Three-dimensional scans of furrows in bulk materials: spatial data set

Three-dimensional scans of furrows in bulk materials: spatial data set | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Three-dimensional scans of furrows in bulk materials: spatial data set Martin Zidek, Lucie Jezerska, Aleksandr Derbenev, Ondrej Kabot, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8287044/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents a comprehensive spatial dataset capturing the three-dimensional (3D) morphology of furrows created by rigid tools moving through granular materials under controlled normal loading conditions. The data cover three types of granular materials: two fractions of silica sand (0.3–1.0 mm and 1.4–2.0 mm), and river silica stone (2–6 mm). The experimental setup consisted of five levels of normal load, three transport speeds, and two types of tools, resulting in a total of 375 measurements. The 3D scans were collected using a high-resolution 3D scanner, providing detailed surface topography data. This dataset enables the derivation of key parameters, such as furrow length and width, tool penetration angle, and material subsidence. The collected data facilitate the quantitative assessment of the impact of grain size and applied load on the formation and structure of the furrow. Additionally, the dataset supports applications in terrain mechanics, discrete element modelling, and various fields, including mobility, geotechnics, waste management, and material handling. This resource aims to aid in the calibration of numerical models and offer a reproducible reference for future research in ground interaction analysis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION We present a spatial data set that captures three-dimensional (3D) morphology of furrows created by a rigid runner passing through granular layers under controlled normal loading. Three granular materials were tested: silica sand (0.3 – 1.0 mm), silica sand (1.4 – 2.0 mm) and river silica stone (2 – 6 mm). For each material was prepared a test bed with dimensions 0.40 m × 1.50 m × 0.075 m (0.045 m³). Five nominal levels of normal load were used for two types of tools moving over bulk material at three transport speeds. Each measurement was repeated five times, resulting in a total of 375 passes. The data file includes 3D raw data and surface topography reconstructions after the tool has passed over the bulk material. From this data, it is possible to derive the length and width of the groove, the angle of penetration of the tool along the path of movement, and the angle of inclination of the material obtained from cross sections. These data enable a quantitative assessment how normal load and grain size affect the size and shape of the furrow, subsidence, and furrow formation. This resource is intended to support benchmarking and calibration of terrain mechanical models, discrete element simulations, and related applications in the field of mobility, geotechnics, waste management, and material handling. Background & Summary Interactions between rigid bodies and bulk materials are crucial for terrain mechanics [1, 2, 3], terrain mobility [4, 5, 6,7] and surface modification [8, 9] in geotechnical and industrial conditions. When the tool moves over granular subsoil, it leaves behind a furrow whose 3D geometry reflects the location of shear, dilation and relaxation after failure [10]. While previous studies often report data on failure or strength, publicly available datasets, that capture the entire surface after passage under controlled loads and systematic material changes, remain limited. This limits the verification of constitutive models and the calibration of discrete element method (DEM) formulations [11, 12, 13] based on geometrically rich results. To fill this gap, we provide a dataset of furrow morphologies created using two tools (tool 1 without an active element, tool 2 with an active element) on three granular materials ranging from fine to coarse: two fractions of silica sand (0.3–1.0 mm and 1.4–2.0 mm) and river silica stone (2–6 mm), for three transport speeds (0.017 m·s − 1 , 0.034 m·s − 1 and 0.51 m·s − 1 ). For each material, experiments were performed on a bed with dimensions 0.40 m × 1.50 m × 0.075 m under five normal loads, with five independent repetitions for each condition, with the exception of tool 2 passing through river silica stone (2–6 mm) which was not performed due to large dynamic shocks, deformations and damage to the tool during the experiment ((3 materials × 2 tools × 3 speeds × 5 loads × 5 repetitions) – 75 = 375 passes). This design isolates the effects of grain size and applied load while allowing for quantification of variability. The records include 3D raw data and reconstructions of the surface topography after the event (Fig. 1 ). From this data it is possible to deduce length and width of the furrow, the development of the angle of penetration of the runner along the path of movement, and the angle of inclination derived from cross sections. Because each combination of load and material is repeated, the data file supports uncertainly assessment (e.g., variability between