Early warning method for instability of surrounding adjacent high-rise building during deep foundation pit excavation

Abstract

The invention discloses an early warning method for instability of surrounding adjacent high-rise buildings during deep foundation pit excavation, and relates to the technical field of early warning of instability of surrounding adjacent high-rise buildings, which comprises the following steps: step 1, collecting field multivariate information data, step 2, establishing a finite element mesh model, step 3, setting a settlement threshold value in the finite element mesh model, and step 4, setting a settlement threshold value in the finite element mesh model. 4, a field real-time monitoring system is arranged in the finite element mesh model, and field multivariate information data comprises ground and building settlement monitoring data collected by a settlement sensor and an inclinometer, deformation monitoring data of the side wall and the bottom of a foundation pit, underground water level change monitoring data and construction load data; the finite element mesh model is formed by modeling a foundation pit and surrounding buildings on the basis of ABAQUS soil body strength reduction method finite element analysis, and the finite element mesh model simulates settlement of surrounding adjacent high-rise buildings in the layered excavation construction process and predicts settlement changes of the buildings.

Description

Technical Field

[0001] The invention relates to the technical field of instability early warning for surrounding adjacent high-rise buildings, and in particular to an early warning method for instability of surrounding adjacent high-rise buildings during deep foundation pit excavation. Background Art

[0002] Deep foundation pit excavation is widely used in urban construction, especially in projects such as high-rise buildings, underground facilities and transportation systems. Foundation pit construction is a complex engineering process that directly affects the safety and stability of surrounding buildings. During the foundation pit excavation process, due to the weight distribution of soil and the change of groundwater level, the surrounding high-rise buildings may sink and deform. These deformations not only affect the structural safety of the building, but also may cause instability risks. The settlement and instability of buildings may cause cracks, tilts and even collapses, causing significant economic losses and safety hazards. Therefore, timely monitoring and early warning of settlement changes are crucial, but they often have problems such as information isolation, data processing lag, and difficulty in comprehensive analysis. These limitations make it difficult to adequately assess the impact on neighboring buildings during construction and to achieve effective early warning.

[0003] The existing early warning methods for the instability of surrounding high-rise buildings have the following defects:

[0004] 1. Patent document CN115662046A discloses an early warning device for detecting instability of a building body, "including: a top mounting plate and a protective shell, the protective shell is movably connected to the bottom end of the top mounting plate through a body status monitoring component; a bottom mounting plate, the bottom mounting plate is fixedly mounted at the bottom end of the protective shell; a bracket, the bracket is fixedly mounted at the middle of the bottom end of the bottom mounting plate; a signal monitoring device, the signal monitoring device is fixedly mounted at the bottom end of the top mounting plate; a wireless vibration monitoring sensor, the wireless vibration monitoring sensor is fixedly mounted at the middle of the bottom end of the top mounting plate. The early warning device for detecting instability of a building body has an intelligent effect on vibration monitoring as a whole, filters useless signals, and reduces the possibility of false alarms by sensors; it can communicate and alarm to the designated unit of the patrol personnel to ensure its information response speed and launch rescue operations in a timely manner", but the existing early warning method for instability of surrounding high-rise buildings. Instability of building settlement may cause cracks, tilts or even collapses, and the failure to timely monitor and warn of settlement changes causes significant economic losses and safety hazards;

[0005] 2. Patent document CN221551306U discloses an early warning device for detecting instability of a building, "which includes several floor mounting plates and several strain sensors, several of which are respectively arranged on the outer walls of each floor of the building, one side of the floor mounting plate is fixedly connected to a support block, a circular hole is opened on the support block, a detection shaft is passed through and fixedly installed inside the circular hole, and a strain sensor is arranged between every two detection shafts; the present application arranges detection shafts between each floor, and a strain sensor is arranged between every two detection shafts. When one floor is unstable and deformed, the amount of unstable deformation will be transmitted to the strain sensor through the detection shaft, so that each floor can be monitored and warned of instability, which has high safety, and by screwing the internal threaded ring onto the threaded shaft one and the threaded shaft two, the strain sensor and the detection shaft can be quickly installed, which is convenient to use", but the existing early warning method for instability of surrounding high-rise buildings is that the instability of the building settlement may cause cracks, tilting or even collapse, and the failure to timely monitor and warn of settlement changes will cause significant economic losses and safety hazards;

