Digital analysis of heterogeneous materials

Abstract

A computer-implemented method for characterizing a heterogeneous material comprising distributed components, in particular distributed particulate components, dispersed within a continuous phase of agglomerated material, the method comprising the steps of: a) providing or selecting a sample of the heterogeneous material to be analyzed; b) recording at least one digital image of the sample with a camera of a computer device, in particular a mobile computer device, a camera connected to the computer device, in particular a mobile computer device, and / or reading at least one digital image pre-recorded with a separate camera with the computer device; c) performing an image analysis, in particular an imaging particle analysis, on the at least one digital image; the method comprises the following steps: extracting one or more of the following properties:-a size parameter (in particular a particle size parameter), a shape parameter (in particular a particle shape parameter), a spatial distribution, an orientation parameter (in particular a particle orientation parameter), a surface parameter (in particular a particle orientation parameter) of a component (in particular a particulate component) identified by image analysis in the at least one digital image; in particular particle surface parameters), roughness parameters (in particular particle roughness parameters), bulk density, separation parameters, color and / or area fractions; and / or-an area fraction of a continuous phase identified by the image analysis in the at least one digital image; d) making said one or more of said properties extracted in step c) available 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 a heterogeneous material comprising distributed components, in particular distributed particulate components, dispersed in a continuous phase of a condensed matter, and to a system having means for implementing the method, and to a computer-readable medium comprising instructions for implementing the method. Background Art

[0002] In the construction industry, construction materials based on curable or cured compositions are widely used for various applications. Examples of such compositions are mortars, concretes, grouts, screeds, adhesives, floor or coating compositions. These compositions typically comprise a curable binder which is optionally combined with water, air, solid aggregates and / or additives.

[0003] Thus, the binder can be selected from mineral binders such as hydraulic binders, and organic binders such as curable polymers. The aggregates are selected according to the desired properties of the curable composition. Typically the aggregates include sand, gravel, stone powder, metal particles and / or polymer particles. Air can be introduced to produce a lightweight structure or to obtain specific properties such as freeze-thaw resistance.

[0004] Curable or cured compositions used in the construction industry are generally heterogeneous materials comprising two or more different components. Thus, in particular, distributed particulate components such as aggregates, air voids and / or polymer particles are dispersed in a continuous phase of a condensed matter, such as in a solvent, a fluid binder material or a solidified binder in the solid state.

[0005] In order to ensure that curable compositions meet the desired requirements during processing and subsequently in the hardened state, various test procedures have been developed.

[0006] For example, the raw materials of curable compositions such as aggregates and fillers are typically inspected according to their particle size distribution to ensure an appropriate grading curve. In addition, it is known to analyze the particle shape of the aggregates and the contamination of recycled aggregates to adjust the mix design of mortar or concrete compositions.

[0007] In this regard, GB 2524130 (LPW Technology Ltd) describes, for example, an analysis system for determining the properties and / or performance of a flowable material such as sand, comprising an attachable analysis device and a smartphone or tablet device. The sample material in the sample container is moved through a flow aperture into an analysis chamber where the smartphone or tablet device captures an image of the sample material. An application installed on the smartphone is configured to analyze the sample by determining the size, shape, color, flow rate and velocity of the sample particles.

[0008] In addition, the processability and consistency of the fresh mineral binder composition can be checked, for example, by known slump flow tests, flow table tests, L-shaped box tests, V-shaped funnel tests, etc. In the hardened state, the composition is typically analyzed in terms of density, compressive strength, flexural strength, tensile strength, Young's modulus, fracture mode, water tightness, surface aesthetics, air voids, and more properties.

[0009] Although many analytical methods are available for characterizing hardenable or hardened compositions, most of these methods (with a few exceptions) are time-consuming, require sophisticated equipment, and are phenomenologically oriented.

[0010] Similarly, for other building materials, such as membranes for roofs and foams for installation, sealing, reinforcement, damping, and / or filling purposes, for example, in the automotive industry, it is necessary to inspect their condition to ensure quality requirements or to evaluate their condition after a certain exposure, especially when crack formation occurs. There are various test methods for such inspections, but these are also typically complex and time-consuming.

[0011] Therefore, there is still a need for improved solutions with fewer or no such drawbacks. Summary of the Invention

[0012] An object of the present invention is to provide an improved solution for analyzing heterogeneous materials, which heterogeneous materials comprise distributed components, in particular distributed particulate components, dispersed in a continuous phase of a cohesive material. In particular, the solution should allow the heterogeneous material to be analyzed in as easy, flexible, and user-friendly a manner as possible without the need for sophisticated equipment.

[0013] Surprisingly, it has been found that the features of claim 1 achieve this object. Thus, the core of the present invention relates to a computer-implemented method for characterizing a heterogeneous material, which heterogeneous material comprises distributed components, in particular distributed particulate components, dispersed within a continuous phase of a cohesive material, the method comprising the following steps:

[0014] a) providing or selecting a sample of the heterogeneous material to be analyzed;

[0015] b) taking at least one digital image of the sample with a camera of a computer device, in particular a mobile computer device, or a camera connected to a computer device, in particular a mobile device, and / or reading at least one digital image pre-recorded with an independent camera by the computer device;

[0016] c) performing image analysis, in particular imaging particle analysis, on the at least one digital image for extracting one or more of the following properties:

[0017] - Size parameters (especially particle size parameters), shape parameters (especially particle shape parameters), spatial distribution, orientation parameters (especially particle orientation parameters), surface parameters (especially particle surface parameters), roughness parameters (especially particle roughness parameters), bulk density, segregation parameter, color and / or area fraction of components (especially particle-forming components) identified by image analysis in the at least one digital image; and / or

[0018] - Area fraction of the continuous phase identified by the image analysis in the at least one digital image;

[0019] d) Making the one or more of the performances extracted in step c) available via a user interface, via a machine interface, and / or on a data storage medium.

[0020] The method of the present invention is a unique method that can be executed on conventional mobile devices, such as smartphones. Therefore, no complex and expensive analysis equipment is required. The method also allows for very fast, flexible, and accurate digital characterization of heterogeneous materials in various performances.

[0021] Therefore, heterogeneous materials with different performances and structures can be easily characterized using the same hardware device. For example, heterogeneous materials in the form of emulsions, foams, suspensions, minerals, and / or organic binder compositions in fluids and in hardened states can be analyzed without special equipment. Thus, hardened heterogeneous materials can be obtained, for example, from fluid heterogeneous materials cured chemically and / or physically.

[0022] In this context, the term "foam" should be understood in a broad sense and especially includes solid and liquid foams made of substantially any kind of material. For example, the foam can be in the form of foamed minerals and / or organic binder compositions, foamed polymer materials, foamed liquids, etc.

[0023] For example, foamed polymer materials can be obtained from a thermally expandable composition comprising a polymer matrix and one or more polymers and a chemical or physical blowing agent. Such a composition can be used, for example, as a baffle and sound damping material in the automotive industry.

[0024] Therefore, the method of the present invention is particularly suitable for characterizing curable or cured compositions used in the construction industry, such as mortars, concretes, grouts, screeds, floor compositions, adhesives, coating compositions, or foams, in their processable as well as cured states.

[0025] Similarly, synthetic materials, such as synthetic membranes for waterproofing and / or roofing, especially those containing air voids and / or cracks, can be characterized.

[0026] In particular, the method of the present invention allows for the characterization of heterogeneous materials at the structural level. In particular, it is possible to directly obtain the size parameters, shape parameters, spatial distribution, separation parameters, color and / or area fraction of the components, as well as the area fraction of the continuous phase. Specifically, it is possible to directly obtain the particle size parameters, particle shape parameters, spatial distribution, separation parameters, color and / or area fraction of the particulate components, as well as the area fraction of the continuous phase. These parameters have a decisive influence on the properties of the heterogeneous materials, such that for many materials, the method of the present invention can replace common testing procedures.

[0027] In addition, the method of the present invention allows for the storage of the extracted data, for example, on a remote server for further use. For example, the extracted data can be combined and / or correlated with further data such as raw material data, mixing design data of the heterogeneous material, and physical and / or chemical properties of the heterogeneous material to obtain a further understanding of the relationship between the structure and properties of the heterogeneous material.

[0028] Due to the fast and easy way of characterizing heterogeneous materials, especially using mobile computer devices, the method of the present invention can be carried out in a highly user-friendly manner.

[0029] Specifically, the method can be carried out in various ways, for example, in the form of a stand-alone application running on a mobile device without the need for any other resources, such as a server system. This is particularly useful in areas where access to the communication network is restricted, such as away from the city center or underground. However, the method can also be carried out in a distributed computing environment, for example, a combination of a mobile device as a client and a dedicated server and / or dedicated processing unit as a storage unit.

[0030] Furthermore, the method of the present invention can be implemented in a flexible manner using known software architectures, such as a native application, a progressive web application (PWA), or a hybrid application (a combination of native and PWA). Therefore, useful internet links to tutorials or support sites, as well as sharing functions (such as via email, Bluetooth, Airdrop, or other communication means) can also be included in these applications. In addition, the method of the present invention can be carried out in a single application, or it can be divided into two or more separate applications with appropriate software interfaces for data exchange between the applications. In addition, the application can be extended with additional functions in a flexible manner.

[0031] The application can be executed for any kind of operating system, such as iOS, Android, Microsoft Windows, and / or Linux.

[0032] It can be made easily accessible to anyone via different distribution channels, such as public download centers (e.g., stores and Google ), websites operated by private companies, and / or dedicated download sites.

[0033] Another aspect of the invention is the subject matter of the further independent claims. Particularly preferred embodiments are outlined throughout the specification and the dependent claims.

[0034] Modes of Carrying Out the Invention

[0035] A first aspect of the invention relates to a computer-executed method for characterizing a heterogeneous material, the heterogeneous material comprising distributed components, in particular particulate components, dispersed within a continuous phase of a condensed matter, the method comprising the steps of:

[0036] a) providing or selecting a sample of the heterogeneous material to be analyzed;

[0037] b) taking at least one digital image of the sample using a camera of a computer device (in particular a mobile computer device), a camera connected to the computer device (in particular a mobile computer device), and / or reading at least one digital image pre-recorded using an independent camera by the computer device;

[0038] c) performing image analysis, in particular imaging particle analysis, on the at least one digital image to extract one or more of the following properties:

[0039] - size parameters (in particular particle size parameters), shape parameters (in particular particle shape parameters), spatial distribution, orientation parameters (in particular particle orientation parameters), surface parameters (in particular particle surface parameters), roughness parameters (in particular particle roughness parameters), packing density, separation parameters, color, and / or area fraction of the components (in particular particulate components) identified by image analysis in the at least one digital image; and / or

[0040] - area fraction of the continuous phase identified by the image analysis in the at least one digital image;

[0041] d) making the one or more of the properties extracted in step c) available via a user interface, via a machine interface, and / or on a data storage medium.

