An in-situ curing construction quality intelligent monitoring evaluation and compensation construction decision method and system

Abstract

The application discloses an in-situ curing construction quality intelligent monitoring evaluation and compensation construction decision method and system, relates to the technical fields of in-situ curing construction intelligent monitoring, construction quality evaluation, compensation construction decision and engineering quality tracing, and comprises the following steps: according to the construction area boundary, the design processing depth, the design block size and the design curing agent mixing amount, the curing area to be cured is divided into multiple three-dimensional grid units; construction process data in the in-situ curing construction process is collected; according to the real-time three-dimensional position of the mixing head and the effective action radius of the mixing head, the construction quality evaluation index of each three-dimensional grid unit is calculated; the construction quality evaluation index is compared with a preset threshold value or an evaluation model, and regions, such as insufficient stirring and insufficient spraying, are identified; compensation construction parameters are generated for abnormal three-dimensional grid units; a construction quality distribution map, an abnormal region distribution map, a compensation construction map and a quality tracing report are generated. Construction process record data is converted into traceable quality evaluation results.

Description

Technical Field

[0001] This invention relates to the fields of intelligent monitoring of in-situ curing construction, construction quality assessment, compensation construction decision-making, and engineering quality traceability, and particularly to a method and system for intelligent monitoring, assessment, and compensation construction decision-making of in-situ curing construction quality. Background Technology

[0002] In-situ solidification is an important construction method in the treatment of marine silt, reclaimed soil, contaminated soft soil, and weak foundations. The basic idea is to mix the solidifying agent with the soil in situ using a mixing head, a solidifying agent pumping system, and construction machinery, forming a solidified body with certain strength, stability, and durability. This technology can reduce the transportation of soil, shorten the treatment cycle, and is applicable to various engineering scenarios such as marine engineering, ports, land reclamation, contaminated soil remediation, and road foundation treatment.

[0003] Existing technologies can already control and record the in-situ curing process to a certain extent. For example, the three-dimensional position of the mixing head can be monitored in real time using GNSS or Beidou positioning systems, mixing head attitude sensors, boom attitude sensors, and vehicle attitude sensors; construction progress, equipment status, and early warning information can be displayed through a vehicle-mounted display and control terminal; and the supply of curing agent, mixing head speed, and mixing time can be recorded through a mud pump or slurry preparation system. These technologies mainly solve the problem of "how to locate, control, and record the construction process."

[0004] Meanwhile, existing online mixing devices for dredged fill and solidifier can add solidifier at the dredged fill pipe head or during transportation, improving the efficiency of rapid mixing of solidifier and dredged fill and reducing the risk of blockage; existing marine sludge solidifier technology focuses on solving problems such as solidifier formulation, erosion resistance and material mechanical properties.

[0005] However, in actual construction, simply recording the location, speed, duration, and flow rate of the mixing head is insufficient to directly determine whether the curing quality of each soil element within the construction area is up to standard. Several shortcomings remain at the construction site, including a lack of unit-level mapping between construction process data and final curing quality; significant differences in curing effects across different areas; difficulty in accurately reflecting the actual dosage of curing agent in each three-dimensional spatial unit; a lack of data allocation mechanisms when the mixing head covers multiple areas; difficulty in timely warning of localized under-mixing or missed areas; a limited number of post-construction sampling points; and a lack of complete construction quality traceability records.

[0006] Therefore, it is necessary to propose an intelligent monitoring, evaluation, and compensation construction decision-making method and system for in-situ solidification construction quality, which will transform construction process data into quantifiable, locatable, and traceable quality evaluation results, and further generate compensation construction parameters and sampling verification schemes. Summary of the Invention