[0006] 3. Patent document CN112525269A discloses a monitoring system and method for deep foundation pits based on the Beidou positioning system. "The monitoring system includes displacement sensors, Beidou navigation positioning base stations, monitoring controllers, terminal data processing equipment, data storage equipment, and Internet of Things transmission equipment. Displacement sensors are pre-buried in the monitoring positions of foundation pits through a grid layout, interconnected and cross-integrated, and the distance changes between the displacement sensors are measured in real time. The detection distance changes are transmitted to the monitoring controller in real time through the base station. After the monitoring controller analyzes the detection distance changes, the deformation of the deep foundation pit is determined. The data terminal The end processing equipment can then use differential technology to correct the errors of the monitoring data and realize real-time monitoring of deep foundation pits. Data processing and error correction can truly reflect the deformation of deep foundation pits, improve monitoring accuracy, and determine whether the displacement and deformation of deep foundation pits are within a safe range. Advance warning can effectively prevent accidents and avoid sudden collapse and instability of the foundation pit, causing casualties, equipment damage, and affecting the safety of surrounding buildings. However, the existing warning methods for the instability of surrounding high-rise buildings are that the instability of building settlement may cause cracks, tilts, or even collapses. Failure to monitor and warn of settlement changes in a timely manner will cause significant economic losses and safety hazards.

[0007] 4. Patent document CN213659596U discloses a slope instability alarm device, "including a buzzer alarm, characterized in that it also includes a displacement sensor assembly fixed on the wall of the building and a threaded steel bar arranged on the retaining wall, the displacement sensor assembly includes a metal sleeve, a spring located in the metal sleeve and a top touch switch, the top touch switch is fixed on the inner bottom wall of the metal sleeve, one end of the threaded steel bar is embedded in the metal sleeve, the other end of the threaded steel bar is fixedly connected to the retaining wall, an observation hole is opened on the metal sleeve, one end of the spring is connected to the threaded steel bar, the other end of the spring is connected to the inner bottom wall of the metal sleeve, and the top touch switch is electrically connected to the buzzer alarm. The utility model has a simple and reliable structure, good warning effect, strong stability, easy inspection and maintenance, and is suitable for large-scale production and equipment", but the existing warning method for the instability of surrounding high-rise buildings. The instability of building settlement may cause cracks, tilting or even collapse, and the failure to timely monitor and warn of settlement changes will cause significant economic losses and safety hazards. Summary of the invention

[0008] The purpose of the present invention is to provide an early warning method for the instability of surrounding high-rise buildings during deep foundation pit excavation, so as to solve the technical problems raised in the above background technology that the instability of building settlement may cause cracks, tilting or even collapse, and the failure to timely monitor and warn of settlement changes may cause significant economic losses and safety hazards.

[0009] To achieve the above object, the present invention provides the following technical solution: a method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation, comprising the following steps:

[0010] Step 1: Collect multivariate information data on site;

[0011] Step 2: Establish a finite element mesh model;

[0012] Step 3, setting a settlement threshold in the finite element mesh model;

[0013] Step 4: Setting up a field real-time monitoring system in the finite element mesh model;

[0014] The on-site multivariate information data includes the settlement monitoring data of the ground and buildings collected by settlement sensors and inclinometers, the deformation monitoring data of the side walls and bottom of the foundation pit, the monitoring data of groundwater level changes and the construction load data. The finite element mesh model is based on the finite element analysis of the soil strength reduction method in ABAQUS to model the foundation pit and surrounding buildings. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, attention state and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel.

[0015] Preferably, the finite element mesh model can adjust model parameters to improve the accuracy of model prediction results.

[0016] Preferably, the settlement threshold of 200 mm to 220 mm is a normal state, 221 mm to 240 mm is a concern state, and 241 mm to 261 mm is an alarm state.

[0017] Preferably, the mesh type of the finite element mesh model is one of a structured mesh, an unstructured mesh, and a hybrid mesh.

[0018] Preferably, the on-site real-time monitoring system uses G, LoRa and NB-IoT, which improves the stability and speed of data transmission, can provide reliable data transmission in wide areas and complex environments, and ensure that on-site data can be transmitted to the finite element mesh model in real time and accurately.

[0019] Preferably, the on-site real-time monitoring system uses Internet of Things technology to electrically connect sensors and cameras to form a monitoring network, which can collect on-site data and images in real time and transmit them to the finite element mesh model.

