Building foundation settlement and stability long-term monitoring method and system

Abstract

The invention discloses a building foundation settlement and stability long-term monitoring method and system. According to the method, building settlement related data are collected through a distributed optical fiber sensing network, an inclination sensor array and a soil pressure sensor; establishing a soil stress memory effect calculation model, and calculating effective stress considering the memory effect; constructing a building foundation settlement total model containing main consolidation settlement, creep settlement and additional settlement caused by environmental factors; according to the method, risk grade division is carried out based on multi-level threshold values, early warning advance is calculated, an emergency response strategy is generated, intelligent monitoring and early warning of building foundation settlement are achieved, and the method has important engineering application value.

Description

Technical Field

[0001] The invention belongs to the technical field of building stability monitoring, and in particular relates to a method and system for long-term monitoring of building foundation settlement and stability. Background Art

[0002] The problem of building foundation settlement is a major safety hazard in engineering construction, which has a significant impact on the safety of building use and structural stability. With the acceleration of urbanization and the deep development of underground space, the problem of building settlement has become increasingly prominent. Traditional building settlement monitoring methods mainly rely on manual measurement and single sensor data collection, with limited monitoring accuracy and low efficiency, and it is difficult to meet the needs of long-term continuous monitoring. At the same time, the existing monitoring system often ignores the impact of soil stress memory effect on settlement deformation, resulting in a large deviation between the prediction results and the actual situation.

[0003] At present, the research on building foundation settlement at home and abroad mainly focuses on two aspects: settlement calculation model and monitoring technology. In terms of settlement calculation model, traditional methods are mostly based on Terzaghi's one-dimensional consolidation theory, which does not fully consider the impact of environmental factors on the settlement process, and does not establish an interaction model between environmental factors, resulting in insufficient accuracy of the prediction results. In terms of monitoring technology, although various types of automated monitoring equipment have begun to be used, the system integration is not high, data collection and analysis are still relatively scattered, and it is difficult to achieve effective fusion and comprehensive analysis of multi-source data; in addition, the existing building foundation settlement monitoring system generally lacks a complete early warning mechanism. Most systems can only perform simple threshold judgments on a single monitoring indicator, and cannot achieve comprehensive evaluation and risk level classification of multiple monitoring indicators, nor can they effectively calculate the early warning lead time, resulting in delayed warnings and difficulty in taking effective preventive measures in a timely manner.

[0004] Therefore, it is urgent to develop a building foundation settlement monitoring method and system that can achieve long-term continuous monitoring and has intelligent early warning functions. Summary of the invention

[0005] In view of the above problems, the present invention proposes a method and system for long-term monitoring of building foundation settlement and stability. The specific technical solutions are as follows:

[0006] In a first aspect, the present invention provides a method for long-term monitoring of building foundation settlement and stability, the method comprising the following steps:

[0007] Step S1, collecting basic data related to building settlement based on deployed sensors and collecting environmental data of the area where the building is located.

[0008] Furthermore, the collection of basic data related to building settlement includes: real-time collection of deformation data of each monitoring point of the building foundation through a distributed optical fiber sensing network, collection of the building's inclination angle data through a tilt sensor array, and collection of soil stress data at different depths of the building foundation through a soil pressure sensor; the environmental data includes at least temperature, humidity, groundwater level and rainfall.

[0009] Step S2, based on the soil stress data, establish a soil stress memory effect calculation model and calculate the effective stress considering the memory effect, and calculate the soil deformation response based on the effective stress.

[0010] Step S3, based on the soil deformation response and in combination with environmental data, a general model of building foundation settlement composed of main consolidation settlement, creep settlement and additional settlement caused by environmental factors is established.

[0011] Furthermore, the overall model of building foundation settlement is: S(t) = S p (t)+S c (t)+S e (t), where S(t) is the total settlement at time t, Sp(t) is the main consolidation settlement considering stress memory effect, Sc(t) is the creep settlement, and Se(t) is the additional settlement caused by environmental factors. The unit of each item is millimeter.

