An underwater mold bag concrete construction quality real-time monitoring method

Abstract

The present application belongs to the technical field of hydraulic engineering, and particularly relates to a real-time monitoring method for construction quality of underwater mold bag concrete, comprising the following steps: S1, real-time construction data acquisition; S2, point cloud three-dimensional model reconstruction; S3, quality instant analysis and evaluation; S4, dynamic visual feedback. The present application can accurately monitor the thickness and flatness of underwater mold bag concrete construction on the basis of real-time calculation, and can visually display, solve the problem of lack of real-time, non-contact and full-coverage quality monitoring means in the underwater concealed construction environment, provide auxiliary decision for construction, effectively improve the construction quality of underwater mold bag concrete, and has a good market application prospect.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy engineering technology, specifically relating to a method for real-time monitoring of the construction quality of underwater formwork concrete. Background Technology

[0002] Underwater geotextile concrete construction technology is widely used in water conservancy projects such as waterway slope protection and dam reinforcement. Its construction quality (thickness uniformity, surface smoothness) directly affects the structure's erosion resistance and service life. In recent years, this field has shown two major development trends: ① Innovation in monitoring methods, shifting from traditional handheld measurements by divers (low efficiency and high risk) to acoustic / optical non-contact detection; ② Digital upgrading, with BIM models gradually replacing two-dimensional drawings, but real-time feedback on dynamic construction quality remains a gap in the industry.

[0003] Currently, there is limited research in China on quality monitoring methods for underwater geotextile concrete construction, with most studies neglecting quality control and monitoring techniques. Existing quality monitoring methods for underwater geotextile concrete construction mainly include: diver exploration, diver-held sonar measurement, shipborne single-beam sonar scanning, and post-construction core sampling.

[0004] Diver probing involves divers carrying probes to explore in real time, but this method is extremely inefficient, has limited coverage, and is highly susceptible to human error. Handheld sonar measurement using a single-beam sonar is also inefficient, taking over 2 minutes per square meter for a single point measurement, and cannot provide full coverage. It only acquires thickness at discrete points, cannot reconstruct surface curvature, and lacks accurate flatness data. Furthermore, the prolonged operation poses significant safety risks for divers.

[0005] Shipborne single-beam sonar scanning generates thickness profiles by using a single-beam sonar mounted on a construction vessel. However, the resolution is insufficient, resulting in a high rate of missed detections. It is also affected by hull swaying and lacks GNSS pose compensation, leading to high thickness errors. Furthermore, it lacks a digital model, making it impossible to dynamically predict and correct deviations, and the output two-dimensional profile cannot be compared with BIM design.

[0006] Post-construction core sampling, a common industry practice, involves drilling and core sampling after hardening to measure thickness. However, this method can damage structural integrity. Furthermore, the testing methods are severely outdated and cannot provide real-time guidance for construction. By the time the concrete is tested, it has already solidified and cannot be repaired. Additionally, the sampling bias is large, resulting in poor representativeness.

[0007] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to provide a real-time monitoring method for the construction quality of underwater geotextile concrete. Based on real-time calculation, the method accurately monitors the thickness and flatness of underwater geotextile concrete and displays the results visually. This solves the problem of lacking real-time, non-contact, and comprehensive quality monitoring methods in underwater concealed construction environments, provides auxiliary decision-making for construction, and effectively improves the construction quality of underwater geotextile concrete.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for real-time monitoring of the construction quality of underwater geotextile concrete includes the following steps:

[0011] S1. Real-time construction data acquisition;

[0012] S2. Point cloud 3D model reconstruction;

[0013] S3. Real-time quality analysis and evaluation;

[0014] S4, Dynamic Visual Feedback.

[0015] As a preferred method, real-time construction data collection is performed in S1, specifically including the following steps:

[0016] S11. Construct a detachable, floating construction platform above the underwater formwork construction area;

[0017] S12. A multi-beam sonar system is installed obliquely on the support at the edge of the construction platform to form a fan-shaped scanning area. Each sound wave emission contains multiple beams arranged at the same interval.

