Method and system for safety monitoring of existing bridge pier and deep foundation linkage based on multi-source data fusion

Abstract

The application discloses a kind of based on multi-source data fusion existing bridge pier and deep foundation linkage safety monitoring method and system, the geological parameters of deep foundation pit construction area are carried out inversion analysis by the multi-source monitoring data of current preset construction step in the method, the geological parameters after inversion of current preset construction step and the multi-source monitoring data of current preset construction step are synchronously mapped into digital twin model, carry out multi-physical field coupling simulation analysis based on updated digital twin model, output the time-varying multi-field distribution prediction result under current preset construction step, to determine the multiple safety state evaluation indexes of current preset construction step, carry out joint failure probability calculation according to multiple safety state evaluation indexes, obtain joint failure probability, to evaluate the construction safety state of current preset construction step, to improve the accuracy and timeliness of risk early warning.

Description

Technical Field

[0001] This invention relates to the field of engineering monitoring technology, and in particular to a method and system for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion. Background Technology

[0002] In urban deep foundation pit projects near existing bridge piers, when deep foundation pits are excavated close to existing bridge piers, the excavation unloading will cause soil stress redistribution, which can easily cause additional settlement, tilting and horizontal displacement of the foundation of the adjacent bridge pier, threatening the safety of the bridge structure. This may lead to the expansion of bridge pier cracks, bearing displacement or even structural instability, and in severe cases, endanger traffic safety.

[0003] Traditional monitoring methods are insufficient to fully reflect the complex impact of foundation pit deformation on bridge pier foundations, which can easily lead to delayed or misjudged risk warnings and fail to meet the safety monitoring needs under complex working conditions. Summary of the Invention

[0004] In view of this, in order to solve the above-mentioned technical problems, the present invention provides a method and system for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion.

[0005] The first aspect of this invention provides a method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion, comprising:

[0006] Acquire multi-source monitoring data of the existing bridge pier structure and deep foundation pit construction area under the current preset construction step, and perform inversion analysis on the geological parameters of the deep foundation pit construction area based on the multi-source monitoring data of the current preset construction step to obtain the inverted geological parameters of the current preset construction step.

[0007] Construct a digital twin model that includes existing bridge piers, deep foundation pits, support structures, and geological bodies. Synchronously map the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step into the digital twin model, and update the digital twin model.

[0008] Multiphysics coupling simulation analysis is performed based on the updated digital twin model, and the time-varying multi-field distribution prediction results under the current preset construction step are output.

[0009] Based on the time-varying multi-field distribution prediction results under the current preset construction step, determine multiple safety status assessment indicators for the current preset construction step.

[0010] The joint failure probability is calculated based on multiple safety status assessment indicators to obtain the joint failure probability, and the construction safety status of the current preset construction step is assessed based on the joint failure probability.

[0011] Preferably, the multi-source monitoring data includes the load on the existing bridge pier, the distance from the center of the existing bridge pier to the preset monitoring point, the foundation depth of the existing bridge pier, and the on-site settlement of the bridge pier foundation; the geological parameters of the deep foundation pit construction area include the elastic modulus of the soil and Poisson's ratio.

[0012] The step of performing inversion analysis on the geological parameters of the deep foundation pit construction area based on the multi-source monitoring data of the current preset construction step to obtain the inverted geological parameters of the current preset construction step includes:

[0013] The Mindlin elasticity solution was used, combined with the load on the existing pier, the distance from the center of the existing pier to the preset monitoring point, the foundation depth of the existing pier, and geological parameters, to determine the calculated settlement of the pier foundation.

[0014] With the optimization objective of minimizing the difference between the calculated settlement of the bridge pier foundation and the on-site settlement of the bridge pier foundation, the geological parameters that minimize the difference between the calculated settlement of the bridge pier foundation and the on-site settlement of the bridge pier foundation are obtained by inversion using the Newton iteration method and are used as the inversion geological parameters for the current preset construction step.

[0015] Preferably, the construction includes digital twin models of existing bridge piers, deep foundation pits, support structures, and geological bodies, comprising:

[0016] Using BIM technology, a geometric twin layer is constructed that includes existing bridge piers, deep foundation pits, support structures, and geological bodies; wherein the spatial positional relationship and geometric characteristics of the geometric twin layer with the existing bridge pier structure and the various physical entities in the deep foundation pit construction area are consistent.

[0017] The geometric twin layer is discretized into a finite element mesh, dividing it into several computational units, and each computational unit is assigned a unique spatial coordinate and number.