[0012] Step S4, based on the total model calculation results of the building foundation settlement, combined with the deformation data and the inclination angle data, risk level classification is performed based on multi-level thresholds, the warning lead time is calculated, and an emergency response strategy is generated.

[0013] Furthermore, the calculation formula of the stress memory kernel function K(t) in step S2 is:

[0014] K(t)=de -ut +(1-d)e -γt ; Wherein, d is the stress memory distribution coefficient, which represents the weight ratio of the two memory components, and its value range is [0, 1]; u is the fast memory decay coefficient, which represents the decay rate of the short-term stress memory effect, and its unit is 1 / day; γ is the slow memory decay coefficient, which represents the decay rate of the long-term stress memory effect, and its unit is 1 / day; t is the time variable, which represents the time interval from the start of loading to the present, and its unit is day;

[0015] The calculation formula of effective stress σm(t) considering memory effect is:

[0016] Among them, σm(t) is the effective stress considering the memory effect, in kPa; σ(τ) is the actual stress at the historical moment τ, in kPa; τ is the integral variable, representing the historical moment, in days.

[0017] Further, the calculation model for the main consolidation settlement amount is as follows:

[0018] where Hi is the thickness of the i-th soil layer, with the unit of m; n is the total number of monitored soil layers, and e 0 is the initial void ratio, dimensionless; C c is the compression index, dimensionless; Δp is the additional stress increment, with the unit of kPa.

[0019] Further, the calculation model for the creep settlement amount is as follows:

[0020] where a is the initial deformation parameter, with the unit of day / mm; b is the long-term deformation parameter, with the unit of 1 / mm.

[0021] Further, the calculation model for the additional settlement amount caused by the environmental factors is as follows:

[0022] where f i (E i (t)) is the influence function value of the i-th environmental factor, k i is the influence coefficient of the i-th environmental factor, m is the number of environmental factors, is the reference value of the i-th environmental factor, taking the historical average value; g i (E i (t) is the non-linear interaction effect value of the i-th environmental factor, λ i is the interaction coefficient, representing the coupling strength between environmental factors, and satisfying 0 ≤ λ i ≤ 1 and ∑λi = 1; α i is the weight coefficient of the i-th environmental factor, and β is the comprehensive influence coefficient of the interaction of environmental factors, with the value range of [0.1, 0.5].

[0023] Further, the risk level classification in step S4 includes:

[0024] Based on the comprehensive evaluation of the total settlement amount S(t), the deformation amount D(t), and the tilt angle θ(t), the building foundation settlement risk is divided into four levels:

[0025] Level I indicates safety: when the total settlement amount S(t) ≤ S1 and the deformation amount D(t) ≤ D1 and the tilt angle θ(t) ≤ θ1;

[0026] Level II indicates warning: when S1 < S(t) ≤ S2 or D1 < D(t) ≤ D2 or θ1 < θ(t) ≤ θ2;

[0027] Level III indicates danger: when S2 < S(t) ≤ S3 or D2 < D(t) ≤ D3 or θ2 < θ(t) ≤ θ3;

[0028] Level IV indicates emergency: when S(t) > S3 or D(t) > D3 or θ(t) > θ3;

[0029] Among them, S1, S2, and S3 are settlement thresholds in millimeters; D1, D2, and D3 are deformation thresholds in millimeters; θ1, θ2, and θ3 are tilt angle thresholds in degrees.