repetitions) and sensitivity assessment to boundary conditions. Expected applications include (i) benchmarking of terrain mechanics and models based on resistive force or plasticity against solved surface geometry; (ii) calibrating DEM contact and damping parameters to reproduce berm formation and subsidence; and (iii) testing control or planning algorithms for ground interactions in robotics and mobility. By offering a consistent set of 3D results differentiated by load and material without prescribing a specific modelling framework, this data descriptor aims to facilitate reproducible comparisons and provide a stable reference for future extensions involving tool geometry, moisture content, or alternative load settings. Methods Bulk materials tested Three samples of bulk materials were examined. Two fractions of natural silica sand with a fraction 0.3–1.0 mm and 1.4–2.0 mm. Third sample is river silica stone 2.0–4.0 mm. The sample measuring 0.3–1.0 mm is jointing silica sand from Sand system, s.r.o. Wienerberger s.r.o., Czech Republic, sample 1.4–2.0 mm is silica sand from Sand system, s.r.o. Czech Republic and River silica stone is a material supplied by Den Braven Czech and Slovak a.s. Czech Republic. All materials are natural, washed (free of dust particles) and sorted into the required fraction. Where available, meta information on bulk materials from technical data sheets or supplier’s specifications in Data citation 1. Experimental Procedure Conventional Measurements The principle of tool movement over bulk material and calibration of the validation device is described in [14]. The device used for the experiment consists of a supporting structure (I) made of aluminium profiles and clear polycarbonate sheets. Furthermore, from the adjustable bottom (II), linear guide (III), and drive (IV). The linear guide is consists of a carriage (V), a TR40x7 treaded rod (VI) and guide rods (VII). A welded assembly (VIII) is attached to the carriage (V) using a detachable joint, to which a flanged carriage with ball bearing guide HGW 45HC Z0H (IX) is attached on the same way. A HIWIN HGR45T-0364 (X) T-type rail is inserted through the ball guide groove. A bracket (XII) is attached to the head of the rail (X) by means of an axis (XI), on which a weight (XIII) is placed. A membrane force sensor (MEG30, 500 N) (XIV) is located at the foot of the rail (X), on which the slide/tool (XV) is mounted. The normal load on bulk material was determined using 0–4 weights (XIII) (each weight weighing 4.4 kg). Furthermore, a digital sliding gauge (XVI) is attached to the device, and the values can be read from the panel (XVII) located on the carriage. The drive (IV) is started using a Sinamics G110 AIN frequency converter (Fig. 2 ). The direction of the movement of the tool is determined by the axis of the TR40x7 threaded rod. The normal force was created by the weight of the loading mechanism with tool (4.52 kg) and the weight (XIII) (4.4 kg). The speed of the tool was achieved using an electric motor and converting the rotary motion into linear motion using a TR40x7 threaded rod. Using a Sinamics G110 AIN frequency converter, three transport speeds were set for frequencies of 5 Hz, 10 Hz, 15 Hz (Table 1 ). Table 1 Conversion of frequency to angular velocity on a threaded rod and linear guide speed Frequency on the converter 5 Hz 10 Hz 15 Hz Average angular velocity on the threaded rod [rpm] 141,49 288,62 436,64 Average linear guide speed [m·s − 1 ] 0,017 0,034 0,051 The speed of the tool moving across the loose material was then recalculated using an angular velocity, which was measured using a Voltcraft DT-30LK laser tachometer and the pitch of the TR40x7 threaded rod. The geometry of the tools was based on the shape of the ski (Fig. 3 ). A 3D model of the tools is added to the data package in STL format (STereoLithography a.k.a. Standard Tessellation Language) so that their dimensions can be read (Skis without active elements.stl, Skis with active elements.stl). Three-dimensional Scans 3D scanning of the resulting furrow was performer using 3D Sense TM 2 scanner from 3DSYSTEMS. The measurement is shown in Fig. 4 . The lab technician held the 3D scanner in his hand and placed it above the scanner geometry so that the scanner’s imaging sensor was always in perpendicular to the scanned surface at a high of 0.3–0.4 m from the scanned surface. The 3D scan was created by moving 3D scanner over the scanned surface along the x-axis across the entire length of the resulting groove (shown by the blue arrows in Fig. 4 ). The 3D model of the surface was automatically recorded by 3D Sense System software. For accurate scanning, the program settings were adjusted to the maximum possible geometry resolution (1 mm grid size) and the maximum possible scanning area with the option of saving the texture of the scanned surface at 30 fps. For accurate geometry network matching during scanning, Sense Track Assist should also be enabled. The raw data was then automatically saved to an OBJ (Object File Format) file. These raw scans may also contain parts of the experimental setup, such as a frame or wall, that were captured along with the scanned surface during