Cast-in-place construction method for displacement support of swivel bridge

Abstract

The invention relates to the technical field of bridge construction, in particular to a swivel bridge ectopic support cast-in-place construction method which comprises the following steps: S1, constructing a three-dimensional model of a bridge and a support by using a BIM technology, performing virtual simulation, obtaining a simulation result by simulating a construction process, and outputting a construction scheme; s2, monitoring the support structure and the overall state of the bridge in real time on the swivel bridge construction site through monitoring equipment, and collecting real-time data of the construction site; when the system is used, equipment idling is reduced, personnel are reasonably distributed, material transportation is optimized, the construction efficiency is improved, intelligent optimization of data driving is achieved through BIM + AI, the construction automation level is improved, accurate analysis of supports and construction data is facilitated, the problem finding efficiency is improved, construction personnel can conveniently and rapidly intervene, construction delay is reduced, and the construction efficiency is improved. Potential risks can be predicted before faults occur, potential safety hazards are reduced, the construction sequence is adjusted through intelligent analysis, and delay caused by abnormal data is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge construction, and in particular to a cast-in-place construction method for an out-of-situ support of a rotary bridge. Background Art

[0002] The off-site support technology for rotating bridges is a unique bridge construction method, often used in areas with complex terrain and unique environments, such as elevated railways or river bridges. The key to this technology lies in the use of an adjustable support system, which, by placing supports at different locations on the bridge, enables complex operations such as rotation, movement, and installation.

[0003] The patent publication number is CN118880746A, which states in its specification that "the present invention discloses a continuous beam with an out-of-situ support cast in situ and a construction method thereof, which relates to the field of bridge construction technology, including: a support mechanism, a connecting mechanism and a fixing mechanism arranged along the length direction of the continuous beam, the continuous beam including a cast-in-situ continuous beam; the cast-in-situ continuous beam is arranged between the support mechanisms. The beneficial effects of the present invention are: through the support mechanism, the connecting mechanism and the fixing mechanism, different foundation treatment methods and support combination forms are adopted for different road cutting slopes and different beam heights, and a concrete hardened foundation and a buckle bracket or an expanded foundation is adopted for a flat and non-sloping beam with a low beam height (less stress). The combined support form of pile foundation, steel pipe support and steel bent frame is adopted. For the slope of the cutting with a large slope and a high beam (with a large force), the protection method of grouting anchor rods and hanging mesh sprayed concrete facing is adopted. The combined support form of pile foundation steel pipe support and steel bent frame is adopted to reduce the excavation volume and improve safety. Although the above technology has successfully reduced the excavation volume and improved construction efficiency and safety by adopting the combined support form of pile foundation steel pipe support and steel bent frame, the overall construction process still relies on traditional construction experience and lacks data statistics, engineering plan analysis and intelligent empowerment. This has led to the failure of the above technology to be fully combined with modern technology, which in turn limits the optimization and efficiency improvement of the construction process.

[0004] In summary, developing a cast-in-place construction method for dysplastic supports of a rotating bridge is still a key issue that needs to be urgently addressed in the field of bridge construction technology. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem in the prior art that although the above-mentioned technology has successfully reduced the excavation volume and improved construction efficiency and safety by adopting a combined support form of pile foundation steel pipe support and steel frame, the overall construction process still relies on traditional construction experience and lacks data statistics, engineering plan analysis and intelligent empowerment, which leads to the above-mentioned technology failing to be fully combined with modern technology for development, thereby limiting the optimization of the construction process and efficiency improvement.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a method for cast-in-situ construction of an off-site support for a rotating bridge, comprising the following steps: S1, constructing a three-dimensional model of the bridge and the support using BIM technology, performing virtual simulation, obtaining simulation results by simulating the construction process, and then outputting a construction plan;

[0008] S2. Use monitoring equipment to monitor the support structure and the overall status of the bridge in real time at the construction site of the rotating bridge, and collect real-time data from the construction site;

[0009] S3. Dynamically optimize support installation sequence and resource allocation by combining real-time data from the construction site, weather forecasts, and construction plans;

[0010] S4. Compare the real-time data from the construction site with the construction plan. If any abnormal data is found, locate the problem area and conduct investigation and correction.

[0011] S5. Summarize and analyze abnormal data based on artificial intelligence systems, combine machine learning models to predict faults, and implement predictive maintenance.

[0012] Furthermore, in step S1, a three-dimensional model of the bridge and support is constructed using BIM technology to perform virtual simulation, and a construction plan is planned by simulating the construction process as follows:

[0013] Collect engineering data related to the bridge and support, including but not limited to bridge design drawings, support design plans, geological and hydrological data, construction environment information, and construction technology and specifications. Use BIM software to build a rotating bridge structure model according to the design drawings. At the same time, build a support model in the BIM software based on the construction plan of the rotating bridge's ectopic support. Perform virtual simulation of the construction process from foundation construction → support erection → formwork installation → concrete pouring → curing and demolding. Control the construction progress of different stages through the timeline, simulate the lifting, concrete pumping, and formwork installation processes, check whether the equipment layout is reasonable and whether it affects the surrounding construction, use BIM software to detect the spatial interference between the support, formwork, and equipment, and perform force analysis. Finally, obtain the simulation results, and then output the construction plan. Support model:

[0014] where Q bridge is the total load borne by the support and foundation during construction, Q concrete is the concrete pouring load, Q equipment is the construction equipment load, Q env It is the load affected by environmental factors such as wind load and temperature load. A base is the bracket base contact area, w s is the bearing capacity of the foundation, Is the bracket safety, Q total is the load borne by the bracket, virtual simulation: Among them E Q is the pile foundation settlement, Q bridge is the total load borne by the support and foundation during construction, R s is the pile length, T s is the elastic modulus of the foundation, A s is the cross-sectional area of ​​the pile, α is the support deflection, Q is the concentrated load, Y is the support span, T is the elastic modulus of the material, U is the section moment of inertia, Q i is the concrete pumping pressure, β is the concrete viscosity, Y o is the length of the delivery pipe, P is the pumping flow rate, a is the pipe diameter, d o (t) is the concrete compressive strength growth curve, d o (28) is the standard curing strength for 28 days, and f and g are empirical coefficients.