[0018] S13. After receiving the echo signal, the sonar system performs acoustic imaging processing to generate a two-dimensional image; the sonar system continuously updates the data and outputs the coordinates of the sonar center based on GNSS positioning.

[0019] S14. Install pressure sensors on the underwater molded bag body to transmit the filling pressure value in real time.

[0020] Preferably, point cloud 3D model reconstruction is performed in S2. The original point cloud data collected by multibeam sonar is reconstructed into an underwater manhole surface model in a global coordinate system through spatiotemporal synchronization and coordinate transformation. Specifically, this includes the following steps:

[0021] S21, Point cloud spatiotemporal synchronization;

[0022] The local coordinate system point cloud acquired by the sonar at time t is as follows:

[0023] ;

[0024] Obtain the global pose matrix of the sonar center using the GNSS system:

[0025] ;

[0026] In the formula, Let be a rotation matrix. These are roll, pitch, and yaw angles, respectively. It is a translation vector;

[0027] S22, Global coordinate transformation of point cloud;

[0028] Map the local point cloud to the global coordinate system:

[0029] ;

[0030] Expanded to:

[0031] ;

[0032] S23, Dynamic noise filtering;

[0033] We use pressure sensor data to construct an adaptive filtering function and define a pressure threshold:

[0034] ;

[0035] In the formula, For the density of water, For design thickness;

[0036] Eliminating suspended noise points:

[0037] ;

[0038] In the formula, For riverbed elevation, This corresponds to the grid pressure value; The concrete filling coefficient;

[0039] S24, Poisson surface reconstruction;

[0040] Filtered point cloud Constructing implicit functions :

[0041] ;

[0042] In the formula, The point cloud normal vector field is locally fitted using PCA. For regularization weights;

[0043] Solving the Poisson equation Obtain the isosurface Extracting triangular mesh .

[0044] As a preferred method, in S3, real-time quality analysis and evaluation are performed. Through spatial geometric calculations and differential geometric analysis, a precise quantitative assessment of the thickness and flatness of the underwater formwork concrete is achieved. Specifically, this includes the following steps:

[0045] S31. Thickness deviation analysis;

[0046] The reconstructed surface model is a triangular mesh:

[0047] ;

[0048] In the formula, V is the vertex set, E is the edge set, and F is the patch set. The design BIM model is parametrically represented as an implicit surface:

[0049] ;

[0050] The thickness deviation calculation process includes: nearest point search, normal distance calculation, and thickness deviation field construction; among which,

[0051] The most recent search results are as follows:

[0052] For each vertex V i Solve for the projection point q i :

[0053] ;

[0054] In the formula, V i For surface model M surface The vertex coordinates of S; BIM To design parametric surfaces for BIM models, q i To design the distance V on the curved surface i The nearest point;

[0055] The calculation of the normal distance is as follows:

[0056] In q i Calculate the unit normal vector n of the design surface. i :

[0057] ;

[0058] Vertex V i The thickness deviation is:

[0059] ;

[0060] In the formula, d design This represents the design thickness of the concrete in the formwork bag; a positive value indicates that it is too thick, and a negative value indicates that it is too thin.

[0061] The thickness deviation field is constructed as follows:

[0062] Define the thickness deviation matrix:

[0063] ;

[0064] Determination of areas exceeding standards:

[0065] ;

[0066] S32. Flatness analysis;

[0067] Surface curvature distribution is evaluated based on discrete differential geometry, including: local surface fitting, curvature tensor calculation, principal curvature extraction, and flatness exceeding limits determination; among which...

[0068] The local surface fitting is as follows:

[0069] For vertex V i Choose its k-neighbor set:

[0070] ;

[0071] Fitting a quadratic surface:

[0072] ;

[0073] parameter vector Solve using least squares:

[0074] ;

[0075] in:

[0076] ;

[0077] The curvature tensor is calculated as follows:

[0078] In the local coordinate system, the curvature tensor The matrix form is as follows:

[0079] ;

[0080] The first fundamental form coefficient is:

[0081] ;

[0082] The second fundamental form coefficient is:

[0083] ;

[0084] The principal curvature extraction is as follows:

[0085] The principal curvature is the curvature tensor The eigenvalues ​​represent the maximum / minimum curvature of the surface in two orthogonal directions, and their characteristic equations are:

[0086] ;

[0087] The analytical solution is:

[0088] ;

[0089] In the formula, K1 represents the maximum degree of bending, and K2 represents the minimum degree of bending;

[0090] The flatness quantification index is root mean square curvature:

[0091] ;

[0092] K RMS The larger the value, the more pronounced the surface undulations.