[0030] Furthermore, the specific steps for calculating the early warning lead time include:

[0031] Based on historical monitoring data, use the sliding time window method to establish prediction models for the settlement S(t), deformation D(t), and tilt angle θ(t), and set the time window length L and prediction step h; conduct trend analysis on the data within each time window;

[0032] Calculate the time when each monitoring index reaches the early warning threshold:

[0033] Among them, t s is the earliest time point when the settlement reaches the early warning threshold, t i is the predicted time series point, S(t i ) is the predicted settlement value at time t i ; S k is the settlement threshold corresponding to the risk level k, where k = 1, 2, 3, corresponding to the three levels of warning, danger, and emergency respectively; t d is the earliest time point when the deformation reaches the early warning threshold, D(t i ) is the predicted deformation value at time t i , D k is the deformation threshold corresponding to the risk level k, t θ is the earliest time point when the tilt angle reaches the early warning threshold, θ(t i ) is the predicted tilt angle value at time t i , θ k is the tilt angle threshold corresponding to the risk level k;

[0034] Comprehensively determine the early warning lead time: T w = min(t s , t d , t θ ) - t 0 ; Among them, T w is the early warning lead time, indicating the time interval from the current moment to the earliest time when the early warning state is reached, min(t s , t d , tθ ) is the earliest time point when the three monitoring indicators reach the warning threshold, t 0 For the current moment.

[0035] Furthermore, the method for deploying the distributed optical fiber sensor network includes: deploying optical fiber sensors in the horizontal and vertical directions at key positions of the building foundation, with a horizontal deployment spacing of 3-5 meters and a vertical deployment depth of 10-15 meters below the bottom surface of the foundation; the tilt sensor array is deployed at the corners and middle of the building, with a spacing of no more than 20 meters; the soil pressure sensor is deployed at a measuring point every 5 meters under the building foundation, with a total depth of no less than 20 meters.

[0036] In a second aspect, the present invention provides a long-term monitoring system for building foundation settlement and stability, which is used to execute the method described in the first aspect. The system includes: a data acquisition module, a memory effect calculation module, a settlement total model calculation module and an early warning response module connected in sequence.

[0037] Furthermore, the data acquisition module is used to collect basic data related to building settlement based on deployed sensors and collect environmental data of the area where the building is located.

[0038] Furthermore, the memory effect calculation module is used to establish a soil stress memory effect calculation model according to the soil stress data and calculate the effective stress considering the memory effect, and calculate the soil deformation response based on the effective stress.

[0039] Furthermore, the settlement total model calculation module is used to establish a total model of building foundation settlement based on the main consolidation settlement, creep settlement and additional settlement caused by environmental factors according to the soil deformation response and combined with environmental data.

[0040] Furthermore, the early warning response module is used to divide the risk level based on the total model calculation results of the building foundation settlement, combined with the deformation data and the inclination angle data, based on the multi-level threshold, calculate the early warning lead time, and generate an emergency response strategy.

[0041] Furthermore, the system also includes a data communication module and a system maintenance module.

[0042] The data communication module is used for real-time collection and transmission of on-site monitoring data, remote transmission and sharing of monitoring data, and integration and synchronization of multi-source data.

[0043] The system maintenance module is used for regular calibration of various sensors, validity verification of monitoring data, and system operation status monitoring and fault handling.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention introduces a soil stress memory effect calculation model and combines it with real-time monitoring data from a distributed optical fiber sensor network to establish a general model that includes main consolidation settlement, creep settlement, and additional settlement caused by environmental factors, thereby achieving a comprehensive assessment of the building settlement process and achieving accurate early warning in conjunction with the calculation of the early warning lead time. The present invention significantly improves the accuracy of settlement prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is a flow chart of the long-term monitoring method for building foundation settlement and stability.

[0047] Figure 2 It is a schematic diagram of the composition of the long-term monitoring system for building foundation settlement and stability of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are 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.

[0049] Example 1

[0050] like Figure 1 As shown, it is a flow chart of the long-term monitoring method of building foundation settlement and stability of the present invention, and the method comprises the following steps:

[0051] Step S1, collecting basic data related to building settlement based on deployed sensors and collecting environmental data of the area where the building is located.

[0052] The basic data collected related to building settlement includes: real-time collection of deformation data of each monitoring point of the building foundation through a distributed optical fiber sensing network, collection of building inclination angle data through a tilt sensor array, and collection of soil stress data at different depths of the building foundation through a soil pressure sensor; the environmental data includes at least temperature, humidity, groundwater level and rainfall.