[0018] Multi-field time-varying constitutive models of existing bridge piers, support structures, and geological bodies are constructed respectively, and the multi-field time-varying constitutive models are embedded into the computational units of the corresponding physical entity types to form an initial physical twin layer;

[0019] Based on the effective stress principle, a control equation is established that couples the stress field, displacement field, and seepage field. The control equation is then coupled and embedded into the initial physical twin layer to form a physical twin layer.

[0020] The physical twin layer and the geometric twin layer are mapped and fused to generate a digital twin model.

[0021] Preferably, the step of synchronously mapping the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step into the digital twin model, and updating the digital twin model, includes:

[0022] The inverted geological parameters replace the parameters of the corresponding calculation units of the geological bodies in the digital twin model, and multi-source monitoring data are applied as boundary conditions to the digital twin model to update the physical state of the digital twin model.

[0023] Preferably, the multiphysics coupled simulation analysis based on the updated digital twin model, outputting the time-varying multifield distribution prediction results under the current preset construction step, includes:

[0024] The inverted geological parameters of the current preset construction step are subjected to db4 wavelet threshold denoising and wavelet packet energy entropy extraction to obtain the energy entropy of the current preset construction step.

[0025] The time calculation step size of the updated digital twin model is determined based on the energy entropy of the current preset construction step.

[0026] Based on the time calculation step, the updated digital twin model is subjected to multiphysics coupling simulation analysis using an iterative method to obtain the time-varying multifield distribution prediction results under the current preset construction step; the time-varying multifield distribution prediction results include stress field distribution, displacement field distribution and seepage field distribution.

[0027] Preferably, the safety status assessment indicators include pier foundation settlement displacement, pier body strain, support structure displacement, support structure strain, and geological volume strain.

[0028] The step of determining multiple safety status assessment indicators for the current preset construction step based on the time-varying multi-field distribution prediction results under the current preset construction step includes:

[0029] The settlement displacement of the bridge pier foundation is determined based on the maximum vertical displacement of all calculation units corresponding to the existing bridge pier in the displacement field distribution.

[0030] The strain of the pier body is determined based on the maximum axial strain of all calculation units corresponding to the existing pier in the stress field distribution.

[0031] The displacement of the support structure is determined based on the maximum horizontal displacement of all calculation units corresponding to the support structure in the displacement field distribution.

[0032] The strain of the support structure is determined based on the maximum axial strain of all calculation units corresponding to the support structure in the stress field distribution.

[0033] Based on the seepage field distribution, determine the maximum head height of all calculation units corresponding to the geological body, determine the pore water pressure corresponding to the geological body, and determine the volumetric strain of the geological body based on the pore water pressure and the maximum stress of the geological body.

[0034] Preferably, the step of calculating the joint failure probability based on multiple safety status assessment indicators to obtain the joint failure probability, and assessing the construction safety status of the current preset construction step based on the joint failure probability, includes:

[0035] Each of the safety status assessment indicators is input into the limit state equation to determine the limit state value of each of the safety status assessment indicators;

[0036] Based on the limit state values ​​of each of the aforementioned safety status assessment indicators, multiple safety status assessment indicators that exceed the limit state are selected as failure mode indicators.

[0037] The subjective and objective weights of each failure mode index are determined by the AHP analytic hierarchy process and the entropy weight method, respectively. The subjective and objective weights of each failure mode index are then weighted and fused to obtain the combined weight of each failure mode index.

[0038] The sampling ratio of each failure mode index is allocated according to the combined weight, and samples of each failure mode index are drawn from the normal probability distribution according to the allocated sampling ratio. The samples of each failure mode index are substituted into the limit state equation, and the failure samples are identified according to the limit state of the samples of each failure mode index, thus forming a failure sample set of each failure mode index.

[0039] The number of failure samples in the failure sample set of each failure mode index and the total number of samples extracted for each failure mode index are counted, and the local failure probability of each failure mode index is calculated.

[0040] The joint failure probability is obtained by weighting the local failure probability of each failure mode index with the combined weight.

[0041] The combined failure probability is compared with a preset combined failure probability threshold. If the combined failure probability is greater than the preset combined failure probability threshold, the construction safety status of the current preset construction step is determined to be unsafe.

[0042] Secondly, the present invention also provides a safety monitoring system for the linkage between existing bridge piers and deep foundation pits based on multi-source data fusion, comprising:

[0043] The geological parameter inversion module is used to acquire multi-source monitoring data of the existing bridge pier structure and the deep foundation pit construction area under the current preset construction step, and to perform inversion analysis on the geological parameters of the deep foundation pit construction area based on the multi-source monitoring data of the current preset construction step to obtain the inverted geological parameters of the current preset construction step.