[0020] Preferably, the on-site real-time monitoring system also includes artificial intelligence and machine learning algorithms to improve the intelligence level of data processing, and realize anomaly detection, pattern recognition and trend prediction through training models, so that the on-site real-time monitoring system can automatically identify abnormal situations and issue early warnings in a timely manner.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. The present invention realizes real-time data collection by installing an automated monitoring system, which can timely reflect the impact of construction on the surrounding environment. Combined with multi-source information fusion technology, this method improves the accuracy of settlement prediction and ensures that the warning threshold can be scientifically set, so as to effectively identify potential risks before they occur. In addition, the dynamic adjustment capability of this method enables the warning mechanism to flexibly adapt to the progress of construction, improves the efficiency of responding to emergencies, and is suitable for various deep foundation pit excavation projects. In particular, through early warning in the construction environment of densely populated urban areas, the potential risks of foundation pit excavation to adjacent high-rise buildings can be effectively reduced. Once the warning is triggered, the system can quickly initiate an emergency response to ensure the safety of adjacent buildings and reduce unexpected events during the construction process and the economic losses caused by them. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a control flow diagram of the present invention;

[0024] Figure 2 The experimental data of the present invention are shown in FIG. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0026] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end", "the other end" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0027] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, it can be understood according to the specific situation.

[0028] Example 1: Please refer to Figure 1 The present invention provides an embodiment: a method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation, comprising the following steps:

[0029] Step 1: Collect multivariate information data on site;

[0030] Step 2: Establish a finite element mesh model;

[0031] Step 3, setting a settlement threshold in the finite element mesh model;

[0032] Step 4: Setting up a field real-time monitoring system in the finite element mesh model;

[0033] The on-site multivariate information data includes the settlement monitoring data of the ground and buildings collected by using settlement sensors and inclinometers, the deformation monitoring data of the side walls and bottom of the foundation pit, the monitoring data of the groundwater level change and the construction load data. The finite element mesh model is based on the finite element analysis of the ABAQUS soil strength reduction method to model the foundation pit and the surrounding buildings. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction process and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, attention state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to Improve the accuracy of the model prediction results. The settlement threshold of 200mm is a normal state, 225mm is a concern state, and 245mm is an alarm state. The mesh type of the finite element mesh model is a structured mesh, an unstructured mesh, or a hybrid mesh. The on-site real-time monitoring system uses G, LoRa, and NB-IoT to improve the stability and speed of data transmission, and can provide reliable data transmission in a wide area and complex environment, ensuring that the on-site data can be transmitted to the finite element mesh model in real time and accurately. The on-site real-time monitoring system uses the Internet of Things technology to electrically connect sensors and cameras to form a monitoring network, and can collect on-site data and images in real time and transmit them to the finite element mesh model. The on-site real-time monitoring system also includes artificial intelligence and machine learning algorithms to improve the intelligence level of data processing. By training models, anomaly detection, pattern recognition, and trend prediction are realized, so that the on-site real-time monitoring system can automatically identify abnormal situations and issue early warnings in a timely manner.

[0034] Example 2: Please refer to Figure 1The present invention provides an embodiment: a method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation, comprising the following steps:

[0035] Step 1: Collect multivariate information data on site;

[0036] Step 2: Establish a finite element mesh model;

[0037] Step 3, setting a settlement threshold in the finite element mesh model;

[0038] Step 4: Setting up a field real-time monitoring system in the finite element mesh model;

[0039] The on-site multivariate information data includes the settlement monitoring data of the ground and buildings collected by using settlement sensors and inclinometers, the deformation monitoring data of the side walls and bottom of the foundation pit, the monitoring data of the groundwater level change and the construction load data. The finite element mesh model is based on the finite element analysis of the ABAQUS soil strength reduction method to model the foundation pit and the surrounding buildings. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction process and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, attention state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to Improve the accuracy of the model prediction results. The settlement threshold of 210mm is a normal state, 230mm is a concern state, and 250mm is an alarm state. The mesh type of the finite element mesh model is a structured mesh, an unstructured mesh, or a hybrid mesh. The on-site real-time monitoring system uses G, LoRa, and NB-IoT to improve the stability and speed of data transmission, and can provide reliable data transmission in a wide area and complex environment, ensuring that the on-site data can be transmitted to the finite element mesh model in real time and accurately. The on-site real-time monitoring system uses the Internet of Things technology to electrically connect sensors and cameras to form a monitoring network, and can collect on-site data and images in real time and transmit them to the finite element mesh model. The on-site real-time monitoring system also includes artificial intelligence and machine learning algorithms to improve the intelligence level of data processing. By training models, anomaly detection, pattern recognition, and trend prediction are realized, so that the on-site real-time monitoring system can automatically identify abnormal situations and issue early warnings in a timely manner.

