, e.g. sand, limestone and / or polymer particles, type of solid particles, e.g. natural, crushed, artificial, regenerated and / or reused; Maximum particle size of solid particles Minimum solid particle size the state of the solid particles, e.g. wet or dry, and / or -Pretreatment applied, e.g. no treatment or washing -descriptive information for the sample of solid particles, in particular Location of solid particle source, e.g. manually or automatically via position sensor, Intended use, e.g. project name and / or customer name, and / or General comments is selected from.

[0105] In particular, "artificial" particles are intended to be aggregates as defined, for example, on page 5 of the European Aggregates Association's Annual Report 2018-2019.

[0106] Preferably, the mobile computing device is configured to interrogate at least one of the attributes of the sample of solid particles, in particular before, during and / or after steps a)-b).

[0107] In particular, a unique identifier for a sample of solid particles is automatically generated.

[0108] In particular, the method is performed at least partly, in particular entirely, on a mobile computing device.

[0109] If the method is performed entirely on a mobile computing device, the entire characterization can be performed on the mobile computing device and does not require any communication network, and the at least one digital image, preferably along with the at least one attribute, can be stored on the mobile computing device, e.g., for sharing, retrieval, and / or future evaluation.

[0110] Preferably, the mobile computing device is configured such that at least one digital image, preferably together with at least one attribute, can be transferred via a communication means, e.g. a communication interface of the mobile device, to an external device for storage. Similarly, the mobile computing device is preferably configured to read the stored data from the external device. According to another preferred embodiment, the image analysis of step c) and / or the providing of step d) are performed on another computing device, e.g. a server.

[0111] In this case, preferably, at least one digital image, preferably together with at least one attribute, is transferred to the external device via a communication means, e.g. a communication interface of the mobile device. Such a distributed solution is advantageous because the computationally intensive step c) can be performed on another device, which helps to increase the runtime of the mobile computing device. Furthermore, steps c) and / or d) can be optimized by updating the software part on the external device side, without the user having to update the software part on the mobile computing device.

[0112] In this case, if no communication network is available, the at least one digital image, preferably together with the at least one attribute, can be temporarily stored on the mobile computing device and later transferred to the external device when a communication network is available.

[0113] In this case, the image, preferably together with the at least one attribute, is stored on an external computing device, for example a server, in particular for storage, retrieval and / or future evaluation.

[0114] In step d) at least one particle size parameter and at least one particle shape parameter are provided via a user interface, via a machine interface and / or on a data storage medium. If desired, at least one digital image can be provided in addition thereto, optionally together with at least one attribute. However, this is not a requirement, since in daily work such images are of little importance to the user.

[0115] If the data is provided via a user interface, this can be done directly on the mobile computing device and / or on an external computing device, whereby for example the at least one particle size parameter and the at least one particle shape parameter are plotted in a graph, e.g. a particle size distribution, and / or presented in an animated plot, e.g. a plot of circularity vs. sphericity or a bar plot of the average particle size parameter vs. sieve opening.

[0116] Providing data via a machine interface allows data to be transferred to an external computing device, such as a server and / or a desktop computer.

[0117] The data may also be provided on a data storage medium, such as an internal data storage medium of the mobile computing device, a storage device attached to the mobile computing device, and / or a storage device of an external computer.

[0118] In particular, at least one particle size parameter and at least one particle shape parameter, optionally together with at least one attribute and optionally at least one image, are written into a data file in a predefined file format. For example, the file format is selected from json, csv, txt, and / or pdf. However, other file formats can also be used. Typically, the data file does not include at least one image. This helps to reduce the file size.

[0119] In particular, the data file is transferred to another application, another mobile computing device, and / or an external computing device. The transfer can be performed by any kind of communication means, for example wireless and / or wired communication. This allows a user to share the characteristics of the solid particles with other users and transfer it to another application for further evaluation and / or to a data storage server. Therefore, preferably, the mobile computing device is configured to transfer the data file to another application, another mobile computing device, and / or an external computing device.

[0120] The size of the solid particles is, for example, in the range of >0 to 125 mm, preferably 10 μm to 32 mm, more preferably 0.063 mm to 16 mm, in particular 0.1 mm to 2 mm. Such types of particles are typically used in curable compositions. However, the method can also be used to characterize other particles.

[0121] In particular, the solid particles of the sample are selected from sand, aggregates, fibers, and / or glass spheres, however, other solid particles can also be used, in particular sand substitutes, artificial sand, crushed and / or recycled building materials, bioaggregates, natural or synthetic fibers, and / or polymer particles.

