As-built rebar modeling server and method using mobile lidar point density analysis

Abstract

This invention provides a server and method for modeling actual building reinforcement using point density analysis based on mobile LiDAR. The server includes at least one processor, and the at least one processor includes a memory storing instructions for performing operations. The operations include: receiving 3D point cloud data acquired by scanning the structure using a LiDAR sensor; preprocessing the received 3D point cloud by removing unwanted regions, adjusting resolution, and removing noise based on a predetermined color range and normal vector; calculating the density distribution of the preprocessed 3D point cloud along each x, y, and z axis, and identifying peak points corresponding to the reinforcement locations through Gaussian smoothing and mean-shift clustering; generating a 3D reinforcement model based on the identified reinforcement locations, and providing parameter information distinguishing the upper and lower reinforcement layers and transverse / main reinforcement for each reinforcement; and making the generated 3D reinforcement model compatible with IFC (Integrated Foundation Control).

Description

Technical Field

[0001] This invention relates to a server and method for modeling actual building steel reinforcement using point density analysis based on mobile LiDAR. More specifically, this invention relates to a server and method for modeling actual building steel reinforcement using point density analysis based on mobile LiDAR. By analyzing the point density distribution in a 3D point cloud generated by mobile LiDAR, the location of the steel reinforcement is identified, visualized as a 3D model, and provided to the user. Background Technology

[0002] In the fields of building and structural engineering, reinforcing steel is a key element in enhancing the strength of concrete. Improper placement of reinforcing steel can lead to problems such as reduced load-bearing capacity and cracking. Therefore, accurately detecting and verifying the placement of reinforcing steel is considered a crucial aspect of building quality control.

[0003] Traditional non-destructive testing (NDT) techniques are typically used to verify the location of reinforcing bars. Ground laser scanning (TLS) is a widely used technique that can generate high-resolution 3D point clouds for analyzing the location of reinforcing bars within a structure. However, these methods often require expensive specialized equipment and skilled technicians for measurement and data interpretation, which presents challenges.

[0004] Meanwhile, advancements in LiDAR sensor technology are increasingly simplifying the collection of spatial information at construction sites. LiDAR sensors use lasers to measure the distance and shape of objects and generate 3D point clouds based on this data.

[0005] However, traditional TLS and mobile LiDAR methods differ in resolution and data density, making it difficult to identify intricate internal structures such as reinforcing bars. 3D point clouds acquired using LiDAR sensors contain vast amounts of point cloud data, requiring technologies capable of effectively processing and analyzing this data. Especially in structures with complex reinforcing bar arrangements, point cloud data from the reinforcing bars and background structures (such as concrete and formwork) may be mixed together, necessitating more precise filtering and data analysis algorithms.

[0006] Furthermore, traditional rebar location detection typically involves predictive measurement, data analysis, and results reporting, making it difficult to verify the rebar condition in real time on-site. This means that measurement data must be post-processed, which can add additional time and cost. Therefore, real-time or near real-time analysis technologies are needed to quickly identify the location of rebar on the construction site.

[0007] Meanwhile, with the advancement of digital twin technology, the integration of building automation with BIM (Building Information Modeling) systems, which utilize three-dimensional spatial data, is becoming increasingly important. BIM provides an integrated data model for the design, construction, and maintenance of building projects, enabling the visualization and management of structural elements such as rebar locations as three-dimensional objects. Therefore, the demand for technologies capable of accurately detecting rebar location information and linking it to the BIM model for future use is growing.

[0008] Therefore, it is necessary to study how to use mobile lidar technology to identify the location of rebar and apply it to real-time analysis and BIM modeling.

[0009] Existing technical documents

[0010] Patent documents

[0011] Patent Document 1: Korean Patent No. 10-2739414 Summary of the Invention

[0012] The problem that the invention aims to solve

[0013] The purpose of this invention is to solve the above problems and provide a server and method for modeling actual building reinforcement using point density analysis based on mobile LiDAR.

