Digital analysis of dissimilar materials

Abstract

A computer-implemented method for characterizing a heterogeneous material comprising dispersed components, particularly particulate components, dispersed within a continuous phase of a condensate comprises the steps of: a) providing or selecting a sample of the heterogeneous material to be analyzed; b) acquiring at least one digital image of the sample by a camera of a computing device, particularly a mobile computing device, or by a camera connected to a computing device, particularly a mobile device, and / or by reading into the computing device at least one digital image previously recorded by a stand-alone camera; and c) determining the following characteristics: size parameters, particularly granularity parameters, shape parameters, and the like, determined by image analysis in the at least one digital image. performing image analysis, in particular image particle analysis, of at least one digital image to extract one or more of the following characteristics: particle shape parameters, in particular particle distribution, orientation parameters, in particular particle orientation parameters, surface parameters, in particular particle surface parameters, roughness parameters, in particular particle roughness parameters, packing density, uneven distribution parameters, color, and / or area fraction of components, in particular particulate components; and / or area fraction of continuous phases identified by image analysis in the at least one digital image; d) making one or more of the extracted characteristics 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 the characterization of heterogeneous materials comprising dispersed components, particularly dispersed particulate components, dispersed within a continuous phase of a condensate, as well as a system with means for carrying out the method and a computer-readable medium containing instructions for carrying out the method. [Background technology]

[0002] In the construction industry, building materials based on settable or set compositions are widely used for a variety of applications. Examples of such compositions are mortar, concrete, grout, screed, adhesive, flooring, or coating compositions. These compositions typically contain a settable binder, optionally in combination with water, air, solid aggregate, and / or additives.

[0003] Thereby, the binder may be selected from inorganic binders, such as hydraulic binders, and organic binders, such as hardenable polymers. The aggregate is selected depending on the desired properties of the hardenable composition. Typically, aggregates include sand, gravel, rock dust, metal particles, and / or polymer particles. Air may also be introduced to produce lightweight structures or to obtain specific properties, such as freeze-thaw resistance.

[0004] Settable or set compositions used in the construction industry are typically heterogeneous materials containing two or more different components, whereby, in particular, dispersed particulate components, such as aggregates, voids, and / or polymeric particles, are dispersed within a continuous phase of the concentrate, such as a solvent, a fluid binder material, or a solid-state set binder.

[0005] Various testing procedures have been developed to ensure that curable compositions meet required requirements during processing and subsequently in the cured state.

[0006] For example, raw materials for settable compositions, such as aggregates and fillers, are typically inspected in terms of particle size distribution to ensure proper grading curves. It is also known to analyze aggregate particle shape and contamination of recycled aggregates in order to adjust the mix design of mortar or concrete compositions.

[0007] In this regard, GB 2524130 (LPW Technology Ltd) describes, for example, an analytical system for determining the characteristics and / or properties of a flowable material (e.g., sand) that includes an attachable analytical device and a smartphone or tablet device. The sample material in the sample container moves through a flow opening into an analysis chamber, where the smartphone or tablet device takes images of the sample material. The application installed on the smartphone is configured to analyze the sample by measuring the size, shape, color, flow rate, and velocity of the sample particles.

[0008] Furthermore, the workability and consistency of the fresh inorganic binder composition can be confirmed by, for example, the well-known slump flow test, flow table test, L-box test, V-funnel test, etc. In the cured state, the composition is usually analyzed for density, compressive strength, flexural strength, tensile strength, Young's modulus, fracture pattern, waterproofing, surface aesthetics, voids, and many more properties.

[0009] Many analytical methods are available for characterizing curable or cured compositions, but most of these methods, almost without exception, tend to be time consuming, require sophisticated instrumentation, and are phenomenological in nature.

[0010] Similarly, other building materials, such as roofing membranes, as well as installation, sealing, reinforcing, damping, and / or filling foams used, for example, in the automotive industry, need to be inspected for their condition to ensure quality requirements or to assess their condition after a period of exposure, especially when cracks form. There are various test methods for such inspections, but they too are usually complex and time-consuming.

[0011] Therefore, there remains a need for improvements that overcome some or all of the aforementioned shortcomings. Summary of the Invention [Means for solving the problem]

[0012] The object of the present invention is to provide an improved solution for analyzing heterogeneous materials comprising dispersed constituents, in particular dispersed particulate constituents, dispersed within a continuous phase of a condensate. In particular, it is desirable that this solution makes it possible to analyze heterogeneous materials in a manner that is as simple, flexible, and user-friendly as possible, without requiring sophisticated equipment.

[0013] Surprisingly, it has been found that the features of claim 1 achieve this object. Thus, the core of the invention is a computer-implemented method for the characterization of heterogeneous materials comprising dispersed components, in particular dispersed particulate components, dispersed within a continuous phase of a condensate, comprising: a) providing or selecting a sample of heterogeneous material to be analyzed; b) acquiring at least one digital image of the sample by reading into the computing device at least one digital image previously recorded by a camera of a computing device, in particular a mobile computing device, or by a camera connected to the computing device, in particular a mobile device, and / or by a stand-alone camera; c) The following characteristics: - size parameters, in particular granularity 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, uneven distribution parameters, color and / or areal coverage of components, in particular particulate components, determined by image analysis of at least one digital image, and / or - the area fraction of the continuous phase determined by image analysis in at least one digital image; performing an image analysis, in particular an image grain analysis, of at least one digital image in order to extract one or more of: d) making one or more of the characteristics extracted in step c) available via a user interface, via a machine interface and / or on a data storage medium. The present invention relates to a method comprising:

[0014] The method of the present invention is a unique approach that can be implemented on conventional mobile devices, such as smartphones, thus eliminating the need for complex and expensive analytical equipment. Furthermore, the method allows for very fast, flexible, and accurate digital characterization of heterogeneous materials in terms of various properties.

[0015] This allows heterogeneous materials of different nature and structure to be easily characterized with the same hardware device. For example, heterogeneous materials in the form of emulsions, foams, suspensions, fluids and inorganic and / or organic binder compositions in the cured state can be analyzed without the need for special equipment. This allows hardened heterogeneous materials to be obtained, for example, by chemically and / or physically hardenable fluid heterogeneous materials.

[0016] In the context of the present invention, the term "foam" is to be interpreted broadly and in particular encompasses solid and liquid foams made from essentially any type of material, for example, foams can take the form of foamed inorganic and / or organic binder compositions, foamed polymeric materials, foamed liquids, etc.

[0017] For example, foamed polymeric materials can be obtained from thermally expandable compositions that include a polymer matrix with one or more polymers and a chemical or physical blowing agent, and such compositions can be used, for example, as baffles or sound absorbers in the automotive industry.

[0018] The method of the present invention is therefore particularly suitable for the characterization of settable or hardened compositions used in the building industry, such as mortars, concretes, grouts, screeds, flooring, adhesives, coatings or foams, both in the workable state as well as in the hardened state.

[0019] Similarly, synthetic materials, such as synthetic membranes used for waterproofing and / or roofing, can be characterized, particularly synthetic materials that contain voids and / or cracks.

[0020] In particular, the method of the present invention enables the characterization of heterogeneous materials at the structural level. In particular, size parameters, shape parameters, spatial distribution, uneven distribution parameters, color, and / or area occupancy of components, as well as the area occupancy of the continuous phase, can be directly obtained. Specifically, particle size parameters, particle shape parameters, spatial distribution, uneven distribution parameters, color, and / or area occupancy of particulate components, as well as the area occupancy of the continuous phase, can be directly obtained. Because these parameters have a decisive influence on the properties of heterogeneous materials, for many materials, the method of the present invention can replace common testing procedures.

[0021] Furthermore, the method of the present invention allows for the extracted data to be stored, for example, on a remote server, for later use. For example, the extracted data can be combined and / or correlated with additional data, such as raw material data, mix design data of the disparate materials, physical and / or chemical properties of the disparate materials, etc., to gain deeper insight into the structure-property relationships of the disparate materials.

[0022] The method of the present invention can be implemented in a very user-friendly manner, especially for a fast and easy way to perform characterization of dissimilar materials using mobile computing devices.

[0023] Specifically, the method can be implemented in various ways, for example, in the form of a standalone application running on a mobile device without requiring additional resources such as a server system. This is particularly useful in areas with limited access to communication networks, for example, areas far from urban centers or underground areas. However, the method can also be implemented in a distributed computing environment, for example, including mobile devices as clients in combination with dedicated servers as storage units and / or dedicated processing units.

[0024] The method of the present invention can also be flexibly implemented in known software architectures, for example, as a native application, a Progressive Web Application (PWA), or a hybrid application (a combination of native and PWA). This allows such applications to include useful internet links to tutorials or support sites, as well as sharing functionality (e.g., via email, Bluetooth, AirDrop, or other communication means). The method of the present invention can also be implemented in a single application or split into two or more separate applications with appropriate software interfaces for data exchange between the applications. Furthermore, the application can be extended with additional functionality in a flexible manner.

[0025] The application can be implemented on any type of operating system, such as iOS, Android, Microsoft Windows and / or Linux.

[0026] Applications are readily available to anyone through various distribution channels, such as public download centers (e.g., Apple® Store or Google Play®), company-run websites, and / or dedicated download sites.

[0027] Further aspects of the invention are the subject of further independent claims. Particularly preferred embodiments are outlined throughout the description and dependent claims.

