A hanging basket construction state monitoring method based on BIM and internet of things

Abstract

This invention relates to a method for monitoring the construction status of hanging baskets based on BIM and the Internet of Things (IoT), belonging to the field of construction monitoring technology. Step 1: Construct a BIM model of the hanging basket construction and simultaneously set up a monitoring point array. The monitoring point array information includes the components to be monitored, the corresponding monitoring locations of the components, and the detection items. Step 2: Based on the monitoring point array in Step 1, deploy corresponding sensors, IoT monitoring terminals, and a data transmission network. Step 3: Establish a dynamic association between the BIM model and real-time monitoring data, mapping the components to be monitored, the corresponding monitoring locations, and the detection items in the monitoring point array information to the corresponding locations in the BIM model, and ensuring that the real-time updated BIM model includes real-time information uploaded by the sensors. Step 4: Perform real-time data analysis and risk level assessment: After receiving the data, the control module determines whether the characteristic values ​​of each individual parameter exceed the threshold.

Description

Technical Field

[0001] This invention belongs to the field of construction monitoring technology, specifically relating to a method for monitoring the construction status of hanging baskets based on BIM and the Internet of Things. Background Technology

[0002] In the construction of long-span bridges, the cantilever formwork method is widely used due to its advantages such as eliminating the need for large scaffolding and minimizing impact on the space beneath the bridge. However, as a temporary load-bearing structure, the formwork must withstand various complex loads during construction, including the self-weight of the concrete, construction loads, and wind loads. Structural instability or damage to critical components can lead to serious safety accidents, causing casualties and economic losses. Therefore, real-time and accurate monitoring of the formwork's construction status is crucial.

[0003] Traditional monitoring methods often rely on manual inspections and local sensor data collection, or a combination of manual inspections and single sensor monitoring. In this approach, manual monitoring depends on technicians using equipment such as total stations and stress meters to conduct periodic measurements on-site. Such methods suffer from problems such as low monitoring frequency, data lag, and large human error, making it difficult to capture the dynamic changes of the hanging basket structure in real time. Therefore, the common approach is to introduce BIM and IoT technologies to monitor the hanging basket.

[0004] With the development of BIM and IoT technologies, their integration and application in the field of building engineering are gradually increasing. However, the following shortcomings still exist in the monitoring of bridge hanging basket construction: BIM models are mostly used for construction scheme design and visualization, without establishing a dynamic connection with real-time monitoring data, and cannot achieve an integrated presentation of "model-data-status"; at the same time, the data analysis capabilities of IoT monitoring systems are weak, mostly remaining at the level of data collection and threshold alarms, lacking predictive analysis of the deformation trend and risk propagation path of the hanging basket structure, making it difficult to meet the needs of early warning and proactive prevention and control.

[0005] Therefore, there is an urgent need for a BIM and IoT-based hanging basket construction status monitoring system that can solve the problems of monitoring lag, fragmentation of elements, and low accuracy of early warning in existing technologies, integrate the advantages of BIM and IoT technologies, and realize real-time monitoring, dynamic analysis, and accurate early warning in bridge hanging basket construction. Summary of the Invention

[0006] To address the aforementioned problems in existing technologies, this invention provides a method for monitoring the construction status of hanging baskets based on BIM and the Internet of Things, which features real-time monitoring, dynamic analysis, and precise early warning.

[0007] The objective of this invention can be achieved through the following technical solutions: A method for monitoring the construction status of hanging baskets based on BIM and IoT includes the following steps: Step 1: Construct a BIM model for hanging basket construction and set up a monitoring point array. The monitoring point array information includes the components that need to be monitored, the monitoring location of the corresponding components, and the detection items. Step 2: Based on the monitoring point array in Step 1, deploy the corresponding sensors, IoT monitoring terminals, and data transmission network; Step 3: Establish a dynamic association between the BIM model and real-time monitoring data, map the components to be monitored, the monitoring locations of the corresponding components, and the detection items in the monitoring point array information to the corresponding locations in the BIM model, and ensure that the real-time updated BIM model includes the real-time information uploaded by the sensors. Step 4: Real-time data analysis and risk level assessment: After receiving the data, the control module first performs noise reduction on the raw monitoring data; then it extracts the characteristic values ​​of the monitoring parameters, including peak stress, displacement change rate and load accumulation, and compares the characteristic values ​​with the design allowable values ​​of the associated components in the BIM model to determine whether the characteristic value of each individual parameter exceeds the threshold.