[0007] This invention provides a method and system for intelligent monitoring, evaluation, and compensation-based construction decision-making for in-situ curing construction quality. The technical problem this invention aims to solve is: 1. To address the problem of insufficient spatial unit-level mapping between construction process data and final curing quality, making it difficult to determine whether each location has received sufficient curing agent dosage and mixing energy; 2. To address the issue that statistics on the supply of hardener based on blocks or time periods cannot accurately reflect the dosage per unit volume of the 3D mesh unit, leading to localized underspraying, overspraying, or uneven distribution; 3. To address the problem of difficulty in rationally allocating construction data according to spatial relationships when the construction trajectory of the mixing head is complex and its effective radius covers multiple areas simultaneously; 4. The problems of local under-mixing, missed construction, repeated construction, and insufficient mixing energy mainly rely on manual experience or post-construction sampling inspection, making it difficult to provide real-time or near-real-time early warning. 5. Address the issues of insufficient coverage in post-event sampling and testing, difficulty in guiding re-stirring and re-spraying and re-sampling, and lack of digital quality traceability records.

[0008] To address the aforementioned technical problems, this invention proposes an intelligent monitoring, evaluation, and compensation construction decision-making method and system for in-situ solidification construction quality. This method is based on a three-dimensional grid of the construction area, using the three-dimensional position and effective radius of the mixing head as the core mapping criteria. Real-time collected construction process data is distributed to three-dimensional grid cells according to spatial weights. At the cell-level scale, construction quality evaluation indicators are calculated, abnormal areas are identified, and compensation construction decisions are output. This method is applicable to quality monitoring, construction quality evaluation, compensation construction decisions, and project quality traceability during in-situ solidification construction of marine silt, reclaimed soil, contaminated soft soil, and weak foundations.

[0009] According to one aspect of this disclosure, an intelligent monitoring, evaluation, and compensation construction decision-making method for in-situ curing construction quality is provided, characterized in that, S1: the area to be cured is divided into multiple three-dimensional grid units according to the construction area boundary, design treatment depth, design block size, and design curing agent dosage, and the design treatment volume, target curing agent dosage, and target mixing parameters are bound to each three-dimensional grid unit; S2: Real-time acquisition of construction process data during in-situ curing construction; S3: Based on the real-time three-dimensional position of the mixing head and the effective radius of the mixing head, determine one or more three-dimensional mesh units covered by the range of action of the mixing head at the current moment, and map the construction process data to the corresponding three-dimensional mesh units according to the spatial action weight; S4: Calculate the construction quality evaluation index for each three-dimensional mesh unit; S5: Compare the construction quality evaluation indicators with preset thresholds or evaluation models to identify areas with insufficient mixing, insufficient spraying, missed construction, overspraying, uneven mixing, and areas requiring review. S6: Generates compensation construction parameters for abnormal 3D mesh elements; S7: Generate a construction quality distribution map, anomaly area distribution map, compensation construction drawing, and quality traceability report based on the quality score of each 3D mesh unit, anomaly identification results, and compensation construction parameters.

[0010] In one possible implementation, the three-dimensional mesh elements in step S1 are cuboids, cubes, columnar bodies, irregular bodies, voxel meshes, or adaptive meshes formed according to the formation boundaries. Each 3D mesh cell should include at least the cell number, planar coordinate range, depth range, designed processing volume, designed curing agent dosage, designed mixing time, and designed mixing coverage times.

[0011] In one possible implementation, the construction process data in step S2 is obtained by one or more of the following: a positioning system, an attitude sensor, a flow meter, a pressure sensor, a speed sensor, a current sensor, a slurry density meter, a moisture content sensor, a vehicle-mounted display and control terminal, or a construction control system. The construction process data also includes one or more of the following: soil moisture content, electrical conductivity, salinity, soil type, grout density, or grout viscosity. The construction process data includes at least the three-dimensional position of the mixing head, mixing depth, mixing head rotation speed, mixing head vertical movement speed, mixing time, curing agent pumping flow rate, cumulative curing agent pumping volume, pumping pressure, and equipment operating status.

[0012] In one possible implementation, the spatial action weight in step S3 is a distance weight, action area weight, volume overlap weight, time weight, velocity weight, or a multi-factor composite weight. When the stirring head's effective range covers multiple three-dimensional grid cells simultaneously, the current amount of curing agent pumped, stirring duration, stirring head rotation speed, pumping pressure, or stirring energy is allocated to each three-dimensional grid cell according to the spatial action weight.