[0014] Methods for solving problems

[0015] To achieve the above objectives, one aspect of the present invention provides a server and method for modeling actual building reinforcement using point density analysis based on mobile LiDAR.

[0016] A server that models actual building reinforcement using point density analysis based on mobile lidar may include at least one processor and a memory that stores instructions for instructing at least one processor to perform multiple operations.

[0017] The operations include: receiving 3D point cloud data obtained by scanning the structure using a LiDAR sensor; preprocessing the received 3D point cloud by removing unnecessary areas, adjusting resolution, and eliminating noise based on a predetermined color range and normal vector; calculating the density distribution of the preprocessed 3D point cloud on the x, y, and z axes, and identifying peak points corresponding to the rebar locations through Gaussian smoothing and mean-shift clustering; generating a 3D rebar model based on the identified rebar locations, providing parameter information to distinguish between upper and lower layers and transverse / main rebars; and ensuring the generated 3D rebar model is compatible with IFC. This may include exporting to a specific format and integrating with a BIM system.

[0018] The method may include: receiving reinforcement layout information from an external server; receiving modeling accuracy information from an external server; combining the received reinforcement layout information to calculate horizontal and vertical layout index scores and layer-by-layer layout index scores; calculating a reinforcement layout information score based on a weighted sum of the calculated horizontal and vertical layout index scores and layer-by-layer layout index scores; combining the received modeling accuracy information to calculate a modeling accuracy index score and a BIM link index score; calculating a modeling accuracy index score based on a weighted sum of the calculated modeling accuracy index score and BIM link index score; calculating a BIM link optimization index score based on a weighted sum of the calculated reinforcement layout information score and modeling accuracy index score; and sorting the calculated BIM link optimization index scores in descending order to generate a list of excellent models.

[0019] The action of selecting the highest-scoring model from the generated list of good models as the recommended model.

[0020] Invention Effects

[0021] When using the actual building reinforcement modeling server and method of the present invention for point density analysis based on mobile LiDAR as described above, biosignals with high reliability and accuracy can be ensured by overcoming the limitations of measurement based on a single ROI.

[0022] In addition, it can dynamically select and weight biological signals based on real-time ROI, and flexibly respond to environmental changes.

[0023] The effects that can be obtained by the present invention are not limited to those mentioned above. Those skilled in the art will clearly understand other effects not mentioned through the following description. Attached Figure Description

[0024] Other aspects, features, and advantages of the above-described preferred embodiments of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings.

[0025] Figure 1 This is an example diagram illustrating the operating environment of a real building reinforcement modeling server using point density analysis based on mobile LiDAR according to an embodiment of the present invention.

[0026] It should be noted that in all the accompanying drawings, the same reference numerals are used to denote the same or similar elements, features and structures. Detailed Implementation

[0027] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] In describing the embodiments, descriptions of technical content that is well-known in the art and not directly related to the present invention will be omitted. This is done to more clearly convey the spirit of the invention without obscuring it by omitting unnecessary explanations.

[0029] For the same reason, some components in the accompanying drawings are exaggerated, omitted, or shown schematically. Furthermore, the dimensions of each component do not perfectly reflect its actual size. Identical or corresponding components in each drawing use the same reference numerals.

[0030] The advantages and features of the present invention, as well as methods for achieving these advantages and features, will become more apparent from the embodiments described in detail below with reference to the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but can be implemented in various different forms. These embodiments are provided only to ensure the completeness of the disclosure of this invention and to fully inform those skilled in the art of the scope of the invention, which is defined only by the scope of the claims. Throughout the specification, the same reference numerals denote the same elements.

