A construction control method and system for a low tower cable-stayed bridge based on artificial intelligence

Abstract

The present invention relates to the field of construction of low-pylon cable-stayed bridges, and specifically provides a construction control method and system for low-pylon cable-stayed bridges based on artificial intelligence, including: obtaining construction indicators of the low-pylon cable-stayed bridge; wherein, engineering technical indicators and three-dimensional layout indicators; according to the three-dimensional layout indicators, performing geometric modeling of the low-pylon cable-stayed bridge; through geometric modeling, dividing the key construction periods of the low-pylon cable-stayed bridge and determining the mechanical models of each key construction period; through the mechanical models and engineering technical indicators, building a depth supervision model of the low-pylon cable-stayed bridge; through the depth supervision model, supervising the construction process and judging whether to optimize the construction process. The invention can realize real-time monitoring and automatic evaluation of the construction process through AI technology, quickly identify problems and optimize them. Before and during the construction, it is necessary to predict the future state of the structure to evaluate risks.

Description

Technical Field

[0001] The present invention relates to the field of cable-stayed bridge construction, and particularly to a construction control method and system for a low tower cable-stayed bridge based on artificial intelligence. Background Art

[0002] At present, the low tower cable-stayed bridge is a novel bridge type between the continuous beam bridge and the cable-stayed bridge. Its shape is similar to that of a cable-stayed bridge, and the main beam deck is similar to that of a continuous beam bridge, with the characteristics of a low tower, a rigid beam, and concentrated cables. Compared with the continuous beam bridge, the low tower cable-stayed bridge has the advantages of novel structure, large spanning ability, simple construction, and economy; compared with the cable-stayed bridge, it has the advantages of convenient construction, material saving, and large main beam stiffness, making the low tower cable-stayed bridge have broad development space.

[0003] In terms of construction supervision, traditional construction monitoring technologies rely on sensors to collect data, artificial analysis of data, and expert systems to provide decision support. In construction control, a large amount of computing resources are required, and the results depend on the accuracy of input parameters. Moreover, it mainly relies on digital technologies of building information models, through image recognition and natural language processing, but it cannot achieve the supervision of the mechanical conditions of the low tower cable-stayed bridge. Summary of the Invention

[0004] The present invention provides a construction control method and system for a low tower cable-stayed bridge based on artificial intelligence, which can solve the problems that traditional construction monitoring technologies rely on sensors to collect data, artificial analysis of data, and expert systems to provide decision support. In construction control, a large amount of computing resources are required, and the results depend on the accuracy of input parameters. Moreover, it mainly relies on digital technologies of building information models, through image recognition and natural language processing, but it cannot achieve the supervision of the mechanical conditions of the low tower cable-stayed bridge.

[0005] In the first aspect, the present invention proposes a construction control method for a low tower cable-stayed bridge based on artificial intelligence, including:

[0006] Obtaining the construction indicators of the low tower cable-stayed bridge; wherein, the construction indicators include: engineering technical indicators and three-dimensional layout indicators;

[0007] According to the three-dimensional layout indicators, geometric modeling of the low tower cable-stayed bridge is carried out;

[0008] Through geometric modeling, the key construction periods of the low tower cable-stayed bridge are divided, and the mechanical model of each key construction period is determined;

[0009] Based on the mechanical model and engineering technical indicators, a depth supervision model of the low tower cable-stayed bridge is built;

[0010] Through the depth supervision model, the construction process is supervised to determine whether construction process optimization is required.

[0011] Combined with the first aspect, the acquisition of the construction indicators of the low tower cable-stayed bridge includes:

[0012] Obtain the project index data of the low tower cable-stayed bridge and input it into the preset process large model to determine the construction process data and the three-dimensional layout schematic diagram;

[0013] According to the construction process data, conduct cost accounting, determine the engineering process technology that meets the cost requirements, and set engineering technical indicators;

[0014] According to the three-dimensional layout schematic diagram, conduct engineering quantity accounting and environmental adaptability calculation to determine the three-dimensional layout indicators that meet the environmental requirements.

[0015] Combined with the first aspect, the geometric modeling of the low tower cable-stayed bridge according to the three-dimensional layout indicators includes:

[0016] Import the three-dimensional layout indicators into the modeling software to obtain the real-scene three-dimensional model of the low tower cable-stayed bridge;

[0017] According to the real-scene three-dimensional model, conduct geometric splitting to determine the geometric structure of the low tower cable-stayed bridge; among them, the geometric structure includes: bridge tower, main beam, stay cables, bridge piers, foundation, bearings, railings, drainage channels;

[0018] Through the geometric structure, conduct geometric modeling of the low tower cable-stayed bridge.

[0019] Combined with the first aspect, the division of the key construction periods of the low tower cable-stayed bridge through geometric modeling includes:

[0020] Associate the geometric model with the engineering technical indicators to determine the construction process of the low tower cable-stayed bridge; among them, the construction process includes beam segment prestressing construction, hanging basket assembly, cantilever casting beam segment construction, side span closure segment construction, mid-span closure segment construction and external prestressing construction;

[0021] According to the construction process, set the quality indicators and inspection measures for each construction period;

[0022] Through the inspection measures, conduct process empowerment for each construction period to determine the key construction indicators;

[0023] And through the key construction indicators, determine the key construction periods.