[0013] In one possible implementation, the curing agent content per unit volume of the i-th three-dimensional mesh cell in step S4 is determined based on the cumulative amount of curing agent mapped to that mesh cell and the processing volume of that mesh cell. The effective mixing time is determined based on the cumulative effect weight of the grid cell at each sampling moment and the sampling time interval; the mixing energy index is determined based on the mixing head rotation speed, effective mixing time, up-and-down movement speed, mixing depth, and processing volume; the comprehensive construction quality score is determined based on the curing agent dosage per unit volume, effective mixing time, mixing coverage, mixing energy index, and curing agent distribution deviation. The construction quality evaluation indicators include at least the curing agent dosage per unit volume, effective mixing time, mixing coverage, number of repeated mixing cycles, mixing energy index, curing agent distribution deviation, and comprehensive construction quality score.

[0014] In one possible implementation, the anomaly identification in step S5 includes: marking a case of underspray when the curing agent dosage per unit volume is below the target range; When the effective mixing time or mixing energy index is below the threshold, it is marked as under-mixing; when there is no effective construction trajectory coverage or the coverage rate is zero, it is marked as missed construction. When the amount of curing agent per unit volume exceeds the design range, it is marked as overspraying; when the difference in dosage, energy or quality score between adjacent grid cells exceeds the threshold, it is marked as uneven mixing or requires verification.

[0015] In one possible implementation, the amount of additional curing agent used in step S6 is determined based on the difference between the target unit volume curing agent dosage, the grid cell volume, and the accumulated mapped curing agent dosage. The remixing time is determined based on the difference between the target mixing energy index and the actual mixing energy index; for adjacent abnormal three-dimensional mesh cells, they are merged into a compensation construction area and a continuous remixing path is generated; The compensation construction parameters include the mixing area, mixing path, mixing depth, mixing time, flow rate of the applied curing agent, amount of applied curing agent, compensation construction sequence, and recommended sampling and testing points.

[0016] A smart monitoring, evaluation, and compensation construction decision-making system for in-situ curing construction quality, employing the method described herein, the system comprising: The system includes a data acquisition module, a 3D mesh modeling module, a data mapping module, a quality index calculation module, an anomaly identification module, a compensation construction decision-making module, and a visualization and traceability module. The data acquisition module is used to collect construction process data. The 3D mesh modeling module is used to create a 3D mesh model of the construction area; The data mapping module is used to map construction process data to three-dimensional mesh cells according to the working range and spatial weight of the mixing head. The quality index calculation module is used to calculate construction quality evaluation indicators; The anomaly identification module is used to identify abnormal areas; The compensation construction decision module is used to generate compensation construction parameters; The visualization and traceability module is used to generate quality distribution maps, abnormal area maps, compensation construction drawings, and quality traceability reports.

[0017] In one possible implementation, the system is communicatively connected to in-situ curing construction equipment, vehicle-mounted display and control terminal, construction control system, remote server or mobile terminal, and outputs quality status labels, mixing paths, spraying dosage, recommended sampling points and quality traceability reports to vehicle-mounted terminal, control room screen, mobile terminal or cloud management platform.

[0018] In one possible implementation, the system is deployed as a computer program in an in-vehicle display and control terminal, an edge computing terminal, a construction control platform, or a cloud server. When the program is stored in a computer-readable storage medium and executed on a processor, it implements the method described above.