[0093] The specific criteria for determining flatness exceeding the standard are as follows:

[0094] ;

[0095] Wherein, threshold K th Derived from the design slope α:

[0096] ;

[0097] In the formula, To design the slope gradient vector;

[0098] S33. Construct a global quality assessment matrix:

[0099] ;

[0100] In the formula, the weight W i Determined by the importance of the location;

[0101] ;

[0102] V critical For key structural points (such as joints), σ represents the range of control weight decay.

[0103] Quality compliance rate calculation:

[0104] .

[0105] As a preferred approach, dynamic visualization feedback is performed in S4, transforming the quality analysis results into a multi-dimensional dynamic visualization interface to achieve real-time spatial location and decision support for construction defects. This specifically includes the following steps:

[0106] Map the out-of-specification areas to the view: insufficient thickness is displayed as a red cube; flatness exceeding the tolerance is displayed as yellow ripples.

[0107] Mass heatmap based on thickness deviation Smoothness K RMS Dynamic color rendering; dynamic rendering of quality heatmaps defines the linear relationship between RGB channels and quality parameters as follows:

[0108] ;

[0109] Wherein, the mixing coefficient is:

[0110] ;

[0111] .

[0112] Compared with the prior art, the present invention has the following beneficial effects:

[0113] (1) The underwater formwork concrete construction quality real-time monitoring method of the present invention adopts fully digital monitoring and pioneers the underwater point cloud-BIM model real-time comparison technology to visualize the hidden works.

[0114] (2) The underwater formwork concrete construction quality real-time monitoring method of the present invention integrates multi-source data, including sonar point cloud (surface morphology), pressure sensing (internal filling degree) and GNSS (spatial positioning) cross-validation, which greatly improves the monitoring accuracy.

[0115] (3) The underwater formwork concrete construction quality real-time monitoring method of the present invention realizes AI quantitative evaluation, identifies flatness abnormalities based on curvature algorithm (sensitivity up to ±2cm), and can automatically output the coordinates of the substandard area.

[0116] (4) The underwater formwork concrete construction quality real-time monitoring method of the present invention can provide immediate feedback during construction, guide the adjustment of pumping parameters in real time, avoid rework afterward, and greatly improve construction quality and efficiency. Attached Figure Description

[0117] Figure 1 This is a flowchart of the method of the present invention;

[0118] Figure 2 This is a schematic diagram of the equipment installation site;

[0119] Figure 3 This is a schematic diagram of the on-site sonar images;

[0120] Figure 4 This is a schematic diagram of the on-site data results. Detailed Implementation

[0121] The technical solution of this invention patent will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0122] In the description of this invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention.

[0123] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0124] See attached document Figure 1 A method for real-time monitoring of the construction quality of underwater geotextile concrete includes the following steps:

[0125] Step 1: Real-time construction data collection;

[0126] Real-time construction data is collected by measuring instruments and sensors. A detachable, floating construction platform is built above the underwater formwork construction area. A multi-beam sonar system is installed obliquely on the support at the edge of the platform to emit sound wave signals at 375KHz, forming a 50°x50° fan-shaped scanning area. Each sound wave emission contains 128x128 beams arranged at the same interval, with each sound wave spacing of 0.39°.

[0127] After receiving the echo signal, the sonar system performs acoustic imaging processing to generate a two-dimensional image (frame); the system updates the data at a rate of 20Hz and outputs the coordinates of the sonar center based on GNSS positioning; pressure sensors are arranged on the bag body in a 5m×5m grid to transmit the filling pressure value in real time.

