Deformation detection method and system for construction of large group residence hybrid structure

Abstract

The invention discloses a deformation detection method and system for large group residence mixed structure construction, and relates to the related field of intelligent construction, and the method comprises the steps: constructing a building digital twin model comprising an assembly type structure and a cast-in-place structure, and associating the model with a construction progress plan; multiple types of sensor networks are deployed at key parts of the structure, and strain, settlement, inclination angle and crack data are collected in real time; uploading the multi-source monitoring data set to a cloud monitoring platform in real time; performing state synchronization on the building digital twin model according to the current construction stage, then fusing with a multi-source monitoring data set, performing virtual-real synchronization of the structure state, predicting the structure deformation trend, and triggering multi-stage early warning; and executing the deformed processing decision, and outputting a recommendation decision. The technical problems that in existing construction deformation detection, the model and the construction working condition are dynamically disjointed, and the real-time performance and accuracy of deformation data are insufficient are solved, and the technical effects that the model and the construction working condition are linked in real time, and the deformation data are accurately and efficiently collected and analyzed are achieved.

Description

Technical Field

[0001] This application relates to the field of intelligent construction, and in particular to deformation detection methods and systems for the construction of mixed structures in large-scale residential communities. Background Technology

[0002] Large-scale residential buildings with mixed structures combine the advantages of efficient prefabricated component construction with the strong integrity of cast-in-place structures, making them a mainstream choice for large-scale construction in the current building industry. Controlling structural deformation during construction directly affects the safety of the main building, construction progress, and long-term durability, making it a core issue requiring precise control in the engineering construction field. Current mainstream technologies for detecting deformation during mixed structure construction mostly employ offline finite element simulation and fixed-point manual monitoring. Specifically, a finite element model of the structure is established before construction to simulate deformation trends. During construction, data from key locations is collected periodically using equipment such as total stations and strain gauges, and the simulated values ​​are compared with measured values ​​to determine the structural condition. However, existing methods suffer from a disconnect between the model and actual construction progress and on-site load changes. Furthermore, manual monitoring suffers from low sampling frequency and delayed data feedback, making it impossible to capture the dynamic deformation characteristics at the junction of prefabricated components and cast-in-place structures in real time. This can easily lead to structural cracking, uneven settlement, and other engineering hazards due to untimely deformation warnings.

[0003] At present, the deformation detection of large-scale residential mixed structures faces technical problems such as a dynamic disconnect between the model and the construction conditions, and insufficient real-time and accuracy of deformation data. Summary of the Invention

[0004] This application provides a deformation detection method and system for the construction of large-scale residential mixed-structure buildings. It employs a digital twin model of the building, integrating prefabricated and cast-in-place structures and linking it to the construction progress. Multiple sensor networks are deployed at key structural locations to collect multi-source data such as strain and settlement. This data is uploaded in real-time to a cloud monitoring platform via the Internet of Things (IoT). The platform synchronizes the digital twin model's status with the current construction stage and integrates the monitoring data to achieve virtual-real synchronization, predict deformation trends, trigger multi-level early warnings when thresholds are exceeded, and executes deformation handling decisions and outputs recommended decisions. This addresses the technical problems of existing deformation detection methods for large-scale residential mixed-structure construction, such as the disconnect between the model and the dynamic construction conditions, and insufficient real-time and accuracy of deformation data. It achieves the technical effect of real-time linkage between the model and the construction conditions, and accurate and efficient collection and analysis of deformation data.

[0005] This application provides a deformation detection method for the construction of mixed-structure buildings in large-scale residential communities, comprising: constructing a digital twin model of the building including prefabricated and cast-in-place structures and linking it to the construction schedule; deploying a multi-type sensor network at key structural locations to collect strain, settlement, tilt, and crack data in real time, forming a multi-source monitoring dataset; uploading the multi-source monitoring dataset to a cloud monitoring platform in real time via an IoT communication module; merging the digital twin model with the multi-source monitoring dataset after synchronizing its state according to the current construction stage on the cloud monitoring platform to achieve virtual-real synchronization of the structural state, predict structural deformation trends, and trigger multi-level early warnings when the deformation exceeds a preset threshold; and executing deformation processing decisions and outputting recommended decisions after the multi-level early warnings are triggered.

[0006] In one possible implementation, a digital twin model of the building, including prefabricated and cast-in-place structures, is constructed and associated with the construction schedule. The following processing is performed: based on the design BIM model, the junctions between prefabricated components and cast-in-place structures, as well as the layered and segmented construction nodes, are marked to construct the digital twin model of the building; the time nodes and load change stages in the construction schedule are bound to the construction status in the digital twin model of the building, and the bound digital twin model of the building can dynamically evolve with the progress.