[0031] At this point, it should be understood that each block in the flowchart and combinations thereof can be executed by computer program instructions. These computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, so that the processor of the computer or other programmable data processing apparatus executes these instructions. The instructions generate means for performing the functions described in the flowchart blocks. These computer program instructions can also be stored in a computer-usable or computer-readable memory that can instruct the computer or other programmable data processing apparatus to implement these functions in a particular manner, such that the instructions stored in the computer-usable or computer-readable memory can also generate an article of art containing instruction means for performing the functions described in the flowchart blocks. The computer program instructions can also be mounted on a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable data processing apparatus to produce a computer-implemented flow, such that the instructions executing on the computer or other programmable data processing apparatus can also provide steps for performing the functions described in the flowchart blocks.

[0032] Furthermore, each block can represent a module, code segment, or portion of code, containing one or more executable instructions for performing specified logical functions. It should also be noted that in some alternative implementations, the functions mentioned in a block may be executed out of order. For example, two blocks displayed consecutively may actually execute substantially simultaneously, or sometimes in reverse order, depending on their respective functions.

[0033] The term "unit" refers to a software or hardware component, such as an FPGA (Field-Programmable Gate Array) or ASIC (Application-Specific Integrated Circuit), and a "unit" performs a specific function. However, a "unit" is not limited to software or hardware. A "unit" can be configured in addressable memory and can be configured to act as one or more processors. Thus, for example, a "unit" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within components and "units" can be combined into a smaller number of components and "units," or further broken down into more components and "units." Furthermore, components and "parts" can be implemented to recreate one or more CPUs within a device or secure multimedia card.

[0034] In describing specific embodiments of the present invention, examples of particular systems will be used as the main subject. However, the key points claimed in this specification can be applied to other communication systems and services with similar technical backgrounds without significantly exceeding the scope disclosed in this specification, and this can be determined by those skilled in the art.

[0035] Figure 1 This is an example diagram illustrating the operating environment of a real building reinforcement modeling server using point density analysis based on mobile LiDAR according to an embodiment of the present invention.

[0036] refer to Figure 1 The server (100) (hereinafter referred to as "server (100)") used for actual construction reinforcement modeling using point density analysis based on mobile LiDAR can obtain 3D point cloud data obtained by scanning the structure using LiDAR sensors through the user terminal (10).

[0037] In addition, the server (100) can connect to at least one external server (110) via a wired or wireless network to receive rebar location information and model accuracy information. In this case, the external server (110) can be configured as a cloud server or implemented in a form coupled to the server (100).

[0038] Here, the user terminal (10) is a desktop computer, laptop, notebook, mobile phone, tablet computer, mobile phone, smartwatch, smart glasses, e-book reader, PMP (portable multimedia player), portable game console, navigation device, digital camera, DMB (digital multimedia broadcasting) player, digital recorder, digital audio player, digital video recorder, digital video player, and it may include PDA (personal digital assistant).

[0039] The information on rebar arrangement may include: horizontal point density change rate, vertical point density change rate, standard deviation of rebar spacing based on point density, average rebar spacing error rate, peak detection rate of rebar location, point distribution uniformity index, average noise removal rate, rebar layer-by-layer point density change rate, standard deviation of rebar arrangement height layer by layer, average value of peak distance between rebar layers, rebar detection rate layer by layer, peak symmetry index of rebar cross-section, rebar height distribution entropy, and layer-by-layer point density change rate.

[0040] The modeling accuracy information may include: rebar position coordinate error, rebar cross-sectional shape consistency rate, rebar arrangement direction deviation, average modeling speed, rebar cross-sectional irregularity rate, automatic feature matching success rate, rebar arrangement alignment irregularity coefficient, BIM format conversion speed, IFC file size optimization rate, BIM software compatibility index, rebar attribute data matching success rate, BIM-based rebar arrangement comparison error rate, BIM data update cycle optimization rate, and BIM automatic report generation speed.

[0041] Here, wired and wireless networks can include various communication methods, such as Bluetooth, BLE (Bluetooth Low Energy), Near Field Communication (NFC), WLAN, Zigbee, Infrared Data Association (IrDA), WFD (Wi-Fi Direct), UWB (Ultra-Wideband), ANT+, Wi-Fi, and RFID (Radio Frequency Identification). They are not limited to these.