[0053] The method for laying out the distributed optical fiber sensor network includes: laying out optical fiber sensors in the horizontal and vertical directions at key positions of the building foundation, with a horizontal spacing of 3-5 meters and a vertical depth of 10-15 meters below the bottom surface of the foundation; the tilt sensor array is laid out at the corners and middle of the building, with a spacing of no more than 20 meters; the soil pressure sensor is laid out at a measuring point every 5 meters under the building foundation, with a total depth of no less than 20 meters.

[0054] Step S2, based on the soil stress data, establish a soil stress memory effect calculation model and calculate the effective stress considering the memory effect, and calculate the soil deformation response based on the effective stress.

[0055] The calculation formula of stress memory kernel function K(t) is:

[0056] K(t)=de -ut +(1-d)e -γt ; Wherein, d is the stress memory distribution coefficient, which represents the weight ratio of the two memory components, and its value range is [0, 1]; u is the fast memory decay coefficient, which represents the decay rate of the short-term stress memory effect, and its unit is 1 / day; γ is the slow memory decay coefficient, which represents the decay rate of the long-term stress memory effect, and its unit is 1 / day; t is the time variable, which represents the time interval from the start of loading to the present, and its unit is day;

[0057] The calculation formula of effective stress σm(t) considering memory effect is:

[0058] Among them, σm(t) is the effective stress considering the memory effect, in kPa; σ(τ) is the actual stress at the historical moment τ, in kPa; τ is the integral variable, representing the historical moment, in days.

[0059] Step S3, based on the soil deformation response and in combination with environmental data, a general model of building foundation settlement composed of main consolidation settlement, creep settlement and additional settlement caused by environmental factors is established.

[0060] The overall model of building foundation settlement is: S(t) = S p (t)+S c (t)+S e (t), where S(t) is the total settlement at time t, Sp(t) is the main consolidation settlement considering stress memory effect, Sc(t) is the creep settlement, and Se(t) is the additional settlement caused by environmental factors. The unit of each item is millimeter.

[0061] The calculation model of main consolidation settlement is:

[0062] Where Hi is the thickness of the i-th soil layer, in meters; n is the total number of monitored coatings, e 0 is the initial porosity ratio, dimensionless; C c is the compression index, dimensionless; Δp is the additional stress increment, unit is kPa.

[0063] The calculation method of additional stress increment Δp is:

[0064] Δp=η∑(q i ×Ii );where: η is the stress diffusion coefficient, which is related to the soil type and has a value range of [0.8, 1.2]; q i is the additional ground pressure generated by the i-th load source, in kPa; I i is the stress influence coefficient of the ith load source on the calculation point, which is calculated by the Boussinesq solution:

[0065] I i =1 / (2π)[3z 3 / (R 2 +z 2 )(5 / 2)]; where: z is the depth of the calculation point from the ground, in meters; R is the horizontal distance from the calculation point to the load application point, in meters.

[0066] The creep settlement calculation model is:

[0067] Among them, a is the initial deformation parameter, the unit is day / mm; b is the long-term deformation parameter, the unit is 1 / mm.

[0068] The calculation model of additional settlement caused by environmental factors is:

[0069] Among them, f i (E i (t)) is the impact function value of the i-th environmental factor, k i is the influence coefficient of the i-th environmental factor, m is the number of environmental factors, is the baseline value of the i-th environmental factor, taking the historical average value; g i (E i (t) is the nonlinear interaction effect value of the i-th environmental factor, λ i is the interaction coefficient, which represents the coupling strength between environmental factors and satisfies 0≤λ i ≤1 and∑λi=1;α i is the weight coefficient of the i-th environmental factor, β is the comprehensive influence coefficient of the interaction of environmental factors, and its value range is [0.1, 0.5].

[0070] Step S4, based on the total model calculation results of the building foundation settlement, combined with the deformation data and the inclination angle data, risk level classification is performed based on multi-level thresholds, the warning lead time is calculated, and an emergency response strategy is generated.