[0015] Furthermore, in step S2, the support structure and the overall status of the bridge are monitored in real time at the construction site of the rotating bridge by monitoring equipment. The method for collecting real-time data at the construction site is as follows:

[0016] Real-time data collection was carried out at the construction site of the rotating bridge through monitoring equipment, including the installation of strain gauges and displacement sensors at the support nodes to monitor stress change data and deformation data in real time, the installation of inclinometers on the bridge deck and on the pier tops to monitor the rotation angle data, the use of laser rangefinders to monitor the displacement data of the bridge relative to the support, the installation of anemometers at the wind outlet of the construction site and on both sides of the bridge to monitor the wind speed data of the construction site, the arrangement of temperature and humidity sensors on the bridge deck and the support to monitor the temperature and humidity data of the construction site, the use of ultrasonic detectors to evaluate the pouring quality, and the monitoring of foundation settlement data and angle data through foundation settlement monitors. Where χ(t) is the instantaneous rotation angle of the bridge, δ(t) is the angular velocity, and the displacement data is:

[0017] Where K(t) is the displacement of the bridge relative to the support, (x2, y2, z2) and (x1, y1, z1) are the coordinate positions of the bridge and the support respectively.

[0018] Furthermore, in step S2, the support structure and the overall status of the bridge are monitored in real time at the construction site of the rotating bridge by monitoring equipment. The method for collecting real-time data at the construction site is as follows:

[0019] Wireless transmission and edge computing gateway are used to transmit, pre-process and summarize the collected real-time data. A local server is used to establish a real-time monitoring system to store the collected real-time data. At the same time, the collected real-time data is mapped to the BIM model to intuitively display the status of the rotating bridge and the ectopic support. The real-time monitoring system: L real-time =c(V transmit ,B BIM ), where L real-time For real-time monitoring system, V transmit It is real-time data, B BIM It is BIM model data. c is used to combine monitoring data and BIM model data to calculate real-time construction status and real-time data mapping: in is the parameter matrix of each node, is the coordinate of the node in three-dimensional space, δ is the stress at the node, ε is the deformation of the node, and φ is the rotation angle of the node.

[0020] Furthermore, in step S3, the method for dynamically optimizing the bracket installation sequence and resource allocation is based on the real-time data of the construction site, weather forecast and construction plan:

[0021] Obtain the weather forecast for the next 3-7 days from the meteorological data platform. The weather forecast includes precipitation, wind speed and direction, temperature and humidity. Combined with the construction plan, including the planned bracket installation sequence, the allocation of personnel, equipment and materials required for construction, and the time schedule for hoisting, welding and pouring processes, if the real-time data indicates that the foundation settlement in area A exceeds the standard, the BIM model will prioritize installing brackets in the area with stable foundations. If the weather forecast indicates strong winds in the next three days, the BIM model will adjust the bracket installation sequence, completing the bracket installation in the area affected by low wind speeds first. The bracket installation sequence will be adjusted due to foundation settlement: Where M is the area where the foundation settlement exceeds the standard, is the stable area of ​​the foundation, N plan This is the bracket installation sequence in the initial construction plan. The bracket installation sequence is adjusted due to wind speed: in is the wind speed in the weather forecast, is the high wind threshold, θwind is the wind direction, low-wind is the low wind speed affected area, It is the bracket installation order after optimization according to wind speed.

[0022] Furthermore, in step S3, the method for dynamically optimizing the bracket installation sequence and resource allocation is based on the real-time data of the construction site, weather forecast and construction plan:

[0023] When real-time data indicates that the hoisting equipment is undergoing maintenance, the BIM model adjusts the construction plan to prioritize bracket welding and reinforcement work in other areas. Based on the optimized construction plan, the BIM model dynamically adjusts personnel division of labor, rearranges crane positions according to the bracket installation sequence, reduces idling time, adjusts material transportation routes, prioritizes steel structure supply in emergency areas, and delivers bracket materials in advance according to the optimized bracket installation sequence. Bracket welding and reinforcement work: in It is the lifting equipment operation area. is the welding area, It is the original construction plan. It is an optimized construction plan, dynamically adjusting the division of labor: in It is the optimized personnel scheduling, It is the currently available personnel.