[0024] Combined with the first aspect, the inspection measures include deformation monitoring and asynchronous hanging basket overturning monitoring in the entire construction process;

[0025] Deformation monitoring is carried out by presetting linear test reference points at the bottom template of the hanging basket structure and conducting inductance tests. The inductance test data includes: measurement time, measuring point temperature (temperature type), absolute displacement value, relative displacement value, zero point value;

[0026] The asynchronous hanging basket overturning monitoring performs multi-directional tests by pre-setting inclinometers on the hanging basket device, and an alarm angle for the hanging basket overturning is set in the inclinometer.

[0027] Combined with the first aspect, the determination of the mechanical model for each key construction period includes:

[0028] According to the construction structure and mechanical indexes during the key construction period, establish sub-mechanical equations for the corresponding construction structure;

[0029] According to the sub-mechanical equations, establish the overall geometric topological relationship of the low tower cable-stayed bridge, and generate a mechanical transformation matrix between different construction structures of different low tower cable-stayed bridges;

[0030] Through the mechanical transformation matrix, determine the stress characteristics and mechanical change laws of each construction structure;

[0031] Through the stress characteristics and mechanical change laws, generate the mechanical model for each key construction period.

[0032] Combined with the first aspect, the construction of the depth supervision model for the low tower cable-stayed bridge through the mechanical model and engineering technical indexes includes:

[0033] According to the mechanical model and engineering technical indexes, establish a finite element model of the low tower cable-stayed bridge; among them, the finite element model is used for the comparison and verification of the measured data and the finite element analysis results during the construction process;

[0034] Combine the finite element model with a pre-set deep neural network to generate a depth supervision model; among them, the deep neural network is used for the identification of the construction process of the low tower cable-stayed bridge.

[0035] Combined with the first aspect, the supervision of the construction process through the depth supervision model to judge whether to optimize the construction process includes:

[0036] Obtain the measured data during the construction process and input it into the depth supervision model for construction process modeling;

[0037] According to the construction process modeling, conduct quality index supervision tests and quality risk supervision tests respectively;

[0038] According to the quality index supervision test, judge whether the process can be optimized and output the process optimization control parameters;

[0039] Optimize the construction process through the process optimization control parameters, and when the target construction quality index still cannot be achieved after multiple construction process optimizations, determine the construction process risk through the quality risk supervision test.

[0040] Combined with the first aspect, the construction process optimization further includes:

[0041] Build an engineering details model according to the construction process; among which, the engineering details model depends on the mechanical model for construction and marks the relevance between different construction structures;

[0042] Based on the engineering details model, conduct quality scoring on the construction technology of each construction structure, and determine the process optimization coefficient through the quality scoring and the target process quality. The process optimization coefficient is associated with the process optimization control parameters.

[0043] In the second aspect, a construction control system for a low-pylon cable-stayed bridge based on artificial intelligence includes:

[0044] Construction index acquisition module: Obtain the construction indexes of the low-pylon cable-stayed bridge; among which, the construction indexes include: engineering technical indexes and three-dimensional layout indexes;

[0045] Geometric modeling module: Conduct geometric modeling of the low-pylon cable-stayed bridge according to the three-dimensional layout indexes;

[0046] Mechanical model construction module: Through geometric modeling, divide the key construction periods of the low-pylon cable-stayed bridge and determine the mechanical model of each key construction period;

[0047] Deep supervision module: Build a deep supervision model of the low-pylon cable-stayed bridge through the mechanical model and engineering technical indexes;

[0048] Process optimization module: Supervise the construction process through the deep supervision model and judge whether to optimize the construction technology.

[0049] The beneficial effects of the present invention are as follows:

[0050] Through AI technology, the present invention can realize real-time monitoring and automatic evaluation of the construction process, quickly identify problems and optimize them. Before and during the construction, it is necessary to predict the future state of the structure to evaluate risks. The AI model can make predictions based on historical data and existing conditions to help managers make more accurate decisions.

[0051] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0052] The drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention.

[0053] In the drawings:

[0054] Figure 1 is the method flow chart of a construction control method for a low-pylon cable-stayed bridge based on artificial intelligence in an embodiment of the present invention;

[0055] Figure 2 This is the longitudinal layout diagram of the deformation monitoring points of the hanging basket in the embodiment of the present invention;

[0056] Figure 3 This is the transverse layout diagram of the deformation monitoring points of the hanging basket in the embodiment of the present invention;

[0057] Figure 4 This is the schematic principle diagram of the inclinometer in the embodiment of the present invention;

[0058] Figure 5 This is the schematic diagram of the sensor installation points for the mechanical model test in the embodiment of the present invention;

[0059] Figure 6 This is the system composition diagram of a construction control system for a low tower cable-stayed bridge based on artificial intelligence in the embodiment of the present invention. Specific implementation manners

[0060] The following is an illustration of the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention and are not intended to limit the present invention.