[0122] Another aspect of the invention is a method for providing a formulation of a hardenable composition comprising at least a binder and solid particles, the method comprising the steps of (i) characterizing a sample of the solid particles as described above and (ii) identifying the nature and / or proportion in the formulation of at least one component of the formulation, in particular the solid particles and / or additives, by taking into account at least one particle size parameter and / or at least one grain shape parameter, wherein the solid particles are different from the binder, in particular the solid particles do not comprise any binder or do not consist of a binder.

[0123] In particular, the formulation may be provided via a user interface, via a machine interface and / or on a data carrier.

[0124] This method for providing a formulation of a hardenable composition can be implemented, for example, in a separate software application, or it can be included within the same software application configured to characterize a sample of solid particles as described above.

[0125] Applications capable of calculating mixes for hardenable compositions are known to those skilled in the art. For example, an application called "Sika Mix Design App" suitable for calculating concrete mixes is available from Sika Services AG, Switzerland. The application contains a database of known raw materials to which new raw materials can be added, e.g. solid particles that can be characterized using the present invention.

[0126] The present invention also relates to a method for the preparation of a curable composition comprising at least a binder and solid particles, comprising the steps of characterising a sample of the solid particles as described above and mixing the solid particles with a binder and any optional further components, preferably at least one particle size parameter and / or at least one particle shape parameter being taken into account for identifying the nature and / or proportion in the composition of at least one of the components, in particular the solid particles and / or additives.

[0127] In these cases the hardenable composition is preferably a composition with a mineral binder, such as a mortar or concrete composition.

[0128] The expression "mineral binder" refers in particular to a binder that reacts in the presence of water by hydration to form solid hydrates or hydrate phases, which can be, for example, a hydraulic binder (e.g. cement or hydraulic stucco), a latent hydraulic binder (e.g. slag), a pozzolanic binder (e.g. fly ash) or a non-hydraulic binder (e.g. gypsum or white lime).

[0129] The mineral binder as a whole comprises at least 5% by weight of hydraulic binder, in particular at least 20% by weight, preferably at least 50% by weight, in particular at least 75% by weight. In another advantageous embodiment, the mineral binder comprises at least 95% by weight of hydraulic binder, in particular cement.

[0130] The mineral binder in particular comprises a hydraulic binder, preferably cement. Particularly preferred is Portland cement, in particular of type CEM I, II, III or IV (according to standard EN 197-1). However, it may also be advantageous for the binder composition to comprise other binders in addition to or instead of the hydraulic binder. These are in particular latent hydraulic binders and / or pozzolanic binders. Examples of suitable latent hydraulic binders and / or pozzolanic binders are slag, fly ash and / or silica dust. In one advantageous embodiment, the mineral binder comprises 5 to 95% by weight, in particular 20 to 50% by weight, of latent hydraulic binders and / or pozzolanic binders.

[0131] In a particular embodiment, the mineral binder comprises a mixture of calcined clay, limestone, and Portland cement.

[0132] The term "clay" refers to a solid material comprising at least 30 wt%, preferably at least 35 wt%, in particular at least 75 wt%, of clay minerals, each with respect to its dry weight. Calcined clays are clay materials which have been heat treated, preferably at temperatures between 500 and 900° C., or subjected to a flash calcination process at temperatures between 800 and 1100° C. According to a particularly preferred embodiment of the invention, the calcined clay is metakaolin.

[0133] In a preferred embodiment of the invention, the chemical composition of limestone and Portland cement is defined in the standard EN197-1:2011. In the alternative, limestone may also represent magnesium carbonate, dolomite and / or a mixture of magnesium carbonate, dolomite and / or calcium carbonate. It is particularly preferred that limestone in the context of this application is a natural limestone that consists mainly of calcium carbonate (typically calcite and / or aragonite) but also typically contains some magnesium carbonate and / or dolomite. Limestone may also be a natural peat soil.

[0134] According to a preferred embodiment, the Portland cement is of type CEM I. According to an embodiment, the content of Portland clinker in the Portland cement of the invention is at least 35 wt%, preferably at least 65 wt%, in particular at least 95 wt%, each based on the total dry weight of the cement.

[0135] According to an embodiment, the mineral binder comprises calcined clay, limestone, and Portland cement in the following weight ratios: P:CC is 33:1 to 1:1, preferably 8:1 to 1:1, CC:L is 10:1 to 1:50, preferably 10:1 to 1:33, more preferably 5:1 to 1:10; P:L is 20:1 to 1:4, preferably 5:1 to 1:1.

[0136] According to an embodiment, the mineral binder comprises at least 65 wt. %, preferably at least 80 wt. %, more preferably at least 92 wt. % of calcined clay, limestone, and Portland cement, each of which is based on the total dry weight of the mineral binder.