[0042] On the one hand, when the server (100) receives the rebar arrangement information, the server (100) can classify the rebar arrangement information into at least one category based on at least one attribute.

[0043] Then, the server (100) can calculate the rebar placement information score by crosslinking the rebar placement information categorized by each category.

[0044] When the server (100) receives modeling accuracy information, it can classify the modeling accuracy information into at least one category based on at least one attribute.

[0045] Then, the server (100) can calculate the modeling accuracy information score by combining the modeling accuracy information of each category.

[0046] On one hand, the server (100) can calculate the BIM connection optimization score by combining the calculated rebar layout information score with the modeling accuracy information score.

[0047] The calculated BIM link optimization scores are sorted in descending order to generate a list of excellent models, and the model with the highest score from the generated list of excellent models is determined as the recommended model.

[0048] The server (100) can use the LiDAR (light detection and ranging) sensor built into the mobile device to scan the structure containing steel bars and generate 3D point cloud data based on the scan results.

[0049] For example, a user can use a user terminal (10) to scan a structure containing reinforcing steel. A LiDAR sensor detects infrared laser light reflected from the surface of the structure to measure distance information and thereby generate 3D point cloud data. For example, the LiDAR sensor used may include the LiDAR module built into Apple's iPhone Pro and iPad Pro, or a ToF (Time-of-Flight) based LiDAR sensor provided by Google or Samsung, but is not limited to these; commercial LiDAR sensors or terminals equipped with LiDAR sensors may also be used.

[0050] The data collected by LiDAR sensors can include the three-dimensional coordinates (x, y, z) of each point, color information (RGB), and normal vectors indicating surface slope and orientation. This data can be processed in real time using frameworks such as Apple ARKit and Google ARCore, and can be further analyzed more precisely using software such as CloudCompare, Autodesk ReCap, and FAROSCENE in the post-processing stage.

[0051] Meanwhile, point cloud data is typically stored in PTS (Point Cloud Data), LAS (LASer), or E57 file formats; for example, PTS format can be used. PTS files have structural characteristics that allow them to efficiently store large amounts of LiDAR data, including the 3D coordinates and color information of each point.

[0052] Furthermore, during the scanning process, information on the shape, size, and distance of structures containing reinforcing bars can be collected simultaneously, and preprocessing can be performed based on this information to identify the reinforcing bars. The resolution and scanning distance of the LiDAR sensor (e.g., up to 5 meters for the iPhone Pro) affect the accuracy of the analysis, so optimal scanning conditions can be set accordingly.

[0053] It can determine the quality of data in subsequent analysis processes.

[0054] Therefore, the server (100) can perform real-time quality checks on the point cloud during the scanning process and apply octree-based sampling technology according to the resolution standard set by the user, thereby avoiding unnecessary increases in data volume. For example, by applying octree-based sampling, the spatial resolution of the LiDAR scan data can be maintained uniformly, thereby improving the accuracy of the subsequent rebar identification process.

[0055] After the scan is completed, the acquired 3D point cloud is transmitted to the subsequent analysis module of the present invention for preprocessing and density analysis in order to analyze the location of the reinforcing bars.

[0056] For example, if the entire scan data of a structure contains more than 1,000 points, octree-based sampling can be used to compress the data, removing unnecessary parts and ultimately retaining about 100 points. This can improve the speed of point cloud data processing and reduce the computational burden in subsequent analysis stages.

[0057] An analysis was performed to identify the reinforcing steel.

[0058] Next, the server (100) can automatically identify the arrangement of reinforcing bars in a structure containing reinforcing bars by analyzing the three-dimensional point cloud data acquired using the lidar sensor. Specifically, it can take into account the structural characteristics of multi-layered reinforcing bars and automatically distinguish between upper and lower reinforcing bars.