[0061] In the prior art, mainly by means of the digital technology of building information models, through image recognition and natural language processing, but it is impossible to monitor the mechanical conditions of low tower cable-stayed bridges. Therefore, the present invention proposes a construction control method for low tower cable-stayed bridges based on artificial intelligence, including:

[0062] Obtain the construction indexes of the low tower cable-stayed bridge; among them, the construction indexes include: engineering technical indexes and three-dimensional layout indexes;

[0063] According to the three-dimensional layout indexes, conduct geometric modeling of the low tower cable-stayed bridge;

[0064] Through geometric modeling, divide the key construction periods of the low tower cable-stayed bridge and determine the mechanical models of each key construction period;

[0065] Build a depth supervision model of the low tower cable-stayed bridge through the mechanical model and engineering technical indexes;

[0066] Supervise the construction process through the depth supervision model and judge whether to optimize the construction process.

[0067] The principle of the above technical solution is as follows:

[0068] As shown in the appendix Figure 1As shown in the figure, during the actual construction of the construction indicators obtained by the present invention, designers usually provide a detailed construction standard data, including various engineering technologies and three-dimensional layout indicators. These indicators are the engineering requirements and three-dimensional structures during the construction process. For example: road grade: first-class highway; vehicle load: Highway-I level; designed driving speed: 80 Km / h; bridge deck width: 29 m; peak ground acceleration value of earthquake motion: 0.1 g, basic intensity VII; designed service life: 100 years; navigation grade: Class III waterway, navigation clearance: 165×10 m; bridge deck paving: 10 cm asphalt concrete + waterproof layer + 6 cm C40 waterproof concrete; safety grade: first-class; bridge deck cross slope: 2%, formed by the inclined placement of the top plate, the longitudinal section is symmetric with the center of the bridge span as the symmetry line, the slope change point is set at K13+477.2, and the bridge deck longitudinal slope is a 2.5% symmetric slope, and the curve radius of the slope change point is 10,000 m. The three-dimensional layout indicators are the geometric parameters of the length, width of the entire bridge body and structures such as the main girder. When performing geometric modeling, three-dimensional scanners, CAD software, etc. are required.

[0069] First, scan and identify all components on the construction drawings, then import these components into the corresponding modeling software, and perform modeling according to the size and position information on the drawings. Set constraint conditions, such as material strength, load specifications, etc., to ensure the feasibility and safety of the modeling results.

[0070] Based on the geometric modeling, divide the construction process and determine each key construction period. The key construction period refers to the stage that has the greatest impact and requires the most attention during the entire construction process. For example, for a steel structure bridge, the main construction stages may be the pouring and closure of the main girder and welding.

[0071] For each key construction period, a corresponding mechanical model needs to be established to monitor and control various parameters during the construction process. When this application is implemented, the mechanical model is a mathematical model constructed based on physical principles and a statistical model based on actual data. The deep supervision model is a model that uses artificial intelligence technology to comprehensively monitor and control the construction process. It can automatically detect abnormal situations during the construction process and provide timely warnings and construction strategies, providing specific construction optimization plans for construction personnel to make timely adjustments and optimizations.

[0072] The beneficial effects of the above technical solutions are as follows:

[0073] Through AI technology, the present invention can achieve real-time monitoring and automatic evaluation of the construction process, quickly identify problems and optimize them. Before and during the construction, it is necessary to predict the future state of the structure to assess risks. The AI model can make predictions based on historical data and existing conditions and generate decision-making opinions.

[0074] As an embodiment of the present invention, the obtaining of the construction indicators of the low tower cable-stayed bridge includes:

[0075] Obtain the project index data of the low tower cable-stayed bridge and input it into a preset process big model to determine the construction process data and the three-dimensional layout schematic diagram;

[0076] According to the construction process data, conduct cost accounting, determine the engineering process technology that meets the cost requirements, and set engineering technical indicators;

[0077] According to the three-dimensional layout schematic diagram, conduct engineering quantity accounting and environmental adaptability calculation to determine the three-dimensional layout indicators that meet the environmental requirements.

[0078] The principle of the above technical solution is as follows:

[0079] In the actual construction process, it is necessary to obtain relevant project index data, such as information on budget, construction period, safety level, etc. In actual implementation, it is obtained through design documents, tender documents or other relevant materials. Then, input these data into a preset process big data model. The process big data model is a big data model containing various process schemes, which is used to predict data in multiple aspects such as the cost, progress, and safety risks of different process schemes.

[0080] Through the process big data model, I determine the construction process data suitable for the current project and the corresponding engineering technical indicators. After determining the construction process data suitable for the current project, conduct cost accounting based on these data to determine the engineering process technology that meets the cost requirements.

[0081] In the process of cost accounting, it is necessary to analyze by combining factors such as engineering quantity accounting, material procurement, and labor costs. In addition, relevant engineering technical indicators need to be set according to the construction process data and the cost accounting results, such as material strength, structural stability, safety, etc. In addition to setting engineering technical indicators according to the construction process data, the engineering technical indicators also need to conduct engineering quantity accounting and environmental adaptability calculation based on the three-dimensional layout schematic diagram to determine the three-dimensional layout indicators that meet the environmental requirements.