[0044] The twin model update module is used to construct a digital twin model including existing bridge piers, deep foundation pits, support structures and geological bodies, and synchronously map the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step into the digital twin model to update the digital twin model.

[0045] The simulation response prediction module is used to perform multiphysics field coupled simulation analysis based on the updated digital twin model and output the time-varying multifield distribution prediction results under the current preset construction step.

[0046] The evaluation index determination module is used to determine multiple safety status evaluation indicators for the current preset construction step based on the time-varying multi-field distribution prediction results under the current preset construction step.

[0047] The construction safety assessment module is used to calculate the joint failure probability based on multiple safety status assessment indicators, obtain the joint failure probability, and assess the construction safety status of the current preset construction step based on the joint failure probability.

[0048] Thirdly, the present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the existing bridge pier and deep foundation pit linkage safety monitoring method based on multi-source data fusion as described in the first aspect.

[0049] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the steps of the method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion as described in the first aspect.

[0050] As can be seen from the above technical solutions, this invention performs inversion analysis on the geological parameters of the deep foundation pit construction area using multi-source monitoring data of the current preset construction step, ensuring the dynamic updating and accuracy of the geological parameters. The inverted geological parameters and multi-source monitoring data of the current preset construction step are synchronously mapped into a digital twin model. Based on the updated digital twin model, multi-physics field coupled simulation analysis is performed, outputting the time-varying multi-field distribution prediction results under the current preset construction step. This determines multiple safety status assessment indicators for the current preset construction step. The joint failure probability is calculated based on these multiple safety status assessment indicators to obtain the joint failure probability, thereby assessing the construction safety status of the current preset construction step. This avoids safety misjudgments caused by static parameter assumptions or single indicator threshold judgments, achieving dynamic, collaborative, and accurate assessment of construction risks, and improving the accuracy and timeliness of risk warnings. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart of a method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion, provided for an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a safety monitoring system for the linkage between existing bridge piers and deep foundation pits based on multi-source data fusion, provided for an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] like Figure 1 As shown in the figure, this application provides a method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion, including the following steps S1 to S5. Wherein:

[0057] S1. Obtain multi-source monitoring data of the existing bridge pier structure and deep foundation pit construction area under the current preset construction step. Based on the multi-source monitoring data of the current preset construction step, perform inversion analysis on the geological parameters of the deep foundation pit construction area to obtain the inverted geological parameters of the current preset construction step.

[0058] The multi-source monitoring data includes, but is not limited to, the load on existing bridge piers, the distance from the center of existing bridge piers to preset monitoring points, the foundation depth of existing bridge piers, and the on-site settlement of bridge pier foundations. Multiple monitoring points are set up on the sides of the existing bridge pier structure and the deep foundation pit construction area, and load sensors, displacement sensors, distance measuring instruments, strain gauges, earth pressure cells, inclinometers, and static levels are installed at the monitoring points to collect multi-source monitoring data in real time, ensuring the accuracy and timeliness of the data.

[0059] Pre-defined construction steps refer to key construction stages pre-set during deep foundation pit construction, including excavation, support, dewatering, pouring, and backfilling. Each construction step corresponds to specific geomechanical behavior characteristics. By combining geological survey reports and multi-source monitoring data to invert and correct the geological parameters of the deep foundation pit construction area, the inverted geological parameters are obtained, improving the accuracy of the geological model.

[0060] S2. Construct a digital twin model that includes existing bridge piers, deep foundation pits, support structures, and geological bodies. Simultaneously map the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step into the digital twin model, and update the digital twin model.

[0061] Among them, the digital twin model is a high-fidelity three-dimensional refined model based on the fusion of physical mechanisms and data-driven approaches. With each construction step as an iterative node, after each construction step is completed (such as excavating 2m), the geological parameters after inversion of the current preset construction step and the multi-source monitoring data of the current preset construction step are synchronously updated into the model. This enables real-time dynamic mapping and synchronous updating of the load boundaries and physical states of the existing bridge piers and deep foundation pit construction process, ensuring that the model is highly consistent with the actual working conditions.

[0062] S3. Perform multi-physics coupling simulation analysis based on the updated digital twin model, and output the time-varying multi-field distribution prediction results under the current preset construction step.

[0063] The multiphysics coupled simulation analysis covers the coupled evolution process of stress field, displacement field, and seepage field. Taking into account the nonlinear constitutive relationship of the soil, the Newton-Raphson iterative method is used to solve the coupled control equations to ensure the convergence and accuracy of the numerical solution. Finally, the time-varying multi-field distribution prediction results under the current preset construction step are output.