[0028] Methods of carrying out the invention A first aspect of the present invention is a computer-implemented method for characterization of heterogeneous materials comprising dispersed components, particularly particulate components, dispersed within a continuous phase of a condensate, comprising: a) providing or selecting a sample of heterogeneous material to be analyzed; b) acquiring at least one digital image of the sample by means of a camera of a computing device, in particular a mobile computing device, or by means of a camera connected to the computing device, in particular a mobile computing device, and / or by reading into the computing device at least one digital image previously recorded by an external camera; c) The following characteristics: - size parameters, in particular granularity 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, uneven distribution parameters, color and / or areal coverage of components, in particular particulate components, determined by image analysis of at least one digital image, and / or - the area fraction of the continuous phase determined by image analysis in at least one digital image; performing an image analysis, in particular an image grain analysis, of at least one digital image in order to extract one or more of: d) making one or more of the characteristics extracted in step c) available via a user interface, via a machine interface and / or on a data storage medium. The present invention is directed to a method comprising:

[0029] In particular, a computing device, especially a mobile computing device, includes human interface devices, especially including input devices and a display, and preferably a communications interface, most preferably a wireless communications interface.

[0030] In the context of the present invention, mobile computing devices specifically refer to handheld computers, i.e., computers small enough to be held and operated in a human hand.

[0031] In particular, the mobile computing device is selected from a mobile phone, a mobile computer, or a portable computer. In particular, the mobile computing device is selected from a smartphone, a phablet, a tablet computer, a portable computer, a smartwatch, and / or a head-mounted display with a camera. Particularly preferred are mobile phones and / or smartphones.

[0032] Mobile phones and smartphones typically include a high-resolution camera, an input device, and a display. Therefore, the input device and the display are typically combined with a touch-sensitive display. Mobile phones and smartphones therefore provide all the hardware components necessary to implement the method of the present invention. Furthermore, these types of devices can be held stably in the hand or mounted on a mobile stand and / or tripod, making them highly suitable for capturing images. At the same time, mobile phones and smartphones typically include displays large enough to display complex data in a sufficiently legible manner.

[0033] The expression "camera of a (mobile) computing device" means an internal camera built into a (mobile) computing device. In contrast, the expression "camera connected to a (mobile) computing device" means an external camera connected to a (mobile) computing device. For example, an external camera is a camera that can be attached to a (mobile) computing device or a standalone camera. The connection between the external camera and the (mobile) computing device can be a wired and / or wireless connection.

[0034] Alternatively or additionally, it is also possible to pre-record at least one digital image in a stand-alone camera and then read the image in the (mobile) computing device, whereby in particular the stand-alone camera has its own memory device for intermediate storage of images, and the at least one digital image can then be transferred from the memory device to the (mobile) computing device in any manner known to those skilled in the art, for example by wired connection, wireless connection and / or physical exchange of the memory device.

[0035] Preferably, the camera is a camera for taking images in the visible spectrum, in particular color images. Color images allow the color characteristics of the components, in particular the particulate components, and / or the continuous phase to be taken into account in step c) of the method of the present invention. Therefore, preferably, at least one digital image taken in step b) is a color image. However, 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, for example, a black and white image.

[0036] It is also possible to use a camera for capturing images outside the visible spectrum, for example in the infrared and / or ultraviolet regions of the electromagnetic spectrum. Such a camera can be used instead of or in addition to other cameras. In this case, properties of the components, especially the particulate components, and / or the continuous phase in spectral regions outside the visible spectrum can be taken into account in step c).

[0037] Preferably, the camera has a resolution of at least 2 megapixels, particularly at least 5 megapixels, preferably at least 8 megapixels, particularly at least 12 megapixels, very preferably at least 20 megapixels, even more preferably at least 50 megapixels or at least more than 100 megapixels.In particular, the camera has a resolution of at least 3,800 pixels x at least 2,100 pixels, or so-called 4K, preferably 8K, more preferably 12K, particularly 16K.The higher the resolution, the more easily it can distinguish smaller components, especially particulate components.However, in special embodiments, a camera with a lower resolution may also be suitable.

[0038] In particular, computing devices, especially mobile computing devices, are configured to automatically recognize the camera resolution.

[0039] In particular, the size of the optical sensor of the camera is at least 1 / 3 inch, in particular at least 1 / 2.5 inch, preferably at least 1 / 1.7 inch, preferably at least 2 / 3 inch, in particular at least 1 / 1.33 inch or at least 1 / 1.2 inch. In particular, the size of the optical sensor of the camera is between 1 / 3 inch and 1 inch.

[0040] In other words, the size of the optical sensor of the camera in terms of width x height is preferably at least 4.8 mm x 3.6 mm, in particular at least 5.7 mm x 4.2 mm, preferably at least 7.6 mm x 5.7 mm, preferably at least 8.8 mm x 6.6 mm, in particular at least 9.6 mm x 7.2 mm, or at least 10.6 mm x 8.0 mm. In particular, the size of the optical sensor of the camera in terms of width x height is between 4.8 mm x 3.6 mm and 13.2 mm x 8.8 mm.

[0041] Generally, the larger the sensor size, the more light the sensor can capture, resulting in better image quality, although cameras with other sensor sizes may also be suitable.

[0042] Additionally, auxiliary lenses may be added to a camera, for example to increase or decrease the focal length of the camera.

[0043] Additionally, if available, additional internal wide-angle and / or magnifying lenses (both optical and / or digital) of the computing device, particularly a mobile computing device, may be used as needed to capture at least one digital image.

[0044] In step b), at least one digital image is preferably taken of a surface, in particular a flat surface, of the sample of the different material, whereby the surface is preferably oriented vertically or horizontally, in particular preferably horizontally.

[0045] In a further preferred embodiment, before and / or during step a), the dissimilar material is crushed, cut, ground, sectioned and / or polished to obtain a flat surface of the sample.

[0046] However, such treatment of the heterogeneous material is optional and can be omitted. In particular, the heterogeneous material provided in step a) can be untreated material, in particular material that has not been crushed, cut, ground, sectioned and / or polished.

[0047] In another particularly preferred embodiment, the sample, in particular the flat surface of the sample, is subjected to a surface treatment to enhance the contrast between the continuous phase and the components, in particular the particulate components, for example the surface treatment is selected from coloring with ink and / or polishing with powder and / or paste.

[0048] In particular, a reference scale may be placed on and / or next to the sample.

[0049] The reference scale may be, for example, a letter, a geometric shape, a ruler, and / or a reference object of known size within the sample area, which allows for an accurate determination of the size of the predetermined sample area and an accurate extraction of one or more properties in step c).

[0050] Preferably, the computing device, in particular the mobile computing device, is configured to automatically determine the size of the sample based on a reference scale.

[0051] In particular, the sample is placed on a predetermined sample area that is larger than the sample in all directions in space, and preferably the sample area includes a reference scale and / or has a known size.

[0052] The predetermined sample area can be a two-dimensional sample area or a three-dimensional sample area.

[0053] The two-dimensional sample area is preferably a planar area, in particular a planar rectangular area, in particular the two-dimensional sample area is horizontally aligned and / or is a horizontal sample area.

[0054] Preferably, the predetermined sample area, in particular a two-dimensional sample area, comprises a reference scale and / or has a known size.

[0055] Preferably, the computing device, in particular a mobile computing device, is configured to automatically determine the size of the predetermined sample area, in particular a two-dimensional sample area, based on a reference scale.

[0056] Additionally or alternatively, the predetermined sample area, in particular a two-dimensional sample area, has a predetermined known size. In this case, the computing device, in particular a mobile computing device, is preferably configured to set the predetermined size, for example from a list of predetermined sizes. In this case, no reference scale is required.

[0057] In particular, a thin sheet material, preferably a thin sheet material of a predetermined size, is used as the predetermined sample area, in particular the two-dimensional sample area. For example, a sheet of paper, for example DIN A5, A4, or A3 paper, is used as the thin sheet material. Other types of paper, for example tabloid, letter, or statement format, can also be used. Furthermore, a reference scale can be present on the thin sheet material. Preferably, the computer device, in particular the mobile computer device, is configured to automatically determine the size of the predetermined sample area, in particular the two-dimensional sample area.

[0058] Paper as a predefined sample area is readily available and relatively inexpensive.

[0059] Generally, the size of a predetermined sample area, particularly a two-dimensional sample area, affects the minimum size at which components, particularly particulate components, can be identified. The smaller the size of the sample area, the smaller the components, particularly particulate components, that can be identified in the predetermined sample area, particularly a two-dimensional sample area. Therefore, a small predetermined sample area, particularly a small two-dimensional sample area, is useful for characterizing small components, particularly small particulate components. Particularly small components are components with a maximum Feret diameter of 0.5 mm or less, preferably 0.1 mm or less, and particularly 0.063 mm or less, measured along the direction of maximum extension of the component. Particularly small particulate components are components with a particle size D90 of 0.5 mm or less, preferably 0.1 mm or less, and particularly 0.063 mm or less.

[0060] The particle size can be measured, for example, by the laser light diffraction method described in ISO 13320:2009.

[0061] In particular, the predetermined sample area, in particular the thin sheet material, has a particular colour that is different from the colour of the components, in particular the particulate components, and / or the continuous phase.

[0062] In particular, the specific color of the predetermined sample area, especially the two-dimensional sample area, is black, which provides high contrast when characterizing dissimilar materials that are commonly used, for example, as hardenable compositions, such as mortar and concrete compositions. However, for other dissimilar materials, a different specific color of the predetermined sample area may be desirable.

[0063] Preferably, the sample area is selected so that the sample is completely surrounded by a frame of a different color, which makes it easier to identify the sample in the digital image.

[0064] According to a further preferred embodiment, a light-emitting surface is used as the predetermined sample area, in particular as a two-dimensional sample area, whereby in particular the light-emitting surface is illuminated with a light source in such a way that the light-emitting surface emits light with a homogeneous light distribution over its entire surface.

[0065] This is particularly advantageous when the sample of the dissimilar material is provided in the form of a partially transparent sample, such as a thin ground section or foam, which can be transilluminated with a light-emitting surface.

[0066] In particular, a light table pad, in particular a light pad, is used as the predetermined sample area. This is an advantageous possibility for providing a light-emitting surface that emits light. The light table includes a horizontally arranged flat light-emitting surface that is illuminated from the back by a light source. In particular, the light-emitting surface consists of a semi-transparent layer that is illuminated from the back by a light source.