[0042] In particular, the computer device, especially the mobile computer device, comprises a human-machine interface device, in particular an input device and a display, and preferably a communication interface, most preferably a wireless communication interface.

[0043] In this document, a mobile computer device particularly refers to a handheld computer, i.e., a computer that is small enough to be held and operated in a person's hand.

[0044] The mobile computer device is particularly selected from mobile phones, mobile computers or portable computers. In particular, the mobile computer device is selected from smart phones, phablets, tablet computers, portable computers, smart watches and / or head-mounted displays with a camera. Highly preferred are mobile phones and / or smart phones.

[0045] Mobile phones and smart phones are usually equipped with a high-resolution camera, an input device and a display. Therefore, the input device and the display are usually combined in a touch-sensitive display. Thus, mobile phones and smart phones provide all the hardware components required to perform the method of the present invention. In addition, such devices can be held stably in the hand or mounted on a mobile stand and / or a tripod, which makes them very suitable for taking images. At the same time, mobile phones and smart phones usually have a display large enough to display complex data in a well-readable manner.

[0046] The expression "(mobile) computer device's camera" means an internal camera integrated in the (mobile) computer device. In contrast, "camera connected to the (mobile) computer device" means an external camera, which is a separate device connected to the (mobile) computer device. For example, the external camera is a camera attachable to the (mobile) computer device or an independent camera. The connection between the external camera and the (mobile) computer device can be a wired and / or wireless connection.

[0047] Optionally or additionally, at least one digital image can be pre-recorded with an independent camera and read into the (mobile) computer device. Thus, in particular, the independent camera has its own memory device for intermediate storage of the image. Later, at least one digital image can be transferred from the memory device to the (mobile) computer device in any manner known to those skilled in the art, such as via a wired connection, a wireless connection and / or physical exchange of the memory device.

[0048] Preferably, the camera is a camera that takes pictures in the visible spectrum, especially color pictures. The color image allows the color performance of the components (especially particulate components) and / or the continuous phase to be considered 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, if color information is not required, other cameras, such as monochrome cameras, can also be used. In this case, the image is a monochrome image, such as a black-and-white image.

[0049] In addition, it is also possible to use a camera to capture images outside the visible spectrum, such as images in the infrared and / or ultraviolet ranges of the electromagnetic spectrum. Such a camera can be used alternatively or in combination with other cameras. In this case, in step c), the components (especially particulate components) and / or the properties of the continuous phase in the spectral range outside the visible spectrum can be considered.

[0050] Preferably, the camera has a resolution of at least 2 megapixels, especially at least 5 megapixels, preferably at least 8 megapixels, particularly at least 12 megapixels, highly preferably at least 20 megapixels, and even more preferably at least 50 megapixels or at least more than 100 megapixels. In particular, the camera has a resolution of at least 3800 pixels to at least 2100 pixels or the so-called 4K, preferably 8K, more preferably 12K, and particularly 16K resolution. The higher the resolution, the smaller the components, especially particulate components, that can be identified. However, a camera with a lower resolution may also be suitable.

[0051] In particular, the computer device, especially the mobile computer device, is configured to automatically identify the camera resolution.

[0052] In particular, the size of the optical sensor of the camera is at least 1 / 3", especially at least 1 / 2.5", preferably at least 1 / 1.7", preferably at least 2 / 3", especially at least 1 / 1.33" or at least 1 / 1.2". In particular, the size of the optical sensor of the camera is from 1 / 3" to 1".

[0053] In other words, the size of the optical sensor of the camera is preferably at least 4.8mm × 3.6mm in width × height, especially at least 5.7mm × 4.2mm, preferably at least 7.6mm × 5.7mm, preferably at least 8.8mm × 6.6mm, especially at least 9.6mm × 7.2mm or at least 10.6mm × 8.0mm. In particular, the size of the optical sensor of the camera is from 4.8mm × 3.6mm to 13.2mm × 8.8mm in width × height.

[0054] Generally, the larger the sensor size, the more light the sensor can capture, which in turn can improve the image quality. However, a camera with other sensor sizes may also be suitable.

[0055] In addition, an auxiliary lens can be added to the camera, for example, to extend or shorten the focal length of the camera.

[0056] Furthermore, if available, additional internal wide-angle and / or magnifying lenses (optical and / or digital) of the computer device (especially the mobile computer device) can be accessed as needed to obtain the at least one digital image.

[0057] In step b), at least one digital image is preferably acquired from the surface (in particular a flat surface) of a sample of the inhomogeneous material. Thus, preferably, the surface is oriented vertically or horizontally, in particular preferably horizontally.

[0058] In a further preferred embodiment, before and / or during step a), the inhomogeneous material is crushed, cut, ground, microtomed and / or polished to obtain a flat surface of the sample.

[0059] However, this treatment of the inhomogeneous material is optional and can be neglected. In particular, the inhomogeneous material provided in step a) can be untreated material, in particular material that has not been crushed, cut, ground, microtomed and / or polished.

[0060] In another particularly preferred embodiment, the sample, in particular the flat surface of the sample, is surface-treated to increase the contrast between the continuous phase and the constituents, in particular the particulate constituents. For example, the surface treatment is selected from coloring with ink and / or polishing with powder and / or paste.

[0061] In particular, a reference scale can be placed on and / or next to the sample.

[0062] For example, the reference scale can be characters, geometric shapes, a ruler and / or a reference object of known dimensions in the sample area. This allows the dimensions of a predefined sample area to be accurately determined in step c) and one or more properties to be accurately extracted.

[0063] Preferably, the computer device, in particular the mobile computer device, is configured to automatically determine the dimensions of the sample based on the reference scale.

[0064] In particular, the sample is placed on a predefined sample area that is larger than the sample in all directions in space, and preferably the sample area contains a reference scale and / or has known dimensions.

[0065] The predefined sample area can be a two-dimensional sample area or a three-dimensional sample area.

[0066] 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 aligned and / or it is a horizontal sample area.

[0067] Preferably, the predefined sample area, in particular the two-dimensional sample area, contains a reference scale and / or has known dimensions.

[0068] Preferably, the computer device, in particular the mobile computer device, is configured to automatically determine the dimensions of the predefined sample area, in particular the two-dimensional sample area, based on the reference scale.

[0069] Additionally or alternatively, a predefined sample area, especially a two-dimensional sample area, has a predefined known size. In this case, preferably, a computer device, especially a mobile computer device, is configured to set the predefined size, for example, from a list of predefined sizes. In this case, no reference scale is required.

[0070] In particular, a sheet material preferably having a predetermined size is used as the predetermined sample area, especially a two-dimensional sample area. For example, a sheet of paper, such as DIN A5, A4, or A3 paper, is used as the sheet material. Additionally, paper with other formats, such as tabloid, letter, or statement format, can be used. Further, a reference scale can be present on the sheet material. Preferably, a computer device, especially a mobile computer device, is configured to automatically determine the size of the predetermined sample area, especially a two-dimensional sample area.

[0071] Paper as the predetermined sample area is easily obtainable and quite inexpensive.

[0072] Generally, the size of the predefined sample area (especially a two-dimensional sample area) affects the minimum recognizable size of the components (especially particulate components). The smaller the size of the sample area, the smaller the components (especially particulate components) that can be recognized in the predefined sample area (especially a two-dimensional sample area). Therefore, for characterizing small components, especially small particulate components, a small predefined sample area, especially a small two-dimensional sample area, is beneficial. In particular, small components are those with a maximum Feret diameter not higher than 0.5 mm, preferably not higher than 0.1 mm, especially not higher than 0.063 mm, measured along the maximum extension direction of the component. In particular, small particulate components are those with a particle size D90 not higher than 0.5 mm, preferably not higher than 0.1 mm, especially not higher than 0.063 mm.

[0073] The particle size can be determined, for example, by laser diffraction as described in ISO 13320:2009.

[0074] In particular, the predetermined sample area, especially the sheet material, has a specific color different from the color of the components (especially particulate components) and / or the continuous phase.

[0075] In particular, the specific color of the predefined sample area (especially a two-dimensional sample area) is black. This results in a high contrast when characterizing inhomogeneous materials (such as mortar or concrete compositions) that are commonly used as curable compositions. However, for other inhomogeneous materials, different specific colors of the predefined sample area may be preferred.

[0076] Preferably, a sample area is selected such that the sample is completely surrounded by frames of different colors. This helps to identify the sample in a digital image.

[0077] According to another preferred embodiment, a luminous luminous surface is used as a predefined sample area, in particular a two-dimensional sample area. Thereby, the luminous surface is illuminated in particular by a light source such that the luminous surface emits light with a uniform light distribution over the entire surface.

[0078] This is particularly advantageous if the sample of the inhomogeneous material is provided in the form of a partially transparent sample (for example, a thin ground part or foam) that can be penetrated by the luminous surface emitting light.

[0079] In particular, a light table pad, in particular a light plate, is used as a predefined sample area. This is an advantageous possible way to provide a luminous surface that emits light. The light table includes a flat luminous surface that is horizontally oriented and is illuminated from the back by a light source. In particular, the luminous surface consists of a translucent layer that is illuminated from the back by a light source.

[0080] In particular, the light plate is a thin light table whose thickness is less than 20% or 10% of the width of the luminous surface and less than 20% or 10% of the length of the luminous surface. Light tables and light plates are known, for example, in the graphics field and are commercially available.

[0081] When using a luminous luminous surface (in particular a light table) as the sample area, the sample is arranged on top of the luminous surface, in particular on the front side of the luminous surface.

[0082] Compared with other predefined sample areas (such as paper), the luminous surface, in particular a light table or a light plate, is particularly beneficial because the shadow of the sample can be omitted, the contrast can be increased, fewer artifacts are generated, the components can be better identified, in particular particle identification, in particular bright components (in particular bright particles) and / or small components (in particular small particles), and the fitting of the calculated contour can be improved, which in turn improves the accuracy of the shape parameters (in particular particle shape parameters) and size determination (in particular particle size determination). In short, the accuracy of the result of step c) can be improved.