[0064] S4. Based on the time-varying multi-field distribution prediction results under the current preset construction step, determine multiple safety status assessment indicators for the current preset construction step.

[0065] The safety status assessment indicators include pier foundation settlement displacement, pier body strain, support structure displacement, support structure strain, and geological volume strain. Among them, pier foundation settlement displacement refers to the vertical displacement change of the bottom of the pier under the influence of construction, reflecting the stability of the foundation; pier body strain reflects the internal stress state of the structure and is used to determine whether the material has entered the plastic stage; support structure displacement and strain assess the deformation performance and bearing capacity of the retaining system; and geological volume strain characterizes the overall compression or expansion trend of the soil, revealing the risk of ground instability.

[0066] S5. Calculate the joint failure probability based on multiple safety status assessment indicators to obtain the joint failure probability, and assess the construction safety status of the current preset construction step based on the joint failure probability.

[0067] Since there is a linkage failure mechanism among the various safety status assessment indicators, exceeding the standard of a single indicator may trigger a chain reaction of other indicators. Therefore, this application calculates the joint failure probability through multiple safety status assessment indicators. The joint failure probability can more comprehensively reflect the overall safety risk level during construction and avoid the one-sidedness caused by single indicator assessment.

[0068] It should be noted that, in this embodiment, the geological parameters of the deep foundation pit construction area are inverted and analyzed using multi-source monitoring data of the current preset construction step, ensuring the dynamic updating and accuracy of the geological parameters. The inverted geological parameters and multi-source monitoring data of the current preset construction step are synchronously mapped into the digital twin model. Based on the updated digital twin model, multi-physics field coupled simulation analysis is performed, and the time-varying multi-field distribution prediction results under the current preset construction step are output. This determines multiple safety status assessment indicators for the current preset construction step. The joint failure probability is calculated based on multiple safety status assessment indicators to obtain the joint failure probability, thereby assessing the construction safety status of the current preset construction step. This avoids safety misjudgments caused by static parameter assumptions or single indicator threshold judgments, and achieves dynamic, collaborative, and accurate assessment of construction risks, improving the accuracy and timeliness of risk warning.

[0069] In one embodiment, the multi-source monitoring data includes the load of the existing bridge pier, the distance from the center of the existing bridge pier to the preset monitoring point, the foundation depth of the existing bridge pier, and the on-site settlement of the bridge pier foundation; the geological parameters of the deep foundation pit construction area include the elastic modulus and Poisson's ratio of the soil; in this case, the geological parameters of the deep foundation pit construction area are inverted based on the multi-source monitoring data of the current preset construction step to obtain the inverted geological parameters of the current preset construction step, including: using the Mindlin elasticity solution, combined with the load of the existing bridge pier, the distance from the center of the existing bridge pier to the preset monitoring point, the foundation depth of the existing bridge pier, and the geological parameters, to determine the calculated settlement of the bridge pier foundation; with the optimization objective of minimizing the difference between the calculated settlement of the bridge pier foundation and the on-site settlement of the bridge pier foundation, the geological parameters that minimize the difference between the calculated settlement of the bridge pier foundation and the on-site settlement of the bridge pier foundation are inverted using the Newton iteration method as the inverted geological parameters of the current preset construction step.

[0070] The settlement of the bridge pier foundation calculated using the Mindlin elasticity solution is as follows:

[0071]

[0072] In the formula, Calculate settlement for bridge pier foundations. The load on the existing bridge piers. Poisson's ratio, The elastic modulus of the soil. The distance from the center of the existing bridge pier to the preset monitoring point. The foundation depth of the existing bridge piers.

[0073] Based on the on-site settlement of the bridge pier foundation measured on site The objective function corresponding to the optimization objective is to minimize the difference between the calculated settlement of the bridge pier foundation and the on-site settlement of the bridge pier foundation, i.e.:

[0074]

[0075] In the formula, n is the number of monitoring points. Calculate the settlement of the bridge pier foundation at monitoring point i. The on-site settlement of the bridge pier foundation at monitoring point i.

[0076] The objective function is solved by Newton's iteration method, and the initial assumed values ​​of soil elastic modulus and Poisson's ratio are continuously corrected until the residual between calculated settlement and on-site settlement converges within the preset accuracy threshold range, thereby obtaining the geological parameters closest to reality at the current construction step.