[0137] According to an embodiment of the present invention, the mineral binder comprises: a) 25 to 100 parts by weight of Portland cement (P); b) calcined clay (CC), in particular matekaolin, 3 to 50 parts by mass; c) Limestone (L) 5~100 parts by mass Contains a mixture of.

[0138] The solid particles in these cases are preferably selected from aggregates such as sand, gravel, stone, ground quartz, and / or limestone.

[0139] The additives may be selected from conventionally used materials, such as concrete plasticizers such as lignosulfonates, sulfonated naphthalene formaldehyde condensates, sulfonated melamine formaldehyde condensates, or polycarboxylate ethers (PCEs), accelerators, corrosion inhibitors, retarders, shrinkage reducers, defoamers, and / or foaming agents.

[0140] Yet another aspect relates to a system including a mobile computing device and, optionally, an additional separate computing device, the system comprising: (i) means for carrying out steps a) to d) of the method; (ii) means for carrying out at least steps a) and b), in particular steps a), b) and d), of the method according to claim 1 and means for transferring at least one digital image of the sample of solid particles, optionally together with at least one attribute, to another computing device; Includes.

[0141] Another aspect of the invention relates to a system including a computing device, the system comprising means for receiving at least one digital image of a sample of solid particles and means for performing at least steps c) and / or d) of the method described above.

[0142] Furthermore, the invention relates to a computer-readable medium comprising instructions which, when executed by a mobile computing device, cause the mobile computing device to perform at least steps a) and b), in particular a) and b) and d), in particular steps a) to d), of the aforementioned method.

[0143] The invention also relates to a computer-readable medium comprising instructions which, when executed by a computing device, cause the computing device to receive at least one digital image, optionally together with at least one attribute, and to perform at least steps c) and / or d) of the aforementioned method.

[0144] Other solutions of the invention that can be implemented independently of the first solution mentioned above are described below.

[0145] A first aspect of the second solution relates to a method for providing a formulation of a curable composition comprising at least a binder and solid particles, the method comprising the steps of (i) obtaining at least one particle size parameter and / or at least one particle shape parameter of the solid particles and (ii) identifying the nature and / or proportion in the formulation of at least one component of the formulation, in particular the solid particles and / or additives, by taking into account the at least one particle size parameter and the at least one particle shape parameter.

[0146] Similarly, another aspect of the second solution relates to a method for producing a curable composition comprising at least a binder and solid particles, the method comprising the steps of (i) obtaining at least one particle size parameter and / or at least one particle shape parameter of the solid particles, and (ii) mixing the solid particles with the binder and any optional further components, preferably the at least one particle size parameter and the at least one particle shape parameter being taken into account for identifying the properties and / or proportions in the composition of at least one of the components, in particular the solid particles and / or additives.

[0147] In these two aspects of the second solution, the at least one particle size parameter and the at least one particle shape parameter of the solid particles can be obtained by a method different from the method described above or the method of claim 1, respectively. For example, these parameters can be obtained by conventional methods, for example by sieving, laser diffraction and / or the methods described in standards EN 933-1:2012 to -7:2012, standard ISO 13320:2009 and / or Blott and Pye, Sedimentology (2008) 55, 31-63.

[0148] However, these parameters are used in combination to specify the nature and / or proportion in the composition of at least one component of the composition, in particular the solid particles and / or additives, which serves to improve the properties of the hardenable composition, in particular when using low grade solid particles, e.g. low grade aggregates.

[0149] In particular, when determining the nature and / or proportions of at least one component of the formulation, or when mixing the solid particles with the binder and any optional further components in step (ii) of the above method, at least one particle size parameter is determined for each of two or more predetermined particle populations, e.g. sieved particle populations.

[0150] Thereby, preferably, for each of at least one particle group, an individual weight parameter is assigned to each particle group. The weight parameter can be used to consider the size dependency of at least one particle shape parameter. For example, the shape of small aggregates is usually less relevant in achieving the desired properties of the hardenable composition compared to the shape of larger aggregates. Therefore, by considering the size dependency of at least one shape parameter, the formulation design of the hardenable composition can be further optimized in terms of the desired properties.

[0151] For example, based on the size and shape of the aggregate, e.g. sand, the properties and / or proportions of the individual components of the composition, e.g. mortar or concrete composition, can be adjusted to achieve the desired properties of the hardenable composition, e.g. the desired viscosity and / or flow properties, thereby taking into account the particle size distribution, sphericity, roundness, and / or aspect ratio. In particular, the weight parameter of the aggregate of particles <0.5 mm is selected such that the influence of the shape parameter of this particle group on the adjustment of the properties and / or proportions of the individual components of the hardenable composition is smaller than the shape parameter of the aggregate of particles ≧0.5 mm.