[0083] In particular, the luminous surface is illuminated such that it emits a color different from the color of the sample, preferably illuminating the luminous surface such that it emits white light.

[0084] In particular, the light source includes a white light source. Optionally, the light source also includes a light source of a color different from white. In particular, the color of the light source can be switched between different colors. A color different from white enhances the detection of whitish, bright samples.

[0085] In particular, the light source is an LED light source. Compared with other light sources, this allows minimizing heat release on the translucent surface or in the sample area, respectively. This reduces the risk of thermally induced changes in the sample.

[0086] LEDs can cause temporal light modulation interference. Temporal light modulation is the variation of the luminous amount or spectral distribution of light over time. Such modulation may lead to undesired visual perceptions such as flicker, stroboscopic effects, and virtual image array effects. Such effects are also referred to as temporal light artifacts. Such effects are described, for example, by J.A. Veitch et al. in “On the state of knowledge concerning the effects of temporal light modulation” Lighting Res. Technol. 2021, 53, 89 - 92. It is preferred to avoid such temporal light modulation and the resulting effects in this context. Thus, according to some embodiments, the light source is configured to avoid the effects caused by temporal light modulation.

[0087] Preferably, the luminous emitting surface, especially a light table or a light panel, is configured such that the light intensity of the emitting surface can be adjusted, especially continuously or in discrete steps. For example, the luminous emitting surface, especially a light table or a light panel, is configured such that the light intensity can be switched between 2 - 5, especially 3 - 4, different light intensities.

[0088] In another preferred embodiment, the luminous emitting surface, especially a light table or a light panel, is configured to emit polarized light. This can be achieved, for example, by using a light source that produces polarized light and / or a polarization filter arranged behind, on top of, and / or inside the emitting surface. The polarization filter can be selected, for example, from foils. The polarized light can be used to further enhance the determination of one or more properties in step c). The foil can also be a colored foil.

[0089] If the luminous emitting surface, especially a light table or a light panel, is configured such that it emits polarized light, then preferably there is an analyzer for polarized light, especially a polarizer, placed between the sample and the camera. In particular, an analyzer in the form of a foil and / or a filter is used. The analyzer can be mounted, for example, on the camera and / or placed between the camera and the sample. Using the analyzer allows, for example, selectively enhancing the light emission from a specific part of the sample and / or reducing the light emission from other parts of the sample. Thus, for example, the contrast in the image can be improved and certain components of the sample can be made visible in the image.

[0090] In a further preferred embodiment, the emitted light is such that it does not cause interference.

[0091] In addition, in particular, the light-emitting surface of the light table or light board may be covered with a protective foil, for example, to increase scratch resistance, wherein preferably, the protective foil is transparent with respect to the emitted light of the light-emitting surface. In particular, the protective foil is made of a synthetic material. In particular, the protective foil is a replaceable foil.

[0092] In particular, the light-emitting light-emitting surface, especially the light table or light board, includes a frame surrounding the light-emitting surface of the emitted light, whereby the frame has a color different from that of the light-emitting surface of the emitted light, especially a darker color than the light-emitting surface of the emitted light, especially black. This helps to identify a predefined sample area in a digital image, especially a two-dimensional sample area.

[0093] For example, due to the size of the light-emitting surface of the emitted light, especially the size of the light table or light board, which is equal to the size of DIN A5, A4, or A3 paper, or has the size of a large newspaper, letter paper, or statement format. Generally, the light board is slightly larger in size than any of the above formats to ensure that a paper of a given format will fit perfectly on such a formatted light board. Therefore, the actual size of the light-emitting surface of the emitted light, especially the actual size of the light table or the light board, may also be slightly larger than any of the above formats. Preferably, a computer device, especially a mobile computer device, is configured to manually and / or automatically determine the size of the light-emitting surface of the emitted light.

[0094] Taking at least one digital image of the sample using a camera is preferably performed under daylight conditions and / or using a light source (such as a flashlight) for illuminating the sample. Therefore, the light used for illuminating the sample is preferably well-dispersed to avoid shadows and light non-uniformities. A computer device, especially a mobile computer device, is preferably configured to automatically adjust the light conditions to obtain balanced exposure.

[0095] In another preferred embodiment, the sample or a part thereof may be treated with a luminescent dye, such as a fluorescent dye and / or a phosphorescent dye. Therefore, the excitation of the dye can be achieved, for example, using a light board and / or a light source pointing at the sample. The luminescent dye can be used to further improve the image quality.

[0096] In another preferred embodiment, the camera is aligned parallel to the plane of the sample and / or the predefined sample area, especially the two-dimensional sample area plane, especially horizontally. Particularly preferably, the camera is aligned parallel to the flat surface plane of the sample, where the flat surface is preferably horizontally oriented.

[0097] In particular, if the optical axis of the camera is perpendicular to the sample, the predefined sample area, and / or the flat surface of the sample, the camera is aligned parallel to the plane. The optical axis is an imaginary line that defines the path of light propagation through the camera system.

[0098] Preferably, the computer device, in particular a mobile computer device, is configured to automatically warn the user and / or to prevent acquisition of the at least one image as long as there is a non-planar parallel alignment. In this case, the computer device, in particular a mobile computer device and / or the camera preferably comprises at least one position sensor which can be evaluated when carrying out the method according to the invention.

[0099] In particular, when acquiring at least one digital image of the sample in step b), the camera is aligned horizontally, in particular horizontally and planar parallel to the sample and / or the predefined sample area. Thus, preferably, the sample and / or the predefined sample area are aligned horizontally.

[0100] In particular, if the optical axis of the camera extends vertically, the camera is aligned horizontally.

[0101] Acquiring at least one digital image of the sample in step b) with a camera aligned horizontally, in particular planar parallel to the sample, greatly simplifies the image capture process. Specifically, by adjusting the height of the camera over the area, the share of the sample in the image can be easily maximized by horizontal alignment.

[0102] Furthermore, when analyzing a fluid sample (such as an emulsion), horizontal alignment of the sample and / or the predefined sample area is beneficial since during the image capture process the fluid will automatically remain stable and at rest in its position. Thus, the predefined sample area can be located, for example, on a table or any other substantially horizontal surface.

[0103] However, the method can also be carried out using a non-planar parallel alignment of the camera.

[0104] In another preferred embodiment, at least two consecutive digital images of the sample are acquired. In this case, preferably, step c) is carried out using at least two digital images. This can help to increase the number of statistical counts and to more precisely determine one or more properties in step c). In particular, in step c), at least two digital images are superimposed.

[0105] In particular, when acquiring at least one image, the camera is aligned such that the share of the sample and / or the sample area in the image is maximized. 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 (such as the focal length of the camera). Thus, preferably, the computer device, in particular a mobile computer device, is configured accordingly.

[0106] Highly preferably, the minimum detectable size of a component, in particular a particulate component, is calculated, in particular the minimum detectable particle size, preferably by taking into account the resolution of the camera, the area share of the sample and / or the sample area in the total area of the image, and the actual size of the sample area. Preferably, the computer device, in particular a mobile computer device, is configured accordingly.

[0107] In particular, if the minimum detectable size is below a predetermined threshold, a warning is provided to the user, an alignment instruction is provided to the user and / or the settings of the camera, such as the focal length, are adjusted. The predetermined threshold can be set manually, for example. This helps to avoid taking images under inappropriate conditions.

[0108] The image analysis of at least one digital image in step c) can be implemented using known and readily available image analysis algorithms, for example by using software packages and / or libraries and / or artificial intelligence algorithms provided by Matlab (by )、OpenCV (see https: / / opencv.org), ImageJ (by Wayne Rasband; see https: / / imageJ.net).

[0109] In particular, at least one particle size parameter is extracted in step c). Preferably, this includes at least one of the following parameters:

[0110] - The particle size distribution of the particle population identified in at least one digital image; and / or

[0111] - At least one statistical parameter selected from the average particle size, the average diameter and / or the D x - value (where x = 0 - 100), in particular D 10 、D 50 、D 85 and D 100 values; and / or

[0112] - The deviation from a predefined nominal value and / or nominal distribution, for example the deviation from the Fuller curve, the standard sieve curve of a specific concrete type; and / or

[0113] - The fineness modulus.

[0114] According to a particularly preferred embodiment, the at least one particle size parameter extracted comprises or is the particle size distribution of the particle-forming population identified in at least one digital image.

[0115] These are highly relevant parameters when formulating a hardenable composition having a particulate component.

[0116] Thus, the extracted particle size distribution specifically refers to the particle size distribution corresponding to that defined in the standard EN 933-1:2012.

[0117] Another possible method for determining the particle size distribution is laser diffraction as described in ISO 13320:2009. Thus, the extracted particle size distribution especially refers to the particle size distribution corresponding to that defined in the standard ISO 13320:2009.

[0118] However, depending on the sample and the information required, the particle size distribution can be defined differently.

[0119] D x value refers to the proportion of a given aggregate of particles for which x% have a particle size smaller than a given value. Thus, D 90 value, for example, refers to the proportion of 90% of the aggregate of particles having a particle size smaller than a given D 90 value. Thus, the average particle size, especially the median particle size, especially corresponds to the D 50 value (50% of the particles are smaller than the given value and 50% are correspondingly larger). In particular, the percentage (%) is volume %.

[0120] Generally, at least one extracted particle size parameter preferably includes at least the particle size distribution of the particle population identified in at least one digital image.

[0121] In particular, for particle sizes below the minimum detectable particle size, the particle size distribution can be extrapolated based on the extracted particle size distribution and / or can be equated to a predetermined standard distribution. For example, polynomial extrapolation is used, especially based on Lagrange interpolation or using Newton's finite difference method to create a Newton series that fits the extracted particle size distribution.

[0122] In particular, in step c), at least one particle shape parameter is extracted. Preferably, it includes at least one of the following parameters:

[0123] - Roundness,

[0124] - Sphericity,

[0125] - Elongation ratio,

[0126] - Roughness,

[0127] - Compactness,

[0128] - Flakiness index,

[0129] - Shape index,

[0130] - Percentage of crushed and broken surfaces, and / or

[0131] - Angularity;

[0132] - The distance and / or angle between the surface structures of each particle.

[0133] These are parameters that have a significant impact on the processability of the curable composition. See, for example, "Correlation between Shape of Aggregate and Mechanical Properties of Asphalt Concrete" Digital Image Processing Approach: Road Materials and Pavement Design: Vol. 12, No 2.

