High-efficiency and precise construction system for curved skew bridges based on intelligent sensing and collaborative control

Abstract

This invention discloses a high-efficiency and precise construction system for curved and skew bridges based on intelligent sensing and collaborative control. The system includes: a construction module for building a comprehensive intelligent sensing network during the construction process, collecting multiple sets of historical construction data through this network; a training module for analyzing and training based on the historical construction data using deep learning algorithms to establish a parameter optimization model for curved and skew bridge construction; a prediction module for predicting the optimal construction parameters of the curved and skew bridge using the model, and determining efficiency monitoring indicators based on these parameters; and a monitoring module for accurately monitoring the construction process of the curved and skew bridge based on the efficiency monitoring indicators and providing phased progress feedback. The comprehensive intelligent sensing network provides accurate data support for the construction process, ensuring the objectivity and accuracy of the data, and maximizing the collaborative operation effect and work efficiency among various construction equipment.

Description

Technical Field

[0001] This invention relates to the field of bridge engineering construction technology, and in particular to a high-efficiency and precise construction system for curved skew bridges based on intelligent sensing and collaborative control. Background Technology

[0002] Due to their unique geometry and stress characteristics, skew bridges face numerous challenges during prefabrication and installation. Traditional construction methods rely on manual experience and conventional measurement techniques, making it difficult to accurately control various parameters during construction. This leads to poor coordination and low efficiency among subsequent construction equipment. For example, the lack of real-time position and attitude monitoring during the hoisting and installation of precast beams results in positioning deviations; ineffective coordination between different construction stages leads to low operating efficiency of bridge erecting machines, beam transport vehicles, and other equipment, resulting in long construction cycles. Furthermore, traditional construction methods are ill-suited to complex and variable construction environments, such as the construction of skew bridges in mountainous terrain, which presents significant construction difficulties and challenges in ensuring quality. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides an efficient and precise construction system for curved skew bridges based on intelligent sensing and collaborative control. This system solves the problem mentioned in the background art where traditional construction methods rely on manual experience and conventional measurement methods, making it difficult to accurately control various parameters during the construction process, resulting in poor coordination and low efficiency among subsequent construction equipment.

[0004] A high-efficiency and precise construction system for curved skew bridges based on intelligent sensing and collaborative control, the system comprising:

[0005] The module is used to build a comprehensive intelligent sensing network during the construction of curved skew bridges, and to collect multiple sets of historical construction data through the comprehensive intelligent sensing network.

[0006] The training module is used to analyze and train based on multiple sets of historical construction data using deep learning algorithms to establish a parameter optimization model for the construction of curved skew bridges.

[0007] The prediction module is used to predict the optimal construction parameters of the curved skew bridge through the parameter optimization model, and to determine the efficiency monitoring indicators based on the optimal construction parameters.

[0008] The monitoring module is used to accurately monitor the construction process of the curved skew bridge based on efficiency monitoring indicators and provide phased work progress feedback.

[0009] Preferably, the building module includes:

[0010] The first submodule is used to determine the workflow for the construction of curved skew bridges, and to determine key material components, key working media, and key operating equipment based on the workflow.

[0011] The second determination submodule is used to determine the state changes of key material components, key working media and key operating equipment during construction, and select different types of sensors according to the state changes.

[0012] A sub-module is constructed to build a comprehensive intelligent sensing network for the construction process of curved skew bridges through various types of sensors and communication units;

[0013] The data acquisition and processing submodule is used to collect and process historical construction data from multiple curved skew bridge projects during their initiation and construction processes using an all-around intelligent sensing network.

[0014] Preferably, the key material component is a precast beam, the key working medium is a bridge pier, and the key operating equipment is a bridge erecting machine;

[0015] Among them, the sensors corresponding to the precast beams are high-precision three-dimensional laser displacement sensors and tilt sensors. The three-dimensional laser displacement sensors are used to monitor the spatial position changes of the precast beams during hoisting and installation, and the tilt sensors are used to measure the tilt angle of the precast beams during hoisting and installation.

[0016] The sensors corresponding to the bridge piers are stress-strain sensors, which are used to monitor the stress state of the bridge pier structure during construction.

[0017] The bridge erecting machine is equipped with stress-strain sensors and tilt sensors. The tilt sensors are used to measure the tilt angle of the bridge erecting machine's outriggers, while the strain sensors are used to monitor the stress state of the main structure of the bridge erecting machine during construction.