[0024] Furthermore, in step S4, based on the comparison between the real-time data of the construction site and the construction plan, after abnormal data is found, the method for locating the problem area and conducting investigation and correction is as follows:

[0025] The standard parameters in the construction plan, including support design standards, construction time schedule, material usage standards and safety limits, are entered into the standard database through the BIM model. The real-time data mapped in the BIM model is then compared with the standard database. The AI ​​anomaly detection system is used for automatic analysis. If the real-time data deviates from the standard data in the standard database, it is determined to be abnormal data. The abnormal data is classified as shown in Table 1:

[0026] Exception Type reason Influence Bracket displacement exceeds standard Construction errors and uneven foundation settlement Affects structural stability Tilt angle exceeds limit Uneven construction, uneven force, and wind impact Causes the support to collapse Abnormal stress Loose connection, local material defects Affecting the bearing capacity of the bracket Construction progress is delayed Material supply issues, weather impact Delay in construction Abnormal environmental factors Sudden strong winds and heavy rain Affecting construction safety

[0027] Automatic analysis: in is the classification function, The bracket displacement exceeds the standard. The tilt angle exceeds the limit. It is abnormal stress. The construction progress is delayed. It's an abnormal environmental factor. is the bracket displacement deviation, φ real ,φ std is the bracket tilt angle, φ real ,φ std is the support force, γ real , γ std is the actual construction time vs. the planned construction time, η real ,η std is the environmental parameter vector, φ max , γmax ,η max are their respective abnormal thresholds.

[0028] Furthermore, in step S4, based on the comparison between the real-time data of the construction site and the construction plan, after abnormal data is found, the method for locating the problem area and conducting investigation and correction is as follows:

[0029] Based on the sensor data, the spatial distribution of abnormal data is determined, the bracket number and construction area where the problem is located are determined, the abnormal location is displayed through the BIM model, and the abnormal area is highlighted. Then the construction technicians go to the abnormal area for secondary inspection to confirm the accuracy of the abnormal data. If the abnormal data is correct, the on-site investigation process is initiated and a targeted correction plan is formulated according to the construction process and specifications. After the correction, the corrected data is remeasured and uploaded to the BIM model for re-inspection. If the abnormal data returns to the standard range, the correction is confirmed to be successful and the construction log is updated. If the abnormal data is still not within the standard range, the secondary optimization is carried out and a new correction plan is formulated. Investigation and correction: in It is real-time data. is the correction function, is the revised construction data, is the original bracket position, is the correction offset, is the corrected bracket position, ι old is the original force value, Δι fix is the force correction value, ι new is the corrected force value, This is the original construction schedule. is the progress modifier, is the optimized construction progress, is the original environmental condition data, is the environmental correction value, It is the corrected environmental condition, and the corrected data is rechecked: in is the deviation of the new data, It is the newly acquired real-time data. It is the construction standard data in the standard database.

[0030] Furthermore, in step S5, the method of summarizing and analyzing abnormal data based on the artificial intelligence system, combining the machine learning model to predict faults, and implementing predictive maintenance is as follows:

[0031] Based on the real-time data and abnormal data, the artificial intelligence system summarizes and analyzes them. The artificial intelligence system includes time series analysis, anomaly detection algorithm, regression analysis and cluster analysis. Time series analysis is used to analyze the deformation trend of the support and predict the future state. The anomaly detection algorithm is used to detect abnormal data during construction and determine whether there are hidden faults. Regression analysis is used to predict foundation settlement and bridge stress changes. Cluster analysis is used to cluster data from different construction stages to discover potential abnormal patterns. The deformation trend of the support:

[0032] in The observation data at time t, is the predicted value at time t+1, Indicates the impact of historical data on current data, j is the autoregressive order, θ1 is used to correct the error term, is the sliding average order, σ t is the random error at the current time t, Is the error term at the past time point, anomaly detection algorithm: in is the data vector at the current moment, is the mean vector in the standard database, Σ is the covariance matrix of the standard database, Σ -1 is the inverse of the covariance matrix, is the difference vector between the data point and the mean, is the Mahalanobis distance, cluster analysis: in is the final clustering assignment result, arg min represents the cluster partitioning scheme that minimizes the objective function. is the number of clusters, is the i-th cluster, H J′ is the J′th data point, τ i is the center point of the i-th cluster, It represents the data point H J′ Belong to cluster ||H J′ -τ i || 2 Represents data point H J′ and cluster center τ i The square of the Euclidean distance between is the sum of all i clusters, is the pair belonging to the cluster All data points Perform the summation.

[0033] Furthermore, in step S5, the method of summarizing and analyzing abnormal data based on the artificial intelligence system, combining the machine learning model to predict faults, and implementing predictive maintenance is as follows:

[0034] Based on the abnormal patterns learned from abnormal data by the AI ​​system and combined with time series analysis, it predicts future failures, generates prediction results, and displays risk points in the future construction phase through the BIM model. The prediction results can be viewed in real time and preventive measures can be taken through the app and web interface. The AI ​​system records each failure prediction, maintenance plan, and execution effect, and continuously optimizes the machine learning model. At the same time, after the construction is completed, the failure frequency and maintenance effect are statistically analyzed to optimize the machine learning model:

[0035] in is the construction monitoring data at the current time t, is the historical construction monitoring data at the past n time points, is the prediction function, M′ t is the machine learning model trained at time t, E′ is the new fault feature dataset, is the model loss function, ξ is the learning rate, M′ t-1 is the new machine learning model after training at time t.

[0036] Beneficial effects

[0037] Compared with the known public technology, the technical solution provided by the present invention has the following advantages:

[0038] Beneficial effects:

[0039] When used, the present invention is conducive to reducing equipment idling, rationally allocating personnel, optimizing material transportation, improving construction efficiency, realizing data-driven intelligent optimization through BIM+AI, improving the level of construction automation, and facilitating accurate analysis of support and construction data, improving the efficiency of problem discovery, facilitating rapid intervention of construction personnel, reducing construction delays, and facilitating the prediction of potential risks before failures occur, reducing safety hazards, adjusting the construction sequence through intelligent analysis, and reducing delays caused by abnormal data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention provides a flow chart of a cast-in-place construction method for an out-of-situ support of a rotating bridge. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0042] It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatuses.