[0067] In particular, a light pad is a thin light table having a thickness of less than 20% or 10% of the width and less than 20% or 10% of the length of the light-emitting surface. Light tables and light pads are known, for example, in the field of graphics, and are commercially available.

[0068] When a light-emitting surface, in particular a light table, is used as the sample area, the sample is placed on the light-emitting surface, in particular in front of the light-emitting surface.

[0069] Compared to other predetermined sample areas, such as paper, a light-emitting surface, in particular a light table or light pad, is particularly advantageous because it can eliminate sample shadows, increase contrast, reduce artifacts, allow better component recognition, in particular particle recognition, in particular bright components, in particular bright particles, and / or small components, in particular small particles, and improve the fit of the calculated contour, thereby increasing the accuracy of shape parameters, in particular particle shape parameters, and size determination, in particular particle size determination. Overall, the accuracy of the results of step c) can be improved.

[0070] In particular, the light emitting surface is illuminated so as to emit a color different from the color of the sample, and preferably the light emitting surface is illuminated so as to emit white light.

[0071] In particular, the light source comprises a white light source. Optionally, the light source further comprises a light source of a color different from white. In particular, the color of the light source is switchable between different colors. Colors other than white improve detection of whitish, light samples.

[0072] In particular, the light source is an LED light source, which, compared to other light sources, minimizes the generation of heat on the semi-transparent surface or in the sample area, thereby reducing the risk of thermal alterations to the sample.

[0073] LEDs can cause transient light modulation disturbances. Temporal light modulation is a change in the photometric quantity or spectral distribution of light over time. Such modulation can result in undesirable visual perceptions such as flicker, stroboscopic effects, and phantom array effects. Such effects are also known as transient light artifacts. Such effects are described, for example, in JAVeitch et al., "On the state of knowledge concerning the effects of temporal light modulation," Lighting Res. Technol. 2021, 53, 89-92. Such transient light modulation and its associated effects are desirable to avoid in this context. Therefore, according to some embodiments, the light source is configured to avoid the effects of transient light modulation.

[0074] Preferably, the light-emitting surface, in particular a light table or light pad, is configured so that the light intensity of the light-emitting surface can be adjusted, in particular so that it can be adjusted in continuous or discrete steps, for example, the light-emitting surface, in particular a light table or light pad, is configured so that the light intensity can be switched between 2 to 5, in particular 3 to 4, different light intensities.

[0075] In a further preferred embodiment, the light-emitting surface, in particular a light table or light pad, is configured to emit polarized light. This can be achieved, for example, by using a polarizing filter arranged behind, on, and / or within the light source and / or light-emitting surface generating the polarized light. The polarizing filter can be selected, for example, from a foil. The polarized light can be used to further improve the determination of one or more properties in step c). The foil can be a colored foil.

[0076] If the light-emitting surface, in particular a light table or light pad, is configured to emit polarized light, an analyzer for polarized light, in particular a polarizer, is preferably arranged between the sample and the camera. In particular, an analyzer in the form of a foil and / or a filter is used. This analyzer can be attached to the camera, for example, and / or arranged between the camera and the sample. The use of the analyzer makes it possible, for example, to selectively enhance the emission from certain parts of the sample and / or reduce the emission from other parts of the sample. This can, for example, improve the contrast of the image and make certain components of the sample visible in the image.

[0077] In a further preferred embodiment, the emitted light does not cause interference.

[0078] Furthermore, the light-emitting surface, in particular the light-emitting surface of a light table or light pad, can be covered with a protective foil, for example to increase scratch resistance, whereby the protective foil is preferably transparent to the light emitted by the light-emitting surface. In particular, the protective foil is made of a synthetic material. In particular, the protective foil is a replaceable foil.

[0079] In particular, the light-emitting surface, in particular a light table or light pad, includes a frame surrounding the light-emitting surface, whereby the frame has a different color than the light-emitting surface, in particular a darker color than the light-emitting surface, in particular black, which facilitates identification of a predetermined sample area, in particular a two-dimensional sample area, in the digital image.

[0080] For example, the size of the light-emitting surface, in particular a light table or light pad, may be equivalent to the size of a DIN A5, A4, or A3 sheet of paper, or may have the size of a tabloid, letter, or statement format. Typically, light pads are slightly larger than any of the aforementioned formats, so that a given format of paper fits snugly into such a light pad. Therefore, the actual size of the light-emitting surface, in particular a light table or light pad, may also be slightly larger than any of the aforementioned formats. Preferably, the computing device, in particular a mobile computing device, is configured to manually and / or automatically determine the size of the light-emitting surface.

[0081] Capturing at least one digital image of the sample with a camera is preferably performed under natural light conditions and / or using a light source, such as a flashlight, to illuminate the sample. The light used to illuminate the sample is preferably sufficiently diffused to avoid shadows and uneven lighting. The computing device, particularly a mobile computing device, is preferably configured to automatically adjust the lighting conditions to obtain a balanced exposure.

[0082] In a further preferred embodiment, the sample or a portion thereof can be treated with luminescent dyes, such as fluorescent and / or phosphorescent dyes, which can be excited, for example, by a light pad and / or a light source directed at the sample. The luminescent dyes can be used to further improve image quality.

[0083] In a further preferred embodiment, the camera is aligned plane-parallel, in particular horizontally, to the sample and / or the predetermined sample area, in particular a two-dimensional sample area. Particularly preferably, the camera is aligned plane-parallel to a flat surface of the sample, whereby preferably the flat surface is oriented horizontally.

[0084] In particular, a camera is aligned plane-parallel if the optical axis of the camera runs perpendicular to the sample, the predetermined sample area, and / or the flat surface of the sample. The optical axis is an imaginary line that defines the path that light travels through the camera system.

[0085] Preferably, the computing device, in particular the mobile computing device, is configured to automatically warn the user and / or prevent the taking of at least one image as long as there is a non-plane-parallel alignment, in which case the computing device, in particular the mobile computing device, and / or the camera preferably comprises at least one position sensor that can be evaluated when performing the method of the present invention.

[0086] In particular, when taking the at least one digital image of the sample in step b), the camera is aligned horizontally, in particular horizontally and plane-parallel, with respect to the sample and / or the predetermined sample area, whereby preferably the sample and / or the predetermined sample area is aligned horizontally.

[0087] In particular, if the optical axis of the camera runs vertically, the camera is aligned horizontally.

[0088] Taking at least one digital image of the sample in step b) with the camera aligned horizontally, and in particular aligned plane-parallel to the sample, greatly simplifies the image acquisition process: in particular, the proportion of the sample in the image that is aligned horizontally can be easily maximized by adjusting the height of the camera over the area.

[0089] Additionally, when analyzing fluid samples, such as emulsions, it is beneficial to align the sample and / or the predetermined sample area horizontally, as the fluid automatically remains stable and motionless in that position during the image acquisition process, allowing the predetermined sample area to be placed, for example, on a table or other essentially horizontal surface.

[0090] However, the method can also be performed with the cameras aligned non-plane-parallel.

[0091] In a further preferred embodiment, at least two consecutive digital images of the sample are taken. In this case, step c) is preferably carried out using at least two digital images. This may help to increase the statistical count number and more accurately determine one or more characteristics in step c). In particular, in step c), at least two digital images are superimposed.

[0092] In particular, when taking at least one image, the camera is aligned to maximize the proportion of the sample and / or sample area in the image. This can be done, 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. Preferably, therefore, the computing device, in particular a mobile computing device, is configured accordingly.

[0093] Particularly preferably, the minimum detectable size, in particular the minimum detectable particle size, of the components, in particular of the particulate components, is calculated, preferably by taking into account the resolution of the camera, the area fraction of the sample and / or sample area in the total area of ​​the image, and the actual size of the sample area. Preferably, the computing device, in particular a mobile computing device, is configured accordingly.

[0094] In particular, if the minimum detectable size falls below a predetermined threshold, a warning may be provided to the user, instructions to adjust alignment may be provided to the user, and / or camera settings, such as focal length, may be adjusted. The predetermined threshold may be set manually, for example. This helps to avoid capturing images under inappropriate conditions.

[0095] The image analysis of the at least one digital image in step c) can be performed using known and readily available image analysis algorithms, for example by using software packages and / or libraries provided in Matlab (by MathWorks®), OpenCV (see https: / / opencv.org), ImageJ (by Wayne Rasband; see https: / / imagej.net) and / or artificial intelligence algorithms.

[0096] In particular, in step c) at least one granularity parameter is extracted, whereby preferably it comprises at least one of the following parameters: - the size distribution of the population of particles identified in at least one digital image; and / or - Average particle size, average diameter, and / or D x Value (x=0~100, especially D 10 , D 50 , D 85 , D 100 at least one statistical parameter selected from the group of deviations from predetermined nominal values ​​and / or nominal distributions, for example from a Fuller curve, a standard sieving curve for a particular concrete type; and / or - Coarse grain rate.

[0097] According to a particularly preferred embodiment, the at least one extracted particle size parameter comprises or is the particle size distribution of the population of particulate constituents identified in the at least one digital image.

[0098] These are highly relevant parameters when formulating a curable composition that includes a particulate component.

[0099] Thereby, in particular the extracted particle size distribution is intended to correspond to the particle size distribution defined in the standard EN 933-1:2012.

[0100] Another method for determining the particle size distribution is the laser light diffraction method described in ISO 13320:2009. The particle size distribution extracted in particular is therefore intended to correspond to the particle size distribution defined in the standard ISO 13320:2009.

[0101] However, depending on the sample and the information required, particle size distribution may be defined in different ways.

[0102] D x A value of D means that x% of a given particle population has a particle size lower than the given value. 90 The value is, for example, the value at which 90% of the particles have a given D 90 Therefore, the average particle size, especially the median particle size, is particularly important when referring to the D 50 corresponds to a value (50% of the particles are smaller than the given value and 50% are correspondingly larger). In particular, the percentage (%) is a volume %.