[0077] In some embodiments, constructing a digital twin model including existing bridge piers, deep foundation pits, support structures, and geological bodies includes: using BIM technology to construct a geometric twin layer containing existing bridge piers, deep foundation pits, support structures, and geological bodies; wherein the spatial positional relationship and geometric features of the geometric twin layer with each physical entity in the existing bridge pier structure and the deep foundation pit construction area are consistent; discretizing the geometric twin layer into a finite element mesh, dividing it into several computational units, and assigning each computational unit a unique spatial coordinate and number; constructing multi-field time-varying constitutive models for the existing bridge piers, support structures, and geological bodies respectively, and embedding the multi-field time-varying constitutive models into computational units of the corresponding physical entity types to form an initial physical twin layer; establishing control equations for the coupling of stress field, displacement field, and seepage field based on the effective stress principle, coupling the control equations, and embedding the coupled control equations into the initial physical twin layer to form a physical twin layer; mapping and fusing the physical twin layer and the geometric twin layer to generate a digital twin model.

[0078] Among them, the geometric twin layer constructed using BIM technology accurately reproduces the spatial topological relationship between the existing bridge piers and the deep foundation pit support structure. The geometric twin layer maintains the same geometric accuracy as the physical entity, providing a high-fidelity digital foundation for subsequent multi-field coupled simulation.

[0079] By discretizing the geometric twin layer using finite element meshes, the continuous geometric domain is divided into discrete computational units (such as solid units of bridge piers, support structure units, and solid units of geological bodies), ensuring the feasibility of numerical solutions for the physical field in space, and assigning each unit a unique spatial coordinate and number.

[0080] Multi-field time-varying constitutive models were then constructed for the existing bridge piers, support structures, and geological bodies. For the existing bridge piers, the concrete material constitutive model considered creep, using the ACI 209 creep model, with the creep coefficient as follows:

[0081]

[0082] In the formula, For loading age, The final creep coefficient is 2.3. For the current age.

[0083] The constitutive model of the support structure adopts an ideal elastoplastic model, and the yield criterion is the Mohr-Coulomb criterion. ,in, For the shear yield strength of the support material, For the maximum principal stress, Minimum principal stress.

[0084] The constitutive model of the geological body type considers creep and adopts the Burgers viscoelastic model, whose creep equation is:

[0085]

[0086] In the formula, For shear creep compliance, For initial stress, , For Maxwell element parameters, , These are the Kelvin element parameters. The above multi-field time-varying constitutive models are embedded into the corresponding types of computational units to form an initial physical twin layer with time-varying material properties.

[0087] Based on the effective stress principle, a set of governing equations coupling the stress field, displacement field, and seepage field is established, including: equilibrium equations, geometric equations, seepage continuity equations, and constitutive relations; among them, the equilibrium equations describe the mechanical equilibrium relationship between the stress field and external forces, namely:

[0088]

[0089] In the formula, For Hamiltonian operators, For the effective stress tensor, The density of the soil. This is the gravitational acceleration vector.

[0090] The geometric equations describe the geometric relationship between the displacement field and the strain field, namely:

[0091]

[0092] In the formula, For strain tensor, Let T be the displacement vector and T be the transpose operator.

[0093] The seepage continuity equation describes the mass conservation relationship of the fluid in the seepage field, namely:

[0094]

[0095] In the formula, The soil permeability coefficient, The water head height, For volumetric strain.

[0096] Constitutive relations characterize the physical response between stress and strain in a material, namely:

[0097]

[0098] In the formula, D is the elasticity matrix.

[0099] Among them, by using constitutive relations as a bridge, the stress field and displacement field are coupled bidirectionally, and the effective stress principle is used ( , Using pore water pressure as a link, the pore water pressure of the seepage field is associated with the effective stress of the stress field, thereby effectively coupling the seepage field and the stress field to form a complete mechanical system with multi-field interaction.

[0100] The coupled governing equations are embedded into the discretized computational units. Then, the geometric twin layer and the physical twin layer are mapped and matched, anchored in the same engineering coordinate system, ensuring that the geometric position completely coincides with the physical computational domain. This allows each component of the geometric twin layer to precisely correspond to the corresponding unit in the physical twin layer, achieving a unified expression of geometric form and physical properties. Based on this, construction actions of the geometric twin layer (such as changes in the geometric boundaries of foundation pit excavation) synchronously trigger the update of boundary conditions in the physical twin layer.

[0101] By using the Newton-Raphson iterative method to solve the governing equations nonlinearly, the equilibrium state of multi-field coupling can be gradually approximated. This enables the simulation of the co-evolution of stress field, displacement field, and seepage field, and outputs the numerical solutions of stress field, displacement field, and seepage field for each computational unit. This accurately reflects the stress field distribution, displacement field changes, and dynamic response of seepage field under multi-field coupling in underground engineering, revealing the time-varying mechanical behavior of geological bodies in complex environments.