[0071] The risk level classification includes: Based on the comprehensive evaluation of total settlement S(t), deformation D(t) and inclination angle θ(t), the building foundation settlement risk is divided into four levels:

[0072] Level I indicates safety: when the total settlement S(t) ≤ S1, the deformation D(t) ≤ D1, and the tilt angle θ(t) ≤ θ1;

[0073] Level II indicates warning: when S1 < S(t) ≤ S2 or D1 < D(t) ≤ D2 or θ1 < θ(t) ≤ θ2;

[0074] Level III indicates danger: when S2 < S(t) ≤ S3 or D2 < D(t) ≤ D3 or θ2 < θ(t) ≤ θ3;

[0075] Level IV indicates emergency: when S(t) > S3 or D(t) > D3 or θ(t) > θ3;

[0076] Among them, S1, S2, and S3 are settlement thresholds in millimeters; D1, D2, and D3 are deformation thresholds in millimeters; θ1, θ2, and θ3 are tilt angle thresholds in degrees.

[0077] These thresholds are set based on engineering practice experience, and a specific value is given:

[0078] Total settlement thresholds (S1, S2, S3): S1 = 30mm: the normal settlement range allowed for general buildings; S2 = 50mm: the warning value that requires close attention; S3 = 80mm: the dangerous value that may cause damage to the building.

[0079] Deformation thresholds (D1, D2, D3): D1 = 20mm: the normal differential settlement range; D2 = 35mm: the warning value of differential settlement that requires attention; D3 = 60mm: the dangerous value of differential settlement that may cause structural problems.

[0080] Tilt angle thresholds (θ1, θ2, θ3): θ1 = 0.3 degrees: the normal tilt range (about 1 / 200); θ2 = 0.6 degrees: the warning tilt value (about 1 / 100); θ3 = 1.0 degrees: the dangerous tilt value (about 1 / 60).

[0081] The specific steps for calculating the early warning lead include:

[0082] Based on historical monitoring data, use the sliding time window method to establish prediction models for settlement S(t), deformation D(t), and tilt angle θ(t), set the time window length L and prediction step h; perform trend analysis on the data within each time window; and conduct multi-time scale predictions.

[0083] Short-term prediction: Predict the change trend in the next 1 - 7 days;

[0084] Medium-term prediction: Predict the change trend in the next 8 - 30 days;

[0085] Long-term forecast: predict the changing trend in the next 31-90 days.

[0086] For any monitoring index y(t) (which can be the settlement S(t), deformation D(t) or tilt angle θ(t)), the prediction model is: Where, l(t) is the horizontal component at time t;

[0087] l(t)=α 1 y(t)+(1-α 1 )[l(t-1)+b(t-1)]; where a 1 is the smoothing coefficient of the horizontal component, ranging from [0, 1]; b(t) is the trend component at time t:

[0088] b(t)=α 2 [l(t)-l(t-1)]+(1-α 2 )b(t-1); where α 2 is the smoothing coefficient of the trend component, with a value range of [0, 1]; s(t) is the seasonal component at time t:

[0089] s(t)=α 3 [(t)-l(t)]|+(1-α 3 )s(tp)

[0090] where α 3 is the smoothing coefficient of the seasonal component, with a value range of [0, 1]; p is the seasonal period, for example, when sampling by day, p = 24 means one day is the period, a 1 、a 2 , α 3 is the weight coefficient of each component and satisfies: a 1 +a 2 +α 3 =1; the value needs to be calibrated according to the actual monitoring data; h is the prediction step, which is set according to the prediction scale: short-term prediction: h∈[1,7]; medium-term prediction: h∈[8,30]; long-term prediction: h∈[31,90].