[0103] In general, the at least one particle size parameter extracted preferably includes at least the particle size distribution of the population of particulate constituents identified in the at least one digital image.

[0104] In particular, for particle sizes below the minimum detectable particle size, the particle size distribution may be extrapolated based on the extracted particle size distribution and / or equated to a predetermined standard distribution, for example, polynomial extrapolation may be used, particularly based on Lagrangian interpolation or using Newton finite difference methods to create a Newton series that fits the extracted particle size distribution.

[0105] In particular, in step c) at least one particle shape parameter is extracted, which preferably comprises at least one of the following parameters: - Roundness, - sphericity, - aspect ratio, - roughness, - solidity, - flakiness index, - shape index, - the proportion of fractured and broken surfaces, and / or - angularity; - The distance and / or angle between the surface structures of the individual particles.

[0106] These are parameters that have a significant impact on the workability of the settable 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.

[0107] In particular, the particle shape parameters are intended to correspond to the shape parameters defined in the standard EN 933-1:2012 to -7:2012.

[0108] In particular, particle shape parameters include aspect ratio, expressed as maximum Feret diameter to minimum Feret diameter.

[0109] Further extractable shape parameters are described in Blot and Pye, Sedimentology (2008) 55, 31-63.

[0110] In particular, at least one particle shape parameter is extracted only for particulate components of a predetermined size, particularly for particulate components larger than a given threshold size. For relatively small particulate components, the particle shape of the particulate component does not significantly affect the properties of the curable or cured composition. For example, at least one particle shape parameter is extracted for particulate components larger than 0.5 mm, particularly larger than 1 mm.

[0111] However, if desired, it is also possible to analyze the shape parameters of all particulate components.

[0112] Furthermore, it is also possible to extract at least one particle shape parameter for particulate constituents larger than a given lower threshold size and for particulate constituents smaller than a given upper threshold size, thereby allowing at least two, at least three or more different particle shape parameters to be simultaneously extracted, in particular within a range between a lower threshold and an upper threshold.

[0113] This allows for the identification of shape parameters of particulate components of particular sizes that are known to affect particular properties of interest of the curable composition, such as, for example, the rheology of the curable composition in its processable state.

[0114] According to another preferred embodiment, at least one individual particle shape parameter is extracted for each of at least two or more predetermined size fractions, e.g., sieve size fractions, of the particulate constituents, whereby, in particular, at least one individual average value of the at least one particle shape parameter is extracted for each size fraction, making it possible to provide detailed information about the size-dependent distribution of the particle shape parameter.

[0115] For example, for each of at least two or more predetermined particle size fractions, e.g., sieve size fractions, the mean value of at least one particle shape parameter is provided separately. Optionally, the particle count per particle size fraction can be normalized by the volume of the particulate component under consideration, e.g., to obtain data comparable to a particle size distribution. Furthermore, in this case, at least two, at least three, or more different particle shape parameters can be provided simultaneously.

[0116] Such data can be presented, for example, in the form of a bar graph, with predetermined particle size fractions as categories and corresponding mean values ​​of at least one particle shape parameter, with heights or lengths proportional to the values ​​they represent.

[0117] Furthermore, it is possible to provide an average value of at least one particle shape parameter for particulate components in several selected size fractions, for example for particulate components present in size fractions above and / or below a given threshold value. As above, this allows for identifying particle shape parameters in size fractions known to affect, for example, a particular property of interest of the hardenable or hardened composition.

[0118] To determine the spatial distribution and / or uneven distribution parameters that may be extracted in step c), the local densities of the constituents, in particular the particulate constituents, can be determined for at least two or more subregions of the sample. If the local densities are the same in all subregions, the spatial distribution is homogeneous. Otherwise, there is an inhomogeneous distribution, e.g., an inhomogeneous distribution caused by uneven distribution of the constituents, in particular the particulate constituents.

[0119] In a particular embodiment, the components, particularly the particulate components, have an elongated shape. In this case, in step c), an orientation parameter, particularly a particle orientation parameter, can be extracted. In particular, the component orientation parameter is a component angle distribution, particularly a particle angle distribution, and / or an average component angle, particularly an average particle angle distribution. The angle can be defined, for example, as the angle between the direction of the longitudinal axis of the elongated component, particularly the particulate component, and a predetermined reference direction, for example, the edge of at least one digital image. The average component angle, particularly the average particle angle, can be defined as the average value, for example, the arithmetic mean value, of all component angles, particularly particle angles. For example, if the arithmetic mean value of all component angles, particularly particle angles, is different from zero, the elongated component, particularly the particulate component, has a predominant orientation in the heterogeneous material.

[0120] In a further preferred embodiment, a contour image is generated for each of the at least one digital image. The contour image, also called a contour image, contains the contours of all identified components, particularly particulate components, in the digital image. The contour image can be made available in step d) via a user interface, via a machine interface, and / or on a data storage medium. For example, the contour image can be displayed within the application and / or stored on an external server. The contour image can be used as a tool to evaluate the quality of the digital image and / or the analysis performed.

[0121] In particular, in step b) at least two, preferably at least three, in particular at least five or at least ten digital images are taken, and in step c) an image analysis is carried out for each image, taking into account each of the at least one characteristic extracted from the at least two images individually, to determine a deviation, in particular a standard deviation, of the at least one characteristic, which can be used as a tool for assessing the quality of the digital images and / or the analysis carried out.

[0122] In particular, if the deviation exceeds a predetermined threshold, a warning can be provided to the user and / or the digital image and / or contour image causing the parameter to deviate can be identified and / or displayed if the deviation exceeds a predetermined threshold.

[0123] Preferably, the method of the present invention further comprises the step of assigning at least one attribute of the sample. In particular, the attribute is selected from the following: - unique identifier of the sample; - the chemical and / or physical properties of the sample, in particular Type of sample, e.g. mortar, concrete, grout, screed, floor adhesive, or coating composition; · Raw materials used to prepare the sample; · Mix design of the sample, in particular the proportions of the individual raw materials, e.g. proportions of binders, aggregates, additives, water, etc.; · Density of the sample; · Application method, e.g. casting, spraying, additive manufacturing, etc.; · Special treatments undergone during manufacture, such as mixing, compaction, vibration, heating, etc.; Processing properties of the sample during production, e.g., rheological properties, e.g., flow properties, slump flow, viscosity, t50 time, yield stress and / or consistency class, of the sample in the processable state, in particular for hardenable samples such as mortar, concrete, grout, etc.; · Hardening properties, e.g., setting time and / or hardening time in the case of hardenable or hardened samples; · Mechanical properties of the specimen, e.g. strength class, compressive strength, flexural strength, hardness; Durability, e.g. exposure class, chemical resistance, freeze-thaw resistance, etc.; · Maximum particle size of particulate constituents; · Minimum particle size of particulate constituents; · The state of the sample, e.g., fluid or hardened state; · Pre-treatments undergone, such as crushing, cutting, grinding, flaking, and / or polishing. - Descriptive information about the sample, in particular: · Location of the sample source, e.g. manually or automatically by a position sensor; Intended use, e.g. project name and / or customer name, and / or General comments.

[0124] Preferably, the computing device, in particular a mobile computing device, is configured to query at least one of the sample attributes, in particular before, during and / or after steps a) to b).

[0125] In particular, a unique identifier for the sample is automatically generated.

[0126] In particular, the method of the present invention is carried out to obtain one or more of the following characteristics of the heterogeneous material, in particular the cured binder composition: - particle size distribution of aggregates in heterogeneous materials; - particle shape of aggregates in heterogeneous materials; - the proportion of binders in the heterogeneous materials, in particular the amount of paste proportions; - the quality of recycled aggregates and / or other raw materials, where quality shall be measured in particular by determining the amount and / or proportion of binder present in the recycled aggregate; - Mixing ratio of different materials with respect to particulate components and continuous phase; - the ratio of coarse aggregate to cement (ca / c) in e.g. mortar or concrete materials; - distribution of aggregates in heterogeneous materials; - the proportion, size and / or distribution of voids in the heterogeneous materials; - the amount and / or quality of the inorganic composition in the heterogeneous material, the quality being measured in particular by determining the proportion of one or more particulate inorganic constituents; - the quality of the production method and / or special treatments undergone during production, for example the quality of compaction (e.g. vibration) and / or the quality of the application method, whereby quality shall be measured in particular by measuring the homogeneity of the sample and / or the distribution of the particulate constituents; - color distribution on the surface of different materials, in particular for measuring the aging of samples, e.g. for characterizing carbonization and / or efflorescence; - Determination of the failure mode of dissimilar materials, in particular by measuring the number, size, shape and / or orientation of cracks in the specimen; - Monitoring of mechanical defects, e.g. cracks, in dissimilar materials; - the area ratio of cracks to the area of ​​the continuous phase, especially in synthetic materials, especially synthetic membranes; - size, shape, spatial distribution, orientation, packing density, and / or uneven distribution of cracks, especially in synthetic materials, especially synthetic membranes; - information regarding processability, in particular rheological properties, such as flow properties, slump flow, viscosity, t50 time, yield stress and / or consistency class, for example by taking one or more photographs of the sample in a processable state, for example of the slump flow of the sample, at predetermined times or at predetermined time intervals; - Predictive modelling, in particular predicting adjustments to produce further samples, for example predicting adjustments to raw materials and / or mix designs, in particular to improve certain properties of the further samples.

[0127] In particular, the method is at least partly, in particular entirely, implemented on a computing device, in particular a mobile computing device.

[0128] When the method is performed entirely on a computing device, particularly a mobile computing device, the complete characterization can be performed on the computing device, particularly a mobile computing device, without the need for a communications network, and the at least one digital image, preferably along with the at least one attribute, can be stored on the computing device, particularly a mobile computing device, for example, for sharing, retrieval, further evaluation, and / or predictive modeling.