[0152] The binder, solid particles and additives are preferably selected as defined above with respect to the description of the mineral binder composition of the first solution. Also, the aforementioned application called "Sika Mix Design App" can be used.

[0153] In particular, the formulation is provided via a user interface using a computing device, via a machine interface and / or on a data carrier. Implementation of this feature can be performed as described above.

[0154] Another aspect of the second solution is a system including a computing device, in particular a mobile computing device, the system including means for performing step (ii) of the aforementioned method, in particular steps (i) and (ii) of the aforementioned method.

[0155] Another aspect of the second solution relates to a computer-readable medium comprising instructions which, when executed by a computing device, cause the computing device to perform step (ii) of the aforementioned method, in particular steps (i) and (ii) of the aforementioned method.

[0156] In particular, any data processed and / or generated by the method of the invention is encrypted to ensure that only specifically authorized users have access to the original data. Similarly, any computer programs and / or applications implementing the method of the invention are encrypted. Encryption methods are known to those skilled in the art.

[0157] Further advantageous configurations of the invention are evident from the exemplary embodiments.

[0158] The drawings used to explain the embodiments show the following: [Brief description of the drawings]

[0159] [Figure 1] 2 shows a flow chart of a computer-implemented method according to the present invention. [Diagram 2] 2 shows a schematic diagram of a system including means for carrying out the method of FIG. 1; [Diagram 3] FIG. 1 shows a schematic diagram of the second step of the method in which a user (not shown) holding a smartphone takes images of a sample of solid particles of different sizes. [Figure 4] An example of the data file structure is shown below. [Diagram 5] 1 shows a flowchart of another computer-implemented method. [Figure 6] Particle sieving. Bar plots of selected particle shape parameters (circularity, sphericity, and aspect ratio) per particle population. [Figure 7] 1 shows an outline image overlaid on a digital image. [Figure 8] 1 shows a schematic diagram of a light pad. [Figure 9] A comparison of a particle sample on the non-illuminated surface of the light pad of FIG. 8 (left side) and the same particle sample on the illuminated surface of the light pad of FIG. 8 (right side) is shown. [Figure 10]Another bar plot of selected particle shape parameters (Circularity / R, Sphericity / SP, and Aspect Ratio / AR) per particle sieve fraction for particles >0.5 mm in size and the corresponding particle counts per particle sieve fraction is shown. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0160] Figure 1 shows a flow chart of a computer-implemented method 10 of the present invention. In a first step 11, a sample of solid particles to be analysed, for example a sand sample with particle size >0-2mm, is provided on a two-dimensional sample area of ​​known size. The sample area is for example formed by a piece of A4 size paper.

[0161] In a second step 12, a digital image of the solid particle sample is taken with a mobile computing device, for example a smartphone camera, the camera having for example 4K resolution.

[0162] Then, in a third step 13, image particle analysis of the digital images is performed to extract at least one particle size parameter, such as particle size distribution, and / or at least one particle shape parameter, such as circularity or sphericity, of the particle population identified in the at least one digital image.

[0163] In a fourth step 14, the at least one particle size parameter and the at least one particle shape parameter are provided via a user interface, for example a display of a mobile computing device.

[0164] In an optional fifth step 15a, a formulation of a hardenable composition comprising at least a binder and solid particles is provided, in which the nature and / or proportion in the formulation of at least one component of the formulation, in particular an additive, e.g. a plasticizer, is identified by taking into account at least one particle size parameter and at least one particle shape parameter.

[0165] Alternatively or in addition, in optional step 15b, a hardenable composition is provided comprising at least a binder and solid particles, step 15b comprising mixing the solid particles with the binder and any optional further components, wherein at least one particle size parameter and at least one particle shape parameter are taken into account for identifying the nature and / or proportion of at least one of the components, in particular the additive, in the composition.

[0166] FIG. 2 shows a schematic overview of a system 20 including means for carrying out the method shown in FIG.

[0167] Specifically, system 20 includes a smartphone 21 , which includes a camera 22 , a touch-sensitive display including an input device 23 and a display 24 , a data processing unit 25 with random access memory, a data storage device 29 , and a wireless communication interface 28 .

[0168] In operation, an application 26 executes within the data processing unit 25, the application being configured to carry out steps 12 and 14 of the method described in FIG.

[0169] Specifically, the application 26 assists the user in taking images of solid particles in a two-dimensional sample area 30 with the built-in smartphone camera 22, where the application is configured to automatically warn the user and / or not take images when an arrangement other than plane-parallel is encountered. This can be achieved by evaluating a position sensor (not shown) of the smartphone. The application is also configured to automatically adjust the light conditions to obtain a balanced exposure. The captured digital images are stored in random access memory and / or data storage 29.