[0082] The three-dimensional layout schematic diagram contains various possible layout schemes, such as changes in bridge span, changes in the direction of the main girder, adjustment of the support position, etc. By conducting engineering quantity accounting and environmental adaptability analysis on these schemes, the optimal three-dimensional layout indicators are determined, and then construction adjustment is carried out.

[0083] The beneficial effect of the above technical solution is as follows:

[0084] The present invention configures the process technology that best conforms to the real-time scenario and environment based on the process big model.

[0085] As an embodiment of the present invention, the geometric modeling of the low tower cable-stayed bridge according to the three-dimensional layout index includes:

[0086] Import the three-dimensional layout index into the modeling software to obtain a real-scene three-dimensional model of the low tower cable-stayed bridge;

[0087] According to the real-scene three-dimensional model, perform geometric splitting to determine the geometric structure of the low tower cable-stayed bridge; wherein, the geometric structure includes: bridge tower, main beam, stay cables, bridge piers, foundation, bearings, railings, drainage channels;

[0088] Perform geometric modeling of the low tower cable-stayed bridge through the geometric structure.

[0089] The principle of the above technical solution is as follows:

[0090] In the actual implementation of the present invention, geometric modeling of the low tower cable-stayed bridge is carried out according to the three-dimensional layout index.

[0091] In this process, first, import the three-dimensional layout index into the modeling software to obtain a real-scene three-dimensional model of the low tower cable-stayed bridge containing all necessary information. Determine different engineering structures, for example: Bridge tower: As the main structure supporting the entire bridge, its design and construction must be carried out strictly in accordance with the design requirements. Main beam: Connects the bridge tower and the stay cables and is the part that bears most of the loads. Stay cables: Used to connect the main beam and the bridge tower and bear part of the loads. Bridge piers: Used to bear all the loads of the bridge tower and the main beam, as well as part of the horizontal force. Foundation: Used to transfer all the vertical and horizontal forces of the bridge tower and the main beam to the ground. Bearings: Used to connect the bridge tower and the main beam and are also the rotating joints of the bridge. Railings: Ensure personal safety and prevent accidental falls. Drainage channels: Ensure the drainage of rainwater and groundwater. Then, perform modeling one by one according to different component structures.

[0092] The beneficial effect of the above technical solution is as follows:

[0093] The present invention performs separate modeling for different component structures of the low tower cable-stayed bridge to ensure the accuracy of engineering modeling.

[0094] As an embodiment of the present invention, the key construction periods of the low tower cable-stayed bridge divided by geometric modeling include:

[0095] Associate the geometric model with engineering technical indicators to determine the construction process of the low tower cable-stayed bridge; wherein, the construction process includes prestressing construction of beam segments, erection of hanging baskets, construction of cantilever cast beam segments, construction of side-span closure segments, construction of mid-span closure segments, and external prestressing construction;

[0096] According to the construction process, set quality indicators and inspection measures for each construction period;

[0097] Perform process empowerment for each construction cycle through detection measures to determine key construction indicators;

[0098] And determine the key construction cycle through the key construction indicators.

[0099] The technical principle of the above technical solution is as follows:

[0100] In the actual implementation process, by creating a geometric model of the low tower cable-stayed bridge and associating it with engineering technical indicators (such as structural strength, stability, material properties, etc.), ensure that the design of the construction process is based on accurate engineering requirements and technical standards;

[0101] In actual implementation, engineering requirements include: Functional requirements: The functional requirements of the structure, such as bearing capacity, service life, wind and earthquake resistance performance, etc. Usage space requirements, including the width, height, traffic capacity of the bridge, etc. Traffic flow line design, such as traffic volume, pedestrian volume, traffic organization, etc. Safety requirements: Structural safety standards to ensure the safety of the structure under normal use and extreme conditions. Safety requirements such as fire prevention and evacuation. Environmental protection requirements, such as noise control, pollution treatment, etc. Economic requirements: Cost control, including construction costs and operation and maintenance costs. Resource optimization, rational use of materials and resources to reduce waste. Aesthetic requirements: Structural appearance design, coordination with the surrounding environment. Aesthetic requirements such as lighting and landscape, etc.

[0102] Technical standards include design standards, material standards, construction standards, safety standards, etc.

[0103] Furthermore, based on the geometric model and engineering technical indicators, clarify each stage of the construction process, such as beam segment prestressing construction, hanging basket assembly, etc., so as to organize and manage the construction process. For each stage of the construction process, set specific quality indicators (such as concrete strength, prestress magnitude, etc.) and corresponding detection measures (such as non-destructive testing, material testing, etc.) to ensure that the construction quality meets the design requirements. Process empowerment is to empower the process of each construction cycle through detection measures, that is, quantify the importance and impact of the construction process according to the detection results to determine which key processes. Determining key construction indicators is based on the results of process empowerment, identifying the key construction indicators that have the greatest impact on construction quality. Key construction indicators are the focus of construction control. Through the key construction indicators, further determine the key stages in the entire construction cycle. The key stages play a decisive role in the overall quality and progress of the project.