scanning. The reconstructions of the scanned areas were than saved in STL, PLY (Stanford Polygon File Format) and VRML (Virtual Reality Modeling Language) formats. The reconstruction was carried out for the purpose of standardizing data and converting it into formats suitable for application. This step made it possible to maintain geometric accuracy, optimize the data structure, and ensure compatibility with subsequent analytical and visualization tools. Experimental plan The experimental design included four factors: bulk material type (three levels), tool used (two levels), tool movement speed (three levels), and normal load (five levels). The experiments were conducted according to a full factorial experimental design with each bulk material for two tools, three movement speeds, and five normal loads. All combinations were repeated five times, ( 3materials × 2 tools × 3 speeds × 5 loads × 5 repetitions) = 450 experiments. Unfortunately, when using Skis with active elements on river silica stone 2–6 mm, there were large dynamic shocks, jamming and damage of the tool. Therefore, we had to remove this part from the results (75 experiments). A total of 375 (450–75) were conducted. Code availability The files provided were created, stored, and reconstructed using 3D System Sence version 3.0.213. Data records The data records were made available at XXXXXXX (Data citation 1) Meta information The file „Additional_bulk_solids_info.pdf“ contains meta information relating to the bulk materials under investigation. The relevant information was obtained from suppliers and technical data sheets, where available. The file „Sense2_Specifications.pdf“ contains meta information about the SenseTM2 3D scanner from 3DSYSTEMS. 3D scanning The data from the three-dimensional scanning of the furrow is stored in three separate ZIP files, one for each bulk material. Each archive has same folder structure, as shown in Fig. 5. The first level contains a maximum of two folders, one for each tool used. On the second level, there is one folder for each tool feed rate. For example, „5 Hz“, where this designation is taken as a parameter set on the frequency converter. The conversion of frequency to movement speed is shown in Table 1 . At the third level, there is one folder for each number of weights used to generate normal force. For example, „0“ for a tool pass without weights, or „4“ for a tool pass with four weights, which deduce a normal force on the bulk material. Each of these folders contains four subfolders named „OBJ_raw“, „PLY_reconstruction“, „STL_reconstruction“, and „VRML_reconstruction“. The folder „OBJ_raw“ contains the original data that was created during 3D scanning using the 3D System Sence version 3.0.213 program. The folders „PLY_reconstruction“, „STL_reconstruction“, and „VRML_reconstruction“ contains reconstructed data that was reconstructed using 3D System Sence version 3.0.213. It should be noted that all folders contain spatial scan data of the furrow with the surface texture of the material inserted. The second level of folders contains the geometry of the given tool in STL format („Skis with active elements.stl“ / „Skis without active elements.stl“). STL meshes were saved in binary format for each measurement. Technical validation 3D scanning The accuracy of surface scanning using the Sense TM 2 3D scanner from 3DSYSTEMS has been examined in several studies [15, 16]. Scanning errors may occur due to several factors, such as the distance of the scanned object from the 3D scanner, the condition of the surface, the geometric complexity of the object, the lighting of the object, and the scanning angle. Based on the above studies, the distance used for scanning these objects was determined to be 0.7–0.8 mm [17]. Tests and comparisons of scanned surfaces confirmed these values. Notes on use It may be useful to convert STL networks from binary encoding to ASCII. Some programs may not support binary STL files. Declarations Data citation 1. XXXXXXXXXXXXXXXXXXXX Acknowledgements This study was financially supported by the Operational Program Just Transition and within the project „Waste as an alternative source of energy“, reg. nr. CZ.02.01.01/00/23_021/0008590 under the Programme Johannes Amos Comenius. Contributions by authors M.Z. designed and planned the experiments. A.D. and D.G. performed and supervised the experiments. L.J. prepared and edited the experimental results according to instructions. M.Z. created the 3D wav data files. R.P. and V.S. created the 3D reconstructed data files. M.Z. compiled the manuscript. O.K. and J.T. revised the manuscript. All authors read and approved the manuscript. Additional informations Conflict of interest: The authors declare that they have no conflict of interest References Salmivaara, A., Miettinen, M., Finér, L., Launiainen, S., Korpunen, H., Tuominen, S., Heikkonen, J., Nevalainen, P., Sirén, M., Ala-Ilomäki, J. & Uusitalo, J. Wheel rut measurements by forest machine-mounted LiDAR sensors – accuracy and potential for operational applications. Int. J. For. Eng. 29 , 41–52 (2018). https://doi.org/10.1080/14942119.2018.1419677 Pierzchała, M., Talbot, B. & Astrup, R. Measuring wheel ruts with close-range photogrammetry. Forestry 89, 383–391 (2016). https://doi.org/10.1093/forestry/cpw009 Baek, S.