[0040] Example 3: Please refer to Figure 1 The present invention provides an embodiment: a method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation, comprising the following steps:

[0041] Step 1: Collect multivariate information data on site;

[0042] Step 2: Establish a finite element mesh model;

[0043] Step 3, setting a settlement threshold in the finite element mesh model;

[0044] Step 4: Setting up a field real-time monitoring system in the finite element mesh model;

[0045] The on-site multivariate information data includes the settlement monitoring data of the ground and buildings collected by using settlement sensors and inclinometers, the deformation monitoring data of the side walls and bottom of the foundation pit, the monitoring data of the groundwater level change and the construction load data. The finite element mesh model is based on the finite element analysis of the ABAQUS soil strength reduction method to model the foundation pit and the surrounding buildings. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction process and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, attention state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to Improve the accuracy of the model prediction results. The settlement threshold of 215mm is a normal state, 235mm is a concern state, and 255mm is an alarm state. The mesh type of the finite element mesh model is a structured mesh, an unstructured mesh, or a hybrid mesh. The on-site real-time monitoring system uses G, LoRa, and NB-IoT to improve the stability and speed of data transmission, and can provide reliable data transmission in a wide area and complex environment, ensuring that the on-site data can be transmitted to the finite element mesh model in real time and accurately. The on-site real-time monitoring system uses the Internet of Things technology to electrically connect sensors and cameras to form a monitoring network, and can collect on-site data and images in real time and transmit them to the finite element mesh model. The on-site real-time monitoring system also includes artificial intelligence and machine learning algorithms to improve the intelligence level of data processing. By training models, anomaly detection, pattern recognition, and trend prediction are realized, so that the on-site real-time monitoring system can automatically identify abnormal situations and issue early warnings in a timely manner.

[0046] Example 4: Please refer to Figure 1 The present invention provides an embodiment: a method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation, comprising the following steps:

[0047] Step 1: Collect multivariate information data on site;

[0048] Step 2: Establish a finite element mesh model;

[0049] Step 3, setting a settlement threshold in the finite element mesh model;

[0050] Step 4: Setting up a field real-time monitoring system in the finite element mesh model;

[0051] The on-site multivariate information data includes the settlement monitoring data of the ground and buildings collected by using settlement sensors and inclinometers, the deformation monitoring data of the side walls and bottom of the foundation pit, the monitoring data of the groundwater level change and the construction load data. The finite element mesh model is based on the finite element analysis of the ABAQUS soil strength reduction method to model the foundation pit and the surrounding buildings. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction process and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, attention state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to Improve the accuracy of the model prediction results. The settlement threshold of 213mm is a normal state, 234mm is a concern state, and 257mm is an alarm state. The mesh type of the finite element mesh model is a structured mesh, an unstructured mesh, or a hybrid mesh. The on-site real-time monitoring system uses G, LoRa, and NB-IoT to improve the stability and speed of data transmission, and can provide reliable data transmission in a wide area and complex environment, ensuring that the on-site data can be transmitted to the finite element mesh model in real time and accurately. The on-site real-time monitoring system uses the Internet of Things technology to electrically connect sensors and cameras to form a monitoring network, and can collect on-site data and images in real time and transmit them to the finite element mesh model. The on-site real-time monitoring system also includes artificial intelligence and machine learning algorithms to improve the intelligence level of data processing. By training models, anomaly detection, pattern recognition, and trend prediction are achieved, so that the on-site real-time monitoring system can automatically identify abnormal situations and issue early warnings in a timely manner.