[0129] Preferably, the computer device, in particular the mobile computer device, is configured to transfer at least one digital image, preferably together with at least one attribute, to an external device for storage via a communication means, e.g., a communication interface of the computer device, in particular the mobile device. Similarly, the computer device, in particular the mobile computer device, is preferably configured to retrieve stored data from the external device. According to another preferred embodiment, the image analysis in step c) and / or the making available in step d) are performed on another computer device, for example a server.

[0130] In this case, preferably, the at least one digital image, preferably together with the at least one attribute, is transferred to the external device via a communication means, e.g., a communication interface of the mobile device. Such a distributed solution is advantageous because the computationally intensive step c) can be performed on another device, which helps to extend the operating time of the computer device, in particular the mobile computer device. Furthermore, steps c) and / or d) can be optimized by updating the software part on the external device side, without the user having to update the software part of the computer device, in particular the mobile computer device.

[0131] This allows at least one digital image, preferably together with at least one attribute, to be temporarily stored on a computer device, in particular a mobile computer device, when a communication network is unavailable, and then transferred to an external device at a later time when a communication network becomes available.

[0132] In this case, the image, preferably together with at least one attribute, is stored on an external computing device, for example a server, in particular for sharing, recalling and / or further evaluation.

[0133] In step d), one or more of the extracted characteristics 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 attribute. However, this is not essential, since in daily work such images are of little importance to the user.

[0134] If the data is made available via a user interface, this can be done directly on the computing device, in particular a mobile computing device, and / or on an external computing device, whereby, for example, one or more of the extracted properties, 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 plot, such as a bar chart of circularity versus sphericity or average particle size parameter versus sieve opening.

[0135] Making the data available via a machine interface allows the data to be transferred to an external computing device, such as a server and / or desktop computer.

[0136] It is also possible to make the data available on a data storage medium, for example on an internal data storage medium of a computer device, in particular a mobile computer device, on a storage device attached to a computer device, in particular a mobile computer device, and / or on a storage device of an external computer.

[0137] In particular, one or more of the extracted characteristics, optionally together with at least one attribute and optionally together with at least one image, are written to a data file in a predetermined 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 allows for a reduction in file size.

[0138] In particular, the data file is transferred to another application, a further computing device, in particular a further mobile computing device, and / or an external computing device. The transfer can be performed by any type of communication means, for example wireless communication and / or wired communication. This allows a user to share the characteristics of the constituents, in particular the solid particles, with other users, transfer them to another application for further evaluation, and / or transfer them to a data storage server. Therefore, preferably, the computing device, in particular the mobile computing device, is configured to transfer the data file to another application, in particular a further mobile computing device, and / or an external computing device.

[0139] In particular, the dispersed components, especially the particulate components, are optically distinguishable from the continuous phase.

[0140] In particular, the dispersed components, especially the dispersed particulate components, have different light absorption and / or light reflection properties than the continuous phase, especially for light having wavelengths in the range of 200 nm to 5,000 nm, especially wavelengths in the range of 380 to 780 nm.

[0141] The size of the components, particularly the particulate components, may be, for example, in the range of more than 0 mm to 125 mm, preferably 10 μm to 32 mm, more preferably 0.063 mm to 16 mm, and especially 0.1 mm to 2 mm. These types of components, particularly particulate components, are typically present in hardenable compositions, such as mortar or concrete compositions. However, the method can also be used to characterize particulate components of other sizes.

[0142] In particular, the heterogeneous material is a curable or hardened binder material, in particular a curable or hardened inorganic binder composition or a curable or hardened organic binder composition.

[0143] The hardenable or cured binder material can exist in a fluid state, for example, during processing and / or the curing process, or can exist in a solid state, for example, after being partially cured or after the curing process is complete.

[0144] The hardenable or hardened binder material is selected from, for example, mortar, concrete, grout, screed, flooring, adhesive, or coating, whereby the binder can be an inorganic binder, an organic binder, or a hybrid binder comprising a combination of inorganic and organic binders.

[0145] "organic binder" particularly a polymeric resin. For example, the organic binder may be an epoxide, polyurethane, acrylate, polyester and / or polychloroprene based resin.

[0146] The expression "inorganic binder" especially refers to a binder that undergoes a hydration reaction in the presence of water to form a solid hydroxide or hydroxide phase, which may be, for example, a hydraulic binder (e.g., cement or hydraulic lime), a latent hydraulic binder (e.g., slag), a pozzolanic binder (e.g., fly ash), or a non-hydraulic binder (e.g., gypsum or white lime).

[0147] The total inorganic binder advantageously comprises a proportion of hydraulic binder of at least 5% by weight, in particular at least 20% by weight, preferably at least 50% by weight, in particular at least 75% by weight. In another advantageous embodiment, the inorganic binder comprises at least 95% by weight of hydraulic binder, in particular cement.

[0148] In particular, the inorganic binder comprises a hydraulic binder, preferably cement. Portland cement, in particular type CEM I, II, III or IV (according to standard EN 197-1), is particularly preferred. However, it may also be advantageous for the binder composition to comprise other binders in addition to or instead of the hydraulic binder. These are in particular latent hydraulic binders and / or pozzolanic binders. Examples of latent hydraulic binders and / or pozzolanic binders are slag, fly ash and / or silica dust. In one advantageous embodiment, the inorganic binder comprises 5 to 95% by weight, in particular 20 to 50% by weight, of latent hydraulic binder and / or pozzolanic binder.

[0149] In a particular embodiment, the inorganic binder comprises a mixture of calcined clay, limestone, and Portland cement.

[0150] The term "clay" refers to a solid material whose dry weight is at least 30% by weight, preferably at least 35% by weight, in particular at least 75% by weight, composed of clay minerals. Calcined clays are clay materials that have been heat-treated, preferably at temperatures between 500 and 900°C, or in a flash-calcination process at temperatures between 800 and 1100°C. According to a particularly preferred embodiment of the invention, the calcined clay is metakaolin.

[0151] 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. Alternatively, the limestone may be magnesium carbonate, dolomite, and / or a mixture of magnesium carbonate, dolomite, and / or calcium carbonate. It is particularly preferred that the limestone in the context of the invention is a natural limestone that consists mainly of calcium carbonate (usually calcite and / or aragonite), but typically also contains some magnesium carbonate and / or dolomite. The limestone may also be a natural marl.

[0152] According to a preferred embodiment, the Portland cement is of type CEM I. According to an embodiment, the Portland clinker content in the Portland cement of the invention is at least 35% by weight, preferably at least 65% by weight, in particular at least 95% by weight, based on the total dry weight of the cement.

[0153] According to an embodiment, the inorganic binder comprises calcined clay, limestone, and Portland cement in the following weight ratios: P:CC is 33:1 to 1:1, preferably 8:1 to 1:1; CC:L is 10:1 to 1:50, preferably 10:1 to 1:33, more preferably 5:1 to 1:10, and P:L is 20:1 to 1:4, preferably 5:1 to 1:1.

[0154] According to an embodiment, the inorganic binder comprises at least 65% by weight, preferably at least 80% by weight, more preferably at least 92% by weight, of calcined clay, limestone, and Portland cement, based on the total dry weight of the inorganic binder.

[0155] According to an embodiment of the present invention, the inorganic binder comprises: a) Portland cement (P) 25 to 100 parts by mass, b) 3 to 50 parts by weight of calcined clay (CC), in particular metakaolin; c) 5 to 100 parts by mass of limestone (L), Contains a mixture of

[0156] According to another preferred embodiment, the heterogeneous material is a synthetic material, in particular a synthetic membrane, made from the group comprising, for example, 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 elastomers (TPO, TPE-O), ethylene propylene diene rubber (EPDM), and mixtures thereof, in particular polyvinyl chloride (PVC) and / or thermoplastic polyolefin (TPO).

[0157] Such a membrane can be, for example, a waterproofing membrane or a roofing membrane.

[0158] Prolonged outdoor exposure of polymeric materials, such as roofing membranes, can cause surface cracks that ultimately lead to product failure. When inspecting the condition of a roof, it is therefore standard procedure to check the condition of the membrane by assessing the extent of cracking on its surface.

[0159] In this case, the constituent may be, for example, a crack, as described in particular below. The method of the present invention allows for direct and efficient analysis of the crack, thereby enabling efficient analysis of the state of the film. Film crack analysis can be carried out, for example, in accordance with the standard EN 13956:2013.

[0160] In particular, the particulate constituents of the sample are solid particles, such as selected from sand, aggregates, natural or synthetic fibers, glass spheres, sand substitutes, artificial sand, crushed and / or recycled building materials, bioaggregates, metal particles, ash, mineral processing waste, and / or polymeric particles, although other particulate constituents may also be present.

[0161] In a particular embodiment, the constituents of the sample, in particular the particulate constituents, are discoloured particulate specks and / or flakes of the sample.

[0162] In another embodiment, the dispersed components, especially dispersed particulate components, are gas-filled pores, especially bugholes, which are surface voids present in hardened binder compositions such as adhesives, coatings, grouts, mortars, or concrete compositions.

[0163] According to a further embodiment, the dispersed components are cracks, in particular unbranched and / or branched cracks, whereby in particular the cracks are gas-filled. Such cracks may for example be partially or completely straight and / or partially or completely curved.

[0164] In a particular embodiment, the crack is a crack in a synthetic material, in particular a synthetic membrane as mentioned above.

[0165] In particular, the heterogeneous material comprises two or more types of dispersed components, in particular particulate components, that are distinguishable in at least one digital image, for example because of different colors, and the extraction of one or more of the properties in step b) is performed separately for each type of distinguishable component, in particular particulate component.

[0166] This allows, for example, to simultaneously analyze samples for different properties, for example, to simultaneously analyze the void distribution and the aggregate distribution in a binder composition.

[0167] The continuous phase can be a solid or a liquid phase. A solid continuous phase is, for example, a hardened binder composition, e.g., containing inorganic and / or organic binders. A liquid continuous phase is, for example, a solvent, e.g., water, alcohol, or a fluid binder, e.g., cement mixed with water, before hardening is complete.