[0134] In particular, the particle shape parameters are intended to correspond to the shape parameters defined in Standards EN 933-1:2012 to -7:2012.

[0135] In particular, the particle shape parameter includes the aspect ratio expressed as the ratio of the maximum Feret diameter to the minimum Feret diameter.

[0136] Other shape parameters that can be extracted are given in Blott and Pye, Sedientology (2008) 55, 31-63.

[0137] In particular, for particulate components of a predetermined size only, especially for particulate components larger than a given threshold size, at least one particle shape parameter is extracted. For relatively small particulate components, the particle shape of the particulate component has less influence on the properties of the curable or cured composition. For example, for particulate components > 0.5 mm, especially > 1 mm, at least one particle shape parameter is extracted.

[0138] However, if desired, the shape parameters of all particulate components can be analyzed.

[0139] In addition, at least one particle shape parameter can be extracted for particulate components larger than a given lower threshold size and particulate components smaller than a given upper threshold size. Thus, in particular, at least two, at least three or more different particle shape parameters can be extracted simultaneously within the range between the lower threshold and the upper threshold.

[0140] This allows the identification of the shape parameters of particulate components of a specific size, which are known, for example, to affect certain properties of interest of the curable composition, such as the rheology of the curable composition in the processable state.

[0141] According to another preferred embodiment, for each of at least two or more predetermined size fractions (e.g., sieve size fractions) of the particulate component, at least one individual particulate parameter is extracted. Thus, in particular, for each size fraction, at least one individual average value of at least one particulate shape parameter is extracted. This allows providing detailed information on the size-dependent distribution of the particulate shape parameter.

[0142] For example, an average value of at least one particulate shape parameter is provided for each of at least two or more predetermined particulate size fractions (e.g., sieve size fractions), respectively. Optionally, the particle count for each size fraction can be normalized by the volume of the particulate component under consideration, so as to obtain, for example, data comparable to the particle size distribution. Further, in this case, at least two, at least three or more different particulate shape parameters can be provided simultaneously.

[0143] Such data can be presented, for example, in a bar chart, where the predefined particle size fractions are taken as categories and the corresponding average values of at least one particulate shape parameter are in the form of bars, where the height or length is proportional to the values they represent.

[0144] In addition, an average value of at least one particulate shape parameter can be provided, which is for the particulate component in some selected size fractions, e.g., the particulate component present in size fractions above and / or below a given fraction threshold. Similar to the above, this allows identifying the shape parameters of the particles in the size fractions, which are known, for example, to affect certain properties of interest of the curable or cured composition.

[0145] To determine the spatial distribution and / or separation parameters that can be extracted in step c), the local density of the component (especially the particulate component) can be determined for at least two or more sub-regions of the sample. If the local density is the same in all sub-regions, the spatial distribution is uniform. Otherwise, there is a non-uniform distribution, for example, caused by the separation of the component, especially the particulate shape component.

[0146] In certain embodiments, the components (especially particulate components) have an elongated shape. In such cases, the orientation parameter (especially the particle orientation parameter) can be extracted in step c). In particular, the component orientation parameter is the component angular distribution (especially the particle angular distribution) and / or the average component angle (especially the average particle angular distribution). The angle can be defined, for example, as the angle between the direction of the longitudinal axis of the elongated component (especially the particulate component) and a predetermined reference direction, such as the edge of at least one digital image. The average component angle, especially the average particle angle, can be defined as the average value, such as the arithmetic mean, of all component angles, especially particle angles. For example, if the arithmetic mean of all component angles, especially particle angles, is non-zero, the elongated components, especially the particulate components, have a predominant orientation in the inhomogeneous material.

[0147] In another preferred embodiment, for each of at least one digital image, an outline image is generated. The outline image is also referred to as a contour image and contains the outlines of all identified components (especially particulate components) in the digital image. The outline image can be obtained in step d) via a user interface, via a machine interface, and / or on a data storage medium. For example, the outline image can be displayed within an application and / or stored on an external server. The outline image can be used as a tool for evaluating the quality of the digital image and / or the analysis performed.

[0148] In particular, in step b), at least two, preferably at least three, particularly at least five or at least ten digital images are taken, and in step c), image analysis is performed for each image, and by considering each of at least one performance separately extracted from at least two images, the deviation of at least one performance, especially the standard deviation, is determined. This deviation can be used as a tool for evaluating the quality of the digital image and / or the analysis performed.

[0149] In particular, if the deviation is higher than a predetermined threshold, a warning can be provided to the user, and / or if the deviation is higher than a predetermined threshold, the digital image and / or the outline image that produced the divergent parameter can be identified and / or indicated.

[0150] Preferably, the method of the present invention further comprises the step of assigning at least one attribute to the sample.

[0151] In particular, the attribute is selected from:

[0152] - A unique identifier of the sample;

[0153] - The chemical and / or physical properties of the sample, especially

[0154] o The type of the sample, such as mortar, concrete, grout, leveling compound, floor adhesive, or coating composition;

[0155] o The raw materials used to prepare the sample;

[0156] o The mixing design of the sample, especially the proportions of the respective raw materials, such as the proportions of binder, aggregate, additives, water, etc.;

[0157] o The density of the sample;

[0158] o The application method, such as casting, spraying, additive manufacturing, etc.;

[0159] o Special treatments received during production, such as mixing, compaction, vibration, heating, etc.;

[0160] o The processability of the sample during production, such as the rheological properties of the sample in the processable state, such as flow properties, slump flow, viscosity, t50 time, yield stress, and / or consistency grade, especially in the case of hardenable samples such as mortar, concrete, grout, etc.;

[0161] o In the case of hardenable or hardened samples, the curing properties, such as setting and / or hardening time;

[0162] o The mechanical properties of the sample, such as strength grade, compressive strength, flexural strength, hardness;

[0163] o Durability, such as exposure grade, chemical resistance, freeze-thaw resistance, etc.;

[0164] o The maximum grain size of the granular component

[0165] o The minimum grain size of the granular component

[0166] o The state of the sample, such as fluid or hardened; and / or

[0167] o Pretreatments received, such as crushing, cutting, grinding, thin sectioning, and / or polishing.

[0168] - Descriptive information of the sample, especially

[0169] o The location of the sample source, such as manually or automatically via a position sensor;

[0170] o The intended use, such as the project name and / or customer name; and / or

[0171] o General comments.

[0172] Preferably, the computer device, especially the mobile computer device, is configured to query at least one of these sample properties, especially before, during, and / or after steps a) to b).

[0173] In particular, a unique identifier of the sample is automatically generated.

[0174] In particular, the method of the present invention is carried out to obtain one or more of the following characteristics of a heterogeneous material (especially a hardened binder composition):

[0175] - The particle size distribution of the aggregate in the heterogeneous material;

[0176] - The particle shape of the aggregate in the heterogeneous material;

[0177] - The binder fraction in the heterogeneous material, especially the amount of the paste fraction;

[0178] - The quality of recycled aggregate and / or other raw materials, especially measured by determining the amount and / or fraction of the binder present on the recycled aggregate;

[0179] - The mixing ratio of the heterogeneous material in terms of particulate components and continuous phase;

[0180] - The ratio of coarse aggregate to cement (ca / c), for example in mortar or concrete materials;

[0181] - The distribution of the aggregate in the heterogeneous material;

[0182] - The fraction, size and / or distribution of air voids in the heterogeneous material;

[0183] - The quantity and / or quality of the mineral composition in the heterogeneous material, where the quality is especially measured by determining the fraction of one or more particulate mineral components;

[0184] - The quality of the production method and / or special treatment received during production, such as the quality of compaction (e.g., vibration) and / or the quality of the application method, where the quality is especially measured by determining the homogeneity of the sample and / or the distribution of the particulate components;

[0185] - The color distribution at the surface of the heterogeneous material, for example for characterizing carbonation and / or weathering, especially for determining the aging of the sample;

[0186] - Determining the failure mode of the heterogeneous material, especially by measuring the number, size, shape and / or orientation of cracks in the sample;

[0187] - Monitoring mechanical defects in the heterogeneous material, such as cracks;

[0188] - The area fraction of cracks relative to the area of the continuous phase, especially in composite materials, especially in composite membranes;

[0189] - The size, shape, spatial distribution, orientation, packing density and / or separation of cracks, especially in composite materials, especially in composite membranes;

[0190] - Information related to processability, especially rheological properties such as flow properties, slump flow, viscosity, t50 time, yield stress, and / or consistency class; for example, by obtaining one or more photos of a sample in a processable state at a predetermined time or predetermined time interval, such as a photo of the slump flow of the sample.

[0191] - Predictive modeling, especially predicting adjustments for producing additional samples, such as adjustments to raw materials and / or mix design adjustments, especially for improving certain properties of additional samples.

[0192] Specifically, the method is at least partially, especially fully, executed on a computer device, especially a mobile computer device.

[0193] In the case where the method is fully executed on a computer device, especially a mobile computer device, full characterization can be performed on the computer device, especially a mobile computer device, without any communication network. Additionally, at least one digital image, preferably together with at least one attribute, can be stored on the computer device, especially a mobile computer device, for example for sharing, calling, further evaluation, and / or predictive modeling.

[0194] Preferably, the computer device, especially the mobile computer device, is configured such that the at least one digital image, preferably together with the at least one attribute, can be transmitted to an external device via a communication means (such as the communication interface of the computer device, especially the mobile device) for storage. Similarly, the computer device, especially the mobile computer device, is preferably configured to call the stored data from the external device. According to another preferred embodiment, the image analysis in step c) and / or the available process in step d) are performed on a separate computer device, such as a server.

[0195] In this case, preferably, the at least one digital image, preferably together with at least one attribute, is transmitted to an external device via a communication means (such as the communication interface of the mobile device). Such a decentralized solution is beneficial because the computationally intensive step c) can be executed on another device, which in turn helps to increase the running time of the computer device (especially the mobile computer device). Additionally, 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 computer device, especially the mobile computer device.

[0196] Thus, if there is no available communication network, the at least one digital image, preferably together with the at least one attribute, can be temporarily stored on the computer device, especially the mobile computer device, and later transmitted to an external device when the communication network is available.

[0197] In this case, the image, preferably stored on an external computer device (such as a server) together with at least one attribute, especially for sharing, calling, and / or further evaluation.