[0018] Preferably, the training module includes:

[0019] The preprocessing submodule is used to perform data cleaning and standardization preprocessing on multiple sets of historical construction data to obtain preprocessed historical construction data.

[0020] The design submodule is used to extract the construction stage characteristics and environmental impact characteristics corresponding to the preprocessed historical construction data through deep learning algorithms, select a deep learning model based on the construction stage characteristics and environmental impact characteristics, and design the model architecture.

[0021] The training submodule is used to divide the preprocessed historical construction data into training set, validation set and test set, and to train the deep learning model through the inspection set to build the trained model.

[0022] The first generation submodule is used to verify and test the accuracy and performance of the trained model using a validation set and a test set. After the verification test is passed, a parameter optimization model for the construction of curved skew bridges is generated.

[0023] Preferably, the prediction module includes:

[0024] The first data acquisition submodule is used to obtain the target design parameters of the current curved skew bridge project, collect environmental parameters of the construction site, and real-time construction data.

[0025] The prediction submodule is used to input the target design parameters, environmental parameters of the construction site, and real-time construction data into the parameter optimization model to predict the optimal construction parameters.

[0026] The third determination submodule is used to determine the collaborative operation parameters between various construction equipment based on the optimal construction parameters, and to determine the scheduling and control parameters for each construction equipment based on the collaborative operation parameters.

[0027] The fourth determination submodule is used to determine the matching efficiency index type based on the scheduling and control parameters of each construction equipment and the construction target, obtain the quantitative index of the matching efficiency index type, and determine the quantitative index as the monitoring index of each type of efficiency index.

[0028] Preferably, the monitoring module includes:

[0029] The second data acquisition submodule is used to determine the reference data system for each efficiency monitoring indicator and to collect the corresponding data of the construction process of the skew bridge according to the reference data system.

[0030] The fifth submodule is used to calculate the efficiency index of each efficiency monitoring indicator based on the collected data, determine the construction qualification based on the efficiency index, and make corresponding optimizations.

[0031] The sixth submodule is used to determine the phased targets for each efficiency monitoring indicator at each construction stage, and to determine the actual progress of the phased construction based on the phased targets and real-time construction data.

[0032] The second generation submodule is used to determine the deviation between the actual and theoretical progress of the phased construction and to identify inefficient construction links to generate a phased work progress feedback report.

[0033] Preferably, the system is also used for:

[0034] Detect abnormal environmental parameters and abnormal event parameters at the construction site, and identify risky construction equipment and potential safety hazards based on these parameters.

[0035] Based on the risk of construction equipment and potential safety hazards, the recommended suspension points of the construction operation mechanism are determined, and the relevant construction equipment and manpower configurations for the recommended suspension points are deployed and adjusted.

[0036] Based on abnormal environmental parameters, the appropriate construction parameters are re-predicted using a parameter optimization model, and then the optimal construction parameters are adjusted based on these appropriate construction parameters.

[0037] Preferably, the location of the stress sensor on the bridge pier is determined by the following method:

[0038] Obtain the volume parameters of the bridge pier, determine the area of ​​the stress zone of the bridge pier based on the volume parameters, and determine the stress zone within the stress zone based on the working structure of the precast beam and the stress zone of the bridge pier.

[0039] The area of ​​the stress region is calculated based on the area of ​​the stressed region, and the stress uniformity of the bridge pier is determined based on the area of ​​the stress region and the overlap characteristics between the stress region and the stressed region.

[0040] Based on the uniformity of force, the direction of force eccentricity and the direction of force non-eccentricity are determined. Stress sensor positions are set on the side of the first pier in the direction of force eccentricity at a first height, and stress sensor positions are set on the side of the second pier in the direction of force non-eccentricity at a second height.

[0041] The distance from the second height to the top of the pier is 1.5 times the distance from the first height to the top of the pier.

[0042] Preferably, the collaborative operation parameters between various construction equipment are determined based on optimal construction parameters, including:

[0043] Determine the prerequisite construction conditions for each construction equipment, and determine the construction task parameters for each construction equipment based on the prerequisite construction conditions;

[0044] The construction objectives are determined based on the construction task parameters. The data state change patterns before and after construction are determined based on the construction objectives. The critical values ​​of the collaborative operation are determined based on the optimal construction parameters and the data state change patterns of each construction equipment before and after construction.

[0045] Based on the state critical values ​​of the collaborative operation of each construction equipment, the synchronous data change parameters of the collaborative operation of each construction equipment are determined, and the collaborative operation parameters between each construction equipment are determined based on the synchronous data change parameters.