[0091] Calculate the time when each monitoring indicator reaches the warning threshold:

[0092] Among them, t s is the earliest time point when the settlement reaches the warning threshold, t i is the predicted time series point, S(t i ) is t i The predicted settlement value at any moment; S k is the settlement threshold corresponding to risk level k, k = 1, 2, 3, corresponding to warning, danger, and emergency levels respectively; t dis the earliest time point when the deformation reaches the warning threshold, D(t i ) is t i The predicted deformation value at the moment, D k is the deformation threshold corresponding to risk level k, t θ is the earliest time point when the tilt angle reaches the warning threshold, θ(t i ) is t i The predicted tilt angle value at the moment, θ k is the tilt angle threshold corresponding to risk level k;

[0093] Comprehensively determine the advance warning amount: T w =min(t s , t d , t θ )-t 0 ; Among them, T w is the warning lead time, which means the time interval from the current time to the earliest warning state, min(t s , t d , t θ ) is the earliest time point when the three monitoring indicators reach the warning threshold, t 0 For the current moment.

[0094] Emergency response strategies include: Level I response: continue routine monitoring and maintain the existing monitoring frequency; Level II response: increase the monitoring frequency, increase the inspection frequency, and analyze the disaster-causing factors; Level III response: activate the emergency plan, add temporary support, and formulate a reinforcement plan; Level IV response: immediately evacuate personnel, take emergency reinforcement measures, and initiate emergency response.

[0095] Example 2

[0096] like Figure 2 As shown, it is a schematic diagram of the composition of the long-term monitoring system for building foundation settlement and stability of the present invention, which is used to execute the method described in Example 1. The system includes: a data acquisition module, a memory effect calculation module, a settlement total model calculation module and an early warning response module connected in sequence.

[0097] The data acquisition module is used to collect basic data related to building settlement based on the deployed sensors and collect environmental data of the area where the building is located.

[0098] The memory effect calculation module is used to establish a soil stress memory effect calculation model according to the soil stress data and calculate the effective stress considering the memory effect, and calculate the soil deformation response based on the effective stress.

[0099] The settlement total model calculation module is used to establish a total model of building foundation settlement based on the main consolidation settlement, creep settlement and additional settlement caused by environmental factors according to the soil deformation response and combined with environmental data.

[0100] The early warning response module is used to divide the risk level based on the total model calculation results of the building foundation settlement, combined with the deformation data and the inclination angle data, based on the multi-level threshold, calculate the early warning lead time, and generate an emergency response strategy.

[0101] The system also includes a data communication module and a system maintenance module.

[0102] The data communication module is used for real-time collection and transmission of on-site monitoring data, remote transmission and sharing of monitoring data, and integration and synchronization of multi-source data.

[0103] The system maintenance module is used for regular calibration of various sensors, validity verification of monitoring data, and system operation status monitoring and fault handling.

[0104] Establish monitoring data quality control for multi-source data fusion; pre-process the raw data collected by sensors: remove outliers, use the triple standard deviation method or box plot method to identify and eliminate abnormal data; smooth data, use sliding average or wavelet transform to eliminate data noise; interpolate data, use linear interpolation or spline interpolation method to repair missing data.

[0105] Establish a data quality evaluation index system, including data integrity index, which evaluates the missing rate and validity of data; data accuracy index, which evaluates the precision and reliability of data; data timeliness index, which evaluates the data collection delay and transmission delay;

[0106] Realize collaborative verification of multi-source data, based on cross-validation of fiber optic sensing and tilt sensing data; based on correlation analysis of deformation data and earth pressure data; and based on correction of monitoring data based on environmental data.

[0107] The regular calibration and maintenance plan includes: sensors are calibrated every 6 months; data acquisition equipment is performance tested every 3 months; and communication systems are tested for connectivity once a month.

[0108] Establish an equipment fault diagnosis model to realize automatic fault identification; formulate a hierarchical fault handling plan, determine the fault response process, set up backup monitoring equipment, and ensure the continuous operation of the system; dynamically adjust the data collection frequency to optimize system resource utilization; optimize the data transmission strategy to improve the real-time performance of the system; update the early warning model parameters to improve the accuracy of the early warning.