[0007] In one possible implementation, a multi-type sensor network is deployed at key structural locations, and the following processes are performed: stress concentration zones, deformation-sensitive zones, and construction joints are determined based on finite element analysis; the multi-type sensor network is deployed in the stress concentration zones, deformation-sensitive zones, and construction joints; strain, settlement, tilt angle, and crack data are collected through the multi-type sensor network to form the multi-source monitoring dataset.

[0008] In a possible implementation, the cloud monitoring platform synchronizes the state of the building digital twin model according to the current construction stage and then merges it with the multi-source monitoring dataset to achieve virtual-real synchronization of the structural state, predict the structural deformation trend, and trigger multi-level early warnings when the deformation exceeds a preset threshold. The following processes are performed: identifying the current construction stage and activating the corresponding structural state and load conditions in the building digital twin model; mapping the multi-source monitoring dataset to the activated building digital twin model and outputting the deformation development trend curve of key parts; and performing multi-level early warnings based on the deformation development trend curve.

[0009] In a possible implementation, the multi-source monitoring dataset is mapped to the activated building digital twin model, and the deformation development trend curve of the key parts is output. The following processing is performed: the multi-source monitoring dataset is mapped to the corresponding position of the activated building digital twin model, and data-model alignment is performed to obtain the fused model; based on the fused model, the future construction stage plan and estimated load are input, and the deformation development trend curve of the key parts is output.

[0010] In a possible implementation, when a multi-level warning is triggered, a deformation handling decision is executed, a recommended decision is output, and the following processing is performed: the warning type and location are read, and historical handling cases are matched from the contingency plan library; the historical handling cases are filtered in combination with the current construction stage, weather conditions and resource status to generate multiple handling suggestions; the building digital twin model is called to simulate and extrapolate the multiple handling suggestions, evaluate the deformation control effect after handling, select the optimal suggestion, and generate the recommended decision.

[0011] In a possible implementation, the following processing is performed: the recommended decision includes process adjustment, reinforcement measures and enhanced monitoring information. After the recommended decision is output, the decision execution process is included in the monitoring project for tracking, and the treatment effect is fed back in real time and the contingency plan database is updated.

[0012] This application also provides a deformation detection system for the construction of mixed-structure residential buildings in large-scale residential communities, including: a building digital twin model construction module for constructing a building digital twin model including prefabricated and cast-in-place structures and associating it with the construction schedule; a multi-source monitoring data acquisition module for deploying a multi-type sensor network at key structural locations to collect strain, settlement, tilt, and crack data in real time, forming a multi-source monitoring dataset; a data upload module for uploading the multi-source monitoring dataset to a cloud monitoring platform in real time via an IoT communication module; a structural deformation trend prediction module for merging the building digital twin model with the multi-source monitoring dataset in the cloud monitoring platform after synchronizing the model's state according to the current construction stage, performing virtual-real synchronization of the structural state, predicting structural deformation trends, and triggering multi-level early warnings when the deformation exceeds a preset threshold; and a recommendation decision output module for executing deformation processing decisions and outputting recommended decisions after multi-level early warnings are triggered.

[0013] The proposed deformation detection method and system for large-scale residential mixed-structure construction, as described in this application, first constructs a digital twin model of the building, including both prefabricated and cast-in-place structures, and links it to the construction schedule. Next, a multi-type sensor network is deployed at key structural locations to collect real-time data on strain, settlement, tilt, and cracks, forming a multi-source monitoring dataset. This dataset is then uploaded to a cloud monitoring platform in real-time via an IoT communication module. On the cloud platform, the digital twin model is synchronized with the multi-source monitoring dataset based on the current construction stage, achieving virtual-real synchronization of the structural state, predicting structural deformation trends, and triggering multi-level early warnings when preset thresholds are exceeded. Finally, after multiple early warnings are triggered, deformation processing decisions are executed, and recommended decisions are output. Through this process, the proposed method and system achieve the technical effects of real-time linkage between the model and construction conditions, and accurate and efficient collection and analysis of deformation data. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1 A schematic flowchart illustrating the deformation detection method for the construction of a large-scale residential mixed structure provided in this application embodiment.

[0016] Figure 2 A schematic diagram of the deformation detection system for the construction of a large-scale residential mixed structure provided in this application embodiment.

[0017] Figure labeling: 10 Building digital twin model construction module, 20 multi-source monitoring data acquisition module, 30 data upload module, 40 structural deformation trend prediction module, and 50 recommendation decision output module. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] This application provides a deformation detection method for the construction of large-scale residential mixed-structure buildings, such as... Figure 1 As shown, the method includes: Step S100: Construct a digital twin model of the building, including prefabricated and cast-in-place structures, and link it to the construction schedule.