[0008] As a preferred embodiment of the present invention, step two further includes: during the construction of the data transmission network, the network includes a monitoring terminal, an edge gateway, and a cloud platform. The monitoring terminal transmits the real-time collected data to the edge gateway via LoRa or Wi-Fi protocols. After preprocessing the data, the edge gateway uploads the data to the cloud platform via 4G / 5G or Ethernet. At the same time, the edge gateway has a local caching function.

[0009] As a preferred embodiment of the present invention, step one further includes: assigning an ID to each component when creating the BIM model; step three further includes: mapping the real-time parameters collected by the strain sensor to the corresponding component attribute column in the BIM model through component ID mapping, so that when the monitoring data is updated, the parameter information in the attribute column of the component in the BIM model is updated synchronously.

[0010] As a preferred embodiment of the present invention, step one further includes: setting four risk levels from 1 to 4; step four further includes: after determining whether the characteristic value of each individual parameter exceeds the threshold, assessing the risk status, if all monitored parameters are within the design allowable range, it is determined to be level 1, if a single non-critical parameter is close to the design threshold range, it is determined to be level 2, if a single critical parameter exceeds the design threshold range, it is determined to be level 3, if multiple parameters exceed the design threshold or critical structures show abnormal displacement, it is determined to be level 4.

[0011] As a preferred technical solution of the present invention, it also includes step five, risk warning and visual control. When the risk level is determined to be level 2, a reminder is sent to the management personnel through the cloud platform, and the component ID corresponding to the single parameter feature value exceeding the threshold is sent. When the risk level reaches level 3, a warning notification is sent, and the risk component is highlighted in yellow in the BIM model. When the risk level reaches level 4, the system automatically triggers an audible and visual alarm, and pushes an emergency response plan at the same time.

[0012] As a preferred technical solution of the present invention, it also includes step six: data storage and historical backtracking analysis; the cloud platform uses a time-series database to store all monitoring data and establishes an index in the format of "component ID-monitoring parameter-time stamp".

[0013] The beneficial effects of this invention are as follows: By establishing a dynamic fusion of monitoring data sensing network and BIM model, and by mapping component IDs to data, real-time monitoring data is directly associated with the corresponding components in the BIM model, realizing the function of real-time data display in the model, improving the level of visualization, and thus facilitating real-time monitoring and dynamic analysis. By setting up multiple sensors to collect multiple parameters, taking into account structural mechanics, geometric deformation, environmental and construction load factors, a comprehensive perception of various parameters of the hanging basket is achieved. By setting up a four-level response mechanism, combined with the BIM interface as a visual management and control interface, construction personnel can quickly locate risky components and further analyze the causes of risks, ensuring real-time monitoring, dynamic analysis, and accurate early warning. Attached Figure Description

[0014] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a schematic diagram of step three of the present invention; Figure 3 This is a schematic diagram of a typical sensor network layout according to the present invention; Figure 4 This is a topology diagram of the various software modules of this invention; Figure 5 This is a reference table for the IDs of the components used in the bridge hanging basket construction monitoring of this invention. Detailed Implementation

[0016] 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.