[0046] Preferably, the system is also used for:

[0047] Based on the target design parameters of the current curved skew bridge project, determine multiple reserved installation positions for the precast beams and obtain the support installation area for each reserved installation position;

[0048] The optimal docking posture between the precast beam and the support at each reserved installation location is determined based on the support installation area at each reserved installation location and the lithological factors at that reserved installation location.

[0049] The optimal docking posture is used to determine the aerial docking configuration of the precast beam at each reserved installation position, and the required rotation angle of the bridge erecting machine's outriggers is determined based on the aerial docking configuration.

[0050] An improvement plan is generated based on the required rotation angle of the outriggers and the current outrigger structure of the bridge erecting machine. Based on the improvement plan, the current outrigger structure is technically improved.

[0051] Based on the target design parameters, generate construction drawings and material dimension drawings for the current curved skew bridge project, and review the construction drawings using the BIM model to identify and provide feedback on any unreasonable processes;

[0052] Based on material dimension drawings, BIM technology is used to model and perform dimensional verification and engineering requirement matching assessment.

[0053] Obtain the environmental parameters of organic soil at the construction site, determine the soil water content based on the organic soil environmental parameters, and determine the water stability of the filler based on the water content.

[0054] The water resistance requirements are determined based on the water stability of the filler, and the target type of curing agent is selected based on the water resistance requirements and the reaction parameters of the curing agent, clay minerals and organic matter.

[0055] Soil looseness is determined based on soil environmental parameters, soil consolidation performance is determined based on soil looseness, and it is confirmed whether the soil consolidation performance meets the construction requirements.

[0056] If not, a new architecture is generated based on the compaction mechanism of inorganic construction waste recycled aggregate and soil consolidation skeleton, as well as the composition of soil micro-network structure.

[0057] The new architecture determines the blending ratio of inorganic construction waste recycled aggregate and soil, and the soil is then optimized based on the blending ratio.

[0058] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0059] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0060] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0061] Figure 1A schematic diagram of the structure of an efficient and precise construction system for curved skew bridges based on intelligent sensing and collaborative control provided by the present invention;

[0062] Figure 2 A schematic diagram of the structure of a construction module in an efficient and precise construction system for curved skew bridges based on intelligent sensing and collaborative control provided by the present invention;

[0063] Figure 3 This is a schematic diagram of the prediction module in an efficient and precise construction system for curved skew bridges based on intelligent sensing and collaborative control, provided by the present invention. Detailed Implementation

[0064] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0065] Due to their unique geometry and stress characteristics, skew bridges face numerous challenges during prefabrication and installation. Traditional construction methods rely on manual experience and conventional measurement techniques, making it difficult to accurately control various parameters during construction. This leads to poor coordination and low efficiency among subsequent construction equipment. For example, the lack of real-time position and attitude monitoring during the hoisting and installation of precast beams results in positioning deviations; ineffective coordination between different construction stages leads to low operating efficiency of bridge erecting machines, beam transport vehicles, and other equipment, resulting in long construction cycles. Furthermore, traditional construction methods are ill-suited to complex and variable construction environments, such as the construction of skew bridges in mountainous terrain, which presents significant construction difficulties and challenges in ensuring quality. To address these issues, this embodiment discloses a highly efficient and precise construction system for skew bridges based on intelligent sensing and collaborative control.

[0066] A high-efficiency and precise construction system for curved skew bridges based on intelligent sensing and collaborative control, such as... Figure 1 As shown, the system includes:

[0067] Module 101 is used to construct an all-round intelligent sensing network during the construction process of the curved skew bridge, and to collect multiple sets of historical construction data through the all-round intelligent sensing network.

[0068] Training module 102 is used to analyze and train based on multiple sets of historical construction data using deep learning algorithms to establish a parameter optimization model for the construction of curved skew bridges.

[0069] Prediction module 103 is used to predict the optimal construction parameters of the curved skew bridge through the parameter optimization model, and determine the efficiency monitoring index based on the optimal construction parameters.

[0070] The monitoring module 104 is used to accurately monitor the construction process of the curved skew bridge based on efficiency monitoring indicators and provide phased work progress feedback.

[0071] In this embodiment, the optimal construction parameters perfectly control the timing of construction for each piece of equipment. For example, during the hoisting of precast beams, the beam transport vehicle accurately transports the precast beams to the area below the bridge erecting machine. Based on the precast beam position information fed back by the intelligent sensing network, the bridge erecting machine automatically adjusts the hoisting position and angle to achieve precise docking.