[0043] The present invention is described in further detail below with reference to the accompanying drawings:

[0044] Example:

[0045] like Figure 1 As shown, the present invention provides a method for cast-in-situ construction of a rotating bridge out-of-situ support, comprising the following steps: S1, constructing a three-dimensional model of the bridge and the support using BIM technology, performing virtual simulation, and obtaining simulation results by simulating the construction process, and then outputting a construction plan;

[0046] Furthermore, in step S1, a three-dimensional model of the bridge and support is constructed using BIM technology to perform virtual simulation, and a construction plan is planned by simulating the construction process as follows:

[0047] Collect engineering data related to the bridge and support, including but not limited to bridge design drawings, support design plans, geological and hydrological data, construction environment information, and construction technology and specifications. Use BIM software to build a rotating bridge structure model according to the design drawings. At the same time, build a support model in the BIM software based on the construction plan of the rotating bridge's ectopic support. Perform virtual simulation of the construction process from foundation construction → support erection → formwork installation → concrete pouring → curing and demolding. Control the construction progress of different stages through the timeline, simulate the lifting, concrete pumping, and formwork installation processes, check whether the equipment layout is reasonable and whether it affects the surrounding construction, use BIM software to detect the spatial interference between the support, formwork, and equipment, and perform force analysis. Finally, obtain the simulation results, and then output the construction plan. Support model:

[0048] where Q bridge is the total load borne by the support and foundation during construction, Q concrete is the concrete pouring load, Q equipment is the construction equipment load, Q env It is the load affected by environmental factors such as wind load and temperature load. Abase is the bracket base contact area, w s is the bearing capacity of the foundation, Is the bracket safety, Q total is the load borne by the bracket, virtual simulation: Among them E Q is the pile foundation settlement, Q bridge is the total load borne by the support and foundation during construction, R s is the pile length, T s is the elastic modulus of the foundation, A s is the cross-sectional area of ​​the pile, α is the support deflection, Q is the concentrated load, Y is the support span, T is the elastic modulus of the material, U is the section moment of inertia, Q i is the concrete pumping pressure, β is the concrete viscosity, Y o is the length of the delivery pipe, P is the pumping flow rate, a is the pipe diameter, d o (t) is the concrete compressive strength growth curve, d o (28) is the standard curing strength for 28 days, and f and g are empirical coefficients.

[0049] In this embodiment, BIM technology is used to construct a three-dimensional model of the bridge and support, and virtual simulation is performed. By simulating the construction process, a construction plan is obtained, which is conducive to rationally arranging the construction progress and equipment layout, and reducing unnecessary shutdowns and adjustments.

[0050] S2. Use monitoring equipment to monitor the support structure and the overall status of the bridge in real time at the construction site of the rotating bridge, and collect real-time data from the construction site;

[0051] Furthermore, in step S2, the support structure and the overall status of the bridge are monitored in real time at the construction site of the rotating bridge by monitoring equipment. The method for collecting real-time data at the construction site is as follows:

[0052] Real-time data collection was carried out at the construction site of the rotating bridge through monitoring equipment, including the installation of strain gauges and displacement sensors at the support nodes to monitor stress change data and deformation data in real time, the installation of inclinometers on the bridge deck and on the pier tops to monitor the rotation angle data, the use of laser rangefinders to monitor the displacement data of the bridge relative to the support, the installation of anemometers at the wind outlet of the construction site and on both sides of the bridge to monitor the wind speed data of the construction site, the arrangement of temperature and humidity sensors on the bridge deck and the support to monitor the temperature and humidity data of the construction site, the use of ultrasonic detectors to evaluate the pouring quality, and the monitoring of foundation settlement data and angle data through foundation settlement monitors. Where χ(t) is the instantaneous rotation angle of the bridge, δ(t) is the angular velocity, and the displacement data is:

[0053] Where K(t) is the displacement of the bridge relative to the support, (x2, y2, z2) and (x1, y1, z1) are the coordinate positions of the bridge and the support respectively.

[0054] Furthermore, in step S2, the support structure and the overall status of the bridge are monitored in real time at the construction site of the rotating bridge by monitoring equipment. The method for collecting real-time data at the construction site is as follows:

[0055] Wireless transmission and edge computing gateway are used to transmit, pre-process and summarize the collected real-time data. A local server is used to establish a real-time monitoring system to store the collected real-time data. At the same time, the collected real-time data is mapped to the BIM model to intuitively display the status of the rotating bridge and the ectopic support. The real-time monitoring system: L real-time =c(V transmit ,B BIM ), where L real-time For real-time monitoring system, V transmit It is real-time data, B BIM It is BIM model data. c is used to combine monitoring data and BIM model data to calculate real-time construction status and real-time data mapping: in is the parameter matrix of each node, is the coordinate of the node in three-dimensional space, δ is the stress at the node, ε is the deformation of the node, and φ is the rotation angle of the node.