[0019] Compared with the prior art, the beneficial effects of the present invention are: 1. It can transform construction process recorded data into quantifiable and location-based construction quality evaluation results, solving the problem of only being able to record but not evaluate; 2. Spatial quality tracking of the construction area through three-dimensional mesh units can accurately locate abnormal areas, avoiding rough statistics based on large blocks; 3. Data mapping based on the mixing head's effective radius and spatial weights better reflects the actual mixing head's effective range and construction trajectory; 4. By comprehensively considering the curing agent dosage per unit volume, effective mixing time, coverage, mixing energy, and distribution deviation, the quality of curing construction can be evaluated more comprehensively. 5. It can identify areas of under-mixing, under-spraying, missed construction, over-spraying, and uneven mixing in real time or near real time, reducing reliance on manual experience; 6. It can automatically generate re-mixing, re-spraying, and sampling verification plans, forming a closed-loop control from monitoring and evaluation to compensation construction; 7. It can generate construction quality distribution maps and traceability reports, which facilitates project acceptance, subsequent operation and maintenance, and accountability. 8. This invention does not depend on a specific curing agent formulation or a specific equipment model, and is compatible with existing in-situ curing construction equipment, vehicle-mounted display and control terminals, and construction control systems. Attached Figure Description

[0020] Figure 1 This is an overall structural diagram of the in-situ curing construction quality intelligent monitoring, evaluation, and compensation construction decision-making system described in this invention; Figure 2 This is a flowchart of the intelligent monitoring, evaluation, and compensation construction decision-making method for in-situ curing construction quality as described in this invention; Figure 3 This is a schematic diagram of the three-dimensional mesh unit division of the construction area as described in this invention; Figure 4 This is a schematic diagram of the construction data mapping described in this invention; Figure 5 This is a schematic diagram of the abnormal area identification and compensation construction decision-making described in this invention. Detailed Implementation

[0021] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0022] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0023] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0024] This invention adopts a layered architecture of "data acquisition layer - data processing layer - quality assessment layer - decision output layer".

[0025] The data acquisition layer is used to collect information such as the three-dimensional position of the mixing head, mixing depth, mixing head rotation speed, mixing head vertical movement speed, mixing time, curing agent pump flow rate, cumulative curing agent pump volume, pumping pressure, construction timestamp, and equipment operating status; The data processing layer is used to establish a three-dimensional mesh model of the construction area and to map the construction process data to each three-dimensional mesh cell according to the spatial position of the mixing head, the effective radius of action, and the action weight. The quality assessment layer is used to calculate the construction quality indicators of each three-dimensional mesh unit and generate quality status labels; The decision output layer is used to generate agitation path, agitation depth, agitation time, curing agent flow rate, curing agent dosage, suggested sampling points, and quality traceability report for abnormal mesh cells.

[0026] In terms of three-dimensional meshing of the construction area, the area to be cured is divided into multiple three-dimensional mesh units based on the construction area boundary, design treatment depth, design block size, and design curing agent dosage. The three-dimensional mesh units can be cuboids, cubes, columns, or irregular shapes formed according to the stratum boundary. Each three-dimensional mesh unit includes at least the unit number, planar coordinate range, depth range, design treatment volume, design curing agent dosage, design mixing time, and design mixing coverage times.

[0027] Regarding data acquisition during construction, multi-source data is collected in real time during the in-situ curing process. This construction process data includes the three-dimensional position of the mixing head, its posture and mixing depth, rotational speed and direction, vertical movement speed and residence time, curing agent pump flow rate and cumulative pumping volume, pumping pressure or pipeline pressure, construction timestamp, construction block number, and operation log. Optionally, parameters such as soil moisture content, electrical conductivity, salinity, soil type, slurry density, and slurry viscosity can also be collected.

[0028] Regarding the matching of construction data with 3D mesh cells, the system determines one or more 3D mesh cells that the mixing head is acting on at the current moment based on the real-time 3D position of the mixing head and its effective radius of action. If the mixing head's range of action covers only one mesh cell, the curing agent pumping volume, mixing time, and mixing energy at that moment are included in that mesh cell. If the mixing head's range of action covers multiple mesh cells simultaneously, the construction process data is allocated to each mesh cell according to distance weight, area weight, volume overlap weight, or a combination of weights.

[0029] For example, at a certain moment, the position of the stirring head is The effective radius of action is R, and the current pumped curing dose is... The time increment is The system first determines The spatial overlap relationship with each 3D mesh element is then calculated, and the weight w_i of each element is then determined. , Parameters such as rotational speed n and pressure p are assigned to the corresponding three-dimensional mesh elements according to their weights.