[0104] The beneficial effects of the above technical solution are as follows:

[0105] Through precise geometric modeling and quality index setting, this application can more effectively control the construction quality and reduce construction errors; determine the key construction period and construction indicators, thereby optimizing the allocation and scheduling of construction resources. By monitoring the key construction indicators, potential safety risks can be detected in a timely manner and preventive measures can be taken.

[0106] As an embodiment of the present invention, the detection measures include deformation monitoring and asynchronous hanging basket overturning monitoring throughout the construction process;

[0107] Deformation monitoring is carried out by presetting linear test reference points on the bottom template of the hanging basket structure and conducting inductance tests. The inductance test data includes: measurement time, measuring point temperature (temperature type), absolute displacement value, relative displacement value, and zero point value;

[0108] Asynchronous hanging basket overturning monitoring is carried out by setting inclinometers on the hanging basket device for multi-directional testing. An alarm angle for the hanging basket overturning is set in the inclinometer.

[0109] The principle of the above technical solution is as follows:

[0110] In the specific implementation of the present invention, the stress test element selects the JMZX-212HAT steel surface steel string strain sensor for steel structures. The installation schematic diagram of the sensor is as Figure 5 , and the detection instrument is the JMZX-3006 steel string reading instrument. Through the strain-frequency calibration curve, the actual strain of the material is converted, and then the stress is deduced according to its elastic modulus. In this hanging basket stress monitoring, an automated integrated test system based on the cloud platform is adopted. Since it is necessary to find the linear test reference points for manual testing of the deformation of the bottom template of the hanging basket structure, JMDL-2220A intelligent digital displacement gauges can be arranged at the lower crossbeam position of the outer web on the outside of the corrugated steel web for deformation monitoring. The intelligent digital displacement gauge is designed and manufactured using the inductance frequency modulation principle, and has the advantages of high sensitivity, high precision, high stability, and temperature influence, and is suitable for long-term observation. The intelligent digital crack gauge has an internal storage chip and has an intelligent memory function. When leaving the factory, parameters such as the sensor model, number, and calibration coefficient have been permanently stored in the sensor, and the measurement results required multiple times can be saved, such as measurement time, measuring point temperature (temperature type), absolute displacement value, relative displacement value, zero point value, etc. In actual implementation, 4 deformation monitoring points are arranged for a single hanging basket, which are respectively arranged at the lower crossbeam position of the outer web at the cantilever end (2 monitoring points) and the poured end (2 monitoring points) of the corrugated steel web as Figure 2 and Figure 3 shown.

[0111] The overall instability and overturning of the hanging basket device during construction is the main form of structural safety accidents. Therefore, it is necessary to install monitoring equipment on the truss of the hanging basket device to measure the inclination angles of the front truss in the longitudinal and transverse directions of the bridge during construction. By using fixed inclinometers installed on the surface of the hanging basket device structure, the inclination angles of the structural members in two mutually perpendicular directions relative to the gravity axis can be measured. Using the geometric dimensions of the installation positions, the deformations of the structural members in the X and Y directions can be calculated, achieving the purpose of monitoring the inclination angles of the upper truss of the hanging basket and the goal of monitoring whether there is a risk of overall overturning of the hanging basket device in the longitudinal and transverse directions of the bridge, providing safety warnings when the structure overturns, such as Figure 4 as shown

[0112] The beneficial effects of the above technical solution are as follows:

[0113] The present invention can detect the overall deformation of the bridge body and monitor the overturning of the asynchronous hanging basket during construction.

[0114] As an embodiment of the present invention, the determination of the mechanical model for each key construction period includes:

[0115] Establish a sub-mechanical equation for the corresponding construction structure according to the construction structure and mechanical indexes during the key construction period;

[0116] According to the sub-mechanical equation, establish the overall geometric topological relationship of the low tower cable-stayed bridge and generate the mechanical transformation matrix between different construction structures of different low tower cable-stayed bridges;

[0117] Through the mechanical transformation matrix, determine the force characteristics and mechanical change laws of each construction structure;

[0118] Generate the mechanical model for each key construction period based on the force characteristics and mechanical change laws.

[0119] The principle of the above technical solution is as follows:

[0120] In the actual implementation process, first, according to the construction structure and mechanical indexes during the key construction period, establish a sub-mechanical equation for the corresponding construction structure. The sub-mechanical equation established through the construction structure and mechanical indexes accurately reflects the mechanical behavior during construction.

[0121] Secondly, according to the sub-mechanical equation, establish the overall geometric topological relationship of the low tower cable-stayed bridge and generate the mechanical transformation matrix between different construction structures of different low tower cable-stayed bridges. In the process of establishing the mechanical sub-equation, tools such as computer simulation and numerical calculation need to be used to generate an accurate mechanical model.