[0109] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A long-term monitoring method for building foundation settlement and stability, characterized in that: The method includes the following steps: Step S1: Based on the deployed sensors, collect the basic data related to the building settlement, and collect the environmental data of the area where the building is located; The collection of the basic data related to the building settlement includes: real-time collection of the deformation data of each monitoring point of the building foundation through a distributed optical fiber sensing network, collection of the inclination angle data of the building through an inclination sensor array, and collection of the soil stress data at different depths of the building foundation through soil pressure sensors; the environmental data includes at least temperature, humidity, groundwater level, and rainfall; Step S2: According to the soil stress data, establish a soil stress memory effect calculation model and calculate the effective stress considering the memory effect, and calculate the soil deformation response based on the effective stress; Step S3: According to the soil deformation response, combined with the environmental data, establish a total model of the building foundation settlement composed of the primary consolidation settlement, creep settlement, and additional settlement caused by environmental factors; The overall model of building foundation settlement is: S(t) = S p (t)+S c (t)+S e (t), where S(t) is the total settlement at time t, Sp(t) is the main consolidation settlement considering stress memory effect, Sc(t) is the creep settlement, and Se(t) is the additional settlement caused by environmental factors. The units of each item are millimeters; Step S4: Based on the calculation results of the total model of the building foundation settlement, combined with the deformation data and the inclination angle data, conduct risk level division based on multiple thresholds, calculate the early warning lead time, and generate an emergency response strategy.

2. The long-term monitoring method for building foundation settlement and stability according to claim 1 is characterized in that: The calculation formula of the stress memory kernel function K(t) in step S2 is: K(t)=de -ut +(1-d)e -γt ; Wherein, d is the stress memory distribution coefficient, which represents the weight ratio of the two memory components, and its value range is [0, 1]; u is the fast memory decay coefficient, which represents the decay rate of the short-term stress memory effect, and its unit is 1 / day; γ is the slow memory decay coefficient, which represents the decay rate of the long-term stress memory effect, and its unit is 1 / day; t is the time variable, which represents the time interval from the start of loading to the present, and its unit is day; The calculation formula of the effective stress σm(t) considering the memory effect is: Among them, σm(t) is the effective stress considering the memory effect, in kPa; σ(τ) is the actual stress at the historical moment τ, in kPa; τ is the integral variable, representing the historical moment, in days.

3. The long-term monitoring method for building foundation settlement and stability according to claim 2 is characterized in that: The primary consolidation settlement calculation model is: Where Hi is the thickness of the i-th soil layer, in meters; n is the total number of monitored coatings; e0 is the initial porosity, dimensionless; C c is the compression index, dimensionless; Δp is the additional stress increment, unit is kPa.

4. The long-term monitoring method for building foundation settlement and stability according to claim 3 is characterized in that: The creep settlement calculation model is: Among them, a is the initial deformation parameter, the unit is day / mm; b is the long-term deformation parameter, the unit is 1 / mm.

5. The long-term monitoring method for building foundation settlement and stability according to claim 4 is characterized in that: The calculation model of the additional settlement caused by environmental factors is: Among them, f i (E i (t)) is the impact function value of the i-th environmental factor, k i is the influence coefficient of the i-th environmental factor, m is the number of environmental factors, is the baseline value of the i-th environmental factor, taking the historical average value; g i (E i (t) is the nonlinear interaction effect value of the i-th environmental factor, λ i is the interaction coefficient, which characterizes the coupling strength between environmental factors and satisfies 0≤λ i ≤1 and∑λi=1;α i is the weight coefficient of the i-th environmental factor, β is the comprehensive influence coefficient of the interaction of environmental factors, and its value range is [0.1, 0.5].