[0128] Step 2: Reconstruction of the 3D point cloud model;

[0129] The raw point cloud data acquired by multibeam sonar is reconstructed into an underwater manhole surface model in a global coordinate system through spatiotemporal synchronization and coordinate transformation. The specific process is as follows:

[0130] ① Spatiotemporal synchronization of point clouds;

[0131] The local coordinate system point cloud acquired by the sonar at time t is as follows:

[0132] ;

[0133] Obtain the global pose matrix of the sonar center using the GNSS system:

[0134] ;

[0135] In the formula, Let be a rotation matrix. These are roll, pitch, and yaw angles, respectively. This is the translation vector (RTK positioning coordinates);

[0136] ② Global coordinate transformation of point cloud;

[0137] Map the local point cloud to the global coordinate system:

[0138] ;

[0139] Expanded to:

[0140] ;

[0141] ③ Dynamic noise filtering;

[0142] We use pressure sensor data to construct an adaptive filtering function and define a pressure threshold:

[0143] ;

[0144] In the formula, For the density of water, For design thickness;

[0145] Eliminating suspended noise points:

[0146] ;

[0147] In the formula, For riverbed elevation, This corresponds to the grid pressure value; The concrete filling coefficient ranges from 0.8 to 1.2.

[0148] ④ Reconstruction of Poisson surface;

[0149] Filtered point cloud Constructing implicit functions :

[0150] ;

[0151] In the formula, The point cloud normal vector field is locally fitted using PCA. This is the regularization weight, ranging from 0.001 to 0.01;

[0152] Solving the Poisson equation Obtain the isosurface Extracting triangular mesh .

[0153] Step 3: Real-time quality analysis and evaluation;

[0154] Through spatial geometric calculations and differential geometric analysis, a precise quantitative assessment of the thickness and flatness of underwater geotextile concrete is achieved. The core algorithm architecture is as follows:

[0155] ①Thickness deviation analysis;

[0156] The reconstructed surface model is a triangular mesh:

[0157] ;

[0158] In the formula, V is the vertex set, E is the edge set, and F is the patch set. The design BIM model is parametrically represented as an implicit surface:

[0159] ;

[0160] The process for calculating thickness deviation includes:

[0161] (1) Nearest point search:

[0162] For each vertex V i Solve for the projection point q i :

[0163] ;

[0164] In the formula, V i For surface model M surface The vertex coordinates, i=1,2,...,N; S BIM To design parametric surfaces for BIM models, q i To design the distance V on the curved surface i The nearest point;

[0165] (2) Calculation of normal distance:

[0166] In q i Calculate the unit normal vector n of the design surface. i :

[0167] ;

[0168] Vertex V i The thickness deviation is:

[0169] ;

[0170] In the formula, d design This represents the design thickness of the concrete in the formwork bag (constant, unit: m), with a positive value indicating over-thickness and a negative value indicating under-thickness;

[0171] (3) Construction of thickness deviation field:

[0172] Define the thickness deviation matrix:

[0173] ;

[0174] Determination of areas exceeding standards:

[0175] ;

[0176] ② Flatness analysis;

[0177] Surface curvature distribution is evaluated based on discrete differential geometry, including:

[0178] (1) Local surface fitting:

[0179] For vertex V i Choose its k-neighbor set:

[0180] ;

[0181] Fitting a quadratic surface:

[0182] ;

[0183] parameter vector Solve using least squares:

[0184] ;

[0185] in:

[0186] ;

[0187] (2) Calculation of curvature tensor:

[0188] In the local coordinate system, the curvature tensor The matrix form is as follows:

[0189] ;

[0190] The first fundamental form coefficient is:

[0191] ;

[0192] The second fundamental form coefficient is:

[0193] ;

[0194] (3) Principal curvature extraction:

[0195] The principal curvature is the curvature tensor The eigenvalues ​​represent the maximum / minimum curvature of the surface in two orthogonal directions, and their characteristic equations are:

[0196] ;

[0197] The analytical solution is:

[0198] ;

[0199] In the formula, K1 represents the maximum degree of bending, and K2 represents the minimum degree of bending;

[0200] The flatness quantification index is root mean square curvature:

[0201] ;

[0202] K RMS The larger the value, the more pronounced the surface undulations.