-H., Shin, G.-B., Lee, S.-H., Yoo, M., & Chung, C.-K. 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16:06:13","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26771,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/dffd8e20c3137a27a08377cb.png"},{"id":97896463,"identity":"9383c9c9-2e02-4b54-bb4f-f05ef9b3941e","added_by":"auto","created_at":"2025-12-10 15:36:35","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28181,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/e29d516045b07d9cb68a15c6.png"},{"id":97812012,"identity":"28cc6d7a-fb35-483c-bbbe-11a9efea95a8","added_by":"auto","created_at":"2025-12-09 16:06:13","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16459,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/74ff12606648fa6b25dfe35b.png"},{"id":97812016,"identity":"7e187dc0-3b75-4a76-ba66-86cfddfb5ac3","added_by":"auto","created_at":"2025-12-09 16:06:13","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34861,"visible":true,"origin":"","legend":"","description":"","filename":"rs82870440structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/2cd4f84afc087eb07786d9cb.xml"},{"id":97898842,"identity":"ebac09e5-f15d-4d64-8982-129ed838cf2d","added_by":"auto","created_at":"2025-12-10 15:39:50","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41194,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/a76c5946795fe4e22c5aea92.html"},{"id":97898672,"identity":"f837dcb6-3b3f-4ccc-ac2b-8f3a35274218","added_by":"auto","created_at":"2025-12-10 15:39:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27422,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial 3D scan of a furrow in silica sand, under the action of Skis without active elements, with a movement speed of 15 Hz for a) no weight, b) one weight, c) two weights, d) three weights, e) four weights\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/a571a31faeecb42b714b7b3a.jpg"},{"id":97811998,"identity":"af8ec880-308c-4204-9c2d-bccf7067c490","added_by":"auto","created_at":"2025-12-09 16:06:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26099,"visible":true,"origin":"","legend":"\u003cp\u003eDescription of the validation device [14]\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/b3c9b1e94c2a813c9030933a.jpg"},{"id":97898307,"identity":"86e9e59f-0c1e-46c4-9466-daab29f57a41","added_by":"auto","created_at":"2025-12-10 15:38:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19321,"visible":true,"origin":"","legend":"\u003cp\u003eTool geometry, a) Skis without active elements, b) Skis with active elements\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/f218bd5a131a66088c3eebf5.jpg"},{"id":97812006,"identity":"43a997d8-bdd0-40fe-890f-f1dc03004bd7","added_by":"auto","created_at":"2025-12-09 16:06:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18292,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of scanning movement\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/0b201447cd6413673936e8e7.jpg"},{"id":97898433,"identity":"f25746c5-15c7-43e8-b09e-e6708cf43ae5","added_by":"auto","created_at":"2025-12-10 15:39:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71592,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of Zip file folders\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/8014c58aca4c1a2dda8a4827.jpg"},{"id":97903250,"identity":"7301a90d-ac1a-4221-a5ec-f18a88079696","added_by":"auto","created_at":"2025-12-10 15:54:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":582242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8287044/v1/b9e939ea-9fb9-4f25-87a9-91fba0a406c7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThree-dimensional scans of furrows in bulk materials: spatial data set\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eWe present a spatial data set that captures three-dimensional (3D) morphology of furrows created by a rigid runner passing through granular layers under controlled normal loading. Three granular materials were tested: silica sand (0.3 \u0026ndash; 1.0 mm), silica sand (1.4 \u0026ndash; 2.0 mm) and river silica stone (2 \u0026ndash; 6 mm). For each material was prepared a test bed with dimensions 0.40 m \u0026times; 1.50 m \u0026times; 0.075 m (0.045 m\u0026sup3;). Five nominal levels of normal load were used for two types of tools moving over bulk material at three transport speeds. Each measurement was repeated five times, resulting in a total of 375 passes. The data file includes 3D raw data and surface topography reconstructions after the tool has passed over the bulk material. From this data, it is possible to derive the length and width of the groove, the angle of penetration of the tool along the path of movement, and the angle of inclination of the material obtained from cross sections. These data enable a quantitative assessment how normal load and grain size affect the size and shape of the furrow, subsidence, and furrow formation. This resource is intended to support benchmarking and calibration of terrain mechanical models, discrete element simulations, and related applications in the field of mobility, geotechnics, waste management, and material handling.