[0056] In this embodiment, at the cast-in-place construction site of the off-site support of the rotating bridge, the monitoring equipment is responsible for real-time monitoring of the support structure and the overall status of the bridge, collecting real-time data from the construction site, and processing, storing and mapping it to the BIM model through wireless transmission and edge computing, forming a complete real-time monitoring system, intuitively displaying the real-time status of the rotating bridge and the support, and providing visual monitoring, which is conducive to real-time acquisition of the support and bridge status, ensuring construction stability and safety, and ensuring the casting quality through ultrasonic detection and foundation settlement monitoring, reducing construction hazards, and combining BIM model visualization analysis data to optimize construction processes and resource scheduling, thereby improving construction efficiency.

[0057] S3. Dynamically optimize support installation sequence and resource allocation by combining real-time data from the construction site, weather forecasts, and construction plans;

[0058] Furthermore, in step S3, the method for dynamically optimizing the bracket installation sequence and resource allocation is based on the real-time data of the construction site, weather forecast and construction plan:

[0059] Obtain the weather forecast for the next 3-7 days from the meteorological data platform. The weather forecast includes precipitation, wind speed and direction, temperature and humidity. Combined with the construction plan, including the planned bracket installation sequence, the allocation of personnel, equipment and materials required for construction, and the time schedule for hoisting, welding and pouring processes, if the real-time data indicates that the foundation settlement in area A exceeds the standard, the BIM model will prioritize installing brackets in the area with stable foundations. If the weather forecast indicates strong winds in the next three days, the BIM model will adjust the bracket installation sequence, completing the bracket installation in the area affected by low wind speeds first. The bracket installation sequence will be adjusted due to foundation settlement: Where M is the area where the foundation settlement exceeds the standard, is the stable area of ​​the foundation, N plan This is the bracket installation sequence in the initial construction plan. The bracket installation sequence is adjusted due to wind speed: in is the wind speed in the weather forecast, is the high wind threshold, θwind is the wind direction, low-wind is the low wind speed affected area, It is the bracket installation order after optimization according to wind speed.

[0060] Furthermore, in step S3, the method for dynamically optimizing the bracket installation sequence and resource allocation is based on the real-time data of the construction site, weather forecast and construction plan:

[0061] When real-time data indicates that the hoisting equipment is undergoing maintenance, the BIM model adjusts the construction plan to prioritize bracket welding and reinforcement work in other areas. Based on the optimized construction plan, the BIM model dynamically adjusts personnel division of labor, rearranges crane positions according to the bracket installation sequence, reduces idling time, adjusts material transportation routes, prioritizes steel structure supply in emergency areas, and delivers bracket materials in advance according to the optimized bracket installation sequence. Bracket welding and reinforcement work: in It is the lifting equipment operation area. is the welding area, It is the original construction plan. It is an optimized construction plan, dynamically adjusting the division of labor: in It is the optimized personnel scheduling, It is the currently available personnel.

[0062] In this embodiment, during the construction process, the weather forecast for the next 3-7 days is obtained from the meteorological data platform, and dynamic optimization is performed in combination with the bracket installation sequence, personnel and equipment allocation, and hoisting and welding schedule in the construction plan. This is conducive to reducing equipment idling, rationally allocating personnel, optimizing material transportation, and improving construction efficiency. Through BIM+AI, data-driven intelligent optimization is achieved to improve the level of construction automation.

[0063] S4. Compare the real-time data from the construction site with the construction plan. If any abnormal data is found, locate the problem area and conduct investigation and correction.

[0064] Furthermore, in step S4, based on the comparison between the real-time data of the construction site and the construction plan, after abnormal data is found, the method for locating the problem area and conducting investigation and correction is as follows:

[0065] The standard parameters in the construction plan, including support design standards, construction time schedule, material usage standards and safety limits, are entered into the standard database through the BIM model. The real-time data mapped in the BIM model is then compared with the standard database. The AI ​​anomaly detection system is used for automatic analysis. If the real-time data deviates from the standard data in the standard database, it is determined to be abnormal data. The abnormal data is classified as shown in Table 1:

[0066] Exception Type reason Influence Bracket displacement exceeds standard Construction errors and uneven foundation settlement Affects structural stability Tilt angle exceeds limit Uneven construction, uneven force, and wind impact Causes the support to collapse Abnormal stress Loose connection, local material defects Affecting the bearing capacity of the bracket Construction progress is delayed Material supply issues, weather impact Delay in construction Abnormal environmental factors Sudden strong winds and heavy rain Affecting construction safety

[0067] Automatic analysis: in is the classification function, The bracket displacement exceeds the standard. The tilt angle exceeds the limit. It is abnormal stress. The construction progress is delayed. It's an abnormal environmental factor. is the bracket displacement deviation, φ real ,φ std is the bracket tilt angle, φ real ,φ std is the force on the bracket, γ real , γ std is the actual construction time vs. planned construction time, η real ,η std is the environmental parameter vector, φ max , γ max ,η max are their respective abnormal thresholds.