[0052] Example 5: Please refer to Figure 1 The present invention provides an embodiment: a method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation, comprising the following steps:

[0053] Step 1: Collect multivariate information data on site;

[0054] Step 2: Establish a finite element mesh model;

[0055] Step 3, setting a settlement threshold in the finite element mesh model;

[0056] Step 4: Setting up a field real-time monitoring system in the finite element mesh model;

[0057] The on-site multivariate information data includes the settlement monitoring data of the ground and buildings collected by using settlement sensors and inclinometers, the deformation monitoring data of the side walls and bottom of the foundation pit, the monitoring data of the groundwater level change and the construction load data. The finite element mesh model is based on the finite element analysis of the ABAQUS soil strength reduction method to model the foundation pit and the surrounding buildings. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction process and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, attention state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to Improve the accuracy of the model prediction results. The settlement threshold of 220mm is a normal state, 240mm is a concern state, and 260mm is an alarm state. The mesh type of the finite element mesh model is a structured mesh, an unstructured mesh, or a hybrid mesh. The on-site real-time monitoring system uses G, LoRa, and NB-IoT to improve the stability and speed of data transmission, and can provide reliable data transmission in a wide area and complex environment, ensuring that the on-site data can be transmitted to the finite element mesh model in real time and accurately. The on-site real-time monitoring system uses the Internet of Things technology to electrically connect sensors and cameras to form a monitoring network, and can collect on-site data and images in real time and transmit them to the finite element mesh model. The on-site real-time monitoring system also includes artificial intelligence and machine learning algorithms to improve the intelligence level of data processing. By training models, anomaly detection, pattern recognition, and trend prediction are realized, so that the on-site real-time monitoring system can automatically identify abnormal situations and issue early warnings in a timely manner.

[0058] Comparative experiment;

[0059] The difference between Comparative Example 1 and Example 1 is that;

[0060] The multivariate information data on site include settlement monitoring data of the ground and buildings collected by settlement sensors and inclinometers, deformation monitoring data of the side walls and bottom of the foundation pit, groundwater level change monitoring data and construction load data. The finite element mesh model is modeled by the foundation pit and surrounding buildings based on the finite element analysis of the soil strength reduction method of ABAQUS. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, concern state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to improve the accuracy of the model prediction results. The settlement threshold is 200mm for the normal state, 225mm for the concern state, and 245mm for the alarm state.

[0061] The difference between Comparative Example 1 and Example 2 is that;

[0062] The multivariate information data on site include settlement monitoring data of the ground and buildings collected by settlement sensors and inclinometers, deformation monitoring data of the side walls and bottom of the foundation pit, groundwater level change monitoring data and construction load data. The finite element mesh model is modeled by the foundation pit and surrounding buildings based on the finite element analysis of the soil strength reduction method of ABAQUS. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, concern state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to improve the accuracy of the model prediction results. The settlement threshold is 210 mm for the normal state, 230 mm for the concern state, and 250 mm for the alarm state.

[0063] The difference between Comparative Example 1 and Example 3 is that;

[0064] The multivariate information data on site include settlement monitoring data of the ground and buildings collected by settlement sensors and inclinometers, deformation monitoring data of the side walls and bottom of the foundation pit, groundwater level change monitoring data and construction load data. The finite element mesh model is modeled by the foundation pit and surrounding buildings based on the finite element analysis of the soil strength reduction method of ABAQUS. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, concern state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to improve the accuracy of the model prediction results. The settlement threshold is 215mm for the normal state, 235mm for the concern state, and 255mm for the alarm state.

[0065] The difference between Comparative Example 1 and Example 4 is that;

[0066] The multivariate information data on site include settlement monitoring data of the ground and buildings collected by settlement sensors and inclinometers, deformation monitoring data of the side walls and bottom of the foundation pit, groundwater level change monitoring data and construction load data. The finite element mesh model is modeled by the foundation pit and surrounding buildings based on the finite element analysis of the soil strength reduction method of ABAQUS. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, concern state, and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to improve the accuracy of the model prediction results. The settlement threshold is 213mm for the normal state, 234mm for the concern state, and 257mm for the alarm state.

[0067] The difference between Comparative Example 1 and Example 5 is that;

[0068] The multivariate information data on site include settlement monitoring data of the ground and buildings, deformation monitoring data of the side walls and bottom of the foundation pit, groundwater level change monitoring data and construction load data collected by settlement sensors and inclinometers. The finite element mesh model is modeled by the foundation pit and surrounding buildings based on the finite element analysis of the soil strength reduction method of ABAQUS. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, concern state and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel. The finite element mesh model can adjust the model parameters to improve the accuracy of the model prediction results. The settlement threshold is 220 mm for the normal state, 240 mm for the concern state, and 260 mm for the alarm state.