[0059] For example, in reinforced concrete structures, a double-layer reinforcement method is used to distribute the load, so reinforcement can be arranged in both the upper and lower parts. If the three-dimensional point cloud is simply analyzed without considering this reinforcement arrangement, the upper and lower reinforcements may be mixed up, making it difficult to accurately identify the reinforcement arrangement. Therefore, the server (100) can automatically classify the upper and lower reinforcements by applying density analysis and filtering techniques in the z-axis (height) direction of the point cloud data.

[0060] Furthermore, parameter information can be assigned to analyze the three-dimensional shape and arrangement characteristics of reinforcing bar objects, thereby distinguishing between transverse and main reinforcing bars in a three-dimensional reinforcing bar model. For example, main reinforcing bars are typically arranged longer along the axis of a column or beam, thus exhibiting relatively greater linearity (straight-line length); while transverse reinforcing bars are arranged in a closed curve (loop) structure that surrounds the transverse reinforcing bars and repeats at regular intervals. Therefore, parameters can be assigned by estimating the length vector and curvature of each reinforcing bar object.

[0061] Specifically, after extracting the centerline of the reinforcing bars from the 3D point cloud, the total length of the centerline is calculated, and its curvature distribution is determined to be either a straight line or a curve. In this way, long straight lines can be classified as main reinforcing bars, while closed curves or repetitive ring structures at certain intervals can be classified as transverse reinforcing bars. Furthermore, the direction cosine of the reinforcing bar axis vector, combined with different axial directions, can distinguish between main and transverse reinforcing bars. During modeling and simulation, parameters for main and transverse reinforcing bars are assigned to manage these classified objects separately. This allows for accurate reproduction of complex reinforcing bar arrangements, and when checking the actual arrangement against construction drawings, main and transverse reinforcing bars can be clearly identified.

[0062] At this point, the distribution of points can be analyzed based on the coordinate values ​​of the z-axis in the 3D point cloud data. To this end, the server (100) can apply density analysis technology based on kernel density estimation (KDE) to distinguish the layers where the reinforcement is concentrated by finding areas where the point density increases rapidly within a specific height (z-value) segment.

[0063] For example, if the height of the structure (z-axis range) ranges from 0 mm to 500 mm, performing a KDE analysis can identify a pattern where the point density increases sharply at specific height intervals. Based on this pattern, the first peak can be classified as bottom reinforcement, and the second peak as top reinforcement.

[0064] Furthermore, if the upper and lower reinforcing bars cannot be clearly distinguished, the server (100) can apply mean-shift clustering to search for density centers in the point cloud by height. Mean-shift clustering determines the cluster center values ​​of the upper and lower reinforcing bars by moving the cluster centers in a direction with higher specific data density.

[0065] At this point, filtering techniques utilizing the normal vectors and color information (RGB values) of the point cloud data can be applied to further remove unnecessary data (concrete surfaces, background noise, etc.). For example, reinforcing bars typically have strong reflectivity and normal vector characteristics at specific angles; therefore, a method can be employed based on these characteristics to remove non-reinforcing bar elements.

[0066] For example, the results of classifying the upper and lower reinforcing bars can be output as a 3D model for visual verification in software such as Autodesk ReCap, CloudCompare, and TrimbleRealWorks.

[0067] Meanwhile, the server (100) can automatically calculate the number of horizontal and vertical reinforcing bars through point density analysis in the x-axis and z-axis directions.

[0068] Point density analysis can be performed along the x-axis to estimate the number of horizontal reinforcing bars. For this purpose, the server (100) can apply a sliding window-based histogram analysis technique. Specifically, after counting the number of points along the x-axis in the point cloud data at intervals (e.g., 10 mm or 20 mm intervals), points where the density changes rapidly (peaks) can be searched to determine the location of the horizontal reinforcing bars.

[0069] For example, assuming a structure has a total horizontal length of 2000mm, point density analysis shows peaks at intervals of approximately 150mm on the x-axis, confirming the presence of 13 horizontal reinforcing bars. By analyzing the point density histogram in this way, the number of horizontal reinforcing bars can be automatically calculated based on the recurring pattern at certain intervals.