[0068] Furthermore, in step S4, based on the comparison between the real-time data of the construction site and the construction plan, after abnormal data is found, the method for locating the problem area and conducting investigation and correction is as follows:

[0069] Based on the sensor data, the spatial distribution of abnormal data is determined, the bracket number and construction area where the problem is located are determined, the abnormal location is displayed through the BIM model, and the abnormal area is highlighted. Then the construction technicians go to the abnormal area for secondary inspection to confirm the accuracy of the abnormal data. If the abnormal data is correct, the on-site investigation process is initiated and a targeted correction plan is formulated according to the construction process and specifications. After the correction, the corrected data is remeasured and uploaded to the BIM model for re-inspection. If the abnormal data returns to the standard range, the correction is confirmed to be successful and the construction log is updated. If the abnormal data is still not within the standard range, the secondary optimization is carried out and a new correction plan is formulated. Investigation and correction: in It is real-time data. is the correction function, is the revised construction data, is the original bracket position, is the correction offset, is the corrected bracket position, ι old is the original force value, Δι fix is the force correction value, ι new is the corrected force value, This is the original construction schedule. is the progress modifier, is the optimized construction progress, is the original environmental condition data, is the environmental correction value, It is the corrected environmental condition, and the corrected data is rechecked: in is the deviation of the new data, It is the newly acquired real-time data. It is the construction standard data in the standard database.

[0070] In this embodiment, the standard parameters of the construction plan are entered through the BIM model and a standard database is established. The AI ​​anomaly detection system is used to automatically compare the standard database, discover abnormal data and classify them, and perform spatial distribution analysis based on sensor data to accurately locate problematic supports and construction areas. This is conducive to accurately analyzing support and construction data, improving problem discovery efficiency, facilitating rapid intervention by construction personnel, and reducing construction delays.

[0071] S5. Summarize and analyze abnormal data based on artificial intelligence systems, combine machine learning models to predict faults, and implement predictive maintenance;

[0072] Furthermore, in step S5, the method of summarizing and analyzing abnormal data based on the artificial intelligence system, combining the machine learning model to predict faults, and implementing predictive maintenance is as follows:

[0073] Based on the real-time data and abnormal data, the artificial intelligence system summarizes and analyzes them. The artificial intelligence system includes time series analysis, anomaly detection algorithm, regression analysis and cluster analysis. Time series analysis is used to analyze the deformation trend of the support and predict the future state. The anomaly detection algorithm is used to detect abnormal data during construction and determine whether there are hidden faults. Regression analysis is used to predict foundation settlement and bridge stress changes. Cluster analysis is used to cluster data from different construction stages to discover potential abnormal patterns. The deformation trend of the support:

[0074] in The observation data at time t, is the predicted value at time t+1, Indicates the impact of historical data on current data, j is the autoregressive order, θ1 is used to correct the error term, is the sliding average order, σ t is the random error at the current time t, Is the error term at the past time point, anomaly detection algorithm: in is the data vector at the current moment, is the mean vector in the standard database, Σ is the covariance matrix of the standard database, Σ -1 is the inverse of the covariance matrix, is the difference vector between the data point and the mean, is the Mahalanobis distance, cluster analysis: in is the final clustering assignment result, arg min represents the cluster partitioning scheme that minimizes the objective function. is the number of clusters, is the i-th cluster, H J′ is the J′th data point, τ i is the center point of the i-th cluster, It represents the data point H J′ Belong to cluster ||H J′ -τ i || 2 Represents data point H J′ and cluster center τ i The square of the Euclidean distance between is the sum of all i clusters, is the pair belonging to the cluster All data points Perform the summation.

[0075] Furthermore, in step S5, the method of summarizing and analyzing abnormal data based on the artificial intelligence system, combining the machine learning model to predict faults, and implementing predictive maintenance is as follows:

[0076] Based on the abnormal patterns learned from abnormal data by the AI ​​system and combined with time series analysis, it predicts future failures, generates prediction results, and displays risk points in the future construction phase through the BIM model. The prediction results can be viewed in real time and preventive measures can be taken through the app and web interface. The AI ​​system records each failure prediction, maintenance plan, and execution effect, and continuously optimizes the machine learning model. At the same time, after the construction is completed, the failure frequency and maintenance effect are statistically analyzed to optimize the machine learning model:

[0077] in is the construction monitoring data at the current time t, is the historical construction monitoring data at the past n time points, is the prediction function, M′ t is the machine learning model trained at time t, E′ is the new fault feature dataset, is the model loss function, ξ is the learning rate, M′ t-1 is the new machine learning model after training at time t.

[0078] In this embodiment, an artificial intelligence system is used to summarize and analyze real-time data and abnormal data, time series analysis is used to predict support deformation trends, anomaly detection algorithms are applied to identify construction anomalies, regression analysis is used to predict foundation settlement and bridge stress changes, and cluster analysis is used to classify construction phase data to discover potential abnormal patterns. This is conducive to predicting potential risks before failures occur, reducing safety hazards, adjusting the construction sequence through intelligent analysis, and reducing delays caused by abnormal data.

[0079] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for cast-in-place construction of a swivel bridge out-of-place support, characterized in that: The following steps are involved: S1. Use BIM technology to build a three-dimensional model of the bridge and support, conduct virtual simulation, and obtain simulation results by simulating the construction process, and then output the construction plan; S2. Use monitoring equipment to monitor the support structure and the overall status of the bridge in real time at the construction site of the rotating bridge, and collect real-time data from the construction site; S3. Dynamically optimize support installation sequence and resource allocation by combining real-time data from the construction site, weather forecasts, and construction plans; S4. Compare the real-time data from the construction site with the construction plan. If any abnormal data is found, locate the problem area and conduct investigation and correction. S5. Summarize and analyze abnormal data based on artificial intelligence systems, combine machine learning models to predict faults, and implement predictive maintenance.

2. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 1, characterized in that: In step S1, a three-dimensional model of the bridge and support is constructed using BIM technology for virtual simulation. By simulating the construction process, the construction plan is planned as follows: Collect engineering data related to the bridge and support, including but not limited to bridge design drawings, support design plans, geological and hydrological data, construction environment information, and construction technology and specifications. Use BIM software to build a rotating bridge structure model according to the design drawings. At the same time, build a support model in the BIM software based on the construction plan of the rotating bridge's ectopic support. Perform virtual simulation of the construction process from foundation construction → support erection → formwork installation → concrete pouring → curing and demolding. Control the construction progress of different stages through the timeline, simulate the lifting, concrete pumping, and formwork installation processes, check whether the equipment layout is reasonable and whether it affects the surrounding construction, use BIM software to detect the spatial interference between the support, formwork, and equipment, and perform force analysis. Finally, obtain the simulation results, and then output the construction plan. Support model: where Q bridge is the total load borne by the support and foundation during construction, Q concrete is the concrete pouring load, Q equipment is the construction equipment load, Q env It is the load affected by environmental factors such as wind load and temperature load. A base is the bracket base contact area, w s is the bearing capacity of the foundation, Is the bracket safety, Q total is the load borne by the bracket, virtual simulation: Among them E Q is the pile foundation settlement, Q bridge is the total load borne by the support and foundation during construction, R s is the pile length, T s is the elastic modulus of the foundation, A s is the cross-sectional area of ​​the pile, α is the support deflection, Q is the concentrated load, Y is the support span, T is the elastic modulus of the material, U is the section moment of inertia, Q i is the concrete pumping pressure, β is the concrete viscosity, Y o is the length of the delivery pipe, P is the pumping flow rate, a is the pipe diameter, d o (t) is the growth curve of concrete compressive strength, d o (28) is the standard curing strength for 28 days, and f and g are empirical coefficients.

3. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 2, characterized in that: In step S2, the support structure and the overall status of the bridge are monitored in real time at the construction site of the rotating bridge by monitoring equipment. The method for collecting real-time data at the construction site is as follows: Real-time data collection was carried out at the construction site of the rotating bridge through monitoring equipment, including the installation of strain gauges and displacement sensors at the support nodes to monitor stress change data and deformation data in real time, the installation of inclinometers on the bridge deck and on the pier tops to monitor the rotation angle data, the use of laser rangefinders to monitor the displacement data of the bridge relative to the support, the installation of anemometers at the wind outlet of the construction site and on both sides of the bridge to monitor the wind speed data of the construction site, the arrangement of temperature and humidity sensors on the bridge deck and the support to monitor the temperature and humidity data of the construction site, the use of ultrasonic detectors to evaluate the pouring quality, and the monitoring of foundation settlement data and angle data through foundation settlement monitors. Where χ(t) is the instantaneous rotation angle of the bridge, δ(t) is the angular velocity, and the displacement data is: Where K(t) is the displacement of the bridge relative to the support, (x2, y2, z2) and (x1, y1, z1) are the coordinate positions of the bridge and the support respectively.

4. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 3 is characterized in that: In step S2, the support structure and the overall status of the bridge are monitored in real time at the construction site of the rotating bridge by monitoring equipment. The method for collecting real-time data at the construction site is as follows: Wireless transmission and edge computing gateways are used to transmit, pre-process, and aggregate the collected real-time data. A local server is used to establish a real-time monitoring system to store the collected real-time data. The collected real-time data is mapped to the BIM model to intuitively display the status of the rotating bridge and dyslocated supports. The real-time monitoring system: L real-time =c(V transmit ,B BIM ), where L real-time For real-time monitoring system, V transmit It is real-time data, B BIM It is BIM model data. c is used to combine monitoring data and BIM model data to calculate real-time construction status and real-time data mapping: in is the parameter matrix of each node, is the coordinate of the node in three-dimensional space, δ is the stress at the node, ε is the deformation of the node, and φ is the rotation angle of the node.

5. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 4, characterized in that: In step S3, the method for dynamically optimizing the bracket installation sequence and resource allocation is as follows, combining the real-time data of the construction site, weather forecast, and construction plan: Obtain the weather forecast for the next 3-7 days from the meteorological data platform. The weather forecast includes precipitation, wind speed and direction, temperature and humidity. Combined with the construction plan, including the planned bracket installation sequence, the allocation of personnel, equipment and materials required for construction, and the time schedule for hoisting, welding and pouring processes, if the real-time data indicates that the foundation settlement in area A exceeds the standard, the BIM model will prioritize installing brackets in the area with stable foundations. If the weather forecast indicates strong winds in the next three days, the BIM model will adjust the bracket installation sequence, completing the bracket installation in the area affected by low wind speeds first. The bracket installation sequence will be adjusted due to foundation settlement: Where M is the area where the foundation settlement exceeds the standard, is the stable area of ​​the foundation, N plan This is the bracket installation sequence in the initial construction plan. The bracket installation sequence is adjusted due to wind speed: in is the wind speed in the weather forecast, is the high wind threshold, θwind is the wind direction, low-wind is the low wind speed affected area, It is the bracket installation order after optimization according to wind speed.

6. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 5, characterized in that: In step S3, the method for dynamically optimizing the bracket installation sequence and resource allocation is as follows, combining the real-time data of the construction site, weather forecast, and construction plan: When real-time data indicates that the hoisting equipment is undergoing maintenance, the BIM model adjusts the construction plan to prioritize bracket welding and reinforcement work in other areas. Based on the optimized construction plan, the BIM model dynamically adjusts personnel division of labor, rearranges crane positions according to the bracket installation sequence, reduces idling time, adjusts material transportation routes, prioritizes steel structure supply in emergency areas, and delivers bracket materials in advance according to the optimized bracket installation sequence. Bracket welding and reinforcement work: in It is the lifting equipment operation area. is the welding area, It is the original construction plan. It is an optimized construction plan, dynamically adjusting the division of labor: in It is the optimized personnel scheduling, It is the currently available personnel.

7. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 6, characterized in that: In step S4, based on the comparison between the real-time data of the construction site and the construction plan, after abnormal data is found, the method for locating the problem area and conducting investigation and correction is as follows: The standard parameters in the construction plan, including support design standards, construction time schedule, material usage standards and safety limits, are entered into the standard database through the BIM model. The real-time data mapped in the BIM model is then compared with the standard database. The AI ​​anomaly detection system is used for automatic analysis. If the real-time data deviates from the standard data in the standard database, it is determined to be abnormal data. The abnormal data is classified as shown in Table 1: Automatic analysis: in is the classification function, The bracket displacement exceeds the standard. The tilt angle exceeds the limit. It is abnormal stress. The construction progress is delayed. It's an abnormal environmental factor. is the bracket displacement deviation, φ real ,φ std is the bracket tilt angle, φ real ,φ std is the force on the bracket, γ real , γ std is the actual construction time vs. planned construction time, η real ,η std is the environmental parameter vector, φ max , γ max ,η max are their respective abnormal thresholds.

8. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 7, characterized in that: In step S4, based on the comparison between the real-time data of the construction site and the construction plan, after abnormal data is found, the method for locating the problem area and conducting investigation and correction is as follows: Based on the sensor data, the spatial distribution of abnormal data is determined, and the bracket number and construction area where the problem is located are determined. The abnormal location is displayed through the BIM model, and the abnormal area is highlighted. Then the construction technicians go to the abnormal area for secondary inspection to confirm the accuracy of the abnormal data. If the abnormal data is correct, the on-site investigation process is initiated and a targeted correction plan is formulated according to the construction process and specifications. After the correction, the corrected data is remeasured and uploaded to the BIM model for re-inspection. If the abnormal data returns to the standard range, the correction is confirmed to be successful and the construction log is updated. If the abnormal data is still not within the standard range, the secondary optimization is carried out and a new correction plan is formulated. Investigation and correction: in It is real-time data. is the correction function, is the revised construction data, is the original bracket position, is the correction offset, is the corrected bracket position, ι old is the original force value, Δι fix is the force correction value, ι new is the corrected force value, This is the original construction schedule. is the progress modifier, is the optimized construction progress, is the original environmental condition data, is the environmental correction value, It is the corrected environmental condition, and the corrected data is rechecked: in is the deviation of the new data, It is the newly acquired real-time data. It is the construction standard data in the standard database.

9. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 8, characterized in that: In step S5, the method of summarizing and analyzing abnormal data based on the artificial intelligence system, combining the machine learning model to predict faults, and implementing predictive maintenance is as follows: Based on the real-time data and abnormal data, the artificial intelligence system summarizes and analyzes them. The artificial intelligence system includes time series analysis, anomaly detection algorithm, regression analysis and cluster analysis. Time series analysis is used to analyze the deformation trend of the support and predict the future state. The anomaly detection algorithm is used to detect abnormal data during construction and determine whether there are hidden faults. Regression analysis is used to predict foundation settlement and bridge stress changes. Cluster analysis is used to cluster data from different construction stages to discover potential abnormal patterns. The deformation trend of the support: in The observation data at time t, is the predicted value at time t+1, Indicates the impact of historical data on current data, j is the autoregressive order, θ1 is used to correct the error term, is the sliding average order, σ t is the random error at the current time t, Is the error term at the past time point, anomaly detection algorithm: in is the data vector at the current moment, is the mean vector in the standard database, ∑ is the covariance matrix of the standard database, ∑ -1 is the inverse of the covariance matrix, is the difference vector between the data point and the mean, is the Mahalanobis distance, cluster analysis: in is the final clustering assignment result, arg min represents the cluster partitioning scheme that minimizes the objective function. is the number of clusters, is the i-th cluster, H J′ is the J′th data point, τ i is the center point of the i-th cluster, It represents the data point H J′ Belong to cluster ‖‖H J′ -τ i ‖‖ 2 Represents data point H J′ and cluster center τ i The square of the Euclidean distance between is the sum of all i clusters, is the pair belonging to the cluster All data points Perform the summation.

10. The method for cast-in-situ construction of a rotating bridge out-of-place support according to claim 8, characterized in that: In step S5, the method of summarizing and analyzing abnormal data based on the artificial intelligence system, combining the machine learning model to predict faults, and implementing predictive maintenance is as follows: Based on the abnormal patterns learned from abnormal data by the AI ​​system and combined with time series analysis, it predicts future failures, generates prediction results, and displays risk points in the future construction phase through the BIM model. The prediction results can be viewed in real time and preventive measures can be taken through the app and web interface. The AI ​​system records each failure prediction, maintenance plan, and execution effect, and continuously optimizes the machine learning model. At the same time, after the construction is completed, the failure frequency and maintenance effect are statistically analyzed to optimize the machine learning model: in is the construction monitoring data at the current time t, is the historical construction monitoring data at the past n time points, is the prediction function, M′ t is the machine learning model trained at time t, E′ is the new fault feature dataset, is the model loss function, ξ is the learning rate, M′ t-1 is the new machine learning model after training at time t.

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