[0170] Additionally, application 26 prompts the user to input one or more attributes of the sample via the input device and assigns a unique identifier to the sample, such as maximum solid particle size, type of solid particle (natural, crushed, man-made, reclaimed, recycled solid particles), condition of the solid particle (wet or dry), pretreatment (washed or untreated), source location of the solid particle, intended use (project name, customer name), and / or general comments.

[0171] The queries may be answered, for example, by posing them in user input fields, selection fields, maps, and / or text entry fields on the display 24 and storing data provided by the user via the input device 23 together with at least the digital image in random access memory and / or data storage 29.

[0172] For example, the location of the particle source may be provided manually by a user by entering geographic coordinates in an input field and / or by marking the location on a map shown on the display 24. However, the location of the source of the solid particles may also be provided automatically by Global Positioning Satellite System sensors, such as GPS, Galileo, Beidou, and / or Gionass sensors. The user may then be requested to confirm the automatically determined location.

[0173] In the system 20 shown in Fig. 2, step 13 of the method shown in Fig. 1 is executed on an external server 21a. In particular, an image captured by the smartphone camera 22, optionally together with attributes, is transferred to the server 21a via a wireless communication interface 28 (or any other communication interface) and a network (e.g., the Internet, not shown). The server 21a receives the data via its communication interface 28a and sends it to an image particle analysis application 26a executed on a processing unit 25a.

[0174] The images, optionally along with attributes, can be stored in data storage 29a of server 21a for later sharing, retrieval, and / or further evaluation.

[0175] The application 26a performs an image particle analysis of the digital images, whereby at least one particle size parameter, such as particle size distribution, and at least one particle shape parameter, such as circularity or sphericity, of a particle population identified in at least one digital image are extracted, whereby one or more attributes may also be taken into account in the analysis.

[0176] The application 26a is implemented using image analysis algorithms, such as software packages and / or libraries provided in Matlab, OpenCV, and / or ImageJ, and / or using artificial intelligence software.

[0177] After the image analysis is completed, at least one particle size parameter, such as particle size distribution, and at least one particle shape parameter, such as circularity or sphericity, are sent back to the smartphone 21 or an application 25 running thereon via communication interfaces 28a, 28, respectively.

[0178] The application 25a provides at least one particle size parameter and / or at least one particle shape parameter via the display 24 or stores these parameters, preferably together with attributes, in the data storage 29 for later sharing, recalling and / or further evaluation. This data can be stored in the form of a data file having a file format selected from json, csv, txt, pdf and / or copyright-protected file formats. In particular, a file format is selected that can be read by the application called “Sika Mix Design App” and / or any other additional application. See FIG. 4 for an example.

[0179] Additionally, the application 25 is configured to share the at least one particle size parameter and the at least one particle shape parameter and attributes with other users by transmitting them, in particular as a data file, via the communication interface 28 to another computing device 40, for example a smartphone of the other user. This can be initiated, for example, by the user via the input device 23, for example by pressing a button shown on the display 24.

[0180] Furthermore, the system 20 may comprise an optional mix design application 27, which may be executed on the processing unit 25. The mix design application is configured to provide a formulation of a hardenable composition comprising at least a binder and solid particles, in which the nature and / or proportion in the formulation of at least one component of the formulation, in particular an additive, e.g. a plasticizer, is identified by taking into account the at least one particle size parameter and at least one particle shape parameter obtained in the first application 26.

[0181] Thus, for example, the first application may send at least one particle size parameter and at least one particle shape parameter and, optionally, attributes to the mix design application 27 via a suitable application interface. The data sent to the mix design application 27 may also be provided in the form of a data file and sent to the mix design application 27. Additionally or alternatively, the mix design application may be configured to load this data from the data storage 29 and / or in the form of a data file.

[0182] The mix design application 27 is configured to present the provided recipe on the display 24 and transmit it via a communication interface 28 to another computer system, such as the server 21a or another smartphone 40, or store it in data storage 29 for later sharing, retrieval and / or further evaluation.

[0183] Figure 3 shows a schematic diagram of step 12 of the method shown in figure 1, where a user (not shown) holds a smartphone 21 in his hand and takes images of samples of solid particles of different sizes L (large), M (medium), S (small), e.g. sand particles with particle diameters in the range >0-2 mm, provided on a white paper of A4 size serving as a two-dimensional sample area 30. The paper is placed on a high-contrast surface, e.g. a black surface 30, which is larger than the two-dimensional sample area 30 in all directions in space. Hence, the two-dimensional sample area 30 is completely surrounded by frames of different colours.