[0069] The early warning method for instability of surrounding adjacent high-rise buildings of Examples 1, 2, 3, 4 and 5 of the present invention and the traditional early warning method for instability of surrounding adjacent high-rise buildings (Comparative Example 1) were used to start an emergency response time experiment, and their values ​​were calculated and statistically analyzed, and the results are shown in the following table;

[0070] Start-up emergency response time (min) Example 1 3 Example 2 2 Example 3 3 Example 4 2 Example 5 3 Comparative Example 1 20

[0071] From the data in Table 1, it can be seen that the start-up emergency response time of the early warning method for instability of the surrounding adjacent high-rise buildings in Examples 1, 2, 3, 4 and 5 of the present invention is 3, 2, 3, 2, 3 respectively, which is obviously lower than the start-up emergency response time in Comparative Example 1. Therefore, it is shown that the present invention realizes real-time data acquisition through an automated monitoring system, which can timely reflect the impact of construction on the surrounding environment. Combined with multi-source information fusion technology, this method improves the accuracy of settlement prediction, ensures that the early warning threshold can be scientifically set, and effectively identifies potential risks before they occur. In addition, the dynamic adjustment ability of this method enables the early warning mechanism to flexibly adapt to the progress of construction, improves the efficiency of responding to emergencies, and is suitable for various deep foundation pit excavation projects, especially in the construction environment of urban dense areas, through early warning, it can effectively reduce the potential risks of foundation pit excavation to adjacent high-rise buildings. Once the early warning is triggered, the system can quickly start the emergency response, ensure the safety of adjacent buildings, and reduce accidents during the construction process and the economic losses caused by them.

[0072] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation, characterized in that: The following steps are involved: Step 1: Collect multivariate information data on site; Step 2: Establish a finite element mesh model; Step 3, setting a settlement threshold in the finite element mesh model; Step 4: Setting up a real-time monitoring system on site within the finite element mesh model; The on-site multivariate information data includes the settlement monitoring data of the ground and buildings collected by settlement sensors and inclinometers, the deformation monitoring data of the side walls and bottom of the foundation pit, the monitoring data of groundwater level changes and the construction load data. The finite element mesh model is based on the finite element analysis of the soil strength reduction method in ABAQUS to model the foundation pit and surrounding buildings. The finite element mesh model simulates the settlement of the surrounding high-rise buildings during the layered excavation construction and predicts the settlement changes of the buildings. The finite element mesh model has built-in existing monitoring data, and the settlement change model prediction results of the building are compared with the existing monitoring data. The settlement thresholds are normal state, attention state and alarm state. The on-site real-time monitoring system uses the data fusion algorithm to analyze and compare the real-time monitoring data with the set settlement threshold to determine whether to trigger an early warning. Once the monitoring data exceeds the set threshold, the system automatically triggers an early warning and sends a notification to relevant personnel.

2. The method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation according to claim 1 is characterized by: The finite element mesh model can adjust model parameters to improve the accuracy of model prediction results.

3. The method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation according to claim 1 is characterized by: The settlement threshold of 200mm-220mm is a normal state, 221mm-240mm is a concern state, and 241mm-261mm is an alarm state.

4. The method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation according to claim 1 is characterized by: The mesh type of the finite element mesh model is one of a structured mesh, an unstructured mesh, and a hybrid mesh.

5. The method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation according to claim 1 is characterized by: The on-site real-time monitoring system uses G, LoRa and NB-IoT to improve the stability and speed of data transmission, and can provide reliable data transmission in wide areas and complex environments, ensuring that on-site data can be transmitted to the finite element mesh model in real time and accurately.

6. The method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation according to claim 5 is characterized by: The on-site real-time monitoring system uses the Internet of Things technology to electrically connect sensors and cameras to form a monitoring network, which can collect on-site data and images in real time and transmit them to the finite element mesh model.

7. The method for early warning of instability of surrounding high-rise buildings during deep foundation pit excavation according to claim 6 is characterized by: The on-site real-time monitoring system also includes artificial intelligence and machine learning algorithms to improve the intelligence level of data processing. By training models, anomaly detection, pattern recognition and trend prediction are realized, so that the on-site real-time monitoring system can automatically identify abnormal situations and issue early warnings in a timely manner.

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