[0198] In step d), one or more of the extracted performances are made available via a user interface, via a machine interface, and / or on a data storage medium. If necessary, at least one digital image can additionally be made available, optionally together with at least one performance. However, this is not necessary, as the image itself is of little importance to the user for daily work.

[0199] If data is obtained via a user interface, this can be done directly on a computer device, especially a mobile computer device and / or an external computer device. Thus, for example, one or more of the extracted performances, such as at least one particle size parameter and at least one particle shape parameter, are plotted in a graph, such as a particle size distribution, and / or presented in a dynamic graph, such as a roundness vs. sphericity graph or a bar graph of average particle size parameter vs. sieve aperture.

[0200] Making the data available via a machine interface allows the data to be transferred to an external computer device, such as a server and / or a desktop computer.

[0201] Moreover, the data can be made available on a data storage medium, such as on an internal data storage medium of a computer device (especially a mobile computer device), on a storage device attached to the computer device (especially a mobile computer device), and / or on a storage device of an external computer.

[0202] In particular, one or more of the extracted performances, optionally together with at least one attribute, and optionally together with at least one image, are written into a data file with 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.

[0203] Specifically, the data file is transferred to another application, another computer device (especially another mobile computer device), and / or an external computer device. The transfer can be performed by any type of communication means (such as wireless communication and / or wired communication). This allows the user to share the characteristics of the components (especially solid particles) with other users, transfer them to another application for further evaluation, and / or transfer them to a data storage server. Thus, preferably, the computer device, especially the mobile computer device, is configured to transfer the data file to another application, another computer device, especially another mobile computer device, and / or an external computer device.

[0204] In particular, the distributed components, especially the particulate components, are optically distinguishable from the continuous phase.

[0205] In particular, the distributed components, especially the distributed particulate components, have different light absorption and / or light reflection properties from the continuous phase, especially for light with wavelengths in the range of 200 nm - 5000 nm, especially in the range of 380 - 780 nm.

[0206] The size range of these components, especially the particulate components, is for example >0 - 125 mm, preferably 10 μm - 32 mm, more preferably 0.063 mm - 16 mm, especially 0.1 mm - 2 mm. Such components, especially the particulate components, are usually present in the curable compositions, such as mortar or concrete compositions. However, the method can also be used to characterize particulate components with other sizes.

[0207] In particular, the inhomogeneous material is a curable or cured binder material, especially a curable or cured mineral binder composition or a curable or cured organic binder composition.

[0208] The curable or cured binder material can exist in a fluid state, for example during processing and / or curing. Moreover, the curable or cured binder material can be in a solid state, for example after partial curing or after the completion of the curing process.

[0209] The curable or cured binder material is for example selected from mortar, concrete, grout, screed, floor, binder or coating. Thus, the binder can be a mineral binder, an organic binder or a hybrid binder comprising a combination of a mineral binder and an organic binder.

[0210] "Organic binder", especially a polymer resin. For example, the organic binder is a resin based on epoxides, polyurethanes, acrylates, polyesters and / or polychloroprenes.

[0211] The term "mineral binder" especially refers to a binder that reacts in a hydration reaction in the presence of water to produce a solid hydrate or hydrated phase. This can be for example a hydraulic binder (such as cement or hydraulic lime), a latent hydraulic binder (such as slag), a pozzolanic binder (such as fly ash) or a non-hydraulic binder (such as gypsum or white lime).

[0212] The entire mineral binder advantageously accounts for at least 5 wt%, especially at least 20 wt%, preferably at least 50 wt%, especially at least 75 wt% of the hydraulic binder. In another advantageous embodiment, the mineral binder comprises at least 95 wt% of the hydraulic binder, especially cement.

[0213] The mineral binder particularly comprises a hydraulic binder, preferably cement. Portland cement is particularly preferred, especially type CEM I, II, III or IV (according to standard EN 197-1). However, in addition to or instead of the hydraulic binder, it is also advantageous for the binder composition to comprise other binders. 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 fume. In an advantageous embodiment, the mineral binder comprises 5-95% by weight, especially 20-50% by weight, of latent hydraulic binder and / or pozzolanic binder.

[0214] In a specific embodiment, the mineral binder comprises a mixture of calcined clay, limestone and Portland cement.

[0215] The term "clay" refers to a solid material consisting of at least 30% by weight, preferably at least 35% by weight, especially at least 75% by weight, of clay minerals (each relative to its dry weight). Calcined clay is a clay material that has undergone heat treatment, preferably by a rapid calcination process at a temperature of 500-900 °C or at a temperature of 800-1100 °C. According to a particularly preferred embodiment of the invention, the calcined clay is metakaolin.

[0216] In a preferred embodiment of the invention, the chemical composition of the limestone and Portland cement is as defined in standard EN 197-1:2011. In an alternative, the limestone can also represent magnesium carbonate, dolomite and / or a mixture of magnesium carbonate, dolomite and / or calcium carbonate. Particularly preferably, the limestone herein is a naturally occurring limestone mainly composed of calcium carbonate (usually calcite and / or aragonite) but usually also containing some magnesium carbonate and / or dolomite. The limestone can also be naturally occurring marl.

[0217] According to a preferred embodiment, the Portland cement is type CEM I. According to an embodiment, the Portland clinker content in the Portland cement of the present invention is at least 35% by weight, preferably at least 65% by weight, especially at least 95% by weight, each based on the total dry weight of the cement.

[0218] According to an embodiment, the mineral binder comprises calcined clay, limestone and Portland cement in the following weight ratios:

[0219] P:CC is from 33:1 to 1:1, preferably from 8:1 to 1:1,

[0220] CC:L is from 10:1 to 1:50, preferably from 10:1 to 1:33, more preferably from 5:1 to 1:10, and

[0221] P:L is from 20:1 to 1:4, preferably from 5:1 to 1:1.

[0222] According to an embodiment, in each case relative to the total dry weight of the mineral binder, at least 65% by weight, preferably at least 80% by weight, more preferably at least 92% by weight of the mineral binder consists of calcined clay, limestone, and Portland cement.

[0223] According to an embodiment of the present invention, the mineral binder comprises a mixture of the following substances:

[0224] a) 25 - 100 parts by mass of Portland cement (P),

[0225] b) 3 - 50 parts by mass of calcined clay (CC), especially metakaolin,

[0226] c) 5 - 100 parts by mass of limestone (L).

[0227] According to another preferred embodiment, the heterogeneous material is a synthetic material, especially a synthetic film, particularly made of polyvinyl chloride (PVC) and / or thermoplastic polyolefin (TPO), for example selected from high - density polyethylene (HDPE), medium - density polyethylene (MDPE), low - density polyethylene (LDPE), polyethylene (PE), polyethylene terephthalate (PET), polystyrene (PS), polyvinyl chloride (PVC), polyamide (PA), ethylene / vinyl acetate copolymer (EVA), chlorosulfonated polyethylene, thermoplastic polyolefin elastomer (TPO, TPE - O), ethylene propylene diene rubber (EPDM), and mixtures thereof.

[0228] Such a film can be, for example, a waterproof film or a roof membrane.

[0229] Under long - term external exposure of a polymeric material (such as a roof membrane), surface cracks will appear and eventually lead to product failure. Therefore, when inspecting the condition of a roof, it is a standard procedure to check the condition of the membrane by evaluating the crack strength on the membrane surface.

[0230] In this case, the component can be, for example, a crack, especially as described below. Due to the method of the present invention, cracks can be analyzed in a direct and effective manner. This allows for an effective analysis of the condition of the membrane. The crack analysis in the membrane can be carried out, for example, according to standard EN 13956:2013.

[0231] In particular, the particulate component of the sample is solid particles, for example selected from sand, aggregate, natural or synthetic fibers, glass beads, sand substitutes, manufactured sand, crushed and / or recycled building materials, bio - aggregate, metal particles, ash, tailings, and / or polymer particles. However, other particulate components can also be present.

[0232] In certain embodiments, the components of the sample, particularly the particulate shape components, are faded particulate spots and / or flakes of the sample.

[0233] In another embodiment, the distributed components, particularly the distributed particulate components, are gas-filled pores, particularly wormholes. Wormholes are surface air voids present in hardened binder compositions such as adhesives, coatings, grouts, mortars, or concrete compositions.

[0234] According to another embodiment, the distributed components are cracks, particularly unbranched and / or branched cracks, where the cracks are particularly gas-filled. Cracks of this type can be, for example, partially or completely straight cracks and / or partially or completely curved cracks.

[0235] In a specific embodiment, the cracks are cracks in a synthetic material, particularly a synthetic film as described above.

[0236] In particular, the heterogeneous material contains more than one type of distributed component (e.g., distinguishable due to different colors) distinguishable in at least one digital image, particularly particulate shape components, and for each type of distinguishable component, particularly particulate shape components, one or more properties in step b) are extracted separately.

[0237] Thus, for example, different properties of the sample can be analyzed simultaneously. For example, the air void distribution and aggregate distribution in a binder composition can be analyzed simultaneously.

[0238] The continuous phase can be a solid phase or a liquid phase. A solid continuous phase is, for example, a hardened binder composition, such as one containing mineral and / or organic binders. A liquid continuous phase is, for example, a solvent such as water, alcohol, or a fluid binder before hardening is complete, such as cement mixed with water.

[0239] In particular, the continuous phase has a different appearance (particularly a different color) from the distributed components (particularly particulate components), and / or there is an interface between the distributed components (particularly particulate components) and the continuous phase, which can be detected in at least one digital image.

[0240] In particular, the heterogeneous material to be characterized is a solid material.

[0241] However, in another preferred embodiment, the heterogeneous material is a fluid material, particularly a liquid material. Heterogeneous materials in fluid form are, for example, selected from emulsions, foams, suspensions, processable binder compositions. However, other fluid forms of heterogeneous materials can also be used.

[0242] In another preferred embodiment, the distributed components, in particular the distributed particulate components, comprise a first type of mineral material, while the continuous phase of the agglomerated material comprises a second type of mineral material different from the first type of mineral material. This is the case, for example, if the method of the invention is used to analyze heterogeneous mineral materials.

[0243] Another aspect relates to a system which comprises a computer device, in particular a mobile computer device, and optionally an additional separate computer device, whereby the system comprises:

[0244] (i) means for performing steps a) to d) of the method as described above, and / or

[0245] (ii) means for performing at least steps a) and b) of the method as described above, in particular steps a) and b) and d), and means for transmitting at least one digital image of a sample, optionally together with at least one attribute, to a separate computer device.