[0122] Then, through this mechanical transformation matrix, determine the force characteristics and mechanical change laws of each construction structure. This is achieved by analyzing the mechanical model to extract the force characteristics and mechanical change laws of each construction structure for monitoring and management during the construction process.

[0123] Finally, based on the force characteristics and mechanical change laws, a mechanical model for each key construction period can be generated. The mechanical model of the key construction period contains various construction information and mechanical data for real-time monitoring and management during the construction process.

[0124] The beneficial effects of the above technical solution are as follows:

[0125] The present invention establishes a mechanical model for each construction structure according to each construction period and mechanical characteristics.

[0126] As an embodiment of the present invention, the depth supervision model of the low tower cable-stayed bridge is built through the mechanical model and engineering technical indicators, including:

[0127] Establish a finite element model of the low tower cable-stayed bridge according to the mechanical model and engineering technical indicators; wherein, the finite element model is used for comparing and verifying the measured data and the finite element analysis results during the construction process;

[0128] Combine the finite element model with a preset deep neural network to generate a depth supervision model; wherein, the deep neural network is used for identifying the construction process of the low tower cable-stayed bridge.

[0129] The principle of the above technical solution is as follows:

[0130] In the actual implementation process, the present invention establishes a finite element model of the low tower cable-stayed bridge according to the mechanical model and engineering technical indicators. The finite element model is a numerical analysis tool that can simplify complex physical problems into a series of simple mathematical operations by discretizing the entity.

[0131] Predict the measured data through the finite element model, and verify and improve the theoretical model. Combine the finite element model with a preset deep neural network to generate a depth supervision model.

[0132] In actual implementation, the deep neural network is a machine learning algorithm that is suitable for adaptively learning and understanding complex non-linear problems.

[0133] Here, use the deep neural network to identify and classify different construction processes and adopt targeted control strategies.

[0134] The beneficial effects of the above technical solution are as follows:

[0135] The present invention can realize the verification and comparison of measured data and modeling data, and can also realize the accurate recognition of measured data by a deep neural network, and automatically perform verification and comparison.

[0136] As an embodiment of the present invention, the supervision of the construction process by a deep supervision model to determine whether to optimize the construction process includes:

[0137] Obtain the measured data during the construction process and input it into the deep supervision model for construction process modeling;

[0138] According to the construction process modeling, respectively conduct quality index supervision tests and quality risk supervision tests;

[0139] According to the quality index supervision test, determine whether process optimization can be carried out and output process optimization control parameters;

[0140] Optimize the construction process through the process optimization control parameters, and when the target construction quality index still cannot be achieved after multiple construction process optimizations, determine the construction process risk through the quality risk supervision test.

[0141] The principle of the above technical solution is as follows:

[0142] In the actual implementation process, the present invention obtains the measured data during the construction process and inputs it into the deep supervision model for construction process modeling.

[0143] In this process, the construction process will be modeled through the measured data, enabling the deep supervision model to more accurately learn and simulate the actual construction process. <J

[0144] According to the construction process modeling, respectively conduct quality index supervision tests and quality risk supervision tests. The quality index supervision test and the quality risk supervision test are used to evaluate the quality status of the construction process. The quality index supervision test is used to check whether the quality in the construction process has reached the expected goal, and the quality risk supervision test is used to detect the possible quality risks in the construction process.

[0145] According to the quality index supervision test, determine whether process optimization can be carried out and output process optimization control parameters. The goal of this step is to judge whether process optimization is needed by measuring quality indicators and risks. If optimization is needed, the corresponding optimization control parameters will be output for application during the construction process. <S

[0146] Optimize the construction process by controlling parameters through process optimization. When the target construction quality index still cannot be achieved after multiple construction process optimizations, determine the construction process risk through quality risk supervision tests. The core part of the construction process optimization will perform multiple construction process optimizations according to the optimization control parameters. If the expected quality index still cannot be achieved after multiple optimizations, then it is necessary to determine whether there is a construction process risk through quality risk supervision tests.

[0147] The beneficial effects of the above technical solution are as follows:

[0148] The present invention gradually tests the construction quality and construction risk, thereby reducing the mismeasurement of construction risk while accurately identifying construction risk and reducing construction costs.

[0149] As an embodiment of the present invention, the construction process optimization further includes:

[0150] Build an engineering detail model according to the construction process modeling; wherein, the engineering detail model depends on the mechanical model for construction and marks the relevance between different construction structures;

[0151] Based on the engineering detail model, perform quality scoring on the construction process of each construction structure, and determine the process optimization coefficient through the quality score and the target process quality. The process optimization coefficient is associated with the process optimization control parameters.

[0152] The principle of the above technical solution is as follows:

[0153] In the process of optimizing the construction process of the present invention, an engineering detail model is first built. The engineering detail model is established based on the mechanical model, and the relevance between different construction structures is marked. Predict and evaluate the actual effects of different construction processes, providing more accurate data support for process optimization.

[0154] When performing quality scoring on the construction process of each construction structure, two factors, namely the quality score and the target process quality, are comprehensively considered. The quality score is mainly used to evaluate the actual effect of each process, while the target process quality is the ideal state that we hope to achieve through process optimization. The division of the two is the process optimization coefficient, thereby reflecting whether the required quality target is achieved during the process optimization.