\u0026nbsp;\u003c/p\u003e"},{"header":"Background \u0026 Summary","content":"\u003cp\u003eInteractions between rigid bodies and bulk materials are crucial for terrain mechanics [1, 2, 3], terrain mobility [4, 5, 6,7] and surface modification [8, 9] in geotechnical and industrial conditions. When the tool moves over granular subsoil, it leaves behind a furrow whose 3D geometry reflects the location of shear, dilation and relaxation after failure [10]. While previous studies often report data on failure or strength, publicly available datasets, that capture the entire surface after passage under controlled loads and systematic material changes, remain limited. This limits the verification of constitutive models and the calibration of discrete element method (DEM) formulations [11, 12, 13] based on geometrically rich results.\u003c/p\u003e\u003cp\u003eTo fill this gap, we provide a dataset of furrow morphologies created using two tools (tool 1 without an active element, tool 2 with an active element) on three granular materials ranging from fine to coarse: two fractions of silica sand (0.3\u0026ndash;1.0 mm and 1.4\u0026ndash;2.0 mm) and river silica stone (2\u0026ndash;6 mm), for three transport speeds (0.017 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 0.034 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.51 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). For each material, experiments were performed on a bed with dimensions 0.40 m \u0026times; 1.50 m \u0026times; 0.075 m under five normal loads, with five independent repetitions for each condition, with the exception of tool 2 passing through river silica stone (2\u0026ndash;6 mm) which was not performed due to large dynamic shocks, deformations and damage to the tool during the experiment ((3 materials \u0026times; 2 tools \u0026times; 3 speeds \u0026times; 5 loads \u0026times; 5 repetitions) \u0026ndash; 75\u0026thinsp;=\u0026thinsp;375 passes). This design isolates the effects of grain size and applied load while allowing for quantification of variability.\u003c/p\u003e\u003cp\u003eThe records include 3D raw data and reconstructions of the surface topography after the event (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). From this data it is possible to deduce length and width of the furrow, the development of the angle of penetration of the runner along the path of movement, and the angle of inclination derived from cross sections. Because each combination of load and material is repeated, the data file supports uncertainly assessment (e.g., variability between repetitions) and sensitivity assessment to boundary conditions.\u003c/p\u003e\u003cp\u003eExpected applications include (i) benchmarking of terrain mechanics and models based on resistive force or plasticity against solved surface geometry; (ii) calibrating DEM contact and damping parameters to reproduce berm formation and subsidence; and (iii) testing control or planning algorithms for ground interactions in robotics and mobility. By offering a consistent set of 3D results differentiated by load and material without prescribing a specific modelling framework, this data descriptor aims to facilitate reproducible comparisons and provide a stable reference for future extensions involving tool geometry, moisture content, or alternative load settings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eBulk materials tested\u003c/p\u003e\u003cp\u003eThree samples of bulk materials were examined. Two fractions of natural silica sand with a fraction 0.3\u0026ndash;1.0 mm and 1.4\u0026ndash;2.0 mm. Third sample is river silica stone 2.0\u0026ndash;4.0 mm. The sample measuring 0.3\u0026ndash;1.0 mm is jointing silica sand from Sand system, s.r.o. Wienerberger s.r.o., Czech Republic, sample 1.4\u0026ndash;2.0 mm is silica sand from Sand system, s.r.o. Czech Republic and River silica stone is a material supplied by Den Braven Czech and Slovak a.s. Czech Republic. All materials are natural, washed (free of dust particles) and sorted into the required fraction.\u003c/p\u003e\u003cp\u003eWhere available, meta information on bulk materials from technical data sheets or supplier\u0026rsquo;s specifications in Data citation 1.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e"},{"header":"Experimental Procedure","content":"\u003cp\u003eConventional Measurements\u003c/p\u003e\n\u003cp\u003eThe principle of tool movement over bulk material and calibration of the validation device is described in [14]. The device used for the experiment consists of a supporting structure (I) made of aluminium profiles and clear polycarbonate sheets. Furthermore, from the adjustable bottom (II), linear guide (III), and drive (IV). The linear guide is consists of a carriage (V), a TR40x7 treaded rod (VI) and guide rods (VII). A welded assembly (VIII) is attached to the carriage (V) using a detachable joint, to which a flanged carriage with ball bearing guide HGW 45HC Z0H (IX) is attached on the same way. A HIWIN HGR45T-0364 (X) T-type rail is inserted through the ball guide groove. A bracket (XII) is attached to the head of the rail (X) by means of an axis (XI), on which a weight (XIII) is placed. A membrane force sensor (MEG30, 500 N) (XIV) is located at the foot of the rail (X), on which the slide/tool (XV) is mounted. The normal load on bulk material was determined using 0\u0026ndash;4 weights (XIII) (each weight weighing 4.4 kg). Furthermore, a digital sliding gauge (XVI) is attached to the device, and the values can be read from the panel (XVII) located on the carriage. The drive (IV) is started using a Sinamics G110 AIN frequency converter (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe direction of the movement of the tool is determined by the axis of the TR40x7 threaded rod. The normal force was created by the weight of the loading mechanism with tool (4.52 kg) and the weight (XIII) (4.4 kg).