[0168] In particular, the continuous phase has a different appearance, in particular a different colour, from the dispersed components, in particular the particulate components, and / or there is an interface between the dispersed components, in particular the particulate components, and the continuous phase, which can be detected in at least one digital image.

[0169] In particular, the heterogeneous material to be characterized is a solid material.

[0170] However, in another preferred embodiment, the heterogeneous material is a fluid material, in particular a liquid material. The fluid form of the heterogeneous material is selected from, for example, emulsions, foams, suspensions, and processable binder compositions. However, other fluid forms of the heterogeneous material can be used as well.

[0171] In another preferred embodiment, the dispersed components, in particular the dispersed particulate components, comprise a first type of inorganic material and the continuous phase of the condensate comprises a second type of inorganic material that is different from the first type of inorganic material, such as occurs, for example, when the method of the present invention is used to analyze heterogeneous inorganic materials.

[0172] Yet another aspect is a system comprising a computing device, in particular a mobile computing device, and optionally an additional separate computing device, comprising: (i) means for carrying out steps a) to d) of the above method; and / or (ii) means for carrying out at least steps a) and b), in particular steps a), b) and d), of the above method, and means for transferring at least one digital image of the sample, optionally together with at least one attribute, to a separate computing device; The present invention is directed to a system comprising:

[0173] Another aspect of the invention relates to a system comprising a computing device, the system comprising means for receiving at least one digital image of a sample of heterogeneous material comprising dispersed components, in particular particulate components, dispersed within a continuous phase of a condensate, and means for performing at least steps c) and / or d) of the method described above.

[0174] Furthermore, the present invention relates to a computer readable medium comprising instructions which, when executed by a computing device, in particular a mobile computing device, cause the computing device to perform at least steps a) and b), in particular a) and b) and d), in particular steps a) to d), of the above method.

[0175] The present invention also relates to a computer readable medium comprising instructions which, when executed by a computing device, cause the computing device to receive at least one digital image, optionally together with at least one attribute, and to perform at least steps c) and / or d) of the above method.

[0176] In particular, the data processed and / or generated by the method of the present invention is encrypted, in particular to ensure that only authorized users can access the original data. Similarly, computer programs and / or applications implementing the method of the present invention are encrypted. Encryption methods are well known to those skilled in the art.

[0177] Further advantageous configurations of the invention are evident from the exemplary embodiments.

[0178] The drawings used to explain the embodiments show: [Brief explanation of the drawings]

[0179] [Figure 1] 1 is a flowchart of a computer-implemented method of the present invention. [Figure 2] 2 is a schematic overview of a system with means for implementing the method of FIG. 1; [Figure 3] Schematic of a second step of the method of FIG. 1 in which a user (not shown) holding a smartphone takes an image of a cubic hardened mortar sample with sand aggregate. [Figure 4]Schematic diagram of a second step of the method of FIG. 1 in which a user (not shown) holding a smartphone takes an image of a vertical wall of a building made of concrete containing aggregates of different sizes. [Figure 5] An example of a data file structure. [Figure 6] Bar graph of selected particle shape parameters (circularity, sphericity, and aspect ratio) by particle sieve size fraction. [Figure 7] 10 is a contour image superimposed on a digital image analyzed with the method of the present invention. [Figure 8a] Photograph of the surface of a concrete floor analyzed by the method of the present invention. [Figure 8b] Detail of Figure 8a. [Figure 9a] Photograph of the polished surface of a concrete drill core analyzed by the method of the present invention. [Figure 9b] Detail of Figure 9a. [Figure 10a] Photograph of the polished surface of granite analyzed by the method of the present invention. [Figure 10b] Detail of Figure 10a. [Figure 11a] Photograph of the surface of an epoxy grout analyzed by the method of the present invention. [Figure 11b] Detail of Figure 11a. [Figure 12a] Photograph of the surface of a liquid aqueous foam analyzed by the method of the present invention. [Figure 12b] Detail of Figure 12a. [Figure 13a] Photograph of cured foam analyzed by the method of the present invention. [Figure 13b] Detail of Figure 13a. [Figure 14a] Photograph of a concrete wall with a bughole analyzed using the method of the present invention. [Figure 14b] Detail of Figure 14a. [Figure 15a] Photo of a concrete wall with discoloration (light areas) caused by efflorescence. [Figure 15b] Detail of Figure 15a. [Figure 16a]Photograph taken during flow table testing of freshly prepared mortar samples. [Figure 16b] Detail of Figure 16a. [Figure 17a] Photograph of a mortar sample with voids that was inverted to determine bearing area and void coverage using the method of the present invention. [Figure 17b] Detail of Figure 17a. [Figure 18] Photograph of some recycled aggregates with cement residues (light areas) analyzed with the method of the present invention. [Figure 19a] Photo of a crack-free synthetic membrane (class 0 according to EN 13956:2013). [Figure 19b] Photo of a cracked synthetic membrane of class 1 according to EN 13956:2013. [Figure 19c] Photo of a cracked synthetic membrane of class 2 according to EN 13956:2013. DETAILED DESCRIPTION OF THE INVENTION

[0180] 1 shows a flowchart of the computer-implemented method 10 of the present invention. In a first step 11, a sample of heterogeneous material to be analyzed, for example a cubic hardened mortar sample having sand aggregate (particulate constituents) with a grain size of >0 mm to 2 mm embedded in a cementitious matrix C (continuous phase of the concentrate), is provided on a two-dimensional sample area of ​​known size. The sample area is formed, for example, by an A4-sized sheet of black paper.

[0181] In a second step 12, a digital image of one of the flat faces of the cubic sample is taken with a camera on a mobile computing device, for example a smartphone, the camera having for example 4K resolution.

[0182] Thereafter, in a third step 13, an image analysis of the digital images is performed to extract at least one particle size parameter, such as particle size distribution, and at least one particle shape parameter, such as circularity or sphericity, of the population of sand particles identified in the at least one digital image. Furthermore, the area fraction of the cementitious matrix C (continuous phase) is determined in the at least one digital image.

[0183] In a fourth step 14, at least one particle size parameter, at least one particle shape parameter and the area fraction of the cementitious matrix are made available via a user interface, for example a display of a mobile computing device.

[0184] FIG. 2 shows a schematic overview of a system 20 equipped with means for implementing the method shown in FIG.

[0185] Specifically, system 20 includes a smartphone 21 with a camera 22, a touch-sensitive display including an input device 23 and a display 24, a data processing unit 25 with random access memory, a data storage device 29, and a wireless communication interface 28.

[0186] During operation, an application 26 is executed on the data processing unit 25, whereby the application is configured to perform steps 12 and 14 of the method described with reference to FIG.

[0187] Specifically, the application 26 assists the user in capturing images of a sample 31 made of dissimilar materials within a two-dimensional sample area 30 using the built-in smartphone camera 22. The application is configured to automatically warn the user and / or prevent image capture, for example, if there is a non-plane-parallel alignment adjustment to the surface of the sample 31. This can be achieved by evaluating the smartphone's position sensor (not shown). The application is also configured to automatically adjust the lighting conditions to obtain a balanced exposure. Captured digital images are stored in random access memory and / or data storage 29.

[0188] Additionally, application 26 prompts the user via the input device to input one or more attributes of the sample and assigns a unique identifier to the sample, such as maximum aggregate size, aggregate type (natural, crushed, manufactured, recycled; recycled solid particles), aggregate source location, cement type, intended use (project name, customer name), and / or general comments.

[0189] The query may be performed, for example, by presenting the user with input fields, selection fields, maps and / or text entry fields on the display 24 and storing data provided by the user via the input device 23 together with at least the digital image in random access memory and / or data storage 29.

[0190] For example, the location of the aggregate source may be provided manually by a user, e.g. by entering geographic coordinates in input fields and / or by marking the location on a map displayed on the display 24. However, it is also possible for the location of the aggregate source to be provided automatically, e.g. by Global Navigation Satellite System sensors, such as GPS, Galileo, Beidou and / or Glonass sensors, whereby the user may be requested to confirm the automatically determined location.

[0191] In the system 20 shown in Fig. 2, step 13 of the method shown in Fig. 1 is performed on an external server 21a. Specifically, images captured by the smartphone camera 22, optionally along with attributes, are transferred to the server 21a via a wireless communication interface 28 (or any other communication interface) and a network (e.g., the Internet; not shown). The server 21a receives the data via the communication interface 28a and transfers it to an image analysis application 26a running in a processing unit 25a.

[0192] The images, optionally along with attributes, may be stored in data storage 29a of server 21a for later sharing, retrieval and / or further evaluation.

[0193] The application 26a performs image analysis of the digital images, such as to extract at least one particle size parameter, such as particle size distribution, and at least one particle shape parameter, such as circularity or sphericity, and area fraction, of a population of particles identified in at least one digital image, so that one or more attributes may also be considered in the analysis.

[0194] The application 26a may be implemented with image analysis algorithms and / or artificial intelligence software, such as, for example, software packages and / or libraries provided in Matlab, OpenCV, and / or ImageJ.

[0195] Once the image analysis is complete, at least one particle size parameter, such as particle size distribution, and at least one particle shape parameter, such as circularity or sphericity, and area occupancy, are sent back to the smartphone 21 or the application 25 running thereon via the communication interfaces 28a, 28b, respectively.

[0196] The application 25a then makes the at least one particle size parameter and / or at least one particle shape parameter and the area fraction available via the display 24 or stores the parameters, preferably together with the attributes, in the data storage 29 for later sharing, recall and / or further evaluation. This data can be stored in the form of a data file having a file format selected from json, csv, txt, pdf and / or a proprietary file format, for example. In particular, a file format that can be read by the application called "Sika Mix Design App" and / or any other additional application is selected. See, for example, FIG. 4.