[0246] Another aspect of the invention relates to a system comprising a computer device, whereby the system comprises means for receiving at least one digital image of a sample of a heterogeneous material which comprises distributed components, in particular particulate components, dispersed in a continuous phase of an agglomerated material, and means for performing at least steps c) and / or d) of the method as described above.

[0247] Furthermore, the invention relates to a computer-readable medium comprising instructions which, when executed by a computer device, in particular a mobile computer device, cause the computer device to perform at least steps a) and b) of the method as described above, in particular a) and b) and d), especially steps a) to d).

[0248] The invention also relates to a computer-readable medium comprising instructions which, when executed by a computer device, cause the computer 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 method as described above.

[0249] In particular, any data processed and / or generated by the method of the invention is encrypted, in particular such that only authorized users can access the original data. Similarly, any computer program and / or application for performing the method of the invention is also encrypted. Encryption methods are known to those skilled in the art.

[0250] Other advantageous configurations of the invention are apparent according to exemplary embodiments.

[0251] Brief Description of the Drawings

[0252] The drawings used to explain the embodiments show:

[0253] Figure 1 is a flowchart of the computer-implemented method of the present invention;

[0254] Figure 2 is a schematic diagram of a system of an apparatus for performing the Figure 1 method;

[0255] Figure 3 is Figure 1 a schematic diagram of the second step of the

[0256] Figure 4 method, in which a user (not shown) holding a smart phone obtains an image of a cuboid hardened mortar sample having sand aggregates; Figure 1 is a schematic diagram of the second step of the

[0257] Figure 5 method, in which a user (not shown) holding a smart phone obtains an image of a vertical wall of a building made of concrete containing aggregates of different sizes;

[0258] Figure 6 is a bar graph of the selected particle shape parameters (roundness, sphericity, and aspect ratio) for each particle sieve size fraction;

[0259] Figure 7 is a contour image overlaid on a digital image analyzed by the method of the present invention;

[0260] Figure 8a is a photograph of a concrete floor surface analyzed by the method of the present invention;

[0261] Figure 8b is Figure 8a a detailed view;

[0262] Figure 9a is a photograph of a polished surface of a concrete core drilled analyzed by the method of the present invention;

[0263] Figure 9b is Figure 9a a detailed view;

[0264] Figure 10a is a photograph of a polished surface of granite analyzed by the method of the present invention;

[0265] Figure 10b is Figure 10a a detailed view;

[0266] Figure 11a is a photograph of an epoxy grout surface analyzed by the method of the present invention;

[0267] Figure 11b isFigure 11a Detailed view;

[0268] Figure 12a is a photograph of the surface of a liquid aqueous foam analyzed by the method of the present invention.

[0269] Figure 12b is Figure 12a Detailed view;

[0270] Figure 13a is a photograph of a hardened foam analyzed by the method of the present invention;

[0271] Figure 13b is Figure 13a Detailed view;

[0272] Figure 14a is a photograph of a concrete wall with wormholes analyzed by the method of the present invention;

[0273] Figure 14b is Figure 14a Detailed view;

[0274] Figure 15a is a photograph of a concrete wall with fading caused by weathering (bright areas);

[0275] Figure 15b is Figure 15a Detailed view;

[0276] Figure 16a is a photograph taken during the flow table test of a freshly prepared mortar sample;

[0277] Figure 16b is Figure 16a Detailed view;

[0278] Figure 17a is a photograph of a mortar sample with air voids, which is inverted to determine the bearing area and the share of air voids by the method of the present invention;

[0279] Figure 17b is Figure 17a Detailed view;

[0280] Figure 18 is a photograph of several recycled aggregates with cement residues (bright areas) analyzed by the method of the present invention;

[0281] Figure 19a is a photograph of a synthetic film without cracks (class 0 according to EN 13956:2013);

[0282] Figure 19b is a photograph of a synthetic film with class 1 cracks according to EN 13956:2013;

[0283] Figure 19c It is a photograph of a synthetic membrane with level 2 cracks according to EN 13956:2013.

[0284] Exemplary embodiment

[0285] Figure 1 A flowchart of a computer-implemented method 10 of the present invention is shown. In a first step 11, a sample of a heterogeneous material to be analyzed is provided on a two-dimensional sample area of known size, such as a cubic hardened mortar sample having sand aggregates (granular components) with a particle size range of >0 to 2 mm embedded in a cement matrix C (continuous phase of the cohesive material). The sample area is formed, for example, by a black paper sheet of A4 size.

[0286] In a second step 12, a digital image of one of the flat surfaces of the cubic sample is taken with a camera of a mobile computer device (such as a smart phone). The camera has, for example, a 4K resolution.

[0287] Thereafter, in a third step 13, image analysis of the digital image is performed to extract at least one particle size parameter (such as particle size distribution) and at least one particle shape parameter (such as roundness or sphericity) of a group of sand particles identified in at least one digital image. Additionally, the area fraction of the cement matrix C (continuous phase) is determined in at least one digital image.

[0288] In a fourth step 14, at least one particle size parameter, at least one particle shape parameter, and the area fraction of the cement matrix are made available via a user interface (such as the display of the mobile computer device).

[0289] Figure 2 Shows a system 20 including a device for performing Figure 1 the method shown in

[0290] Specifically, the system 20 includes a smart phone 21 having a camera 22, a touch-sensitive display including an input device 23 and a display 24, a data processing unit 25 having a random access memory, a data storage device 29, and a wireless communication interface 28.

[0291] In operation, an application 26 is executed in the data processing unit 25, whereby the application is configured to implement steps 12 and 14 of the method described using Figure 1

[0292] ​Specifically, the application 26 helps the user to take an image of a sample 31 composed of a heterogeneous material in a two-dimensional sample area 30 using the built-in smartphone camera 22. Thus, the application is configured, for example, to automatically warn the user and / or to prevent the taking of an image whenever there is a non-planar parallel alignment with the surface of the sample 31. This can be achieved by evaluating the position sensors of the smartphone (not shown). In addition, the application is configured to automatically adjust the light conditions to obtain a balanced exposure. The digital image taken is stored in the random access memory and / or data memory 29.

[0293] In addition, the application 26 requires the user to input one or more properties of the sample via an input device and assigns a unique identifier to the sample. The properties are, for example, the maximum grain size of the aggregate, the type of aggregate (natural, crushed, manufactured, recycled, reused solid particles), the location of the aggregate source, the type of cement, the intended use (project name, customer name); and / or general comments.

[0294] The query can be made, for example, by presenting input fields, selection fields, maps, and / or text input fields to the user on the display 24 and storing the data provided by the user via the input device 23 together with at least the digital image in the random access memory and / or data storage device 29.

[0295] For example, the location of the aggregate source can be provided manually by the user, for example, by entering geographical coordinates into an input field and / or by marking the location on a map displayed on the display 24. However, the location of the aggregate source can be provided automatically, for example, by sensors of the global navigation satellite system (such as GPS, Galileo, Beidou, and / or Glonass sensors). Thus, the user can be requested to confirm the automatically determined location.

[0296] In Figure 2 the system 20 shown, Figure 1 step 13 of the method shown is executed on the external server 21a. Specifically, the image taken with the smartphone camera 22, optionally together with the properties, is transmitted via the wireless communication interface 28 (or any other communication interface) and a network (such as the Internet, not shown) to the server 21a. The server 21a receives the data via its communication interface 28a and forwards it to the image analysis application 26a running in the processing unit 25a.

[0297] The image, optionally together with the properties, can be stored on the data memory 29a of the server 21a for later sharing, calling, and / or further evaluation.

[0298] The application 26a performs an image analysis of digital images, thereby extracting, for example, at least one particle size parameter (such as particle size distribution) and at least one particle shape parameter (such as roundness or sphericity) of a population of particles identified in at least one digital image, as well as an area fraction. One or more attributes can thus also be taken into account in the analysis.

[0299] The application 26a is implemented, for example, using image analysis algorithms (such as software packages and / or libraries available in Matlab, OpenCV, and / or ImageJ) and / or using artificial intelligence software.

[0300] After the image analysis is completed, at least one particle size parameter (such as particle size distribution), at least one particle shape parameter (such as roundness or sphericity), and the area fraction are respectively sent back to the smartphone 21 or the application 25 running thereon via the communication interfaces 28a, 28.

[0301] Then, the application 25a makes at least one particle size parameter and / or at least one particle shape parameter and the area fraction available via the display 24, or preferably saves the parameters together with the attributes on the data memory 29 for later sharing, calling, and / or further evaluation. This data can be stored, for example, in the form of a data file with a file format selected from json, csv, txt, pdf, and / or proprietary file formats. In particular, a file format that can be read by an application that can be called the "Sika Mix DesignApp" and / or any other additional application is selected. See, for example Figure 4 .

[0302] In addition, the application 25 is configured to share at least one particle size parameter, at least one particle shape parameter, the area fraction, and performance (especially as a data file) with another user by sending them to another computer device 40 (such as another user's smartphone) via the communication interface 28. This can be initiated, for example, by the user via the input device 23, such as by pressing a button displayed on the display 24.

[0303] Figure 3 Shows Figure 1 A schematic diagram of step 12 of the method shown. Thus, a user (not shown) holds the smartphone 21 and takes an image of the sample 31, which is, for example, a cubic hardened mortar sample with sand aggregates. Therefore, aggregates of solid sand particles L (large), M (medium), and S (small) of different sizes from 0 to 2 mm are embedded in a cement-based matrix C. The sample 31 is provided on an A4-sized black paper serving as a two-dimensional sample area 30, which is larger than the sample 31 in both horizontal directions of space. The smartphone 21 is held in a horizontal orientation and the plane is parallel to the surface of the sample 31.

[0304] Figure 4 shows Figure 1 Another schematic diagram of step 12 of the method shown in. In this case, the smart phone 21 is held in a vertical orientation and the plane is parallel to the vertical surface of the sample 31'. For example, the sample 31' is a wall of a building made of concrete, and the concrete contains aggregates of different sizes from >0 to 12 mm of gravel particles L' (large), sand particles M' (medium) and sand particles S' (small), which are embedded in a cement-based matrix C'. In this case, the black frame-shaped mark R' has been attached and / or marked on the sample surface. The mark R' serves as a reference scale that can be used to determine the dimensions of the particle shape. If the mark R' is visible in the picture and the size of the mark R' is set in the smart phone, the true size of the particle shape components can be determined.