[0072] The working principle of the above technical solution is as follows: Multiple historical construction data points are collected during the construction of the skew bridge through a comprehensive intelligent sensing network. This historical construction data is standardized and complete. Intelligent sensors replace manual inspection, improving accuracy and efficiency. Then, deep learning algorithms are used to determine the collaborative and synchronized working parameters between various construction equipment based on multiple sets of construction data, constructing a parameter optimization model for the skew bridge construction. Next, the current engineering parameters of the skew bridge are obtained, and the operating parameters of each construction equipment are determined based on these parameters. Finally, based on the calibrated operating parameters and the real-time operating parameters of each construction equipment, the model determines the optimal construction parameters—that is, the collaborative and efficient construction parameters between the various construction equipment. During construction, efficiency monitoring indicators are established to monitor construction efficiency in real time. Based on the monitoring results, the phased progress of the project is summarized, compiled, and feedback is provided.

[0073] The beneficial effects of the above technical solution are as follows: by constructing a comprehensive intelligent sensing network, it can provide accurate data support for the construction process, ensuring the objectivity and accuracy of the data. Furthermore, by constructing a parameter optimization model for the construction of curved skew bridges to predict the optimal construction parameters, it can maximize the collaborative operation effect and work efficiency between various construction equipment, improve stability and practicality, and solve the problem mentioned in the existing technology that the traditional construction mode relies on manual experience and conventional measurement methods, making it difficult to accurately control various parameters in the construction process, resulting in poor coordination and low efficiency of subsequent construction equipment.

[0074] In one embodiment, such as Figure 2 As shown, the construction module 101 includes:

[0075] The first determining submodule 1011 is used to determine the workflow of the construction of the curved skew bridge, and to determine the key material components, key working media and key operating equipment based on the workflow.

[0076] The second determination submodule 1012 is used to determine the state changes of key material components, key working media and key operating equipment during construction, and select different types of sensors according to the state changes.

[0077] Submodule 1013 is constructed to build an all-round intelligent sensing network for the construction process of curved skew bridges through various types of sensors and communication units;

[0078] The data acquisition and processing submodule 1014 is used to collect and process historical construction data from multiple curved skew bridge projects during their construction process using an all-around intelligent sensing network.

[0079] The beneficial effects of the above technical solution are as follows: determining the relevant components and equipment according to the construction workflow can ensure the reliability of the construction process; furthermore, selecting sensors based on the status changes of the relevant components and equipment, constructing an all-round intelligent sensing network and collecting historical construction data can ensure the accuracy and precision of the data, while improving the efficiency of data collection.

[0080] In one embodiment, the key material component is a precast beam, the key working medium is a bridge pier, and the key operating equipment is a bridge erecting machine;

[0081] Among them, the sensors corresponding to the precast beams are high-precision three-dimensional laser displacement sensors and tilt sensors. The three-dimensional laser displacement sensors are used to monitor the spatial position changes of the precast beams during hoisting and installation, and the tilt sensors are used to measure the tilt angle of the precast beams during hoisting and installation.

[0082] The sensors corresponding to the bridge piers are stress-strain sensors, which are used to monitor the stress state of the bridge pier structure during construction.

[0083] The bridge erecting machine is equipped with stress-strain sensors and tilt sensors. The tilt sensors are used to measure the tilt angle of the bridge erecting machine's outriggers, while the strain sensors are used to monitor the stress state of the main structure of the bridge erecting machine during construction.

[0084] In one embodiment, the training module includes:

[0085] The preprocessing submodule is used to perform data cleaning and standardization preprocessing on multiple sets of historical construction data to obtain preprocessed historical construction data.

[0086] The design submodule is used to extract the construction stage characteristics and environmental impact characteristics corresponding to the preprocessed historical construction data through deep learning algorithms, select a deep learning model based on the construction stage characteristics and environmental impact characteristics, and design the model architecture.

[0087] The training submodule is used to divide the preprocessed historical construction data into training set, validation set and test set, and to train the deep learning model through the inspection set to build the trained model.

[0088] The first generation submodule is used to verify and test the accuracy and performance of the trained model using a validation set and a test set. After the verification test is passed, a parameter optimization model for the construction of curved skew bridges is generated.

[0089] The beneficial effects of the above technical solution are as follows: preprocessing historical data can ensure data consistency; further, extracting the construction stage characteristics and environmental impact characteristics corresponding to historical construction data and training and verifying the model can improve the model's performance and accuracy, enhance the model's reliability; further, generating a parameter optimization model for the construction of curved skew bridges can improve the accuracy and quality of the construction process and reduce construction errors.