[0184] 4 shows an example of the structure of data file 50 in pdf file format. Data file 50 includes a table 51 of user provided attributes such as type of solid particle (natural, crushed, manmade, reclaimed, recycled solid particles), condition of the solid particle (wet or dry), pretreatment (washed or untreated), source location of the solid particle, intended use (project name, customer name), and / or general comments.

[0185] File 50 also includes a graph 52 showing particle size distribution, a table 53 including the calculated sieve size passing percentage of solid particles and / or the calculated percentage of solid particles remaining, and a 10 , D 50 , D 85 , and D 100 In addition to the values, it includes Table 54 of statistical parameters such as the coarseness ratio of the analyzed solid particles.

[0186] Further, file 50 includes a two-dimensional plot 55 showing the average particle shape (e.g., circularity or sphericity) with marker 55a. Additionally, file 50 includes a bar plot 57 displaying the average values ​​of selected particle shape parameters (e.g., circularity, sphericity, and aspect ratio) per particle sieve population. A more detailed view of bar plot 57 is shown in FIG.

[0187] Figure 5 shows a flow chart of another computer-implemented method 60, in which a particle size distribution and at least one particle shape parameter of a sample of solid particles are provided in a first step 61. These parameters can be obtained by the method shown in Figure 1 or by any other method, e.g. manually.

[0188] Thereafter, in a next step 62a, a formulation of a hardenable composition comprising at least a binder and solid particles is provided, whereby the nature and / or proportion in the formulation of at least one component of the formulation, in particular an additive, e.g. a plasticizer, is identified by taking into account the particle size distribution and at least one particle shape parameter.

[0189] This can be achieved by the aforementioned mix design application, namely mix design application 27, which is configured to provide the proposed recipe on the display 24, transmit it via a communication interface 28 to another computer system, for example the server 21a or another smartphone 40, and / or store it on a data storage 29 for later sharing, recalling and / or further evaluation.

[0190] Alternatively or in addition, in optional step 62b, a hardenable composition is provided comprising at least a binder and solid particles, where step 62b comprises mixing the solid particles with the binder and any optional other components, where at least one particle size parameter and / or at least one particle shape parameter is taken into account to specify the nature and / or proportion of at least one of the components, in particular the additive, in the composition.

[0191] As a representative example, a hardening mortar composition containing cement (CEM I), water, plasticizer (Sika® Viscocrete® 111P), and quartz sand (D85 value=2.36 mm) was prepared as follows:

[0192] 1. The particle size distribution of the sand used, as well as the sphericity, circularity, and aspect ratio (=grain shape parameters) were determined according to the method shown in Figure 1.

[0193] 2. The particle size distribution and particle shape parameters were transferred to the “Sika Mix Design App” (available from Sika Services AG) for the calculation of a mortar composition with a given slump flow of 370 mm (according to EN 12350-5:2019) by adjusting the proportions of cement, water, debulking agent, and silica sand to the target slump flow.

[0194] 3. Then, the mortar compositions calculated by the “Sika Mix Design App” were prepared by mixing cement, water, plasticizer, and silica sand in their respective proportions. Then, the slump flow of the mortar compositions thus prepared was determined (according to EN 12350-5:2019) and the actual slump flow of the prepared mortar compositions was compared with the target slump flow of 370mm.

[0195] 4. For comparison, steps 2 and 3 were repeated under otherwise identical conditions, without considering the particle shape parameters (sphericity, circularity, and aspect ratio) in “Sika Mix Design App”. Hence, here only the particle size distribution was considered in “Sika Mix Design App” when calculating the mortar composition.

[0196] The results were as follows:

[0197] [Table 1]

[0198] Therefore, by considering the geometric parameter, one can predict a mortar composition that has a slump flow very close to the desired target value. Without the geometric parameter, the deviation from the target slump flow would be much larger.

[0199] Similar tests were performed on other sands (not shown here). Statistical evaluation of the data shows that taking into account grain shape parameters for calculating mortar compositions with desired target slump flows is generally very relevant and gives significantly better predictions compared to calculations without considering shape parameters.

[0200] 7 shows a contour plot superimposed on a corresponding digital image. The contour plot can further be used to check the quality of the digital image and / or the analysis performed.

[0201] Figure 8 shows a schematic diagram of a light pad 70 that can be used as the two-dimensional sample area 30 in place of the paper used in the apparatus of Figure 3. The light pad consists of a light-emitting surface 71 made of a semi-transparent layer that is back-illuminated with a white LED light source 73 (shown as a dashed rectangle). The light-emitting surface 71 has the size of a DIN A4 sheet of paper and is completely surrounded by a black frame 72.