[0020] Specifically, building information modeling (BIM) software is used to import design drawings of prefabricated components and cast-in-place structures for large-scale residential buildings. Core parameters such as component dimensions, material properties, and connection methods are extracted to construct a basic 3D model. For the different characteristics of prefabricated and cast-in-place structures, corresponding physical property parameters are assigned to the model, such as the elastic modulus of prefabricated wall panels and the strength growth curve of cast-in-place concrete. Through a schedule association interface, information such as the process divisions and time nodes in the construction schedule are written into the model's attribute fields, establishing a one-to-one correspondence between model components and construction processes. For example, the prefabricated column hoisting process is bound to the prefabricated column component in the model, and the status of the corresponding component in the model is set to switch to "installed" when the process begins.

[0021] In one possible implementation, a digital twin model of the building, including prefabricated and cast-in-place structures, is constructed and linked to the construction schedule. Step S100 further includes step S110, which, based on the design BIM model, marks the junctions between prefabricated components and cast-in-place structures, as well as the layered and segmented construction nodes, to construct the building digital twin model. Specifically, the layer data of the design building information model is called, and the junctions between prefabricated components and cast-in-place structures are identified using a component type filtering tool. For example, the overlap between prefabricated beams and cast-in-place floor slabs, and the connection between prefabricated wall panels and cast-in-place structural columns are marked in red in the model, and junction attribute labels are added. According to the layered and segmented scheme in the construction organization design, construction nodes are divided in the model according to floors and construction flow sections. For example, floors 1 to 5 of a residential complex are divided into the first construction section, and floors 6 to 10 into the second construction section. Node markers are set at the start and end positions of each construction section. The spatial coordinates, geometric features, and other information of the marked junctions and construction nodes are written into the model database to form a complete building digital twin model.

[0022] Step S120 involves binding the time nodes and load change stages in the construction schedule with the construction status in the building digital twin model. The bound building digital twin model can dynamically evolve with the progress. Specifically, a schedule-model binding method is used to bind key time nodes in the construction schedule, such as the hoisting time of precast components, the pouring time of cast-in-place concrete, and the curing completion time, with the state parameters of the corresponding components in the building digital twin model. It is set that when the hoisting time node is reached, the state of the precast components in the model changes from "awaiting hoisting" to "hoisted." The load changes at different construction stages are analyzed, such as the construction live load during the floor slab pouring stage, the hoisting load during the component hoisting stage, and the self-weight load during the curing stage. The load values ​​and their application times are written into the load attribute module of the model, establishing a correspondence between load change stages and construction time nodes. For example, when the cast-in-place floor slab pouring time node is reached, the model automatically loads a construction live load of 2500 N / m². A dynamic evolution trigger mechanism for the model is set up. By comparing timestamps, when the system time reaches the bound time node, the corresponding component status and load conditions are automatically activated, so as to realize the synchronous evolution of the model and the construction progress.

[0023] Step S200: Deploy a multi-type sensor network at key structural locations to collect strain, settlement, tilt angle, and crack data in real time, forming a multi-source monitoring dataset.

[0024] Specifically, sensor deployment locations are determined based on structural stress analysis results. Sensor networking technology is employed to connect different types of sensors, such as strain sensors, settlement sensors, tilt sensors, and crack sensors, via wired or wireless means to form a monitoring network. Each sensor is assigned a unique identification code and its coordinates are bound to the corresponding key components in the building's digital twin model, ensuring that the collected data accurately corresponds to the specific location within the model. The sensor data acquisition frequency is set, for example, strain sensors collect data every 5 minutes, and settlement sensors every 10 minutes. The raw data collected by the sensors is converted into a unified format recognizable by the database through a data acquisition terminal, forming a multi-source monitoring dataset.

[0025] In one possible implementation, a multi-type sensor network is deployed at key structural locations. Step S200 further includes step S210, which involves determining stress concentration zones, deformation-sensitive zones, and construction joint locations based on finite element analysis. Specifically, the structural parameters of the building's digital twin model are imported into finite element analysis software to establish a finite element model of the hybrid structure. Mesh elements are then defined, for example, prefabricated components are divided into tetrahedral meshes, and cast-in-place structures are divided into hexahedral meshes. Load boundary conditions for the construction phase are set, such as self-weight, construction live loads, and hoisting loads. Finite element simulation calculations of the construction process are performed, outputting stress cloud maps and displacement cloud maps of the structure. Stress concentration zones are identified through the stress cloud maps, such as the stress peak area at the junction of prefabricated components and cast-in-place structures. Deformation-sensitive zones are identified through the displacement cloud maps, such as the top floor slabs and cantilever components of high-rise residential buildings. Simultaneously, construction joint locations are determined in conjunction with construction drawings, such as construction joints in cast-in-place concrete and splicing joints in prefabricated components. The spatial coordinates of the stress concentration zones, deformation-sensitive zones, and construction joint locations obtained from the analysis are exported to form a list of sensor deployment points.