[0017] Please see Figure 1-5 A method for monitoring the construction status of hanging baskets based on BIM and the Internet of Things includes the following steps: Step 1: Construct a BIM model for hanging basket construction and set up a monitoring point array. The monitoring point array information includes the components that need to be monitored, the monitoring location of the corresponding components, and the detection items. Step 2: Based on the monitoring point array in Step 1, deploy the corresponding sensors, IoT monitoring terminals, and data transmission network; Step 3: Establish a dynamic association between the BIM model and real-time monitoring data, map the components to be monitored, the monitoring locations of the corresponding components, and the detection items in the monitoring point array information to the corresponding locations in the BIM model, and ensure that the real-time updated BIM model includes the real-time information uploaded by the sensors. Step 4: Real-time data analysis and risk level assessment: After receiving the data, the control module first performs noise reduction on the raw monitoring data; then it extracts the characteristic values ​​of the monitoring parameters, including peak stress, displacement change rate and load accumulation, and compares the characteristic values ​​with the design allowable values ​​of the associated components in the BIM model to determine whether the characteristic value of each individual parameter exceeds the threshold. In step one, based on the hanging basket design drawings, construction plan and bridge structural parameters, BIM modeling software is used to construct a detailed BIM model containing information such as the main structure of the hanging basket, anchoring system and construction load. The main structure of the hanging basket includes load-bearing beams, slings and bottom formwork. The model accuracy of the above detailed model is LOD400. The components and testing items that need to be monitored for the hanging baskets are shown in the table below: For the monitoring locations of the corresponding components: strain sensors are installed on the load-bearing beams and slings, which are key load-bearing components; displacement sensors are installed on the bottom formwork and the ends of the main beams; wind speed and direction sensors and temperature sensors are installed on the top of the hanging basket; and weight sensors are installed in the concrete pouring area. All monitoring sensors have data acquisition, local storage and wireless transmission functions. Specifically, step two also includes: during the construction of the data transmission network, the network includes a monitoring terminal, an edge gateway, and a cloud platform. The monitoring terminal transmits the real-time collected data to the edge gateway via LoRa or Wi-Fi protocols; after the edge gateway preprocesses the data, it uploads the data to the cloud platform via 4G / 5G or Ethernet; at the same time, the edge gateway has a local caching function. Step one also includes: assigning an ID to each component when creating the BIM model; Step three also includes: mapping the real-time parameters collected by the strain sensor to the corresponding component attribute column in the BIM model through component ID mapping. When the monitoring data is updated, the parameter information in the attribute column of the component in the BIM model is updated synchronously, realizing the linkage of "model visualization - real-time data". Then, based on the time dimension information of the BIM model (such as the construction schedule), the monitoring data is associated with the construction stages (before, during, and after concrete pouring) to form a three-dimensional data matrix of "time-location-parameters", which provides data support for subsequent staged risk analysis. BIM models utilize model engines (such as AutodeskForge, BentleyiModel.js) and the WebSocket real-time communication protocol to achieve real-time communication with sensors or cloud platforms; At this time, when the monitoring data is updated, the parameter information of the components in the BIM model is updated synchronously, realizing the linkage of "model visualization - data real-time", including the synchronous update of monitoring data and BIM model component parameters. This is achieved by relying on a four-level technical architecture of "IoT data acquisition layer - edge gateway preprocessing layer - cloud platform data association layer - BIM model engine interaction layer". Each layer is connected through standardized data interfaces and protocols to ensure the real-time and accuracy of data flow and model updates. Specifically, step three also includes the following sub-steps: Step A1: Establish standardized data mapping rules; In the initial stage of system deployment, the basic mapping configuration between monitoring data and BIM model components is completed to lay the rule foundation for subsequent synchronous updates. The specific steps are as follows: A11. Standardized definition of BIM component ID: In the modeling stage of step S1, a unique and structured ID is assigned to each key component. The ID format is uniformly "component type-number-location", and the ID is stored in the "extended attribute bar" of the BIM model component. A12. Binding of monitoring terminals and component IDs: When deploying IoT monitoring terminals, the cloud platform's "Equipment Management Module" associates