[0202] Figure 9 shows a particle sample (left side) on the unilluminated surface of light pad 70 of Figure 8 and the same particle sample (right side) on the illuminated surface of light pad 70 of Figure 8. As is evident from the comparison, the contrast on the right side is clearly better and no shadows are visible.

[0203] Figure 10 shows the particle counts of sand particles >0.5 mm in size per particle sieve (right axis) and additionally the corresponding average values ​​of three particle shape parameters (roundness / R, sphericity / SP, and aspect ratio / AR) per particle sieve (left axis).

[0204] The data shown in Figure 10 was obtained using the method and system described in Figures 1 and 2. The application 26 used in the method may be further configured to calculate an overall average value of the particle shape parameter for a selected range of particle sieving particles, for example from a lower threshold of 4 mm to an upper threshold of 8 mm, or alternatively from a lower threshold of 1 mm to a maximum sieving particle size of 16 mm.

[0205] Those skilled in the art will recognize that the present invention may be embodied in other specific forms without departing from its spirit or essential characteristics, and therefore, the examples and embodiments disclosed herein are to be considered in all respects as illustrative and not restrictive.

[0206] For example, instead of using server 21a, application 26 may be configured as a stand-alone application capable of performing all of steps 11, 12, 13, 14, and optionally 15a of the method of FIG.

[0207] Similarly, optional features of system 20, such as sharing data with other computing devices or adding additional functionality, such as automatically retrieving location data via a location sensor, may be excluded.

[0208] The method can also be used to characterize solid particles other than sand.

[0209] It is also possible to provide a system 20 having the mix design application 27 but not the application 26. Such a system is for example suitable for carrying out the method shown in Figure 5. In this case, the at least one particle size parameter and the at least one particle shape parameter can be manually entered and / or retrieved from the data storage 29 and / or any other data storage, for example the data storage 29a of the server 21.

Claims

1. A computer-implemented method for characterizing solid particles, particularly sand particles, comprising: a) providing a sample of the solid particles to be analyzed within a predetermined sample area; b) taking at least one digital image of the sample of the solid particles with a camera of a mobile computer device or a camera connected to the mobile device; c) performing image particle analysis on the at least one digital image to extract at least one particle size parameter and / or at least one particle shape parameter of a particle population identified in the at least one digital image; d) providing the at least one particle size parameter and / or the at least one particle shape parameter via a user interface, via a machine interface, and / or on a data storage medium. A method as described above.

2. The method according to claim 1, wherein the mobile computer device comprises a human interface device, particularly an input device and a display, and preferably a wireless communication interface.

3. The method according to claim 1 or 2, wherein the mobile computer device is selected from a mobile phone, a mobile computer or a portable computer, and / or a head-mounted display with a camera.

4. The method according to claim 1 or 2, wherein the camera is a camera for taking images in the visible spectrum, particularly color images.

5. The method according to claim 1 or 2, wherein the camera has a resolution of at least 2 million pixels, particularly at least 5 million pixels, preferably at least 8 million pixels, particularly at least 12 million pixels, particularly preferably at least 20 million pixels, even more preferably at least 50 million pixels, or more than at least 100 million pixels.

6. The method according to claim 1 or 2, wherein the sample area includes a reference scale and / or has a known size.

7. The method according to claim 1 or 2, wherein the predetermined sample area is a two-dimensional sample area, preferably a thin sheet material, particularly a thin sheet material of a predetermined size.

8. The method according to claim 1 or 2, wherein the particles of the sample are selected from sand, aggregates, fibers, and / or glass spheres.

9. When taking the image, the camera is arranged such that the allocation of the sample area in the image is maximized by, in particular, providing the user with an instruction for the arrangement and / or by automatically adjusting at least one setting of the camera, such as the focal length of the camera. The method according to claim 1 or 2.

10. The minimum detectable particle size is calculated by taking into account the resolution of the camera, the allocation of the length of the sample area in the total area of the image, and the actual length of the sample area. The method according to claim 1 or 2.

11. If the minimum detectable particle size is smaller than a predetermined threshold value, a warning is provided to the user, an instruction for the arrangement is provided to the user, and / or a setting of the camera, such as the focal length, is automatically adjusted. The method according to claim 1 or 2.

12. The at least one extracted particle size parameter includes the particle size distribution of the particle population identified in the at least one digital image. The method according to claim 1 or 2.

13. For each of the at least one digital image, an outline image is generated, and preferably, the outline image is provided via a user interface, via a machine interface, and / or on a data storage medium in step d). The method according to claim 1 or 2.