[0102] In one embodiment, the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step are synchronously mapped into the digital twin model to update the digital twin model. This includes replacing the parameters of the calculation unit corresponding to the geological body in the digital twin model with the inverted geological parameters and applying the multi-source monitoring data as boundary conditions to the digital twin model, thereby updating the physical state of the digital twin model.

[0103] Specifically, the parameters of the corresponding computational units of the geological bodies in the digital twin model are replaced with the inverted geological parameters. This involves dynamically correcting key parameters such as soil permeability coefficient, elastic modulus, and Poisson's ratio based on the latest inversion results, ensuring that the model's material properties remain consistent with the actual geological response. Simultaneously, multi-source monitoring data is applied to the digital twin model in real-time as boundary conditions. This allows for the simulation of the actual stress state and conditions at the current construction step. Driven by the construction step, multi-field coupled control equations (equilibrium equations, geometric equations, seepage continuity equations, and constitutive relations) are substituted into the equations for nonlinear iterative solutions. This enables the synchronous evolution of the digital twin model and the actual engineering state, effectively improving prediction accuracy.

[0104] In one embodiment, multiphysics coupling simulation analysis is performed based on the updated digital twin model to output the time-varying multifield distribution prediction results under the current preset construction step. This includes: performing db4 wavelet threshold denoising and wavelet packet energy entropy extraction on the inverted geological parameters of the current preset construction step to obtain the energy entropy of the current preset construction step; determining the time calculation step size of the updated digital twin model based on the energy entropy of the current preset construction step; and performing multiphysics coupling simulation analysis on the updated digital twin model using an iterative method based on the time calculation step size to obtain the time-varying multifield distribution prediction results under the current preset construction step. The time-varying multifield distribution prediction results include stress field distribution, displacement field distribution, and seepage field distribution.

[0105] The inverted geological parameters are decomposed into three levels using db4 wavelets. Let the k-th wavelet coefficient of the j-th level after decomposition be... A soft thresholding function is used for denoising, and the denoised data is then subjected to a four-level wavelet packet decomposition to calculate the energy of each frequency band.

[0106]

[0107] In the formula, N represents the energy of frequency band m, where m is the frequency band number. m This is the data length for that frequency band;

[0108] Further calculation of energy entropy:

[0109]

[0110] In the formula, It represents the energy entropy.

[0111] Among them, energy entropy Used to characterize the time-varying fluctuation of data, the higher the energy entropy, the faster the response of the physical entity changes, and the smaller the time calculation step of the digital twin model will be accordingly. The relationship between energy entropy and the time calculation step of the digital twin model is as follows:

[0112]

[0113] In the formula, As the reference step size, The time step is denoted by H, where H is the energy entropy.

[0114] By determining the time calculation step, the accuracy and efficiency of simulation iteration are dynamically adjusted to ensure that the digital twin model has a higher time resolution during periods of drastic physical changes, thereby accurately capturing the transient evolution characteristics of stress, displacement and seepage fields during construction and improving the reliability and timeliness of prediction results.

[0115] By employing the Newton-Raphson iterative method, the nonlinear equations are solved with a defined time step, ensuring that each iteration satisfies the convergence condition, thereby accurately obtaining the transient distribution of the stress field, displacement field, and seepage field under the current construction step.

[0116] The time-varying multi-field distribution prediction results include stress field distribution, displacement field distribution and seepage field distribution. Among them, the stress field distribution reflects the stress state of each point inside the model, the displacement field distribution reflects the changes in the spatial position of each point in the model, and the seepage field distribution describes the changes in the water head height of groundwater or pore water pressure in the model.

[0117] In one embodiment, the safety status assessment indicators include pier foundation settlement displacement, pier body strain, support structure displacement, support structure strain, and geological volumetric strain. In this case, based on the time-varying multi-field distribution prediction results under the current preset construction step, multiple safety status assessment indicators for the current preset construction step are determined, including: determining the pier foundation settlement displacement based on the maximum vertical displacement of all calculation units corresponding to the existing pier in the displacement field distribution; determining the pier body strain based on the maximum axial strain of all calculation units corresponding to the existing pier in the stress field distribution; determining the support structure displacement based on the maximum horizontal displacement of all calculation units corresponding to the support structure in the displacement field distribution; determining the support structure strain based on the maximum axial strain of all calculation units corresponding to the support structure in the stress field distribution; determining the pore water pressure corresponding to the geological body based on the maximum water head height of all calculation units corresponding to the geological body determined by the seepage field distribution; and determining the geological volumetric strain based on the pore water pressure and the maximum stress of the geological body.