6. The long-term monitoring method for building foundation settlement and stability according to claim 5, characterized in that: The risk level division in step S4 includes: Based on the comprehensive evaluation of the total settlement S(t), deformation amount D(t), and inclination angle θ(t), the building foundation settlement risk is divided into four levels: Level I indicates safety: when the total settlement S(t) ≤ S1 and the deformation amount D(t) ≤ D1 and the inclination angle θ(t) ≤ θ1; Level II indicates warning: when S1 < S(t) ≤ S2 or D1 < D(t) ≤ D2 or θ1 < θ(t) ≤ θ2; Level III indicates danger: when S2 < S(t) ≤ S3 or D2 < D(t) ≤ D3 or θ2 < θ(t) ≤ θ3; Level IV indicates emergency: when S(t) > S3 or D(t) > D3 or θ(t) > θ3; Among them, S1, S2, and S3 are settlement thresholds in millimeters; D1, D2, and D3 are deformation amount thresholds in millimeters; θ1, θ2, and θ3 are inclination angle thresholds in degrees.

7. The long-term monitoring method for building foundation settlement and stability according to claim 6, characterized in that: The specific steps for calculating the early warning lead time include: Based on the historical monitoring data, use the sliding time window method to establish prediction models for the settlement S(t), deformation amount D(t), and inclination angle θ(t), and set the time window length L and the prediction step h; conduct trend analysis on the data within each time window; Calculate the time when each monitoring index reaches the early warning threshold: Among them, t s is the earliest time point when the settlement reaches the warning threshold, t i is the predicted time series point, S(t i ) is t i The predicted settlement value at any moment; S k is the settlement threshold corresponding to risk level k, k = 1, 2, 3, corresponding to warning, danger, and emergency levels respectively; t d is the earliest time point when the deformation reaches the warning threshold, D(t i ) is t i The predicted deformation value at the moment, D k is the deformation threshold corresponding to risk level k, t θ is the earliest time point when the tilt angle reaches the warning threshold, θ(t i ) is t i The predicted tilt angle value at the moment, θ k is the tilt angle threshold corresponding to risk level k; Comprehensively determine the warning advance time: T w =min(t s , t d , t θ )-t0; where T w is the warning lead time, which means the time interval from the current time to the earliest warning state, min(t s , t d , t θ ) is the earliest time point when the three monitoring indicators reach the warning threshold, and t0 is the current time.

8. The long-term monitoring method for building foundation settlement and stability according to claim 7, characterized in that: The method for laying out the distributed optical fiber sensor network includes: laying out optical fiber sensors in the horizontal and vertical directions at key positions of the building foundation, with a horizontal spacing of 3-5 meters and a vertical depth of 10-15 meters below the bottom surface of the foundation; the tilt sensor array is laid out at the corners and middle of the building, with a spacing of no more than 20 meters; the soil pressure sensor is laid out at a measuring point every 5 meters under the building foundation, with a total depth of no less than 20 meters.

9. A long-term monitoring system for building foundation settlement and stability, used to implement the method according to any one of claims 1 to 8, characterized in that: The system comprises: a data acquisition module, a memory effect calculation module, a settlement total model calculation module and an early warning response module connected in sequence; The data acquisition module is used to collect basic data related to building settlement based on the deployed sensors and collect environmental data of the area where the building is located; The memory effect calculation module is used to establish a soil stress memory effect calculation model according to the soil stress data and calculate the effective stress considering the memory effect, and calculate the soil deformation response based on the effective stress; The settlement total model calculation module is used to establish a total model of building foundation settlement based on the main consolidation settlement, creep settlement and additional settlement caused by environmental factors according to the soil deformation response and in combination with environmental data; The early warning response module is used to divide the risk level based on the total model calculation results of the building foundation settlement, combined with the deformation data and the inclination angle data, based on the multi-level threshold, calculate the early warning lead time, and generate an emergency response strategy.

10. The long-term monitoring system for building foundation settlement and stability according to claim 9, characterized in that: The system also includes a data communication module and a system maintenance module; The data communication module is used for real-time collection and transmission of on-site monitoring data, remote transmission and sharing of monitoring data, and integration and synchronization of multi-source data; The system maintenance module is used for regular calibration of various sensors, validity verification of monitoring data, and system operation status monitoring and fault handling.

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