[0030] In calculating construction quality indicators, the following parameters are calculated for each three-dimensional grid cell: curing agent dosage per unit volume, effective mixing time, mixing coverage, number of repeated mixing cycles, mixing uniformity, mixing energy index, curing agent distribution deviation, and comprehensive construction quality score. For example, the curing agent dosage per unit volume can be expressed as: (1) Where, q i M represents the curing agent content per unit volume of the i-th 3D mesh element. iV represents the cumulative amount of curing agent mapped to this grid cell. i This represents the processing volume of the grid cell.

[0031] The effective stirring time can be expressed as: (2) Among them, T i w represents the effective mixing time for the i-th grid cell. i This represents the influence weight of the grid cell at the current moment. This represents the data sampling time interval.

[0032] Stirring Energy Index E i It can be calculated by weighting the stirring head rotation speed, stirring duration, vertical movement speed, stirring depth, and working volume. For example, it can be expressed as: (3) Where, n i T represents the average stirring head rotation speed corresponding to the grid cell. i For effective mixing time, v i Let d be the vertical movement speed. i V represents the stirring depth. i To handle volume; the function f can be determined based on engineering experience, equipment parameters, or on-site calibration results.

[0033] Overall construction quality score Q i The curing agent dosage per unit volume, effective stirring time, stirring coverage, stirring energy index, and curing agent distribution deviation can be comprehensively determined. For example, the following comprehensive scoring function can be used: (4) Among them, C i For mixing coverage, D i The distribution deviation of the curing agent is represented by F, which is a normalization function or a comprehensive scoring function. F can be further expanded into a weighted scoring model, a rule-based threshold model, a fuzzy evaluation model, or a machine learning model. The above formula is used to illustrate the evaluation logic of this invention and does not limit the specific function form used in actual engineering. In practical applications, the weights and thresholds can be determined according to the type of curing agent, soil properties, treatment depth, and acceptance criteria.

[0034] For 3D mesh elements that are substandard or require re-verification, the amount of curing agent to be applied can be determined based on the difference between the target dosage and the actual cumulative dosage. For example, for the i-th mesh element, the amount of curing agent to be applied can be expressed as: (5) Where, q targetV represents the target curing agent dosage per unit volume. i M is the volume of the mesh cell. i This represents the accumulated mapped curing dose. The remixing time can be determined based on the difference between the target mixing energy index and the actual mixing energy index. For multiple adjacent abnormal grid cells, the system can merge them into a compensation construction area and generate a continuous remixing path.

[0035] In terms of intelligent identification of abnormal areas, the actual construction quality indicators of each three-dimensional grid unit are compared with preset thresholds, and quality status labels are generated. Quality status labels may include qualified, under-mixed, under-sprayed, missed construction, over-sprayed, unevenly mixed, requiring verification, and requiring compensation construction.

[0036] Table 1. Comparison Table of Quality Status, Judgment Criteria, and Handling Recommendations , Example 1: System Overall Architecture and Data Acquisition like Figure 1 As shown, the system in this embodiment includes a data acquisition layer, a data processing layer, a quality assessment layer, and a decision output layer. The data acquisition layer is used to collect information such as the three-dimensional position of the mixing head, mixing depth, mixing head rotation speed, mixing head vertical movement speed, mixing time, curing agent pump flow rate, cumulative curing agent pump volume, pumping pressure, construction timestamp, and equipment operating status; optionally, it can also collect parameters such as soil moisture content, electrical conductivity, soil layer type, slurry density, and slurry viscosity.

[0037] The data processing layer is used to establish a 3D mesh model of the construction area and map the collected construction process data to each 3D mesh cell according to the spatial location, effective radius, and weight of the mixing head. The quality assessment layer is used to calculate the construction quality indicators of each 3D mesh cell and generate quality status labels based on preset thresholds or evaluation models. The decision output layer is used to generate supplementary mixing paths, supplementary mixing depths, supplementary mixing times, supplementary curing agent flow rates, supplementary curing agent dosages, suggested sampling points, and quality traceability reports for abnormal mesh cells.