[0155] The beneficial effects of the above technical solution are as follows:

[0156] The present invention can score the construction process. By associating the process optimization coefficient with the process optimization control coefficient, the accuracy of process optimization can be guaranteed.

[0157] Second aspect, an artificial-intelligence-based construction control system for a low-pylon cable-stayed bridge, which includes: Construction index acquisition module: acquiring the construction indexes of the low-pylon cable-stayed bridge; wherein, the construction indexes include engineering technical indexes and three-dimensional layout indexes;

[0158] Geometric modeling module: performing geometric modeling of the low-pylon cable-stayed bridge according to the three-dimensional layout indexes;

[0159] Mechanical model building module: through geometric modeling, dividing the key construction periods of the low-pylon cable-stayed bridge and determining the mechanical models of each key construction period;

[0160] Deep supervision module: building a deep supervision model of the low-pylon cable-stayed bridge through the mechanical model and engineering technical indexes;

[0161] Process optimization module: supervising the construction process through the deep supervision model and judging whether to optimize the construction process.

[0162] The principle of the above technical solution is as follows:

[0163] As Figure 6 shown, in actual construction, designers pre-obtain construction standard data, where the construction standard data includes various engineering technical and three-dimensional layout indexes. These indexes are the engineering requirements and three-dimensional structures in the construction process. For example: road grade: first-class highway; vehicle load: highway-I level; designed driving speed: 80 Km / h; bridge deck width: 29 m; peak ground acceleration value of earthquake: 0.1 g, basic intensity VII; designed service life: 100 years; navigation grade: III-level waterway, navigation clearance: 165×10 m; bridge deck paving: 10 cm asphalt concrete + waterproof layer + 6 cm C40 waterproof concrete; safety grade: first-class; cross slope of bridge deck: 2%, formed by inclined top plate, the longitudinal section is symmetric about the center line of the bridge span, the slope change point is set at K13+477.2, the longitudinal slope of the bridge deck is a 2.5% symmetric slope, and the radius of the curve at the slope change point is 10,000 m. The three-dimensional layout indexes are the length and width of the entire bridge body and the geometric parameters of structures such as the main girder. When performing geometric modeling, three-dimensional scanners, CAD software, etc. are required.

[0164] First, scan and identify all components on the construction drawings, then import these components into the corresponding modeling software, and perform modeling according to the size and position information on the drawings. Set constraint conditions, such as material strength, load specifications, etc., to ensure the feasibility and safety of the modeling results.

[0165] Based on the geometric modeling, divide the construction process and determine each key construction period. The key construction period refers to the stage that has the greatest impact and requires the most attention during the entire construction process. For example, for a steel structure bridge, the main construction stages may be the pouring and closure of the main girder and welding.

[0166] For each key construction cycle, a corresponding mechanical model needs to be established to monitor and control various parameters during the construction process. When this application is implemented, the mechanical model is a mathematical model constructed based on physical principles, and the statistical model is based on actual data. The deep supervision model is a model that uses artificial intelligence technology to comprehensively monitor and control the construction process. It can automatically detect abnormal situations during the construction process, provide timely warnings and construction strategies, and provide specific construction optimization plans for construction personnel to make timely adjustments and optimizations.

[0167] The beneficial effects of the above technical solution are as follows:

[0168] Through AI technology, the present invention can achieve real-time monitoring and automatic evaluation of the construction process, quickly identify problems and optimize them. Before and during the construction process, it is necessary to predict the future state of the structure to assess risks. The AI model can make predictions based on historical data and existing conditions and generate decision-making opinions.

[0169] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A construction control method for a low-tower cable-stayed bridge based on artificial intelligence, characterized in that: include: Obtain construction indicators for a low-tower cable-stayed bridge; construction indicators include engineering technical indicators and three-dimensional layout indicators; Conduct geometric modeling of low-tower cable-stayed bridges based on three-dimensional layout indicators; Through geometric modeling, the critical construction period of the low-tower cable-stayed bridge is divided and the mechanical model of each critical construction period is determined; A deep supervision model for a low-tower cable-stayed bridge is constructed using a mechanical model and engineering technical indicators. The deep supervision model for a low-tower cable-stayed bridge is constructed using a mechanical model and engineering technical indicators, including: Based on the mechanical model and engineering technical indicators, a finite element model of the low-tower cable-stayed bridge was established. The finite element model was used to compare and verify the measured data and finite element analysis results during the construction process. The finite element model is combined with a preset deep neural network to generate a deep supervision model; the deep neural network is used to identify the construction process of the low-tower cable-stayed bridge; The construction process is supervised through a deep supervision model to determine whether construction process optimization is necessary.

2. The artificial intelligence-based construction control method for a low-tower cable-stayed bridge according to claim 1, characterized in that: The obtaining of the construction index of the low-tower cable-stayed bridge includes: Obtain project index data for a low-tower cable-stayed bridge, input the pre-set process model, and determine the construction process data and three-dimensional layout diagram; Conduct cost accounting based on construction process data, determine engineering process technology that meets cost requirements, and set engineering technology indicators; According to the three-dimensional layout diagram, carry out engineering quantity accounting and environmental adaptability calculation to determine the three-dimensional layout indicators that meet environmental requirements.