[0026] Step S220: Arrange the multi-type sensor network in the stress concentration area, the deformation-sensitive area, and the construction joint. Specifically, strain sensors are arranged in the stress concentration area using an adhesive method, where strain gauges are glued to the structural surface with strong adhesive, for example, on the side of the beam end at the junction of a precast beam and a cast-in-place column, ensuring a tight fit between the strain gauges and the structural surface without air bubbles or wrinkles. Settlement sensors and tilt sensors are arranged in the deformation-sensitive area. The settlement sensors are installed using a static leveling method, with the sensor's reference point set on a stable foundation and the measurement point set on the floor slab or wall in the deformation-sensitive area, connected to the reference point and the measurement point via a connecting pipe. The tilt sensors are installed using a magnetic method, adsorbed onto the vertical or horizontal surface of the structure, for example, adsorbed onto the bottom of the top floor slab. Crack sensors are arranged at the construction joint, with the two probes of the crack sensor fixed on both sides of the joint, ensuring a firm connection between the probes and the structural surface. Wireless communication modules, such as narrowband IoT modules, are used to connect all sensors to the data acquisition terminal for network debugging, testing the stability of sensor data transmission, and ensuring that data collected by each sensor can be transmitted to the terminal in real time.

[0027] Step S230: Strain, settlement, tilt, and crack data are collected through the multi-type sensor network to form the multi-source monitoring dataset. Specifically, the acquisition parameters of the data acquisition terminal are set, for example, the acquisition range of the strain sensor is ±2000 microstrain, the acquisition accuracy of the settlement sensor is 0.1 mm, the acquisition accuracy of the tilt sensor is 0.01 degrees, and the acquisition range of the crack sensor is 0 to 5 mm. The sensor network is started, and the data acquisition terminal collects raw data from each sensor at the set frequency. The raw data is preprocessed, including abnormal data removal, such as deleting data exceeding the sensor's range, and data smoothing, using a moving average method to smooth data with large fluctuations and eliminate noise interference. Attribute information such as timestamp, sensor code, and corresponding structural coordinates are added to each data point. The preprocessed data is stored according to data type, for example, strain data is stored in a strain dataset table, settlement data is stored in a settlement dataset table, and finally integrated to form a multi-source monitoring dataset, which is stored in a local database.

[0028] Step S300: The multi-source monitoring dataset is uploaded to the cloud monitoring platform in real time via the IoT communication module.

[0029] Specifically, an IoT communication module, such as a 4G or 5G module, is installed on the data acquisition terminal. The module's network parameters, including access point name, IP address, and port number, are configured to ensure internet access. A communication protocol is established between the local database and the cloud monitoring platform, using Hypertext Transfer Protocol (HTTP) for data transmission to ensure security. A data upload trigger mechanism is set up, employing a real-time upload mode. After the data acquisition terminal completes a data acquisition and preprocessing cycle, it immediately uploads the data to the cloud monitoring platform via the IoT communication module. The cloud monitoring platform's database receives the data, parses and stores it according to a preset data format, and establishes a data index for subsequent queries and retrieval.

[0030] Step S400: In the cloud monitoring platform, the digital twin model of the building is synchronized with the current construction stage and then fused with the multi-source monitoring dataset to synchronize the virtual and real structural states, predict the structural deformation trend, and trigger multi-level early warnings when the value exceeds a preset threshold.

[0031] Specifically, the cloud-based monitoring platform obtains current construction stage information through the construction progress management module. For example, if the current stage is the pouring of the 10th-floor cast-in-place slab, it updates the status of the corresponding part in the building's digital twin model to "pouring" and applies the corresponding load according to preset status binding rules. Sensor data from the multi-source monitoring dataset is mapped to the corresponding parts of the building's digital twin model according to coordinate matching relationships. Using time series prediction algorithms, such as the autoregressive integral moving average model, and with historical monitoring data as input, the platform predicts the deformation trend of key structural components over a future period and outputs a deformation development curve. Multi-level warning thresholds are set; for example, the first-level warning threshold is 80% of the safety limit, the second-level threshold is 90%, and the third-level threshold is exceeding the safety limit. When the predicted or real-time data exceeds the corresponding threshold, the platform automatically triggers the corresponding level of warning.

[0032] In one possible implementation, in the cloud monitoring platform, after synchronizing the state of the building digital twin model according to the current construction stage, it is fused with the multi-source monitoring dataset to achieve virtual-real synchronization of the structural state, predict the structural deformation trend, and trigger multi-level early warnings when the value exceeds a preset threshold. Step S400 further includes step S410, identifying the current construction stage and activating the corresponding structural state and load conditions in the building digital twin model. Specifically, the construction progress management module of the cloud monitoring platform obtains the current construction stage information in real time by connecting with the progress reporting system of the construction site. For example, the current construction stage is determined by the report from the construction personnel that the 10th floor cast-in-place slab is being poured. The model state-construction stage association database is retrieved to find the structural state and load conditions corresponding to the construction stage. For example, the structural state corresponding to the 10th floor cast-in-place slab pouring stage is that the floor slab reinforcement is completed and the concrete is being poured, and the load conditions are the floor slab self-weight load + 2500 N / m² construction live load. The system sends a state activation command to the building digital twin model via the model state activation interface, updates the state parameters of the 10th floor slab in the model to "under pouring", and loads the corresponding load values ​​to complete the synchronization of the model state.