[0038] The system can be connected to a positioning system, attitude sensor, flow meter, pressure sensor, speed sensor, current sensor, slurry density meter, moisture content sensor, vehicle-mounted display and control terminal, or construction control system. This invention does not limit the specific sensor model, as long as it can obtain the aforementioned construction process data.

[0039] Example 2: 3D Meshing and Construction Data Mapping like Figure 2 and Figure 3As shown, when the area to be processed consists of several construction blocks, the system divides each construction block into a three-dimensional grid with a planar dimension of 5m × 5m and a depth of 0.5m or 1m. Each grid cell has a preset target curing agent dosage, target mixing time, and target mixing coverage times. During construction, the system collects the position, depth, rotation speed, flow rate, and pressure of the mixing head in real time. When the mixing head enters the effective range of a certain grid cell, the construction data at that moment is mapped to the corresponding cell and accumulated. After construction is completed, the system calculates the quality score for each cell and generates a quality distribution map.

[0040] like Figure 4 As shown, if the mixing head's effective range covers only one grid cell at a given moment, then the curing agent pumping volume, mixing time, and mixing energy at that moment are included in that grid cell. If the mixing head's effective range covers multiple grid cells simultaneously, then the construction process data are allocated according to distance weight, effective area weight, or volume overlap weight. Composite weight can simultaneously consider distance, overlap area, overlap volume, mixing head speed, and soil layer parameters.

[0041] Example 3: Quality Indicator Calculation and Anomaly Identification In this embodiment, the system calculates the curing agent dosage per unit volume, effective mixing time, mixing coverage, number of repeated mixing cycles, mixing uniformity, mixing energy index, curing agent distribution deviation, and comprehensive construction quality score for each three-dimensional grid cell. If the curing agent dosage per unit volume of a grid cell is lower than the design value, it is marked as undersprayed; if the effective mixing time or mixing energy index is lower than the design threshold, it is marked as undermixed; if there is no mixing trajectory coverage, it is marked as missed construction.

[0042] For areas with significant differences in admixture or energy between adjacent units, the system can mark them as unevenly mixed; for areas with indicators approaching thresholds or abnormal sensor data, the system can mark them as requiring verification. These identification results can be displayed on the construction terminal, the control room screen, or the cloud management platform via quality status labels.

[0043] Example 4: Compensation Construction Decision-Making, Verification Sampling, and Quality Traceability like Figure 5 As shown, for 3D mesh cells that are substandard or require verification, the system automatically generates a compensation construction plan. The compensation construction plan may include the mixing area, mixing path, mixing depth, mixing time, curing agent flow rate, curing agent dosage, compensation construction sequence, and suggested sampling and testing points.

[0044] For multiple adjacent abnormal grid cells, the system can merge them into a compensation construction area and generate a continuous mixing path to reduce the number of equipment movements and improve the efficiency of remedial construction. For areas with quality scores close to the threshold, abnormal sensor data, or significant differences between adjacent cells, the system automatically generates suggested sampling and testing points. Sampling points are preferentially placed at the boundaries of under-mixing, under-spraying, or leaking construction, or at the center of the grid cell with the lowest quality score, thereby improving the representativeness of the detection.

[0045] Based on the construction quality score and anomaly identification results of each 3D grid cell, the system generates a construction quality distribution map, an anomaly area distribution map, compensation construction drawings, and a quality traceability report. The quality traceability report may include construction area information, construction parameters for each block, quality scores for each 3D grid cell, a list of anomaly areas, a remedial mixing and spraying plan, suggested sampling points, and construction quality acceptance recommendations. This report can serve as digital data for construction process quality control, project acceptance, subsequent operation and maintenance, and accountability.