[0090] In one embodiment, such as Figure 3 As shown, the prediction module 103 includes:

[0091] The first data acquisition submodule 1031 is used to acquire the target design parameters of the current curved skew bridge project, collect environmental parameters of the construction site, and real-time construction data.

[0092] The prediction submodule 1032 is used to input the target design parameters, environmental parameters of the construction site, and real-time construction data into the parameter optimization model to predict the optimal construction parameters.

[0093] The third determination submodule 1033 is used to determine the collaborative operation parameters between various construction equipment based on the optimal construction parameters, and to determine the scheduling and control parameters for each construction equipment based on the collaborative operation parameters.

[0094] The fourth determination submodule 1034 is used to determine the matching efficiency index type based on the scheduling control parameters of each construction equipment and the construction target, obtain the quantitative index of the matching efficiency index type, and determine the quantitative index as the monitoring index of each type of efficiency index.

[0095] The beneficial effects of the above technical solution are as follows: Based on the environmental parameters of the construction site and real-time construction data, the optimal construction parameters are predicted, thereby determining the collaborative operation parameters between various construction equipment and the scheduling and control parameters of each piece of equipment. This ensures efficient collaboration between equipment, reduces construction quality problems caused by unreasonable equipment scheduling, allows for timely adjustment of scheduling strategies, and ensures construction quality. Furthermore, determining quantitative indicators matching efficiency index types clarifies key links and bottlenecks in the construction process, enabling targeted optimization. Simultaneously, it provides a clear understanding of construction progress and resource utilization, thereby optimizing the construction process and improving efficiency.

[0096] In one embodiment, the monitoring module includes:

[0097] The second data acquisition submodule is used to determine the reference data system for each efficiency monitoring indicator and to collect the corresponding data of the construction process of the skew bridge according to the reference data system.

[0098] The fifth submodule is used to calculate the efficiency index of each efficiency monitoring indicator based on the collected data, determine the construction qualification based on the efficiency index, and make corresponding optimizations.

[0099] The sixth submodule is used to determine the phased targets for each efficiency monitoring indicator at each construction stage, and to determine the actual progress of the phased construction based on the phased targets and real-time construction data.

[0100] The second generation submodule is used to determine the deviation between the actual and theoretical progress of the phased construction and to identify inefficient construction links to generate a phased work progress feedback report.

[0101] The beneficial effects of the above technical solution are as follows: by determining the efficiency index of each efficiency monitoring indicator based on the relevant data of the construction process, and by evaluating and optimizing the qualification of the construction, it is possible to accurately locate the weak links in the construction process and optimize them in a targeted manner to improve the construction quality. Furthermore, by identifying the inefficient links based on the deviation between the actual progress and the theoretical progress of the phased construction and generating a report, it is possible to make targeted adjustments to the inefficient links and improve the construction efficiency of each link. At the same time, it is possible to ensure the visualization of the construction progress.

[0102] In one embodiment, the system is further configured to:

[0103] Detect abnormal environmental parameters and abnormal event parameters at the construction site, and identify risky construction equipment and potential safety hazards based on these parameters.

[0104] Based on the risk of construction equipment and potential safety hazards, the recommended suspension points of the construction operation mechanism are determined, and the relevant construction equipment and manpower configurations for the recommended suspension points are deployed and adjusted.

[0105] Based on abnormal environmental parameters, the appropriate construction parameters are re-predicted using a parameter optimization model, and then the optimal construction parameters are adjusted based on these appropriate construction parameters.

[0106] The beneficial effects of the above technical solution are as follows: By identifying safety hazards and recommended suspension points based on abnormal environmental and event parameters during construction, and subsequently redeploying manpower and equipment, construction safety can be improved. Simultaneously, the efficiency of equipment and manpower can be maximized. Furthermore, determining suitable construction parameters and adjusting the optimal parameters allows for precise control of the construction process, while simultaneously improving construction quality and efficiency. On one hand, a multi-equipment collaborative control system enables emergency adjustments to construction equipment to ensure construction safety; on the other hand, an AI-based construction parameter optimization model recalculates and generates adjusted construction parameters based on new circumstances. Construction personnel continue construction according to the adjusted parameters, ensuring the continuity and accuracy of the construction process.