[0033] Step S420: Map the multi-source monitoring dataset to the activated building digital twin model and output the deformation trend curve of key components. Specifically, establish a data-model coordinate matching database to store the correspondence between the code of each sensor and the coordinates of structural parts in the building digital twin model. Extract sensor data from the multi-source monitoring dataset, find the corresponding model coordinates according to the sensor code, and use a coordinate mapping algorithm to accurately map the data to the corresponding parts of the model. Use a trend prediction algorithm to train a prediction model with historical monitoring data as the training set. The input of the model is the deformation data of the historical time series, and the output is the deformation prediction data of the future time series. Import the real-time mapped data and the prediction data into a curve generation tool to generate the deformation trend curve of key components, such as the trend curve of the settlement of a 10-story slab over time, with the horizontal axis representing time and the vertical axis representing settlement.

[0034] Step S430: Implement multi-level early warning based on the deformation development trend curve. Specifically, a multi-level early warning threshold database is set up on the cloud monitoring platform to store multi-level early warning thresholds for different structural parts. For example, the first-level early warning threshold for precast beams is 800 microstrain, the second-level threshold is 900 microstrain, and the third-level threshold is 1000 microstrain. The data in the deformation development trend curve is compared with the threshold database in real time, and a threshold comparison algorithm is used to determine whether the data exceeds the corresponding threshold. When the data exceeds the first-level early warning threshold, the platform automatically sends an early warning message to the construction management personnel's mobile APP, indicating that the structural deformation is approaching the warning value and that monitoring needs to be strengthened. When the second-level early warning threshold is exceeded, in addition to sending the APP message, an audible and visual early warning is triggered on the platform. When the third-level early warning threshold is exceeded, an early warning message is immediately sent to the construction management personnel, supervision unit, and construction unit, and an on-site early warning broadcast is triggered, while construction in the relevant area is suspended.

[0035] In one possible implementation, the multi-source monitoring dataset is mapped to the activated building digital twin model, and the deformation development trend curves of key parts are output. Step S420 further includes step S421, mapping the multi-source monitoring dataset to the corresponding positions of the activated building digital twin model, performing data-model alignment, and obtaining the fused model. Specifically, the sensor code, acquisition time, monitoring value, and other information of each data point are extracted from the multi-source monitoring dataset, and the corresponding model spatial coordinates are searched in the coordinate matching database according to the sensor code. A spatial alignment algorithm, such as the least squares method, is used to calibrate the spatial position of the sensor acquisition data with the model coordinates to eliminate position deviations caused by installation errors. A time alignment algorithm is used to synchronize the acquisition time of the sensor data with the time node of the model state, for example, matching the strain data acquired at 10:00:00 with the structural state of the model at 10:00:00. The aligned data is written into the attribute fields of the building digital twin model, so that each key part of the model has real-time monitoring data attributes, forming the fused model.

[0036] Step S422: Based on the fused model, input the future construction stage plan and estimated load, and output the deformation development trend curve of the key parts. Specifically, retrieve the future construction stage plan from the construction schedule database, for example, the construction stages for the next 7 days are the curing stage of the 10th floor cast-in-place slab and the hoisting stage of the 11th floor precast beam. According to the construction load calculation specifications, determine the estimated load for each future construction stage, for example, the load for the curing stage is the self-weight load of the slab, and the load for the hoisting stage is the self-weight load of the precast beam + 12000 Newtons hoisting load. Input the future construction stage plan and estimated load into the fused model, and use the finite element simulation algorithm of the construction process to simulate the structural stress of each future construction stage, and output the deformation simulation data of the key parts. Combining historical monitoring data and simulation data, use an autoregressive integral moving average model for trend prediction, setting the input parameters of the model to historical deformation data, future load data, and construction stage information, and the output parameter to the deformation amount of the future time series. Import the predicted data into the curve generation tool to generate deformation trend curves for key parts, such as the settlement change curve of a 10-story floor slab over the next 7 days.

[0037] Step S500: When a multi-level warning is triggered, a modified processing decision is executed, and a recommended decision is output.

[0038] Specifically, a deformation response plan database is established, storing historical response cases for different warning types and structural components. For example, cases for handling excessive strain include increasing monitoring frequency, adjusting construction loads, and reinforcing the structure. When a warning is triggered, historical cases in the database are retrieved and filtered based on the current construction stage, weather conditions, and resource availability to generate multiple response suggestions. These suggestions are then input into a digital twin model of the building for simulation and evaluation of the deformation control effects of different suggestions. For example, simulating whether structural deformation decreases after structural reinforcement is performed. The suggestion with the best deformation control effect is selected as the recommended decision, and the decision content is output, including process adjustments, reinforcement measures, and enhanced monitoring information. Simultaneously, the decision execution process is incorporated into the monitoring project, providing real-time feedback on the response effects and updating the response cases to the database.