\u003c/p\u003e\n\u003cp\u003eThe speed of the tool was achieved using an electric motor and converting the rotary motion into linear motion using a TR40x7 threaded rod. Using a Sinamics G110 AIN frequency converter, three transport speeds were set for frequencies of 5 Hz, 10 Hz, 15 Hz (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConversion of frequency to angular velocity on a threaded rod and linear guide speed\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFrequency on the converter\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 Hz\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10 Hz\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15 Hz\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage angular velocity on the threaded rod [rpm]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436,64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage linear guide speed [m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe speed of the tool moving across the loose material was then recalculated using an angular velocity, which was measured using a Voltcraft DT-30LK laser tachometer and the pitch of the TR40x7 threaded rod.\u003c/p\u003e\n\u003cp\u003eThe geometry of the tools was based on the shape of the ski (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). A 3D model of the tools is added to the data package in STL format (STereoLithography a.k.a. Standard Tessellation Language) so that their dimensions can be read (Skis without active elements.stl, Skis with active elements.stl).\u003c/p\u003e\n\u003cp\u003eThree-dimensional Scans\u003c/p\u003e\n\u003cp\u003e3D scanning of the resulting furrow was performer using 3D Sense\u003csup\u003eTM\u003c/sup\u003e2 scanner from 3DSYSTEMS. The measurement is shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The lab technician held the 3D scanner in his hand and placed it above the scanner geometry so that the scanner\u0026rsquo;s imaging sensor was always in perpendicular to the scanned surface at a high of 0.3\u0026ndash;0.4 m from the scanned surface. The 3D scan was created by moving 3D scanner over the scanned surface along the x-axis across the entire length of the resulting groove (shown by the blue arrows in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe 3D model of the surface was automatically recorded by 3D Sense System software. For accurate scanning, the program settings were adjusted to the maximum possible geometry resolution (1 mm grid size) and the maximum possible scanning area with the option of saving the texture of the scanned surface at 30 fps. For accurate geometry network matching during scanning, Sense Track Assist should also be enabled.\u003c/p\u003e\n\u003cp\u003eThe raw data was then automatically saved to an OBJ (Object File Format) file. These raw scans may also contain parts of the experimental setup, such as a frame or wall, that were captured along with the scanned surface during scanning.\u003c/p\u003e\n\u003cp\u003eThe reconstructions of the scanned areas were than saved in STL, PLY (Stanford Polygon File Format) and VRML (Virtual Reality Modeling Language) formats. The reconstruction was carried out for the purpose of standardizing data and converting it into formats suitable for application. This step made it possible to maintain geometric accuracy, optimize the data structure, and ensure compatibility with subsequent analytical and visualization tools.\u003c/p\u003e\n\u003cp\u003eExperimental plan\u003c/p\u003e\n\u003cp\u003eThe experimental design included four factors: bulk material type (three levels), tool used (two levels), tool movement speed (three levels), and normal load (five levels). The experiments were conducted according to a full factorial experimental design with each bulk material for two tools, three movement speeds, and five normal loads. All combinations were repeated five times, ( 3materials \u0026times; 2 tools \u0026times; 3 speeds \u0026times; 5 loads \u0026times; 5 repetitions)\u0026thinsp;=\u0026thinsp;450 experiments.\u003c/p\u003e\n\u003cp\u003eUnfortunately, when using Skis with active elements on river silica stone 2\u0026ndash;6 mm, there were large dynamic shocks, jamming and damage of the tool. Therefore, we had to remove this part from the results (75 experiments).\u003c/p\u003e\n\u003cp\u003eA total of 375 (450\u0026ndash;75) were conducted.