[0070] Similarly, in estimating the number of vertical reinforcing bars, point density analysis along the z-axis can be performed. For example, KDE technology can be applied to generate a point density function (PDF: probability density function), and peak points can be detected based on this. The server (100) can find locations where the point density increases rapidly at a specific height (z-axis value) and identify the corresponding locations as the positions of vertical reinforcing bars.

[0071] For example, if point cloud data analysis shows peaks at intervals of approximately 200 mm along the z-axis, this can be used to estimate that 10 vertical reinforcing bars have been placed. Furthermore, applying a Gaussian smoothing filter can minimize noise-induced errors, thus providing a more accurate estimate of the number of reinforcing bars.

[0072] Next, the server (100) can export the steel reinforcement quantity information in IFC (Industrial Basic Class) format and link it to the building structure model.

[0073] Specifically, based on the parameters such as the centerline, diameter, and placement angle of the reinforcing bars identified through the density analysis and clustering described above, three-dimensional reinforcing bar objects can be created and exported in an IFC-compatible format. For example, by utilizing the "IfcReinforcingBar" class defined in the IFC4 standard, the reinforcing bar object can be specified in detail, including its shape (Geometry), diameter (NominalDiameter), material (Material), and even its position and orientation (Placement).

[0074] Files exported in this IFC format can be directly imported into BIM software such as Autodesk Revit, Tekla Structures, and Graphisoft ArchiCAD to check for interference with the overall project model or to compare with construction drawings to verify the actual reinforcement status.

[0075] For example, users can use the clash detection function of BIM software to verify whether the rebar model extracted from the scanned site collides with concrete members and other facility elements. Differences between the actual rebar layout and the design drawings can be visualized and reflected in revision notes or process management. Furthermore, data exported in IFC-compatible formats can be linked to a project management system (PMS) or a cloud-based collaboration platform, allowing multiple stakeholders to access and review it simultaneously, thus enabling structural engineers and construction managers to easily and intuitively utilize as-built rebar information.

[0076] The method according to the invention can be implemented in the form of program commands, which can be executed by various computer devices and recorded on a computer-readable medium. The computer-readable medium may contain program instructions, data files, and data structures, which may be contained individually or in combination. The program instructions recorded on the computer-readable medium may be specifically designed and configured for this invention, or may be known and available to those skilled in the art of computer software.

[0077] Examples of computer-readable media may include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions may include machine language code (e.g., code generated by a compiler) and high-level language code that can be executed by a computer using an interpreter. The aforementioned hardware device may be configured to operate together with at least one software module to perform the operations of the present invention, and vice versa.

[0078] Furthermore, the above-described methods or apparatus can be implemented individually by combining all or part of their configurations or functions.

[0079] The present invention has been described above with reference to preferred embodiments; however, those skilled in the art should understand that various modifications and variations can be made to the present invention without departing from the spirit and scope of the invention as set forth in the following claims.

Claims

1. A server for modeling realistic building reinforcement using point density analysis based on mobile lidar, characterized in that, include: At least one processor; and A memory-store instruction that instructs at least one processor to perform multiple operations. The above operations include: Actions that receive 3D point cloud data obtained by scanning a structure using a LiDAR sensor; The preprocessing operations for the received 3D point cloud include removing unnecessary regions based on a predetermined color range and normal vector, adjusting the resolution, and removing noise; The process involves calculating the density distribution of the preprocessed 3D point cloud along the x, y, and z axes, and identifying the peak points corresponding to the rebar locations using Gaussian smoothing and mean-shift clustering. Based on the identified rebar locations, a 3D rebar model is created, and operations are provided to distinguish the upper and lower rebar layers and transverse / main rebars of each rebar. The generated 3D steel reinforcement model is compatible with IFC, and can be exported in a format that allows for linking with BIM systems.

Images