[0046] Optional alternative implementation methods 1. Three-dimensional mesh cells can be replaced by two-dimensional blocks with depth layering, or irregular meshes, triangular meshes, voxel meshes, or adaptive meshes based on formation boundaries can be used; 2. Construction data mapping weights can be based on distance weights, area weights, volume overlap weights, time weights, speed weights, or multi-factor composite weights; 3. The mixing energy index can be the product of rotational speed and mixing time, or further parameters such as torque, current, pressure, vertical movement speed, mixing head diameter, and soil resistance can be introduced; 4. The overall quality score can be achieved using rule-based thresholding, weighted scoring, fuzzy evaluation, analytic hierarchy process, machine learning models, or expert rule models; 5. Compensation construction plans can be automatically generated by the system, or the system can provide suggestions which will then be manually confirmed and implemented. 6. The data acquisition module can be directly connected to the existing construction control system, or it can collect data through independent sensors, edge computing terminals, or remote servers; 7. The visualization results can be displayed on the vehicle terminal, the control room screen, the mobile terminal, or the cloud management platform; 8. This invention can be applied to various construction objects such as marine silt, dredged fill, contaminated soft soil, pre-mixed solidified fluid soil, underwater solidified fluid soil, and ordinary soft foundation treatment.

[0047] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for intelligent monitoring, evaluation, and compensatory construction decision-making for in-situ curing construction quality, characterized in that, S1: Based on the construction area boundary, design treatment depth, design block size and design curing agent dosage, the area to be cured is divided into multiple three-dimensional grid units, and the design treatment volume, target curing agent dosage and target mixing parameters are bound to each three-dimensional grid unit. S2: Real-time acquisition of construction process data during in-situ curing construction; S3: Based on the real-time three-dimensional position of the mixing head and the effective radius of the mixing head, determine one or more three-dimensional mesh units covered by the range of action of the mixing head at the current moment, and map the construction process data to the corresponding three-dimensional mesh units according to the spatial action weight; S4: Calculate the construction quality evaluation index for each three-dimensional mesh unit; S5: Compare the construction quality evaluation indicators with preset thresholds or evaluation models to identify areas with insufficient mixing, insufficient spraying, missed construction, overspraying, uneven mixing, and areas requiring review. S6: Generates compensation construction parameters for abnormal 3D mesh elements; S7: Generate a construction quality distribution map, anomaly area distribution map, compensation construction drawing, and quality traceability report based on the quality score of each 3D mesh unit, anomaly identification results, and compensation construction parameters.

2. The method for intelligent monitoring, evaluation, and compensation-based construction decision-making for in-situ curing construction quality as described in claim 1, characterized in that, The three-dimensional mesh elements in step S1 can be cuboids, cubes, columnar bodies, irregular bodies, voxel meshes, or adaptive meshes formed according to the formation boundaries; Each 3D mesh cell should include at least the cell number, planar coordinate range, depth range, designed processing volume, designed curing agent dosage, designed mixing time, and designed mixing coverage times.

3. The method for intelligent monitoring, evaluation, and compensation-based construction decision-making for in-situ curing construction quality as described in claim 1, characterized in that, The construction process data in step S2 is obtained by one or more of the following: positioning system, attitude sensor, flow meter, pressure sensor, speed sensor, current sensor, slurry density meter, moisture content sensor, vehicle-mounted display and control terminal, or construction control system. The construction process data also includes one or more of the following: soil moisture content, electrical conductivity, salinity, soil type, grout density, or grout viscosity. The construction process data includes at least the three-dimensional position of the mixing head, mixing depth, mixing head rotation speed, mixing head vertical movement speed, mixing time, curing agent pumping flow rate, cumulative curing agent pumping volume, pumping pressure, and equipment operating status.

4. The method for intelligent monitoring, evaluation, and compensation-based construction decision-making for in-situ curing construction quality as described in claim 1, characterized in that, The spatial impact weight in step S3 can be distance weight, impact area weight, volume overlap weight, time weight, velocity weight, or a composite weight of multiple factors. When the stirring head's effective range covers multiple three-dimensional grid cells simultaneously, the current amount of curing agent pumped, stirring duration, stirring head rotation speed, pumping pressure, or stirring energy is allocated to each three-dimensional grid cell according to the spatial action weight.