3. The artificial intelligence-based construction control method for a low-tower cable-stayed bridge according to claim 1, characterized in that: The geometric modeling of the low-tower cable-stayed bridge based on the three-dimensional layout indicators includes: Import the three-dimensional layout indicators into the modeling software to obtain a realistic three-dimensional model of the low-tower cable-stayed bridge; Based on the real-life 3D model, perform geometric decomposition to determine the geometric structure of the low-tower cable-stayed bridge. The geometric structure includes: towers, main beams, stay cables, piers, foundations, supports, railings, and drainage channels. Through geometric structure, geometric modeling of low-tower cable-stayed bridge is carried out.

4. The artificial intelligence-based construction control method for a low-tower cable-stayed bridge according to claim 1, characterized in that: The key construction period of the low-tower cable-stayed bridge is divided into the following parts through geometric modeling: The geometric model was linked to engineering technical indicators to determine the construction process of the low-tower cable-stayed bridge. The construction process included prestressing of beam segments, assembly with hanging baskets, cantilever casting of beam segments, construction of side span closure segments, construction of mid-span closure segments, and external prestressing. According to the construction process, set quality indicators and inspection measures for each construction cycle; Empower the process of each construction cycle through testing measures and determine key construction indicators; Determine the critical construction period through key construction indicators.

5. The artificial intelligence-based construction control method for a low-tower cable-stayed bridge according to claim 4, characterized in that: The detection measures include deformation monitoring and asynchronous gantry overturning monitoring throughout the construction process; Deformation monitoring is done by pre-setting a linear test reference point on the bottom template of the hanging basket structure and performing an inductance test. The inductance test data includes: measurement time, measurement point temperature value, absolute displacement value, relative displacement value, and zero point value; The asynchronous basket overturning monitoring is carried out by pre-installing an inclinometer on the basket device for multi-directional testing, and the inclinometer is set with an alarm angle for the basket overturning.

6. The artificial intelligence-based construction control method for a low-tower cable-stayed bridge according to claim 1, characterized in that: The mechanical model for each key construction period is determined, including: According to the construction structure and mechanical indicators within the key construction period, the sub-mechanical equations corresponding to the construction structure are established; Based on the submechanical equations, the overall geometric topological relationship of the low-tower cable-stayed bridge is established, and the mechanical transformation matrix between different construction structures of different low-tower cable-stayed bridges is generated. Determine the stress characteristics and mechanical change laws of each construction structure through the mechanical transformation matrix; Based on the stress characteristics and mechanical change laws, a mechanical model of each key construction cycle is generated.

7. The artificial intelligence-based construction control method for a low-tower cable-stayed bridge according to claim 1, characterized in that: The construction process is supervised by the deep supervision model to determine whether to optimize the construction process, including: Obtain measured data during the construction process and input it into the deep supervision model to model the construction process; Based on the construction process modeling, quality indicator supervision tests and quality risk supervision tests are carried out respectively; Based on the quality index supervision test, determine whether process optimization is possible and output the process optimization control parameters; The construction process is optimized through process optimization control parameters. When the target construction quality indicators cannot be achieved after multiple construction process optimizations, the construction process risks are determined through quality risk supervision tests.

8. The artificial intelligence-based construction control method for a low-tower cable-stayed bridge according to claim 7, characterized in that: The construction process optimization also includes: Based on the construction process modeling, a detailed engineering model is built. The detailed engineering model relies on the mechanical model and the correlation between different construction structures is marked. Based on the engineering detail model, the construction process of each construction structure is scored, and the process optimization coefficient is determined by the quality score and the target process quality. The process optimization coefficient is associated with the process optimization control parameters.

9. An artificial intelligence-based construction control system for a low-tower cable-stayed bridge, characterized in that: include: Construction index collection module: obtains the construction index of the low-tower cable-stayed bridge; the construction index includes: engineering technical index and three-dimensional layout index; Geometric modeling module: performs geometric modeling of low-tower cable-stayed bridges based on three-dimensional layout indicators; Mechanical model building module: Through geometric modeling, the key construction period of the low-tower cable-stayed bridge is divided and the mechanical model of each key construction period is determined; Deep Supervision Module: Build a deep supervision model for low-tower cable-stayed bridges through mechanical models and engineering technical indicators; The deep supervision model for low-tower cable-stayed bridges is constructed by using mechanical models and engineering technical indicators, including: Based on the mechanical model and engineering technical indicators, a finite element model of the low-tower cable-stayed bridge was established. The finite element model was used to compare and verify the measured data and finite element analysis results during the construction process. The finite element model is combined with a preset deep neural network to generate a deep supervision model; the deep neural network is used to identify the construction process of the low-tower cable-stayed bridge; Process optimization module: supervises the construction process through a deep supervision model to determine whether to optimize the construction process.

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