[0107] In one embodiment, the location of the stress sensor on the bridge pier is determined in the following way:

[0108] Obtain the volume parameters of the bridge pier, determine the area of ​​the stress zone of the bridge pier based on the volume parameters, and determine the stress zone within the stress zone based on the working structure of the precast beam and the stress zone of the bridge pier.

[0109] The area of ​​the stress region is calculated based on the area of ​​the stressed region, and the stress uniformity of the bridge pier is determined based on the area of ​​the stress region and the overlap characteristics between the stress region and the stressed region.

[0110] Based on the uniformity of force, the direction of force eccentricity and the direction of force non-eccentricity are determined. Stress sensor positions are set on the side of the first pier in the direction of force eccentricity at a first height, and stress sensor positions are set on the side of the second pier in the direction of force non-eccentricity at a second height.

[0111] The distance from the second height to the top of the pier is 1.5 times the distance from the first height to the top of the pier.

[0112] The beneficial effects of the above technical solution are as follows: by setting the sensor position according to the stress distribution, it can be ensured that the stress change data of the bridge pier can be detected in all directions, so as to comprehensively understand the stress change of the bridge pier, ensuring the accuracy, reliability and integrity of the data, and at the same time, it can potentially avoid the safety accident of precast beam collapse due to uneven stress of the bridge pier, thus improving safety.

[0113] In one embodiment, determining the collaborative operation parameters between various construction equipment based on optimal construction parameters includes:

[0114] Determine the prerequisite construction conditions for each construction equipment, and determine the construction task parameters for each construction equipment based on the prerequisite construction conditions;

[0115] The construction objectives are determined based on the construction task parameters. The data state change patterns before and after construction are determined based on the construction objectives. The critical values ​​of the collaborative operation are determined based on the optimal construction parameters and the data state change patterns of each construction equipment before and after construction.

[0116] Based on the state critical values ​​of the collaborative operation of each construction equipment, the synchronous data change parameters of the collaborative operation of each construction equipment are determined, and the collaborative operation parameters between each construction equipment are determined based on the synchronous data change parameters.

[0117] The beneficial effects of the above technical solution are as follows: by determining the pre-construction conditions of each construction equipment and then determining the reference data, it is possible to intuitively determine which data each construction equipment should refer to for operation. At the same time, the synchronous changes in the working status of each construction equipment during the operation process determine the collaborative operation parameters, which is faster and more intuitive.

[0118] In one embodiment, the system is further configured to:

[0119] Based on the target design parameters of the current curved skew bridge project, determine multiple reserved installation positions for the precast beams and obtain the support installation area for each reserved installation position;

[0120] The optimal docking posture between the precast beam and the support at each reserved installation location is determined based on the support installation area at each reserved installation location and the lithological factors at that reserved installation location.

[0121] The optimal docking posture is used to determine the aerial docking configuration of the precast beam at each reserved installation position, and the required rotation angle of the bridge erecting machine's outriggers is determined based on the aerial docking configuration.

[0122] An improvement plan is generated based on the required rotation angle of the outriggers and the current outrigger structure of the bridge erecting machine. Based on the improvement plan, the current outrigger structure is technically improved.

[0123] Based on the target design parameters, generate construction drawings and material dimension drawings for the current curved skew bridge project, and review the construction drawings using the BIM model to identify and provide feedback on any unreasonable processes;

[0124] Based on material dimension drawings, BIM technology is used to model and perform dimensional verification and engineering requirement matching assessment.

[0125] Obtain the environmental parameters of organic soil at the construction site, determine the soil water content based on the organic soil environmental parameters, and determine the water stability of the filler based on the water content.

[0126] The water resistance requirements are determined based on the water stability of the filler, and the target type of curing agent is selected based on the water resistance requirements and the reaction parameters of the curing agent, clay minerals and organic matter.

[0127] Soil looseness is determined based on soil environmental parameters, soil consolidation performance is determined based on soil looseness, and it is confirmed whether the soil consolidation performance meets the construction requirements.

[0128] If not, a new architecture is generated based on the compaction mechanism of inorganic construction waste recycled aggregate and soil consolidation skeleton, as well as the composition of soil micro-network structure.

[0129] The new architecture determines the blending ratio of inorganic construction waste recycled aggregate and soil, and the soil is then optimized based on the blending ratio.