[0197] Furthermore, the application 25 is configured to share the at least one particle size parameter, the at least one particle shape parameter, the area fraction and the attributes with other users by transmitting them, in particular as a data file, to a further computing device 40 of the other user, for example a smartphone, via the communication interface 28. This can be initiated by the user via the input device 23, for example by pressing a button shown on the display 24.

[0198] FIG. 3 shows a schematic diagram of step 12 of the method shown in FIG. 1. A user (not shown) holding a smartphone 21 takes an image of a sample 31, e.g., a cubic hardened mortar sample with sand aggregate. This results in solid sand particles L (large), M (medium), and S (small) of different sizes, ranging from over 0 mm to 2 mm, embedded within a cementitious matrix C. The sample 31 is provided on an A4-sized sheet of black paper, which serves as a two-dimensional sample area 30, whose spatial dimensions in both horizontal directions are larger than the sample 31. The smartphone 21 is held in a horizontal orientation, plane-parallel to the surface of the sample 31.

[0199] FIG. 4 shows another schematic diagram of step 12 of the method shown in FIG. 1. In this case, the smartphone 21 is held in a vertical orientation, plane-parallel to the vertical surface of the sample 31′. The sample 31′ may be, for example, a building wall made of concrete containing aggregates of different sizes, ranging from over 0 mm to 12 mm, including large gravel particles L′ (large), medium sand particles M′ (medium), and small sand particles S′ (small), embedded in a cementitious matrix C′. In this case, a black framed marking R′ is attached and / or marked on the sample surface. The marking R′ serves as a reference scale that can be used to determine the size of the particle features. By viewing the marking R′ in the photograph and adjusting the size of the marking R′ on the smartphone, the actual size of the particle features can be determined.

[0200] 5 shows an example of the structure of a data file 50 in pdf file format. Data file 50 includes a table 51 with user-provided attributes, such as aggregate type (natural, crushed, manufactured, recycled; recycled solid particles), location of aggregate source, intended use (project name, customer name), and / or general comments.

[0201] The file 50 also includes a graph 52 representing the particle size distribution, a table 53 containing the calculated sieve size passing percentage of solid particles and / or the calculated percentage of solid particles retained, and statistical parameters, e.g., D 10 , D 50 , D 85 , D 100 The present invention includes Table 54 with the values, as well as the coarseness ratio of the analyzed particulate constituents.

[0202] Additionally, file 50 includes a two-dimensional plot 55 showing the average particle shape (e.g., circularity or sphericity) at marker 55a. Additionally, file 50 includes a bar graph 57 showing the average values ​​of selected particle shape parameters (e.g., circularity, sphericity, and aspect ratio) by particle sieve size fraction. A more detailed view of bar graph 57 is shown in Figure 6. Of course, the content of data file 50 shown in Figure 5 and the bar graph shown in Figure 6 can be adapted to a particular sample and / or required information.

[0203] 7 shows a schematic diagram of a contour image superimposed on a corresponding digital image, which can additionally be used to verify the quality of the digital image and / or the analysis performed.

[0204] Figure 8a shows a photograph of the surface of a concrete floor containing aggregates of different sizes and shapes that was analyzed with the method of the present invention to obtain information about the floor structure, such as the cement to coarse aggregate ratio, mix design analysis, areal occupancy of the cement phase, and production quality (assessed based on the effectiveness of vibration; homogeneity of aggregate distribution).

[0205] Figure 8b shows an enlarged section of Figure 8a, which shows the identified particulate aggregates in a schematic manner.

[0206] Figure 9a is a photograph of the polished surface of a concrete drill core analyzed using the method of the present invention to verify the concrete mix design. As can be seen from the photograph, there is a non-uniform aggregate distribution, which can be quantified using the method of the present invention by determining the spatial distribution of the particulate components. The cement-to-coarse aggregate ratio, mix design, and / or binder coverage can also be analyzed. When combined with information about the application method, it is possible to determine whether the method was performed properly, for example, whether vibration compaction was sufficient.

[0207] Figure 9b shows an enlarged section of Figure 9a, which shows the identified particulate aggregates in a schematic manner.

[0208] Figure 10a is a photograph of a polished surface of a granite rock analyzed by the method of the present invention to determine the mineral composition or amount of important mineral phases such as biotite. In this case, the surface is composed of 42% orthoclase, 15% plagioclase, 35% quartz, and 8% biotite.

[0209] FIG. 10b shows an enlarged section of FIG. 10a, which shows a schematic representation of the identified particulate inorganic phase.

[0210] FIG. 11a shows a photograph of the surface of an epoxy grout analyzed by the method of the present invention to identify the bearing area (area occupied by the continuous phase) and the amount of voids (particulate holes).

[0211] FIG. 11b shows an enlarged section of FIG. 11a, which shows the identified voids in a schematic manner.

[0212] FIG. 12a shows 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.

[0213] FIG. 12b shows an enlarged section of FIG. 12a, which shows the identified voids in a schematic manner.

[0214] FIG. 13a shows a photograph of a cured foam analyzed by the method of the present invention to quantify the air content and support area of ​​the foam.

[0215] FIG. 13b shows an enlarged section of FIG. 13a, which shows the identified voids in a schematic manner.

[0216] FIG. 14a shows a photograph of a concrete wall with a cavity that was analyzed with the method of the present invention to verify the quality of the wall (assessed by quantifying the area fraction of the cavity).

[0217] FIG. 14b shows an enlarged section of FIG. 14a, which shows the identified cavities in a schematic manner.

[0218] Figure 15a shows a photograph of a concrete wall with discoloration (light areas) caused by efflorescence. The area percentage of the discolored areas was quantified, which can be used to determine, for example, the age of the concrete wall.

[0219] FIG. 15b shows an enlarged section of FIG. 15a, which shows the discolored area in a simplified manner.

[0220] Figure 16a shows a photograph taken during a flow table test of a freshly prepared mortar sample. Using the method of the present invention, the size of the mortar sample (the dark circular area) can be measured at a given time to obtain the workability and / or consistency of the composition. Figure 16b shows an enlarged section of Figure 16a.

[0221] Figure 17a shows a photograph of a mortar sample with voids that was inverted to determine the bearing area and void occupancy using the method of the present invention, and Figure 17b shows an enlarged section of Figure 17a, which shows a schematic of the identified voids.

[0222] Figure 18 shows a photograph of several recycled aggregates with cement residues (light areas). The method of the present invention can be used to determine the amount of binder present in recycled aggregate to determine the quality of the aggregate.

[0223] Figures 19a-c show photographs of the surfaces of three different synthetic membranes in different states, whereby the photographs were taken with a camera equipped with a magnifying lens. In Figure 19a, the membrane surface contains only surface texture and no cracks (no cracks; Class 0 according to EN 13956:2013). In Figure 19b, the membrane surface contains unbranched and branched cracks (Class 1 according to EN 13956:2013). In Figure 19c, the membrane surface contains a high density of primarily branched cracks (Class 2 according to EN 13956:2013). Using the method of the present invention, for example, the area coverage of cracks relative to the membrane's surface can be determined to determine the membrane's actual state. This allows, for example, the type and class of cracks to be determined directly from the photograph.

[0224] Those skilled in the art will recognize that the present invention may be embodied in other specific forms without departing from its spirit or essential characteristics, and therefore the presently disclosed aspects and embodiments are considered in all respects to be illustrative and not restrictive.

[0225] For example, instead of using server 21a, application 26 may be configured as a standalone application capable of performing all of steps 11, 12, 13, 14, and optionally 15a of the method of FIG.

[0226] Similarly, it is possible to omit optional features of the system 20, such as sharing data with other computing devices, or adding additional functionality, such as automatically obtaining location data via a positioning sensor.

[0227] Also, instead of or in addition to the particle size parameters and particle shape parameters, the spatial distribution of the particulate constituents, particle orientation parameters, particle surface parameters, particle roughness parameters, packing density, uneven distribution parameters, color, and / or area occupancy can be determined in step 13.

[0228] - particle size distribution of aggregates in heterogeneous materials; - particle shape of aggregates in heterogeneous materials; - the proportion of binder in the heterogeneous materials, in particular the amount of paste proportion; - the amount of binder attached to the aggregate in, for example, mortar or concrete materials; - the mixing ratio of the different materials with respect to the particulate components and the continuous phase; - The ratio of coarse aggregate to cement (ca / c) in e.g. mortar or concrete materials. - distribution of aggregates in heterogeneous materials; - occupancy, size and / or distribution of voids in heterogeneous materials; - the amount and / or quality of inorganic components in the heterogeneous material; - the quality of the manufacturing method and / or any special treatments that have been subjected to during manufacturing, such as the quality of compaction (e.g. vibration) and / or the quality of the method of application. - color distribution on the surface of different materials, for example by characterizing carbonization and / or efflorescence, to determine the aging of the sample; - Determining failure modes of dissimilar materials; - Monitoring of mechanical defects, e.g. cracks, in dissimilar materials; - processability-related information, in particular rheological properties, such as flow properties, slump flow, viscosity, t50 time, yield stress and / or consistency class; for example by taking one or more photographs of the sample in a processable state, such as the slump flow of the sample, at a predefined time or at predefined time intervals; - Predictive modelling, in particular the prediction of sample adjustments, such as adjustments to ingredients and / or mix design, in particular to improve specific properties of the sample.

[0229] In Figure 3, instead of or in addition to placing the mortar sample 31 in the sample area 30, a reference scale, such as a ruler, a geometric shape and / or a letter code, can be placed and / or marked on the sample 31, as in Figure 4. In this case, it is sufficient to take a photograph with a smaller image area. As long as the reference scale is visible in the photograph, the size of the particulate components can be determined.

[0230] It is also possible to omit the reference scale and / or sample area, thereby allowing the dimensions to be manually set in the computing device if necessary. It is noteworthy that even without the reference scale and dimensions, it is still possible to determine, for example, the occupancy of the continuous phase and / or the homogeneity of the sample.

[0231] Furthermore, the structure of the data file 50 shown in FIG. 5 may be in other file formats and / or the respective information may be presented in a graphical user interface, such as a dashboard.