[0305] Figure 5 shows an example of the structure of a data file 50 in PDF file format. The data file 50 contains a table 51 with properties provided by the user, such properties as the type of aggregate (natural, crushed, manufactured, recycled, reused solid particles), the location of the aggregate source, the intended use (project name, customer name); and / or general comments.

[0306] In addition, the file 50 contains a graph 52 representing the particle size distribution, a table 53 including the sieve size passing rate of the calculated solid particles and / or the proportion of the retained solid particles, and a table 54 with statistical parameters such as D 10 、D 50 、D 85 and D 100 values and the fineness modulus of the analyzed granular components.

[0307] In addition, the file 50 contains a two-dimensional graph 55 indicating the average particle shape (such as roundness or sphericity) with a mark 55a. Additionally, the file 50 contains a bar graph 57 showing the average values of the selected particle shape parameters (such as roundness, sphericity and aspect ratio) for each particle sieve size fraction. A more detailed view of the bar graph 57 is shown in Figure 6 Of course, Figure 5 The content of the data file 50 shown in and Figure 6 The bar graph shown in can be adapted to the specific samples and / or information required.

[0308] Figure 7 shows a schematic representation of a contour image overlaid on a corresponding digital image. The contour image can additionally be used to check the quality of the digital image and / or the analysis performed.

[0309] Figure 8aA photograph of the surface of a concrete floor containing aggregates of different sizes and shapes is shown, which is analyzed by the method of the present invention to obtain information about the floor structure, such as the ratio of cement to coarse aggregates, mix design analysis, area fraction of the cement phase, and production quality (effectiveness of vibration, evaluated based on the uniformity of aggregate distribution).

[0310] Figure 8b Shows Figure 8a An enlarged portion of which the identified granular aggregates have been depicted.

[0311] Figure 9a A photograph of the polished surface of a concrete core drilled is shown, which is analyzed by the method of the present invention to examine the mix design of the concrete. It can be seen from the photograph that there is an uneven aggregate distribution, which can be quantified by the method of the present invention by determining the spatial distribution of the granular components. In addition, the ratio of cement to coarse aggregates, mix design, and / or binder fraction can be analyzed. When combined with information about the application method, for example, it can be judged whether the method is correctly carried out, such as regarding the adequacy of compaction by vibration.

[0312] Figure 9b Shows Figure 9a An enlarged portion of which the identified granular aggregates have been depicted.

[0313] Figure 10a A photograph of the polished surface of granite analyzed by the method of the present invention to examine the mineral composition or the amount of key mineral phases (such as biotite) is shown. In this case, the surface consists of 42% orthoclase, 15% plagioclase, 35% quartz, and 8% biotite.

[0314] Figure 10b Shows Figure 10a An enlarged portion of which the identified granular mineral phases have been depicted.

[0315] Figure 11a A photograph of the surface of an epoxy grout analyzed by the method of the present invention to identify the bearing area (area fraction of the continuous phase) and the amount of air voids (pores in the granular) is shown.

[0316] Figure 11b Shows Figure 11a An enlarged portion of which the identified air voids have been depicted.

[0317] Figure 12a A photograph of the surface of a liquid aqueous foam analyzed by the method of the present invention to quantify the air content in the foam is shown.

[0318] Figure 12b Shows Figure 12a An enlarged portion of which the identified air voids have been depicted.

[0319] Figure 13a Shows a photograph of a hardened foam analyzed by the method of the present invention to quantify the air content and the foam-bearing area of the foam.

[0320] Figure 13b Shows Figure 13a a magnified portion of, in which the identified air voids have been depicted.

[0321] Figure 14a Shows a photograph of a concrete wall with wormholes, which is analyzed by the method of the present invention to inspect the quality of the wall (evaluated by quantifying the area fraction of the wormholes).

[0322] Figure 14b Shows Figure 14a a magnified portion of, in which the identified wormholes have been depicted.

[0323] Figure 15a Shows a photograph of a concrete wall with fading (bright areas) caused by weathering. The area fraction of the faded areas is quantified. This allows, for example, determination of the age of the concrete wall.

[0324] Figure 15b Shows Figure 15a a magnified portion of, in which the faded areas have been depicted.

[0325] Figure 16a Shows photographs taken of a freshly prepared mortar sample during a flow table test. Using the method of the present invention, the dimensions of the mortar sample (black circular area) at a given time can be measured to obtain the workability and / or consistency of the composition. Figure 16b Shows Figure 16a a magnified portion of.

[0326] Figure 17a Shows a photograph of a mortar sample with air voids, which is inverted to determine the bearing area and the fraction of air voids by the method of the present invention. Figure 17b Shows Figure 17a a magnified portion of, in which the identified air voids have been depicted.

[0327] Figure 18 Shows a photograph of some recycled aggregates with cement residues (bright areas). Using the method of the present invention, the amount of binder present on the recycled aggregates can be determined to judge the aggregate quality.

[0328] Figure 19a -c Shows photographs of the surfaces of three different synthetic membranes under different conditions, where the photographs are obtained with a camera having a magnifying lens. At Figure 19ain which the membrane surface only contains surface texture but no cracks (no cracks; class 0 according to EN13956:2013). In Figure 19b in which the membrane surface contains unbranched as well as branched cracks (class 1 according to EN 13956:2013). In Figure 19c in which the membrane surface includes a high density of mainly branched cracks (class 2 according to EN 13956:2013). With the method according to the invention, it is for example possible to determine the area share of the cracks relative to the surface of the membrane in order to determine the actual condition of the membrane. This allows for example to determine the type and class of fracture directly from a photograph.

[0329] Those skilled in the art should understand that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the presently disclosed implementations and embodiments are considered illustrative in all respects and not restrictive.

[0330] For example, instead of using server 21a, the application 26 can be configured to be capable of performing Figure 1 all steps 11, 12, 13, 14 and optionally 15a of the method of an independent application.

[0331] Similarly, the optional functions of the system 20 can be omitted, such as sharing data with other computer devices, or additional functions can be added such as automatically retrieving location data via a positioning sensor.

[0332] Furthermore, as an alternative or supplement to the grain size parameter and the particle shape parameter, the spatial distribution of the particulate component, the particle orientation parameter, the particle surface parameter, the particle roughness parameter, the bulk density, the separation parameter, the color and / or the area share can be determined in step 13.

[0333] - the grain size distribution of the aggregate in the inhomogeneous material;

[0334] - the particle shape of the aggregate in the inhomogeneous material;

[0335] - the binder share in the inhomogeneous material, in particular the amount of the paste share;

[0336] - the amount of the binder attached to the aggregate (e.g., in mortar or concrete material);

[0337] - the mixing ratio of the inhomogeneous material with respect to the particulate component and the continuous phase;

[0338] - the ratio of coarse aggregate to cement (ca / c), e.g., in mortar or concrete material;

[0339] - the distribution of the aggregate in the inhomogeneous material;

[0340] - the share, size and / or distribution of the air voids in the inhomogeneous material;

[0341] - The quantity and / or quality of mineral compositions in a heterogeneous material;

[0342] - The quality of the production method and / or special treatment received during production, such as the quality of compaction (e.g., vibration) and / or the quality of the application method;

[0343] - The color distribution at the surface of a heterogeneous material, e.g., by characterizing carbonation and / or weathering to determine the aging of a sample;

[0344] - Determining the failure mode of a heterogeneous material;

[0345] - Monitoring mechanical defects in a heterogeneous material, such as cracks;

[0346] - Information related to processability, especially rheological properties, such as flow properties, slump flow, viscosity, t50 time, yield stress, and / or consistency class; for example, by taking one or more photos of a sample in a processable state at a predetermined time or predetermined time interval, such as a photo of the slump flow of a sample;

[0347] - Predictive modeling, especially predicting adjustments to a sample, such as raw material and / or mix design adjustments, especially for improving certain properties of a sample.

[0348] In Figure 3 as an alternative or supplement to placing the mortar sample 31 in the sample area 30, similar to Figure 4 , a reference scale, such as a ruler, geometric shape, and / or letter code, can be placed on top of and / or marked on the sample 31. In this case, it is sufficient to take a picture with a smaller image portion. As long as the reference scale is visible in the picture, the dimensions of the particle shape components can be determined.

[0349] Furthermore, any reference scale and / or sample area can be omitted. Thus, if needed, the dimensions can be set manually in a computer device. It is worth noting that even without any reference scale or dimensions, it is still possible to determine, for example, the share of the continuous phase and / or the homogeneity of a sample.

[0350] Furthermore, Figure 5 the structure of the data file 50 shown can be any other file format and / or the corresponding information can be presented in a graphical user interface (such as a dashboard).

[0351] This method can also be used to characterize other samples, such as other suspensions, emulsions, foams, adhesives, air voids in walls and ceilings, aesthetic surface features, etc.

Claims

1. A computer-implemented method for characterizing a heterogeneous material, the heterogeneous material comprising distributed components, in particular distributed particulate components, dispersed within a continuous phase of a cohesive substance, the method comprising the steps of: a) providing or selecting a sample of the heterogeneous material to be analyzed; b) taking at least one digital image of the sample using a camera of a computer device (in particular a mobile computer device), or a camera connected to a computer device (in particular a mobile computer device), and / or reading at least one digital image pre-recorded using an independent camera by the computer device; c) performing image analysis, in particular imaging particle analysis, on the at least one digital image to extract one or more of the following properties: - size parameters (in particular particle size parameters), shape parameters (in particular particle shape parameters), spatial distribution, orientation parameters (in particular particle orientation parameters), surface parameters (in particular particle surface parameters), roughness parameters (in particular particle roughness parameters), bulk density, separation parameters, color and / or area fraction of the components (in particular particulate components) identified by image analysis in the at least one digital image; and / or - the area fraction of the continuous phase identified by the image analysis in the at least one digital image; d) making available one or more of the properties extracted in step c) via a user interface, via a machine interface and / or on a data storage medium.

2. The method according to claim 1, wherein the distributed components, in particular the particulate components, are optically distinguishable from the continuous phase.

3. The method according to any one of the preceding claims, wherein for light having a wavelength in the range of 200 nm - 5000 nm, in particular in the range of 380 - 780 nm, the distributed components, in particular the particulate components, have different light absorption and / or light reflection properties from the continuous phase.

4. The method according to any one of the preceding claims, wherein the heterogeneous material is a hardened binder material, in particular a hardened mineral binder composition or a hardened organic binder composition.