[0203] (4) Judgment of flatness exceeding the standard:

[0204] ;

[0205] Wherein, threshold K th Derived from the design slope α:

[0206] ;

[0207] In the formula, To design the slope gradient vector;

[0208] ③ Construct a global quality assessment matrix:

[0209] ;

[0210] In the formula, the weight W i Determined by the importance of the location;

[0211] ;

[0212] V critical For key structural points (such as joints), σ represents the range of control weight decay.

[0213] Quality compliance rate calculation:

[0214] .

[0215] Step 4: Dynamic visual feedback;

[0216] The quality analysis results are transformed into a multi-dimensional dynamic visualization interface to achieve real-time spatial positioning and decision support for construction defects; the excess areas are mapped to the view: insufficient thickness is displayed as a red cube; flatness deviation is displayed as a yellow ripple.

[0217] Mass heatmap based on thickness deviation Smoothness K RMS Dynamically rendered colors provide visual warnings for areas exceeding standards. This visualization module is implemented using secondary development of mature commercial software (such as Unity). The dynamic rendering of the quality heatmap defines the linear relationship between the RGB channels and quality parameters as follows:

[0218] ;

[0219] Wherein, the mixing coefficient is:

[0220] ;

[0221] .

[0222] In this embodiment, the equipment installation site is as shown in Appendix Figure 2 First, the installation location, installation method, equipment layout, and protection methods of the equipment are set. It is determined that an iron frame for fixed support will be welded on the lower right deck, and the transducer will be fixed to the iron frame by connecting it with steel pipes. Inertial navigation and differential antenna tubes will be installed on the deck platform, sensors will be arranged in the bag, and the power supply unit and computer host will be placed in the engine room for operation.

[0223] After all equipment units are installed and connected, communication and the sonar system should be tested. Communication testing should verify the absence of latency and data packet loss. After system parameters are configured, the sonar image should be adjusted to be sufficiently clear and complete, as shown in the attached image. Figure 3 As shown.

[0224] Real-time monitoring was conducted on the concrete formwork under construction, observing changes in thickness and flatness. The on-site data results are attached. Figure 4 By adjusting the pumping parameters, the bag-flushing speed can be controlled, and divers can be effectively guided in underwater auxiliary operations.

[0225] The underwater formwork concrete construction quality real-time monitoring method of this invention is the first of its kind to adopt the "sonar point cloud → Poisson reconstruction → BIM comparison" technology chain, which quantifies the two quality parameters (thickness and flatness) in real time, realizes the visualization of hidden works, can solve the problem of underwater invisibility, is very practical, and has good market application prospects.