5. The method for intelligent monitoring, evaluation, and compensation-based construction decision-making for in-situ curing construction quality as described in claim 1, characterized in that, In step S4, the curing agent dosage per unit volume of the i-th three-dimensional mesh cell is determined based on the cumulative amount of curing agent mapped to that mesh cell and the processing volume of that mesh cell. The effective mixing time is determined based on the cumulative effect weight of the grid cell at each sampling moment and the sampling time interval; the mixing energy index is determined based on the mixing head rotation speed, effective mixing time, up-and-down movement speed, mixing depth, and processing volume; the comprehensive construction quality score is determined based on the curing agent dosage per unit volume, effective mixing time, mixing coverage, mixing energy index, and curing agent distribution deviation. The construction quality evaluation indicators include at least the curing agent dosage per unit volume, effective mixing time, mixing coverage, number of repeated mixing cycles, mixing energy index, curing agent distribution deviation, and comprehensive construction quality score.

6. The method for intelligent monitoring, evaluation, and compensation-based construction decision-making for in-situ curing construction quality as described in claim 1, characterized in that, The anomaly identification in step S5 includes: marking a case of underspray when the amount of curing agent per unit volume is lower than the target range; When the effective mixing time or mixing energy index is below the threshold, it is marked as under-mixing; when there is no effective construction trajectory coverage or the coverage rate is zero, it is marked as missed construction. When the amount of curing agent per unit volume exceeds the design range, it is marked as overspraying; when the difference in dosage, energy or quality score between adjacent grid cells exceeds the threshold, it is marked as uneven mixing or requires verification.

7. The method for intelligent monitoring, evaluation, and compensation-based construction decision-making for in-situ curing construction quality as described in claim 1, characterized in that, The amount of curing agent used in step S6 is determined based on the difference between the target unit volume curing agent dosage, the grid cell volume, and the accumulated mapped curing agent dosage. The remixing time is determined based on the difference between the target mixing energy index and the actual mixing energy index; for adjacent abnormal three-dimensional mesh cells, they are merged into a compensation construction area and a continuous remixing path is generated; The compensation construction parameters include the mixing area, mixing path, mixing depth, mixing time, flow rate of the applied curing agent, amount of applied curing agent, compensation construction sequence, and recommended sampling and testing points.

8. An intelligent monitoring, evaluation, and compensation construction decision-making system for in-situ curing construction quality, employing the method described in any one of claims 1 to 7, characterized in that, The system includes: The system includes a data acquisition module, a 3D mesh modeling module, a data mapping module, a quality index calculation module, an anomaly identification module, a compensation construction decision-making module, and a visualization and traceability module. The data acquisition module is used to collect construction process data. The 3D mesh modeling module is used to create a 3D mesh model of the construction area; The data mapping module is used to map construction process data to three-dimensional mesh cells according to the working range and spatial weight of the mixing head. The quality index calculation module is used to calculate construction quality evaluation indicators; The anomaly identification module is used to identify abnormal areas; The compensation construction decision module is used to generate compensation construction parameters; The visualization and traceability module is used to generate quality distribution maps, abnormal area maps, compensation construction drawings, and quality traceability reports.

9. The intelligent monitoring, evaluation, and compensation construction decision-making system for in-situ curing construction quality as described in claim 8, characterized in that, The system communicates with in-situ curing construction equipment, vehicle-mounted display and control terminal, construction control system, remote server or mobile terminal, and outputs quality status label, mixing path, spraying dosage, suggested sampling points and quality traceability report to vehicle-mounted terminal, control room screen, mobile terminal or cloud management platform.

10. The intelligent monitoring, evaluation, and compensation construction decision-making system for in-situ curing construction quality as described in claim 8 or 9, characterized in that, The system is deployed as a computer program on an in-vehicle display and control terminal, an edge computing terminal, a construction control platform, or a cloud server. When the program is stored in a computer-readable storage medium and executed on a processor, it implements the method described in any one of claims 1 to 7.

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