5. The method according to any one of the preceding claims, wherein the heterogeneous material is a hardened concrete composition, a hardened mortar composition or a hardened grout composition.

6. The method according to any one of the preceding claims, wherein the heterogeneous material is an emulsion, a foam or a suspension.

7. The method according to any one of the preceding claims, wherein the inhomogeneous material is a synthetic material, in particular a synthetic film, especially made of polyvinyl chloride (PVC) and / or thermoplastic polyolefin (TPO), for example selected from the group comprising: high density polyethylene (HDPE), medium density polyethylene (MDPE), low density polyethylene (LDPE), polyethylene (PE), polyethylene terephthalate (PET), polystyrene (PS), polyvinyl chloride (PVC), polyamide (PA), ethylene / vinyl acetate copolymer (EVA), chlorosulfonated polyethylene, thermoplastic polyolefin elastomer (TPO, TPE-O), ethylene propylene diene rubber (EPDM) and mixtures thereof.

8. The method according to any one of the preceding claims, wherein the component is a distributed particulate component in the form of solid particles, in particular an aggregate, a fiber and / or a glass bead, especially an aggregate in the form of sand and / or gravel.

9. The method according to any one of the preceding claims, wherein the component is a distributed particulate component in the form of inflated pores, especially wormholes (surface air voids).

10. The method according to any one of the preceding claims, wherein the distributed component is a crack, especially an unbranched and / or branched crack, wherein the crack is especially inflated.

11. The method according to any one of the preceding claims, wherein the continuous phase is a solid.

12. The method according to any one of the preceding claims, wherein the continuous phase comprises a hardened binder material, especially a hardened organic and / or hardened mineral binder, especially a hardened mineral binder and / or a hardened curable polymer.

13. The method according to any one of the preceding claims, wherein the continuous phase of the agglomerated material is a liquid, especially the continuous phase of the agglomerated material comprises water, an alcohol and / or a fluid binder.

14. The method according to any one of the preceding claims, wherein the component, especially the distributed particulate component, comprises a first type of mineral material, and the continuous phase of the agglomerated material comprises a second type of mineral material, the second type of mineral material being different from the first type of mineral material.

15. The method according to any one of the preceding claims, wherein in step b), the at least one digital image is obtained from the surface, especially a flat surface, of a sample of the inhomogeneous material.

16. The method according to claim 15, wherein before and / or during step a), the inhomogeneous material is crushed, cut, ground and / or polished to obtain a flat surface of the sample.

17. The method according to any one of the preceding claims, wherein the sample or the flat surface is surface-treated to increase the contrast between the continuous phase and the component, especially the particulate component, wherein for example the surface treatment is selected from coloring with ink and / or polishing with powder and / or paste.

18. The method according to any one of the preceding claims, wherein the computer device, in particular the mobile computer device, comprises a human-machine interface device, in particular comprising an input device and a display, and preferably comprises a wireless communication interface.

19. The method according to any one of the preceding claims, 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.

20. The method according to any one of the preceding claims, wherein the camera is a camera for taking images in the visible spectrum, in particular color images.

21. The method according to any one of the preceding claims, wherein the camera has a resolution of at least 2 megapixels, in particular at least 5 megapixels, preferably at least 8 megapixels, particularly at least 12 megapixels, highly preferably at least 20 megapixels, even more preferably at least 50 megapixels or at least more than 100 megapixels.

22. The method according to any one of the preceding claims, wherein the sample is placed on a predetermined sample area, which is larger than the sample in all spatial directions, and preferably, the sample area comprises a reference scale and / or has known dimensions.

23. The method according to claim 22, wherein the predefined sample area is a two-dimensional sample area, preferably a sheet material, in particular a sheet material having predefined dimensions and a characteristic background color, such as black.

24. The method according to any one of the preceding claims, wherein when taking an image, in particular by providing alignment instructions to the user and / or by automatically adjusting at least one setting of the camera, such as the focal length of the camera, the camera is aligned so that the share of the sample in the image is maximized.

25. The method according to any one of the preceding claims, wherein the minimum detectable size, in particular the minimum detectable particle size, of the component, in particular the particulate component, is calculated by considering the resolution of the camera, the length share of the sample in the total area of the image, and the actual length of the sample.

26. The method according to any one of the preceding claims, wherein if the minimum detectable size, in particular the particle size, is below a predetermined threshold, a warning is provided to the user, alignment instructions are provided to the user and / or the settings of the camera, such as the focal length, are automatically adjusted.

27. The method according to any one of the preceding claims, wherein for each of the at least one digital image, a contour image, an inverted image and / or a color thresholded image is generated and used as the image in step c).

28. The method according to claim 27, wherein in step d), the contour image, the inverted image and / or the color thresholded image is made available via a user interface, via a machine interface and / or on a data storage medium.

29. The method according to any one of the preceding claims, wherein in step b), at least two, preferably at least three, particularly at least five or at least ten digital images are taken, and in step c), imaging analysis is performed for each image, and by considering one or more properties extracted separately from the at least two images, in particular each of the particle size parameter and / or the particle shape parameter, a deviation, in particular a standard deviation, of the one or more properties, in particular the particle size parameter and / or at least one particle shape parameter, is determined.

30. The method according to claim 29, wherein if the deviation is higher than a predetermined threshold, a warning is provided to the user, and / or wherein the digital image and / or the contour image that generates the divergent parameter is identified and / or indicated.

31. The method according to any one of the preceding claims, wherein the one or more properties extracted in step c) include a particle size parameter and / or a particle shape parameter.

32. The method according to claim 31, wherein the particle size parameter comprises at least one statistical parameter selected from average particle size, average diameter, D value for x = 0 - 100, and / or fineness modulus. x value and / or fineness modulus.

33. The method according to any one of claims 31-32, wherein the particle size parameter includes the particle size distribution of the group of particulate components identified in the at least one digital image.

34. The method according to any one of claims 31-33, wherein at least one of the extracted particle size parameters includes a deviation from a predetermined nominal value and / or a nominal distribution.

35. The method according to any one of claims 31-34, wherein for particle sizes below the minimum detectable particle size, the particle size distribution is extrapolated based on the extracted particle size distribution.

36. The method according to any one of claims 31-35, wherein at least one of the extracted particle shape parameters includes roundness, sphericity, aspect ratio, roughness, density, flake index, shape index, percentage of fragmentation and fracture surface, distance and / or angle between the surface structures of individual particles, and / or angularity.

37. The method according to any one of claims 31-36, wherein the particle shape parameter is extracted only for particles of a predetermined size, in particular for particles larger than a given threshold size, or wherein for each of at least two or more predetermined size fractions of the particles, at least one separate particle shape parameter is extracted, in particular at least one separate average value of the particle shape parameter is extracted.

38. The method according to any one of the preceding claims, wherein the area fraction of the continuous phase is extracted, in particular to determine the binder fraction in the sample of the heterogeneous material.

39. The method according to any one of the preceding claims, comprising the step of assigning at least one attribute to the sample of the heterogeneous material to be analyzed.

40. The method according to any one of the preceding claims, wherein the method is performed to obtain one or more of the following characteristics of the heterogeneous material, in particular a hardened binder composition or a synthetic material: - The particle size distribution of the aggregate in the heterogeneous material; - The particle shape of the aggregate in the heterogeneous material; - The binder fraction in the heterogeneous material, in particular the amount of the paste fraction; - The amount of binder attached to the aggregate (e.g., in mortar or concrete materials); - The mixing ratio of the heterogeneous material in terms of particulate components and continuous phase; - The ratio of coarse aggregate to cement (ca / c), e.g., in mortar or concrete materials; - The distribution of the aggregate in the heterogeneous material; - The share, size, and / or distribution of air voids in the heterogeneous material; - The amount and / or quality of the mineral composition in the heterogeneous material, where the quality is measured in particular by determining the share of one or more particulate mineral components; - The quality of the production method and / or the special treatment received during production, e.g., the quality of compaction (e.g., vibration) and / or the application method, where the quality is measured in particular by determining the homogeneity of the sample and / or the distribution of the particulate components; - The color distribution at the surface of the heterogeneous material, e.g., for characterizing carbonation and / or weathering, especially for determining the aging of the sample; - Determining the failure mode of the heterogeneous material, especially by measuring the number, size, shape, and / or orientation of cracks in the sample; - Monitoring mechanical defects in the heterogeneous material, e.g., cracks; - The area share of cracks relative to the area of the continuous phase, especially in composite materials, particularly in composite films; - The size, shape, spatial distribution, orientation, packing density, and / or separation of cracks, especially in composite materials, particularly in composite films; - Information related to processability, especially rheological properties, such as flow properties, slump flow, viscosity, t50 time, yield stress, and / or consistency class; e.g., by taking one or more photos of the sample in a processable state at a predetermined time or time interval, e.g., a photo of the slump flow of the sample; - Predictive modeling, especially predicting adjustments for producing additional samples, e.g., adjustments of raw materials and / or mix design adjustments, especially for improving certain properties of additional samples.

41. The method according to any one of the preceding claims, wherein the method is performed at least partially, especially completely, on the computer device, especially the mobile computer device.

42. The method according to any one of the preceding claims, wherein the image analysis in step c) and / or the available process in step d) are performed on a separate computer device, e.g., on a server.

43. The method according to any one of the preceding claims, wherein the image, preferably together with the at least one attribute and optionally the contour image, inverted image, and / or thresholded image, is stored on an external computer device, e.g., a server, especially for sharing, calling, and / or further evaluation.

44. A system comprising a computer device, especially a mobile computer device, and optionally an additional separate computer device, wherein the system comprises: (i) means for performing steps a) to d) of the method according to claim 1, and / or (ii) Apparatus for performing at least steps a) and b) of the method of claim 1, in particular steps a), b) and d), and apparatus for transmitting at least one digital image of a sample, optionally together with at least one attribute, to a separate computer device.

45. A system comprising a computer device, wherein the system comprises apparatus for receiving at least one digital image of a sample of a heterogeneous material to be analyzed and apparatus for performing steps c) and / or d) of the method according to claim 1.

46. A computer-readable medium comprising instructions which, when executed by a computer device, in particular a mobile computer device, cause the computer device to perform at least steps a) to b) of the method according to claim 1, in particular a), b) and d), in particular steps a) to d).

47. A computer-readable medium comprising instructions which, when executed by a computer device, cause the external computer device to receive at least one digital image and perform steps c) and / or d) of the method according to claim 1.

Images