14. In step b), at least two, preferably at least three, in particular at least five, or at least ten digital images are taken, and for each image, image particle analysis is performed in step c). By taking into account each of the at least one particle size parameter and / or the at least one particle shape parameter individually extracted from the at least two images, the deviation, in particular the standard deviation, of the at least one particle size parameter and / or the at least one particle shape parameter is determined. The method according to claim 1 or 2.

15. If the deviation is greater than a predetermined threshold value, a warning is provided to the user, and / or thereby, the digital image and / or the outline image in which the parameter difference occurs is identified and / or indicated. The method according to claim 14.

16. The at least one particle size parameter includes at least one statistical parameter selected from the group of average particle size, average diameter, D value where x = 1 - 100, and / or coarse particle ratio, the method according to claim 1 or 2. ｘ The method according to claim 1 or 2, comprising at least one statistical parameter selected from the group of average particle size, average diameter, D value where x = 1 - 100, and / or coarse particle ratio.

17. The at least one extracted particle size parameter includes a deviation from a predetermined nominal value and / or a nominal distribution. The method according to claim 1 or 2.

18. The method according to claim 1 or 2, wherein for particle sizes smaller than the minimum detectable particle size, the particle size distribution is estimated based on the extracted particle size distribution.

19. The method according to claim 1 or 2, wherein the at least one extracted particle shape parameter includes roundness, sphericity, aspect ratio, roughness, area coverage, flake index, shape index, percentage of broken and damaged surfaces, and / or inclination.

20. The method according to claim 1 or 2, wherein the at least one particle shape parameter is extracted only for particles of a predetermined size, particularly for particles larger than a certain threshold size, or for each of at least two or more predetermined particle groups of the particles, at least one individual particle shape parameter is extracted, particularly at least one individual average value of the particle shape parameters is extracted.

21. The method according to claim 1 or 2, comprising the step of assigning at least one attribute to the sample of solid particles.

22. The method according to claim 1 or 2, which is at least partially, particularly fully executed on the mobile computer device.

23. The method according to claim 1 or 2, wherein the image analysis of step c) and / or the providing of step d) are executed on another computer device, for example, on a server.

24. The method according to claim 1 or 2, wherein the image is preferably shared, called, and / or further evaluated on an external computer device, for example, on a server, together with the at least one attribute and optionally the external shape image.

25. A system comprising a mobile computer device and optionally another additional computer device, (i) means for executing steps a) to d) of the method according to claim 1, and / or (ii) means for executing at least steps a) and b) of the method according to claim 1, particularly steps a), b), and d), and means for transferring at least one digital image of a sample of solid particles to the other computer device, optionally together with at least one attribute, comprising the system.

26. A system comprising a computer device, comprising means for receiving at least one digital image of a sample of solid particles and means for executing steps c) and / or d) of the method according to claim 1. Claim 27 A computer-readable medium that, when executed by a mobile computer device, includes instructions to cause the mobile computer device to perform at least steps a) to b) of the method according to claim 1, particularly a), b) and d), and particularly steps a) to d). Claim 28 A computer-readable medium that, when executed by a computer device, includes instructions to receive at least one digital image by the external computer device and to cause the external computer device to perform step c) and / or d) of the method according to claim 1. Claim 29 A method for providing a formulation of a curable composition comprising at least a binder and solid particles, wherein the solid particles are different from the binder, particularly the solid particles do not contain any binder or consist of a binder, the method comprising: (i) obtaining at least one particle size parameter and / or at least one particle shape parameter of the solid particles; and (ii) specifying at least one component of the formulation, particularly the nature and / or proportion of the solid particles and / or additives during the formulation, by considering the at least one particle size parameter and the at least one particle shape parameter. Claim 30 The method according to claim 29, wherein the formulation is provided using a computer device, via a user interface, via a machine interface, and / or on a data storage medium. Claim 31 A system comprising a computer device, the system including means for performing at least step (ii) of the method according to claim 29, particularly steps (i) and (ii) of the method according to claim 29. Claim 32 A computer-readable medium that, when executed by a computer device, includes instructions to cause the computer device to perform at least step (ii) of the method according to claim 29, particularly steps (i) and (ii) of the method according to claim 29. Claim 33 A method for producing a curable composition containing at least a binder and solid particles, wherein the solid particles are different from the binder, in particular the solid particles do not contain any binder or are not composed of a binder, the method comprising: (i) obtaining at least one particle size parameter and / or at least one particle shape parameter of the solid particles; and (ii) mixing the solid particles with the binder and any optional other components, wherein the at least one particle size parameter and the at least one particle shape parameter are considered for specifying the properties and / or proportions of at least one of the components, in particular the solid particles and / or additives, in the composition.

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