[0118] Among them, the maximum vertical displacement of all calculation units corresponding to the existing piers in the displacement field distribution is used as the settlement displacement of the pier foundation, which can effectively reflect the maximum change in the overall settlement of the pier during the construction process.

[0119] Using the maximum axial strain of all calculation units corresponding to the existing bridge piers in the stress field distribution as the pier strain can accurately characterize the stress deformation state of the structure under load and reflect the maximum accumulation of internal stress in the material.

[0120] By taking the maximum horizontal displacement of all computational elements corresponding to the support structure in the displacement field distribution as the displacement of the support structure, the maximum deformation response of the support structure under lateral earth pressure can be accurately assessed, reflecting its spatial stability. By taking the maximum axial strain of all computational elements corresponding to the support structure in the stress field distribution as the strain of the support structure, the maximum stress on the support structure can be effectively identified.

[0121] To determine the maximum water head height of all computational units corresponding to the geological body, the seepage field distribution is obtained. This parameter is highly correlated with the construction step (e.g., the dewatering step in the foundation pit will cause a drop in water head in the excavation area). Based on the construction step time nodes, the maximum water head height of the computational units corresponding to the geological body is obtained, and then combined with the unit weight of water (taken as 10 kN / m³). 3 The pore water pressure is obtained, i.e.:

[0122]

[0123] In the formula, Pore ​​water pressure, The density of water, This represents the maximum water head height.

[0124] The essence of seepage field changes is pore water pressure changes, while soil deformation (including volumetric strain) is controlled by effective stress. Therefore, the volumetric strain of the geological body can be calculated using pore water pressure as follows:

[0125]

[0126] In the formula, For the volumetric strain of the geological body, The maximum stress of the geological body is obtained by measuring the total stress of the geological body at the corresponding calculation unit in the stress field distribution.

[0127] In one embodiment, a joint failure probability is calculated based on multiple safety status assessment indicators to obtain a joint failure probability. The construction safety status of the current preset construction step is then assessed based on the joint failure probability. This includes: inputting each safety status assessment indicator into the limit state equation to determine the limit state value of each indicator; selecting multiple safety status assessment indicators that exceed the limit state based on their limit state values ​​as failure mode indicators; determining the subjective and objective weights of each failure mode indicator using the Analytic Hierarchy Process (AHP) and entropy weighting methods respectively; weighting and fusing the subjective and objective weights of each failure mode indicator to obtain a combined weight for each indicator; and allocating the sampling ratio of each failure mode indicator according to the combined weight. Samples of each failure mode index are drawn from the normal probability distribution according to the allocated sampling ratio. Each sample of each failure mode index is substituted into the limit state equation, and failure samples are identified based on the limit states of the samples of each failure mode index, forming a failure sample set for each failure mode index. The number of failure samples in the failure sample set of each failure mode index and the total number of samples drawn for each failure mode index are counted, and the local failure probability of each failure mode index is calculated. The local failure probability of each failure mode index is weighted and calculated with a combined weight to obtain the joint failure probability. The joint failure probability is compared with a preset joint failure probability threshold. If the joint failure probability is greater than the preset joint failure probability threshold, the construction safety status of the current preset construction step is determined to be unsafe.

[0128] The limit state equation quantifies the difference between the actual response value and the safety threshold, thus determining whether a single failure mode has occurred. The simultaneous application of limit state equations for multiple failure modes forms the basis for calculating the combined failure probability. Its general form is:

[0129] Z(X) = R(X) - S(X)

[0130] In the formula, Z(X) is the limit state value under the safety status assessment index X, R(X) is the safety threshold of the safety status assessment index allowed by the specification / design, and S(X) is the measured value of the safety status assessment index X. When Z(X)≤0, the sample is judged to have failed. Based on this, the failure situation under a single safety status assessment index can be statistically obtained, and the safety status assessment index that exceeds the limit state can be screened as the failure mode index.

[0131] The subjective weights of each failure mode index are obtained by constructing a judgment matrix A using the Analytic Hierarchy Process (AHP) and then calculating the weight vector using the judgment matrix A and performing a consistency check. At the same time, the entropy value of each failure mode index is calculated based on the measured data of each failure mode index using the entropy weight method, thereby determining the objective weights.

[0132] By introducing weight coefficients for subjective and objective weights (e.g., 0.6 and 0.4 respectively) to weight and fuse subjective and objective weights, a combined weight for each failure mode indicator is obtained, which reflects both expert experience and the inherent laws of the data.