[0039] In one possible implementation, after a multi-level warning is triggered, a deformation handling decision is executed, and a recommended decision is output. Step S500 further includes step S510, which reads the warning type and location, and matches historical handling cases from the contingency plan library. Specifically, the warning processing module of the cloud monitoring platform reads the warning type field and location field from the warning information in real time. It retrieves the contingency plan library, which uses a categorized storage structure, classifying warnings by warning type and structural location. For example, strain exceedance warnings are divided into subcategories such as precast beam strain exceedance and cast-in-place column strain exceedance. A case matching algorithm, such as a similarity-based matching algorithm, is used to calculate the similarity between the current warning information and historical cases in the contingency plan library. Similarity indicators include warning type, structural location, and deformation amount. Multiple historical handling cases with the highest similarity are matched; for example, three historical cases of precast beam strain exceedance are matched, with case content including: increasing the monitoring frequency to once every 2 minutes, reducing construction live load, and reinforcing with carbon fiber cloth.

[0040] Step S520: The historical treatment cases are screened based on the current construction stage, weather conditions, and resource availability, generating multiple treatment suggestions. Specifically, the cloud monitoring platform connects with the environmental monitoring system at the construction site to obtain current weather conditions. The current resource status is obtained through the resource management module, such as sufficient carbon fiber cloth inventory, idle static pile driving equipment, and sufficient construction personnel. Based on current construction stage information, such as the top floor slab pouring and curing stage, the matched historical cases are screened, for example, cases requiring large hoisting equipment and currently occupied by such equipment are removed, as are cases unsuitable for the curing stage. The screened cases are optimized and adjusted, for example, reducing the frequency of monitoring from once every 5 minutes to once every 2 minutes in historical cases, and supplementing with reinforcement using stockpiled carbon fiber cloth based on current resource availability, ultimately generating multiple treatment suggestions, such as suggestion 1: increase the monitoring frequency to once every 2 minutes to reduce construction live load; suggestion 2: reinforce with carbon fiber cloth and suspend the upper construction process.

[0041] Step S530: The building digital twin model is invoked to simulate and extrapolate the multiple treatment suggestions, evaluate the deformation control effect after treatment, select the optimal suggestion, and generate the recommended decision. The recommended decision includes process adjustments, reinforcement measures, and enhanced monitoring information. After outputting the recommended decision, the decision execution process is incorporated into the monitoring project for tracking, real-time feedback on the treatment effect, and updates to the contingency plan database. Specifically, each treatment suggestion is converted into parameters recognizable by the model. For example, the encrypted monitoring frequency is converted into a data acquisition interval adjusted to 2 minutes, and the application of carbon fiber reinforcement is converted into adding carbon fiber properties to the corresponding parts of the model, with an elastic modulus of 230 gigapascals. The parameters are input into the building digital twin model, and a finite element simulation algorithm for the construction process is used to simulate the structural stress and deformation after the implementation of each treatment suggestion. Evaluation indicators such as the deformation amount and deformation trend curve after treatment are output. An effect evaluation system is established, for example, using the reduction in deformation after treatment, treatment cost, and construction convenience as evaluation indicators. A weighted scoring method is used to score each suggestion, and the suggestion with the highest score is the optimal suggestion. The optimal suggestions are compiled into recommended decisions, clarifying the content of process adjustments. After outputting the recommended decision, the decision execution process is set as a special monitoring project to collect deformation data after the treatment in real time, provide feedback on the treatment effect, update the treatment case and effect data to the treatment plan library, and improve the content of the plan library.

[0042] This application's embodiment employs a digital twin model of a building that integrates prefabricated and cast-in-place structures and links it to the construction progress. Multiple types of sensor networks are deployed at key structural locations to collect multi-source data such as strain and settlement. This data is then uploaded in real-time to a cloud-based monitoring platform via the Internet of Things (IoT). The platform synchronizes the digital twin model's status with the current construction phase and integrates the monitoring data to achieve virtual-real synchronization, predict deformation trends, trigger multi-level warnings when thresholds are exceeded, and executes deformation handling decisions and outputs recommended decisions after each warning is triggered. This approach solves the technical problems of existing deformation detection methods for large-scale residential mixed-structure construction, such as the disconnect between the model and the dynamic construction conditions, and insufficient real-time and accuracy of deformation data. It achieves the technical effect of real-time linkage between the model and the construction conditions, and accurate and efficient collection and analysis of deformation data.

[0043] In the above text, refer to Figure 1 A deformation detection method for the construction of a large-scale residential mixed structure according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 Describing a deformation detection system for the construction of a large-scale residential mixed structure according to an embodiment of the present invention.