[0232] The method can also be used to characterize other samples, such as other suspensions, emulsions, foams, adhesives, wall and ceiling voids, aesthetic surface features, etc.

Claims

1. 1. A computer-implemented method for characterization of heterogeneous materials comprising dispersed components, particularly dispersed particulate components, dispersed within a continuous phase of a condensate, comprising: a) providing or selecting a sample of heterogeneous material to be analyzed; b) acquiring at least one digital image of the sample by reading into the computing device at least one digital image previously recorded by a camera of a computing device, in particular a mobile computing device, or by a camera connected to a computing device, in particular a mobile device, and / or by a stand-alone camera; c) 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, packing density, uneven distribution parameters, color and / or areal coverage of said components, in particular said particulate components, as determined by image analysis of said at least one digital image, and / or - the area coverage of the continuous phase determined by the image analysis in the at least one digital image; - performing an image analysis, in particular an image grain analysis, of said at least one digital image in order to extract one or more of: d) making one or more of the characteristics extracted in step c) available via a user interface, via a machine interface and / or on a data storage medium; A method comprising:

2. The method of claim 1 , wherein the dispersed component, in particular the particulate component, is optically distinguishable from the continuous phase.

3. 3. The method according to claim 1 or 2, wherein the dispersed components, in particular the particulate components, have different light absorption and / or light reflection properties than the continuous phase for light having wavelengths in the range of 200 nm to 5,000 nm, in particular in the range of 380 to 780 nm.

4. The method according to any one of claims 1 to 3, wherein the dissimilar material is a hardened binder material, in particular a hardened inorganic binder composition or a hardened organic binder composition.

5. The method of any one of claims 1 to 4, wherein the dissimilar material is a hardened concrete composition, a hardened mortar composition, or a hardened grout composition.

6. The method of any one of claims 1 to 5, wherein the heterogeneous material is an emulsion, a foam or a suspension.

7. 7. The method according to any one of claims 1 to 6, wherein the heterogeneous material is a synthetic material, in particular a synthetic membrane, made 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 elastomers (TPO, TPE-O), ethylene propylene diene rubber (EPDM), and mixtures thereof, in particular from polyvinyl chloride (PVC) and / or thermoplastic polyolefin (TPO).

8. 8. The method according to any one of claims 1 to 7, wherein the components are dispersed particulate components in the form of solid particles, in particular aggregates, fibres and / or glass spheres, in particular aggregates in the form of sand and / or gravel.

9. The method according to any one of claims 1 to 8, wherein the constituents are dispersed particulate constituents in the form of gas-filled pores, in particular bugholes (surface voids).

10. The method according to any one of claims 1 to 9, wherein the dispersed components are cracks, in particular unbranched and / or branched cracks, in particular the cracks are filled with gas.

11. The method of any one of claims 1 to 10, wherein the continuous phase is a solid.

12. 12. The method according to any one of claims 1 to 11, wherein the continuous phase comprises a hardened binder material, in particular a hardened organic binder and / or a hardened inorganic binder, in particular a hardened inorganic binder and / or a hardened curable polymer.

13. The method according to any one of claims 1 to 12, wherein the continuous phase of the concentrate is liquid, in particular the continuous phase of the concentrate comprises water, alcohol and / or a fluid binder.

14. 14. The method according to any one of claims 1 to 13, wherein the components, in particular the dispersed particulate components, comprise a first type of inorganic material and the continuous phase of the concentrate comprises a second type of inorganic material, the second type of inorganic material being different from the first type of inorganic material.

15. The method according to any one of the preceding claims, wherein in step b) said at least one digital image is acquired of a surface, in particular a flat surface, of said sample of dissimilar material.

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

17. 17. The method according to any one of claims 1 to 16, wherein the sample or the flat surface is subjected to a surface treatment in order to increase the contrast between the continuous phase and the components, in particular the particulate components, e.g. the surface treatment is selected from colouring with ink and / or polishing with powder and / or paste.

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

19. The method of any one of claims 1 to 18, wherein the mobile computing device is selected from a mobile phone, a mobile or 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 acquiring images in the visible spectrum, in particular colour images.

21. 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, in particular at least 12 megapixels, very preferably at least 20 megapixels, even more preferably at least 50 megapixels, or at least more than 100 megapixels.

22. 22. The method according to any one of claims 1 to 21, wherein the sample is placed in a predetermined sample area, the sample area being larger than the sample in all spatial directions, preferably the sample area comprising a reference scale and / or having a known size.

23. 23. The method according to claim 22, wherein the predetermined sample area is a two-dimensional sample area, preferably a thin sheet material, in particular of a predetermined size, with a characteristic background color, e.g. black.

24. 24. A method according to any one of claims 1 to 23, wherein when acquiring the image the camera is aligned to maximize the proportion of the sample in the image, in particular by providing alignment instructions to the user and / or by automatically adjusting at least one setting of the camera, for example the focal length of the camera.

25. 25. The method according to any one of claims 1 to 24, wherein the minimum detectable size, in particular the minimum detectable granularity, of the components, in particular the particulate components, is calculated by taking into account the resolution of the camera, the proportion of the length of the sample in the total area of ​​the image, and the actual length of the sample.

26. 26. The method of any one of claims 1 to 25, wherein if the minimum detectable size, in particular granularity, falls below a predetermined threshold, a warning is provided to the user, alignment adjustment instructions are provided to the user, and / or settings of the camera, for example focal length, are automatically adjusted.

27. 27. The method of any one of claims 1 to 26, wherein for each of the at least one digital image a contour image, a reverse image and / or a color thresholded image is generated and used as the image in step c).

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

29. 29. The method according to any one of claims 1 to 28, wherein in step b) at least two, preferably at least three, in particular at least five or at least ten digital images are acquired, and an image analysis is carried out in step c) for each image, and a deviation, in particular a standard deviation, of the one or more characteristics, in particular the particle size parameter and / or the at least one particle shape parameter, is determined by taking into account each of the one or more characteristics, in particular the particle size parameter and / or the particle shape parameter, extracted individually from the at least two images.

30. 30. The method of claim 29, wherein if the deviation exceeds a predetermined threshold, a warning is provided to the user and / or the digital image and / or contour image giving rise to the deviating parameters are identified and / or displayed.

31. The method of any one of the preceding claims, wherein the one or more properties extracted in step c) comprise particle size parameters and / or particle shape parameters.

32. The particle size parameters are the average particle size, the average diameter, and D ｘ 32. The method of claim 31, comprising at least one statistical parameter selected from the group of values ​​and / or coarse-grainedness.

33. 33. The method of claim 31 or 32, wherein the particle size parameter comprises a particle size distribution of a population of particulate components identified in the at least one digital image.

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

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

36. 36. The method of any one of claims 31 to 35, wherein the at least one particle shape parameter extracted comprises circularity, sphericity, aspect ratio, roughness, solidity, flaky index, shape index, percentage of fractured and broken surfaces, distances and / or angles between surface structures of individual particles, and / or angularity.

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

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

39. A method according to any preceding claim, comprising assigning at least one attribute to the sample of the disparate material being analysed.

40. The method comprises the steps of: - particle size distribution of the aggregates in said heterogeneous material; - the particle shape of the aggregates in said heterogeneous material; - the proportion of binder in the heterogeneous material, in particular the amount of paste; - the amount of binder bound to the aggregate, for example in mortar or concrete materials; - the mixing ratio of said different materials with respect to said particulate components and said continuous phase; - the ratio of coarse aggregate to cement (ca / c) in e.g. mortar or concrete materials; - the distribution of aggregates in said heterogeneous materials; - the proportion, size and / or distribution of voids in said heterogeneous material; the amount and / or quality of the inorganic composition in said heterogeneous material, said quality being measured in particular by measuring the proportion of one or more particulate inorganic constituents; - the quality of the production method and / or of any special treatments undergone during production, such as the quality of compaction (e.g. vibration) and / or the quality of the application method, said quality being measured in particular by measuring the homogeneity of the sample and / or the distribution of the particulate constituents; - color distribution on the surface of said heterogeneous materials, in particular for measuring the ageing of said samples, for example for characterizing carbonization and / or efflorescence; - determining the failure mode of said dissimilar materials, in particular by measuring the number, size, shape and / or orientation of cracks in said samples; - monitoring mechanical defects, such as cracks, in said dissimilar materials; the area ratio of cracks to the area of ​​the continuous phase, in particular in synthetic materials, in particular synthetic membranes; - size, shape, spatial distribution, orientation, packing density and / or uneven distribution of cracks, especially in synthetic materials, especially synthetic membranes; - information regarding processability, in particular rheological properties, such as flow properties, slump flow, viscosity, t50 time, yield stress and / or consistency class; for example by taking one or more photographs of said sample in a processable state, such as of the slump flow of said sample, at predetermined times or at predetermined time intervals; predictive modelling, in particular the prediction of adjustments for producing further samples, for example the prediction of raw material adjustments and / or mix design adjustments, in particular to improve certain properties of said further samples; 40. The method of any one of claims 1 to 39, performed to obtain one or more of:

41. The method according to any one of claims 1 to 40, wherein the method is implemented at least partly, in particular entirely, on the computer device, in particular on 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 making available in step d) is performed on another computing device, for example on a server.

43. 43. The method according to any one of claims 1 to 42, wherein said image, preferably together with said at least one attribute, and optionally said contour image, inverted image and / or thresholded image, is stored on an external computing device, e.g. a server, in particular for sharing, retrieval and / or further evaluation.

44. A system comprising a computing device, in particular a mobile computing device, and optionally an additional separate computing device, (i) means for carrying out steps a) to d) of the method of claim 1; and / or (ii) means for carrying out at least steps a) and b), in particular steps a), b) and d) of the method according to claim 1, and means for transferring at least one digital image of said sample, optionally together with at least one attribute, to said separate computing device; A system equipped with

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

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

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

Images