[0226] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for real-time monitoring of the construction quality of underwater geotextile concrete, characterized in that, Includes the following steps: S1. Real-time construction data acquisition; S2. Point cloud 3D model reconstruction; S3. Real-time quality analysis and evaluation; S4, Dynamic Visual Feedback; In S3, real-time quality analysis and evaluation are performed. Through spatial geometric calculations and differential geometric analysis, accurate quantitative evaluation of the thickness and flatness of the underwater formwork concrete is achieved. Specifically, the following steps are included: S31. Thickness deviation analysis; The reconstructed surface model is a triangular mesh: ； In the formula, V is the vertex set, E is the edge set, and F is the patch set. The design BIM model is parametrically represented as an implicit surface: ； The thickness deviation calculation process includes: nearest point search, normal distance calculation, and thickness deviation field construction; among which, The most recent search results are as follows: For each vertex V i Solve for the projection point q i : ； In the formula, V i For surface model M surface The vertex coordinates of S; BIM To design parametric surfaces for BIM models, q i To design the distance V on the curved surface i The nearest point; The calculation of the normal distance is as follows: In q i Calculate the unit normal vector n of the designed surface. i : ； Vertex V i The thickness deviation is: ； In the formula, ddesign is the design thickness of the formwork concrete, with a positive value indicating over-thickness and a negative value indicating under-thickness; The thickness deviation field is constructed as follows: Define the thickness deviation matrix: ； Determination of areas exceeding standards: ； S32. Flatness analysis; Surface curvature distribution is evaluated based on discrete differential geometry, including: local surface fitting, curvature tensor calculation, principal curvature extraction, and flatness exceeding limits determination; among which... The local surface fitting is as follows: For vertex V i Choose its k-neighbor set: ； Fitting a quadratic surface: ； parameter vector Solve using least squares: ； in: ； The curvature tensor is calculated as follows: In the local coordinate system, the curvature tensor The matrix form is as follows: ； The first fundamental form coefficient is: ； The second fundamental form coefficient is: ； The principal curvature extraction is as follows: The principal curvature is the curvature tensor The eigenvalues ​​represent the maximum / minimum curvature of the surface in two orthogonal directions, and their characteristic equations are: ； The analytical solution is: ； In the formula, K1 represents the maximum degree of bending, and K2 represents the minimum degree of bending; The flatness quantification index is root mean square curvature: ； K RMS The larger the value, the more pronounced the surface undulations. The specific criteria for determining flatness exceeding the standard are as follows: ； Wherein, threshold K th Derived from the design slope α: ； In the formula, To design the slope gradient vector; S33. Construct a global quality assessment matrix: ； In the formula, the weight W i Determined by the importance of the location; ； V critical For key structural points, σ ​​represents the range of control weight decay. Quality compliance rate calculation: 。 2. The method for real-time monitoring of underwater formwork concrete construction quality according to claim 1, characterized in that, Real-time construction data collection is performed in S1, specifically including the following steps: S11. Construct a detachable, floating construction platform above the underwater formwork construction area; S12. A multi-beam sonar system is installed obliquely on the support at the edge of the construction platform to form a fan-shaped scanning area. Each sound wave emission contains multiple beams arranged at the same interval. S13. After receiving the echo signal, the sonar system performs acoustic imaging processing to generate a two-dimensional image; the sonar system continuously updates the data and outputs the coordinates of the sonar center based on GNSS positioning. S14. Install pressure sensors on the underwater molded bag body to transmit the filling pressure value in real time.

3. The method for real-time monitoring of underwater formwork concrete construction quality according to claim 1, characterized in that, In S2, point cloud 3D model reconstruction is performed. The original point cloud data collected by multibeam sonar is reconstructed into an underwater manhole surface model in a global coordinate system through spatiotemporal synchronization and coordinate transformation. The specific steps include: S21, Point cloud spatiotemporal synchronization; The local coordinate system point cloud acquired by the sonar at time t is as follows: ； Obtain the global pose matrix of the sonar center using the GNSS system: ； In the formula, Let be a rotation matrix. These are roll, pitch, and yaw angles, respectively. It is a translation vector; S22, Global coordinate transformation of point cloud; Map the local point cloud to the global coordinate system: ； Expanded to: ； S23, Dynamic noise filtering; We use pressure sensor data to construct an adaptive filtering function and define a pressure threshold: ； In the formula, For the density of water, For design thickness; Eliminating suspended noise points: ； In the formula, For riverbed elevation, This corresponds to the grid pressure value; The concrete filling coefficient; S24, Poisson surface reconstruction; Filtered point cloud Constructing implicit functions : ； In the formula, The point cloud normal vector field is locally fitted using PCA. For regularization weights; Solving the Poisson equation Obtain the isosurface Extracting triangular mesh .

4. The method for real-time monitoring of underwater formwork concrete construction quality according to claim 1, characterized in that, In S4, dynamic visualization feedback is used to transform the quality analysis results into a multi-dimensional dynamic visualization interface, enabling real-time spatial location and decision support for construction defects. Specifically, this includes the following steps: Map the out-of-specification areas to the view: insufficient thickness is displayed as a red cube; flatness exceeding the tolerance is displayed as yellow ripples. Mass heatmap based on thickness deviation Peace and flatness K RMS Dynamic color rendering; dynamic rendering of quality heatmaps defines the linear relationship between RGB channels and quality parameters as follows: ； Wherein, the mixing coefficient is: ； 。

Images