[0044] The deformation detection system for large-scale mixed-structure residential construction according to embodiments of the present invention addresses the technical problems of existing deformation detection methods for large-scale mixed-structure residential construction, namely, the dynamic disconnect between the model and the construction conditions, and the insufficient real-time performance and accuracy of deformation data. It achieves the technical effect of real-time linkage between the model and the construction conditions, and accurate and efficient acquisition and analysis of deformation data. The deformation detection system for large-scale mixed-structure residential construction includes: a building digital twin model construction module 10, a multi-source monitoring data acquisition module 20, a data upload module 30, a structural deformation trend prediction module 40, and a recommendation decision output module 50.

[0045] The building digital twin model construction module 10 is used to construct a building digital twin model including prefabricated and cast-in-place structures and associate it with the construction schedule; the multi-source monitoring data acquisition module 20 is used to deploy a multi-type sensor network at key parts of the structure to collect strain, settlement, tilt angle and crack data in real time to form a multi-source monitoring dataset; the data upload module 30 is used to upload the multi-source monitoring dataset to the cloud monitoring platform in real time through the Internet of Things communication module; the structural deformation trend prediction module 40 is used to synchronize the status of the building digital twin model with the multi-source monitoring dataset in the cloud monitoring platform according to the current construction stage, to synchronize the virtual and real structural status, predict the structural deformation trend, and trigger multi-level early warning when it exceeds a preset threshold; the recommendation decision output module 50 is used to execute deformation processing decisions and output recommended decisions when multi-level early warnings are triggered.

[0046] The detailed description of the specific configuration of the building digital twin model construction module 10 is explained as follows: As mentioned above, a building digital twin model including prefabricated structure and cast-in-place structure is constructed and associated with the construction schedule. The building digital twin model construction module 10 may further include: a building digital twin model construction unit used to mark the junction of prefabricated components and cast-in-place structure, and the layered and segmented construction nodes based on the design BIM model, to construct the building digital twin model; and a binding unit used to bind the time nodes and load change stages in the construction schedule with the construction status in the building digital twin model. The bound building digital twin model can dynamically evolve with the progress.

[0047] The detailed description of the specific configuration of the multi-source monitoring data acquisition module 20 is explained below: As mentioned above, a multi-type sensor network is deployed at key parts of the structure. The multi-source monitoring data acquisition module 20 may further include: a finite element analysis unit for determining stress concentration areas, deformation-sensitive areas, and construction joints based on finite element analysis; a multi-type sensor network arrangement unit for arranging the multi-type sensor network in the stress concentration areas, deformation-sensitive areas, and construction joints; and a data acquisition unit for acquiring strain, settlement, tilt angle, and crack data through the multi-type sensor network to form the multi-source monitoring dataset.

[0048] The detailed description of the specific configuration of the structural deformation trend prediction module 40 is explained as follows: As mentioned above, in the cloud monitoring platform, after synchronizing the state of the building digital twin model according to the current construction stage, it is fused with the multi-source monitoring dataset to perform virtual-real synchronization of the structural state, predict the structural deformation trend, and trigger multi-level early warning when it exceeds a preset threshold. The structural deformation trend prediction module 40 may further include: a current construction stage identification unit for identifying the current construction stage and activating the corresponding structural state and load conditions in the building digital twin model; a data mapping unit for mapping the multi-source monitoring dataset to the activated building digital twin model and outputting the deformation development trend curve of key parts; and a multi-level early warning unit for issuing multi-level early warnings based on the deformation development trend curve.

[0049] Specifically, the multi-source monitoring dataset is mapped to the activated building digital twin model, and the deformation development trend curve of the key parts is output. The data mapping unit may further include: a data-model alignment subunit for mapping the multi-source monitoring dataset to the corresponding position of the activated building digital twin model, performing data-model alignment to obtain the fused model; and a deformation development trend curve output subunit for inputting the future construction stage plan and estimated load based on the fused model, and outputting the deformation development trend curve of the key parts.

[0050] The specific configuration of the recommended decision output module 50 is described in detail below: As mentioned above, when a multi-level early warning is triggered, a deformation handling decision is executed, and a recommended decision is output. The recommended decision output module 50 may further include: a plan matching unit for reading the early warning type and location, and matching historical handling cases from the plan library; a filtering unit for filtering the historical handling cases in combination with the current construction stage, weather conditions, and resource status, and generating multiple handling suggestions; and a simulation and deduction unit for calling the building digital twin model to simulate and deduct the multiple handling suggestions, evaluate the deformation control effect after handling, filter the optimal suggestion, and generate the recommended decision.

[0051] The simulation and deduction unit may further include: the recommended decision includes process adjustment, reinforcement measures and monitoring enhancement information; after the recommended decision is output, the decision execution process is included in the monitoring project for tracking, the treatment effect is fed back in real time and the contingency plan database is updated.