\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eThe files provided were created, stored, and reconstructed using 3D System Sence version 3.0.213.\u003c/p\u003e\n\u003ch3\u003eData records\u003c/h3\u003e\n\u003cp\u003eThe data records were made available at XXXXXXX (Data citation 1)\u003c/p\u003e\n\u003ch3\u003eMeta information\u003c/h3\u003e\n\u003cp\u003eThe file \u0026bdquo;Additional_bulk_solids_info.pdf\u0026ldquo; contains meta information relating to the bulk materials under investigation. The relevant information was obtained from suppliers and technical data sheets, where available.\u003c/p\u003e\n\u003cp\u003eThe file \u0026bdquo;Sense2_Specifications.pdf\u0026ldquo; contains meta information about the SenseTM2 3D scanner from 3DSYSTEMS.\u003c/p\u003e\n\u003cp\u003e3D scanning\u003c/p\u003e\n\u003cp\u003eThe data from the three-dimensional scanning of the furrow is stored in three separate ZIP files, one for each bulk material. Each archive has same folder structure, as shown in Fig. 5. The first level contains a maximum of two folders, one for each tool used. On the second level, there is one folder for each tool feed rate. For example, \u0026bdquo;5 Hz\u0026ldquo;, where this designation is taken as a parameter set on the frequency converter. The conversion of frequency to movement speed is shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. At the third level, there is one folder for each number of weights used to generate normal force. For example, \u0026bdquo;0\u0026ldquo; for a tool pass without weights, or \u0026bdquo;4\u0026ldquo; for a tool pass with four weights, which deduce a normal force on the bulk material. Each of these folders contains four subfolders named \u0026bdquo;OBJ_raw\u0026ldquo;, \u0026bdquo;PLY_reconstruction\u0026ldquo;, \u0026bdquo;STL_reconstruction\u0026ldquo;, and \u0026bdquo;VRML_reconstruction\u0026ldquo;. The folder \u0026bdquo;OBJ_raw\u0026ldquo; contains the original data that was created during 3D scanning using the 3D System Sence version 3.0.213 program. The folders \u0026bdquo;PLY_reconstruction\u0026ldquo;, \u0026bdquo;STL_reconstruction\u0026ldquo;, and \u0026bdquo;VRML_reconstruction\u0026ldquo; contains reconstructed data that was reconstructed using 3D System Sence version 3.0.213. It should be noted that all folders contain spatial scan data of the furrow with the surface texture of the material inserted. The second level of folders contains the geometry of the given tool in STL format (\u0026bdquo;Skis with active elements.stl\u0026ldquo; / \u0026bdquo;Skis without active elements.stl\u0026ldquo;). STL meshes were saved in binary format for each measurement.\u003c/p\u003e\n\u003ch3\u003eTechnical validation\u003c/h3\u003e\n\u003cp\u003e3D scanning\u003c/p\u003e\n\u003cp\u003eThe accuracy of surface scanning using the Sense\u003csup\u003eTM\u003c/sup\u003e2 3D scanner from 3DSYSTEMS has been examined in several studies [15, 16]. Scanning errors may occur due to several factors, such as the distance of the scanned object from the 3D scanner, the condition of the surface, the geometric complexity of the object, the lighting of the object, and the scanning angle. Based on the above studies, the distance used for scanning these objects was determined to be 0.7\u0026ndash;0.8 mm [17]. Tests and comparisons of scanned surfaces confirmed these values.\u003c/p\u003e\n\u003ch3\u003eNotes on use\u003c/h3\u003e\n\u003cp\u003eIt may be useful to convert STL networks from binary encoding to ASCII. Some programs may not support binary STL files.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData citation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. XXXXXXXXXXXXXXXXXXXX\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by the Operational Program Just Transition and within the project „Waste as an alternative source of energy“, reg. nr. CZ.02.01.01/00/23_021/0008590 under the Programme Johannes Amos Comenius.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions by authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.Z. designed and planned the experiments. A.D. and D.G. performed and supervised the experiments. L.J. prepared and edited the experimental results according to instructions. M.Z. created the 3D wav data files. R.P. and V.S. created the 3D reconstructed data files. M.Z. compiled the manuscript. O.K. and J.T. revised the manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional informations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflict of interest: The authors declare that they have no conflict of interest\u003c/p\u003e"},{"header":"References","content":" \u003col\u003e\n\u003cli\u003eSalmivaara, A., Miettinen, M., Fin\u0026eacute;r, L., Launiainen, S., Korpunen, H., Tuominen, S., Heikkonen, J., Nevalainen, P., Sir\u0026eacute;n, M., Ala-Ilom\u0026auml;ki, J. \u0026amp; Uusitalo, J. Wheel rut measurements by forest machine-mounted LiDAR sensors \u0026ndash; accuracy and potential for operational applications. \u003cem\u003eInt. J. For. 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Terramech.