each monitoring terminal with a corresponding BIM component ID, forming a "Monitoring Terminal ID - BIM Component ID" mapping table, which is stored in the cloud platform's relational database. A13. Data Format Standardization Conventions: Define a standardized JSON format for monitoring data, ensuring that the data includes six core fields: "Monitoring Terminal ID, BIM Component ID, Parameter Type (Strain / Displacement), Parameter Value, Acquisition Timestamp, and Data Quality Identifier (Normal / Abnormal)". See the example of the ID table mapping. Figure 5 ; Then, step A2 is executed, and the monitoring data triggers the update of BIM component parameters: After the IoT monitoring terminal collects new data and uploads it, the system realizes the automatic synchronization update of BIM model component parameters through the following process, including the following sub-steps: A21. Data Reception and Preprocessing: The monitoring terminal transmits raw data to the edge gateway via LoRa / Wi-Fi protocol. The gateway first uses Kalman filtering algorithm to remove data noise, such as electromagnetic interference fluctuations in strain data, and then converts the data structure according to standardized JSON format. At the same time, it verifies the validity of "BIM component ID" (such as whether it exists in the mapping table). If the data is abnormal (such as the component ID not existing), it is marked as "abnormal data" and temporarily stored locally. Normal data is uploaded to the cloud platform via 4G / 5G. A22. Data Association and Matching: After receiving standardized data uploaded by the edge gateway, the cloud platform's "Data Association Module" queries the "Component Attribute Mapping Table" in the relational database through the "BIM Component ID" to determine the "Parameter Storage Path" corresponding to the component in the BIM model (e.g., the strain parameter of "Sling-03-Left Front End" is stored in the "Extended Attribute-Real-Time Monitoring-Strain Value" field). At the same time, it retrieves the component's design parameters (e.g., allowable strain 1500με) and compares them with the real-time monitoring value to generate associated data of "Real-Time Value-Design Value-Deviation Rate". A23. Synchronous Update of BIM Model Parameters: The cloud platform pushes information such as "BIM component ID, parameter storage path, real-time parameter value, and deviation rate" to the BIM model engine (e.g., AutodeskForge) via the WebSocket real-time communication protocol. After receiving the data, the model engine calls the "component attribute update interface," automatically locates the corresponding BIM component (e.g., "sling-03-left front end"), and updates the value of its "extended attribute-real-time monitoring-strain value" field to 1600με. At the same time, it writes "+6.7%" ((1600-1500) / 1500) in the "deviation rate" field, completing the synchronous update of the component parameters. A3 data visualization is then performed: While the BIM model component parameters are updated synchronously, the following methods are used to achieve the linkage between "model visualization" and "real-time data," allowing managers to intuitively perceive data changes: A31. Real-time overlay display of parameter values: The BIM model engine overlays and displays real-time parameter values ​​and deviation rates on the 3D model of the component, using a "floating text box" attached to the surface of the component (e.g., displaying "Strain: 1600με (+6.7%)" in the middle of the "Sling-03" model). The color of the text box changes with the deviation rate (e.g., black for deviation rate ≤10%, yellow for 10% < deviation rate ≤20%, and red for deviation rate >20%), reflecting whether the parameter exceeds the standard. A32. Component Color Dynamic Change Early Warning: When the monitored parameters exceed the preset threshold (such as strain exceeding the standard and entering Level III risk), the model engine automatically triggers the "component color rendering rule", switching the model color of the corresponding component from the default gray to yellow (Level III risk) or red (Level IV risk), accompanied by a slight flashing effect (such as flashing once per second). At the same time, a "risk warning bubble" pops up on the model interface, displaying the risk level, the parameter exceeding the standard, and the suggested measures, realizing the visual linkage of "data anomaly - model early warning". A33. Time-dimensional data traceability and linkage: When managers click on any component (such as "sling-03") in the BIM model interface, they can retrieve the historical monitoring data of the component (such as the strain change curve of the past 24 hours) through the "historical data query" function. The curve is displayed synchronously with the model interface. Clicking on any time point on the curve will automatically trace back to the component parameter status at that time point (such as displaying "2024-05-20T14:00:00 strain: 1450με (-3.3%)"), realizing the time-dimensional linkage of "real-time data - historical data - model status". By establishing a dynamic fusion of monitoring data sensing network and BIM model, and by associating component IDs with data, real-time monitoring data is directly linked to the corresponding components in the BIM model, enabling the real-time display of data in the model, improving the level of visualization, and thus facilitating real-time monitoring and dynamic analysis.