[0052] The deformation detection system for the construction of large-scale residential mixed structures provided in this embodiment of the invention can execute the deformation detection method for the construction of large-scale residential mixed structures provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0053] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A deformation detection method for the construction of large-scale residential mixed structures, characterized in that, include: Construct a digital twin model of the building, including both prefabricated and cast-in-place structures, and link it to the construction schedule. Deploy a multi-type sensor network in key structural parts to collect strain, settlement, tilt angle and crack data in real time, forming a multi-source monitoring dataset; The multi-source monitoring dataset is uploaded to the cloud monitoring platform in real time via the IoT communication module; In the cloud monitoring platform, the digital twin model of the building is synchronized with the current construction stage and then fused with the multi-source monitoring dataset to synchronize the virtual and real structural states, predict structural deformation trends, and trigger multi-level early warnings when the values ​​exceed preset thresholds. When a multi-level warning is triggered, a modified processing decision is executed, and a recommended decision is output.

2. The deformation detection method for the construction of large-scale residential mixed structures as described in claim 1, characterized in that, Construct a digital twin model of the building, including both prefabricated and cast-in-place structures, and link it to the construction schedule, including: Based on the design BIM model, the junctions between prefabricated components and cast-in-place structures, as well as the layered and segmented construction nodes, are marked to construct the building's digital twin model; The time nodes and load change stages in the construction schedule are bound to the construction status in the building digital twin model. The bound building digital twin model can dynamically evolve with the progress.

3. The deformation detection method for the construction of large-scale residential mixed structures as described in claim 1, characterized in that, Deploy multiple types of sensor networks in critical structural components, including: The stress concentration zone, deformation-sensitive zone, and construction joint location were determined based on finite element analysis. The multi-type sensor network is arranged in the stress concentration area, the deformation sensitive area, and the construction joint. The strain, settlement, tilt angle and crack data are collected through the multi-type sensor network to form the multi-source monitoring dataset.

4. The deformation detection method for the construction of large-scale residential mixed structures as described in claim 1, characterized in that, In the cloud-based monitoring platform, the digital twin model of the building is synchronized with its current construction stage and then fused with the multi-source monitoring dataset to achieve virtual-real synchronization of the structural state, predict structural deformation trends, and trigger multi-level early warnings when preset thresholds are exceeded, including: Identify the current construction stage and activate the corresponding structural state and load conditions in the building digital twin model; The multi-source monitoring dataset is mapped to the activated digital twin model of the building, and the deformation development trend curve of the key parts is output. Multi-level early warning is provided based on the aforementioned deformation development trend curve.

5. The deformation detection method for the construction of large-scale residential mixed structures as described in claim 4, characterized in that, The multi-source monitoring dataset is mapped to the activated building digital twin model, and the deformation development trend curves of key components are output, including: The multi-source monitoring dataset is mapped to the corresponding position in the activated building digital twin model, and data-model alignment is performed to obtain the fused model; Based on the fused model, input the planned and estimated loads for future construction phases, and output the deformation development trend curves of the key components.

6. The deformation detection method for the construction of large-scale residential mixed structures as described in claim 1, characterized in that, When a multi-level warning is triggered, a modified processing decision is executed, and a recommended decision is output, including: Read the warning type and location, and match historical response cases from the contingency plan database; By considering the current construction phase, weather conditions, and resource availability, the historical disposal cases were screened to generate multiple disposal suggestions; The building's digital twin model is invoked to simulate and extrapolate the multiple treatment suggestions, evaluate the deformation control effect after treatment, select the optimal suggestion, and generate the recommended decision.

7. The deformation detection method for the construction of large-scale residential mixed structures as described in claim 6, characterized in that, The recommended decisions include process adjustments, reinforcement measures, and enhanced monitoring information. After the recommended decisions are output, the decision implementation process is incorporated into the monitoring project for tracking, and the treatment effect is fed back in real time and the contingency plan database is updated.

8. A deformation detection system for the construction of large-scale residential mixed-structure buildings, characterized in that, The system is used to implement the deformation detection method for the construction of large-scale residential mixed structures as described in any one of claims 1-7, and the system comprises: The building digital twin model building module is used to build building digital twin models, including prefabricated structures and cast-in-place structures, and to associate them with the construction schedule. The multi-source monitoring data acquisition module is used to deploy a network of multiple types of sensors in key parts of the structure to collect strain, settlement, tilt angle and crack data in real time, forming a multi-source monitoring dataset; The data upload module is used to upload the multi-source monitoring dataset to the cloud monitoring platform in real time via the Internet of Things communication module; The structural deformation trend prediction module is used to synchronize the status of the building digital twin model with the multi-source monitoring dataset according to the current construction stage in the cloud monitoring platform, perform virtual-real synchronization of the structural status, predict the structural deformation trend, and trigger multi-level early warning when the deformation exceeds a preset threshold. The recommended decision output module is used to execute modified processing decisions and output recommended decisions when multi-level warnings are triggered.

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