[0130] The beneficial effects of the above technical solution are as follows: by generating an improved solution and then improving the technical structure of the current support structure based on the improved solution, the stability of the precast beam handling can be guaranteed, and the precise posture docking of the precast beam and the support can be achieved, as well as the precise positioning of the precast box girder of the large-angle skew curve bridge. This improves the positioning accuracy, docking stability and work efficiency. Furthermore, by selecting the type of curing agent and optimizing the soil treatment, the problem of poor water stability of the filler can be solved, while the soil durability can be increased, improving the practicality and the fit with the construction conditions.

[0131] Those skilled in the art should understand that the "first" and "second" in this invention simply refer to different application stages.

[0132] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0133] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A high-efficiency and precise construction system for curved skew bridges based on intelligent sensing and collaborative control, characterized in that, The system includes: The module is used to build a comprehensive intelligent sensing network during the construction of curved skew bridges, and to collect multiple sets of historical construction data through the comprehensive intelligent sensing network. The training module is used to analyze and train based on multiple sets of historical construction data using deep learning algorithms to establish a parameter optimization model for the construction of curved skew bridges. The prediction module is used to predict the optimal construction parameters of the curved skew bridge through the parameter optimization model, and to determine the efficiency monitoring indicators based on the optimal construction parameters. The monitoring module is used to accurately monitor the construction process of the curved skew bridge based on efficiency monitoring indicators and provide phased work progress feedback. The prediction module includes: The first data acquisition submodule is used to obtain the target design parameters of the current curved skew bridge project, collect environmental parameters of the construction site, and real-time construction data. The prediction submodule is used to input the target design parameters, environmental parameters of the construction site, and real-time construction data into the parameter optimization model to predict the optimal construction parameters. The third determination submodule is used to determine the collaborative operation parameters between various construction equipment based on the optimal construction parameters, and to determine the scheduling and control parameters for each construction equipment based on the collaborative operation parameters. The fourth submodule is used to determine the matching efficiency index type based on the scheduling and control parameters of each construction equipment and the construction target, obtain the quantitative index of the matching efficiency index type, and determine the quantitative index as the monitoring index of each type of efficiency index. The building module includes: The first submodule is used to determine the workflow for the construction of curved skew bridges, and to determine key material components, key working media, and key operating equipment based on the workflow. The second determination submodule is used to determine the state changes of key material components, key working media and key operating equipment during construction, and select different types of sensors according to the state changes. A sub-module is constructed to build a comprehensive intelligent sensing network for the construction process of curved skew bridges through various types of sensors and communication units; The data acquisition and processing submodule is used to collect and process historical construction data from multiple curved skew bridge projects during their initiation and construction processes using an all-round intelligent sensing network. The key material component is a precast beam, the key working medium is a bridge pier, and the key operating equipment is a bridge erecting machine; Among them, the sensors corresponding to the precast beams are high-precision three-dimensional laser displacement sensors and tilt sensors. The three-dimensional laser displacement sensors are used to monitor the spatial position changes of the precast beams during hoisting and installation, and the tilt sensors are used to measure the tilt angle of the precast beams during hoisting and installation. The sensors corresponding to the bridge piers are stress-strain sensors, which are used to monitor the stress state of the bridge pier structure during construction. The bridge erecting machine is equipped with stress-strain sensors and tilt sensors. The tilt sensors are used to measure the tilt angle of the bridge erecting machine's outriggers, while the strain sensors are used to monitor the stress state of the main structure of the bridge erecting machine during construction.

2. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control as described in claim 1, characterized in that, The training module includes: The preprocessing submodule is used to perform data cleaning and standardization preprocessing on multiple sets of historical construction data to obtain preprocessed historical construction data. The design submodule is used to extract the construction stage characteristics and environmental impact characteristics corresponding to the preprocessed historical construction data through deep learning algorithms, select a deep learning model based on the construction stage characteristics and environmental impact characteristics, and design the model architecture. The training submodule is used to divide the preprocessed historical construction data into training set, validation set and test set, and to train the deep learning model through the inspection set to build the trained model. The first generation submodule is used to verify and test the accuracy and performance of the trained model using a validation set and a test set. After the verification test is passed, a parameter optimization model for the construction of curved skew bridges is generated.

3. The efficient and precise construction system for curved skew bridges based on intelligent sensing and collaborative control as described in claim 1, characterized in that, The monitoring module includes: The second data acquisition submodule is used to determine the reference data system for each efficiency monitoring indicator and to collect the corresponding data of the construction process of the skew bridge according to the reference data system. The fifth submodule is used to calculate the efficiency index of each efficiency monitoring indicator based on the collected data, determine the construction qualification based on the efficiency index, and make corresponding optimizations. The sixth submodule is used to determine the phased targets for each efficiency monitoring indicator at each construction stage, and to determine the actual progress of the phased construction based on the phased targets and real-time construction data. The second generation submodule is used to determine the deviation between the actual and theoretical progress of the phased construction and to identify inefficient construction links to generate a phased work progress feedback report.

4. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control as described in claim 1, characterized in that, The system is also used for: Detect abnormal environmental parameters and abnormal event parameters at the construction site, and identify risky construction equipment and potential safety hazards based on these parameters. Based on the risk of construction equipment and potential safety hazards, the recommended suspension points of the construction operation mechanism are determined, and the relevant construction equipment and manpower configurations for the recommended suspension points are deployed and adjusted. Based on abnormal environmental parameters, the appropriate construction parameters are re-predicted using a parameter optimization model, and then the optimal construction parameters are adjusted based on these appropriate construction parameters.

5. The efficient and precise construction system for curved skew bridges based on intelligent perception and collaborative control according to claim 1, characterized in that, The location of the stress sensors on the bridge piers is determined in the following way: Obtain the volume parameters of the bridge pier, determine the area of ​​the stress zone of the bridge pier based on the volume parameters, and determine the stress zone within the stress zone based on the working structure of the precast beam and the stress zone of the bridge pier. The area of ​​the stress region is calculated based on the area of ​​the stressed region, and the stress uniformity of the bridge pier is determined based on the area of ​​the stress region and the overlap characteristics between the stress region and the stressed region. Based on the uniformity of force, the direction of force eccentricity and the direction of force non-eccentricity are determined. Stress sensor positions are set on the side of the first pier in the direction of force eccentricity at a first height, and stress sensor positions are set on the side of the second pier in the direction of force non-eccentricity at a second height. The distance from the second height to the top of the pier is 1.5 times the distance from the first height to the top of the pier.

6. The efficient and precise construction system for curved skew bridges based on intelligent sensing and collaborative control according to claim 1, characterized in that, Based on the optimal construction parameters, determine the collaborative operation parameters between various construction equipment, including: Determine the prerequisite construction conditions for each construction equipment, and determine the construction task parameters for each construction equipment based on the prerequisite construction conditions; The construction objectives are determined based on the construction task parameters. The data state change patterns before and after construction are determined based on the construction objectives. The critical values ​​of the collaborative operation are determined based on the optimal construction parameters and the data state change patterns of each construction equipment before and after construction. Based on the state critical values ​​of the collaborative operation of each construction equipment, the synchronous data change parameters of the collaborative operation of each construction equipment are determined, and the collaborative operation parameters between each construction equipment are determined based on the synchronous data change parameters.

7. The efficient and precise construction system for curved skew bridges based on intelligent sensing and collaborative control according to claim 1, characterized in that, The system is also used for: Based on the target design parameters of the current curved skew bridge project, determine multiple reserved installation positions for the precast beams and obtain the support installation area for each reserved installation position; The optimal docking posture between the precast beam and the support at each reserved installation location is determined based on the support installation area at each reserved installation location and the lithological factors at that reserved installation location. The optimal docking posture is used to determine the aerial docking configuration of the precast beam at each reserved installation position, and the required rotation angle of the bridge erecting machine's outriggers is determined based on the aerial docking configuration. An improvement plan is generated based on the required rotation angle of the outriggers and the current outrigger structure of the bridge erecting machine. Based on the improvement plan, the current outrigger structure is technically improved. Based on the target design parameters, generate construction drawings and material dimension drawings for the current curved skew bridge project, and review the construction drawings using the BIM model to identify and provide feedback on any unreasonable processes; Based on material dimension drawings, BIM technology is used to model and perform dimensional verification and engineering requirement matching assessment. Obtain the environmental parameters of organic soil at the construction site, determine the soil water content based on the organic soil environmental parameters, and determine the water stability of the filler based on the water content. The water resistance requirements are determined based on the water stability of the filler, and the target type of curing agent is selected based on the water resistance requirements and the reaction parameters of the curing agent, clay minerals and organic matter. Soil looseness is determined based on soil environmental parameters, soil consolidation performance is determined based on soil looseness, and it is confirmed whether the soil consolidation performance meets the construction requirements. If not, a new architecture is generated based on the compaction mechanism of inorganic construction waste recycled aggregate and soil consolidation skeleton, as well as the composition of soil micro-network structure. The new architecture determines the blending ratio of inorganic construction waste recycled aggregate and soil, and the soil is then optimized based on the blending ratio.

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