\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 1\u0026ndash;11 (2017). https://doi.org/10.1016/j.jterra.2017.06.001\u003c/li\u003e\n\u003cli\u003eHaas, J., Ellh\u0026ouml;ft, K. H., Schack-Kirchner, H., \u0026amp; Lang, F. Using photogrammetry to assess rutting caused by a forwarder\u0026mdash;A comparison of different tires and bogie tracks. \u003cem\u003eSoil \u0026amp; Tillage Res.\u003c/em\u003e \u003cstrong\u003e163\u003c/strong\u003e, 14\u0026ndash;20 (2016). https://doi.org/10.1016/j.still.2016.04.008\u003c/li\u003e\n\u003cli\u003eFederolf, P., Roos, M., Lu\u0026uml;thi, A., \u0026amp; Dual, J. Finite element simulation of the ski\u0026ndash;snow interaction of an alpine ski in a carved turn. \u003cem\u003eSports Eng.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 123\u0026ndash;133 (2010). https://doi.org/10.1007/s12283-010-0038-z\u003c/li\u003e\n\u003cli\u003eAgarwal, S., Karsai, A., Goldman, D. I., \u0026amp; Kamrin, K. Surprising simplicity in the modeling of dynamic granular intrusion. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, eabe0631 (2021). https://doi.org/10.1126/sciadv.abe0631\u003c/li\u003e\n\u003cli\u003eAikins, K. A., Jensen, T. A., Antille, D. L., Barr, J. B., Ucgul, M., \u0026amp; Desbiolles, J. M. A. Evaluation of bentleg and straight narrow point openers in cohesive soil. \u003cem\u003eSoil Tillage Res.\u003c/em\u003e \u003cstrong\u003e208\u003c/strong\u003e, 105004 (2021). https://doi.org/10.1016/j.still.2021.105004\u003c/li\u003e\n\u003cli\u003eFoldager, F. F., Pedersen, J. M., Skov, E. H., Evgrafova, A., \u0026amp; Green, O. LiDAR-Based 3D Scans of Soil Surfaces and Furrows in Two Soil Types. Sensors 19, 661 (2019). https://doi.org/10.3390/s19030661\u003c/li\u003e\n\u003cli\u003eAikins, K. A., Barr, J. B., Jensen, T. A., \u0026amp; Antille, D. L. Measuring soil surface and furrow profiles using a portable and affordable 3D scanner. ASABE Paper No. 1900038 (2019). https://doi.org/10.13031/aim.201900038\u003c/li\u003e\n\u003cli\u003eAikins, K. A., Barr, J. B., Antille, D. L., Ucgul, M., Jensen, T. A., \u0026amp; Desbiolles, J. M. A. Analysis of effect of bentleg opener geometry on performance in cohesive soil using the discrete element method. Biosyst. Eng. 208, 105004 (2021). https://doi.org/10.1016/j.biosystemseng.2021.06.007\u003c/li\u003e\n\u003cli\u003eMakange, N. R., Ji, C., Nyalala, I., Sunusi, I. I., \u0026amp; Opiyo, S. Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin. Sci. Rep. 11, 11082 (2021). https://doi.org/10.1038/s41598-021-90682-w\u003c/li\u003e\n\u003cli\u003eAgarwal, S., Senatore, C., Zhang, T., Kingsbury, M., Iagnemma, K., Goldman, D. I., \u0026amp; Kamrin, K. Modeling of the interaction of rigid wheels with dry granular media. J. 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AI EDAM 38, e7 (2024). https://doi.org/10.1017/S0890060424000064\u003c/li\u003e\n\u003cli\u003eFan, Y., Xu, X. \u0026amp; Wang, M. A surface‑based spatial registration method based on Sense three‑dimensional scanner. J. Craniofac. Surg. 28(1), 157\u0026ndash;160 (2017). https://doi.org/10.1097/SCS.0000000000003283\u003c/li\u003e\n \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"a5f284ca-e59a-4ffe-aada-b8c2c4eb4ecd","identifier":"10.13039/501100001823","name":"Ministerstvo Školství, Mládeže a Tělovýchovy","awardNumber":"CZ.02.01.01/00/23_021/0008590","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8287044/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8287044/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comprehensive spatial dataset capturing the three-dimensional (3D) morphology of furrows created by rigid tools moving through granular materials under controlled normal loading conditions. The data cover three types of granular materials: two fractions of silica sand (0.3–1.0 mm and 1.4–2.0 mm), and river silica stone (2–6 mm). The experimental setup consisted of five levels of normal load, three transport speeds, and two types of tools, resulting in a total of 375 measurements. The 3D scans were collected using a high-resolution 3D scanner, providing detailed surface topography data. This dataset enables the derivation of key parameters, such as furrow length and width, tool penetration angle, and material subsidence. The collected data facilitate the quantitative assessment of the impact of grain size and applied load on the formation and structure of the furrow. Additionally, the dataset supports applications in terrain mechanics, discrete element modelling, and various fields, including mobility, geotechnics, waste management, and material handling. This resource aims to aid in the calibration of numerical models and offer a reproducible reference for future research in ground interaction analysis.\u003c/p\u003e","manuscriptTitle":"Three-dimensional scans of furrows in bulk materials: spatial data set","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 16:06:08","doi":"10.21203/rs.3.rs-8287044/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28894ff8-3bd2-4679-9524-421f8f7d8978","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T16:06:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 16:06:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8287044","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8287044","identity":"rs-8287044","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}