[0018] Step one also includes: setting four risk levels from 1 to 4; Step four also includes: after determining whether the characteristic value of each single parameter exceeds the threshold, assessing the risk status. If all monitored parameters are within the design allowable range, it is determined to be Level 1; if a single non-critical parameter is close to the design threshold range, it is determined to be Level 2; if a single non-critical parameter reaches the first 10% or the last 10% of the design threshold range boundary, it is determined to be Level 3; if multiple parameters exceed the design threshold or the critical structure shows abnormal displacement, it is determined to be Level 4. Optionally, the system automatically triggers the corresponding early warning mechanism based on the risk level, and simultaneously marks the risk area with color in the BIM model, i.e., proceed to step five: Specifically, the first or last 10% of the design threshold range for a single non-critical parameter means that, assuming a non-critical parameter is x and the design threshold range is [a, b], when a≤x≤a+(ba)×0.1 or b-(ba)×0.1≤x≤b, then this non-critical parameter has reached the first or last 10% of the design threshold range.

[0019] Step 5: Risk Warning and Visualized Control. When the risk level is determined to be Level 2, a reminder is sent to the management personnel via the cloud platform, along with the component ID corresponding to the single parameter characteristic value exceeding the threshold. When the risk level reaches Level 3, a warning notification (including the location of the risky component, the parameter exceeding the standard, and suggested measures) is sent to the management personnel's mobile APP, and the risky component is highlighted in yellow in the BIM model. When the risk level reaches Level 4, in addition to sending an emergency warning notification, an audible and visual alarm device (installed in the construction site duty room) is automatically triggered, and the risky component is displayed in red flashing in the BIM model. At the same time, an emergency response plan (such as stopping pouring, unloading the load, and reinforcing the anchorage) is pushed out.

[0020] It also includes step six: data storage and historical backtracking analysis; the cloud platform uses a time-series database to store all monitoring data and creates an index in the format of "component ID-monitoring parameter-time stamp"; After construction is completed, retrospective analysis can be conducted based on historical data: by comparing monitoring data and risk status at different construction stages, the stress law and risk evolution characteristics of the hanging basket structure can be summarized, providing data support for optimizing monitoring schemes for similar bridge hanging basket construction in the future.

[0021] 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 simple 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 method for monitoring the construction status of hanging baskets based on BIM and the Internet of Things, characterized in that: Includes the following steps: Step 1: Construct a BIM model for hanging basket construction and set up a monitoring point array. The monitoring point array information includes the components that need to be monitored, the monitoring location of the corresponding components, and the detection items. Step 2: Based on the monitoring point array in Step 1, deploy the corresponding sensors, IoT monitoring terminals, and data transmission network; Step 3: Establish a dynamic association between the BIM model and real-time monitoring data, map the components to be monitored, the monitoring locations of the corresponding components, and the detection items in the monitoring point array information to the corresponding locations in the BIM model, and ensure that the real-time updated BIM model includes the real-time information uploaded by the sensors. Step 4: Real-time data analysis and risk level assessment: After receiving the data, the control module first performs noise reduction processing on the raw monitoring data; Subsequently, characteristic values ​​of the monitoring parameters are extracted, including peak stress, displacement rate of change, and load accumulation. These characteristic values ​​are then compared with the design allowable values ​​of the associated components in the BIM model to determine whether the characteristic value of each individual parameter exceeds the threshold.

2. The method for monitoring the construction status of hanging baskets based on BIM and IoT according to claim 1, characterized in that: Step two further includes: during the construction of the data transmission network, the network includes a monitoring terminal, an edge gateway, and a cloud platform. The monitoring terminal transmits the real-time collected data to the edge gateway via LoRa or Wi-Fi protocols. After preprocessing the data, the edge gateway uploads the data to the cloud platform via 4G / 5G or Ethernet. At the same time, the edge gateway has a local caching function.

3. The method for monitoring the construction status of hanging baskets based on BIM and IoT as described in claim 1, characterized in that: Step one also includes: assigning an ID to each component when creating the BIM model; Step three also includes: mapping the real-time parameters collected by the strain sensor to the corresponding component attribute column in the BIM model through component ID mapping, so that when the monitoring data is updated, the parameter information in the component attribute column in the BIM model is updated synchronously.

4. The method for monitoring the construction status of hanging baskets based on BIM and IoT as described in claim 1, characterized in that: Step one also includes setting four risk levels, 1 to 4. Step four also includes assessing the risk status after determining whether the characteristic value of each individual parameter exceeds the threshold. If all monitored parameters are within the design allowable range, it is determined to be Level 1. If a single non-critical parameter reaches the first 10% or the last 10% of the design threshold range boundary, it is determined to be Level 2. If a single critical parameter exceeds the design threshold range, it is determined to be Level 3. If multiple parameters exceed the design threshold or the critical structure shows abnormal displacement, it is determined to be Level 4.

5. The method for monitoring the construction status of hanging baskets based on BIM and IoT as described in claim 1, characterized in that: It also includes step five, risk warning and visual control. When the risk level is determined to be level 2, a reminder is sent to the management personnel through the cloud platform, and the component ID corresponding to the single parameter feature value that exceeds the threshold is sent. When the risk level reaches level 3, a warning notification is sent, and the risk component is highlighted in yellow in the BIM model. When the risk level reaches level 4, the system automatically triggers an audible and visual alarm and pushes an emergency response plan.

6. The method for monitoring the construction status of hanging baskets based on BIM and IoT according to claim 1, characterized in that: It also includes step six: data storage and historical backtracking analysis; the cloud platform uses a time-series database to store all monitoring data and creates an index in the format of "component ID-monitoring parameter-time stamp".

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