[0133] A sampling strategy based on combined weights can effectively improve the representativeness of failure samples. By allocating sampling proportions for each failure mode indicator through combined weights, samples of each failure mode indicator are drawn from a normal probability distribution according to the allocated sampling proportions. This ensures that the drawn samples conform to a normal probability distribution, closely resembling the random distribution characteristics of parameters in actual engineering. The normal probability distribution is determined by obtaining the mean and standard deviation of each failure mode indicator from historical monitoring data, ensuring that the statistical characteristics of the samples are consistent with actual operating conditions. After the drawn samples are judged by the limit state equation, a failure sample set is formed, which is used for subsequent calculation of local failure probabilities.

[0134] The ratio of the number of failure samples in the failure sample set of each failure mode indicator to the total number of samples extracted for each failure mode indicator is used to determine the local failure probability of each failure mode indicator. The joint failure probability is obtained by weighting the local failure probability of each failure mode indicator with the combined weight.

[0135] By comparing the combined failure probability with a preset combined failure probability threshold (e.g., 0.5), if the combined failure probability exceeds the threshold, the construction safety status of the current preset construction step is determined to be unsafe, and relevant personnel need to be notified to activate the emergency plan, adjust construction parameters, or optimize the process flow to reduce the risk; otherwise, it indicates that the construction safety status of the current preset construction step is safe.

[0136] Based on the same inventive concept, this application also provides a multi-source data fusion-based safety monitoring system for existing bridge piers and deep foundation pits, used to implement the above-mentioned multi-source data fusion-based safety monitoring method for existing bridge piers and deep foundation pits.

[0137] The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the safety monitoring system for the linkage between existing bridge piers and deep foundation pits based on multi-source data fusion provided below can be found in the limitations of the safety monitoring method for the linkage between existing bridge piers and deep foundation pits based on multi-source data fusion above, and will not be repeated here.

[0138] like Figure 2 As shown, this application provides a multi-source data fusion-based safety monitoring system for the linkage between existing bridge piers and deep foundation pits, including:

[0139] The geological parameter inversion module 100 is used to acquire multi-source monitoring data of the existing bridge pier structure and the deep foundation pit construction area under the current preset construction step, and to perform inversion analysis on the geological parameters of the deep foundation pit construction area based on the multi-source monitoring data of the current preset construction step to obtain the inverted geological parameters of the current preset construction step.

[0140] The twin model update module 200 is used to construct a digital twin model including existing bridge piers, deep foundation pits, support structures and geological bodies. It synchronously maps the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step into the digital twin model and updates the digital twin model.

[0141] The simulation response prediction module 300 is used to perform multi-physics field coupled simulation analysis based on the updated digital twin model and output the time-varying multi-field distribution prediction results under the current preset construction step.

[0142] The evaluation index determination module 400 is used to determine multiple safety status evaluation indicators for the current preset construction step based on the prediction results of the time-varying multi-field distribution under the current preset construction step.

[0143] The construction safety assessment module 500 is used to calculate the joint failure probability based on multiple safety status assessment indicators, obtain the joint failure probability, and assess the construction safety status of the current preset construction step based on the joint failure probability.

[0144] like Figure 3 As shown in the figure, this application provides an electronic device. The electronic device 10 includes a memory 20 and a processor 30. The memory 20 stores a computer program. When the computer program is executed by the processor 30, the processor 30 performs the steps of the existing bridge pier and deep foundation pit linkage safety monitoring method based on multi-source data fusion as described in the above embodiment.

[0145] This application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed, it implements the steps of the existing bridge pier and deep foundation pit linkage safety monitoring method based on multi-source data fusion as described in the above embodiments.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, and computer storage media described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0147] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0148] In the embodiments provided by this invention, it should be understood that the disclosed systems, electronic devices, computer storage media, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0151] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of the present invention through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0152] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion, characterized in that, include: Acquire multi-source monitoring data of the existing bridge pier structure and the deep foundation pit construction area under the current preset construction step, and perform inversion analysis on the geological parameters of the deep foundation pit construction area based on the multi-source monitoring data of the current preset construction step to obtain the inverted geological parameters of the current preset construction step. Constructing a digital twin model including existing bridge piers, deep foundation pits, support structures, and geological bodies; synchronously mapping the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step into the digital twin model; and updating the digital twin model; the construction of the digital twin model including existing bridge piers, deep foundation pits, support structures, and geological bodies includes: Using BIM technology, a geometric twin layer is constructed that includes existing bridge piers, deep foundation pits, support structures, and geological bodies; wherein the spatial positional relationship and geometric characteristics of the geometric twin layer with the existing bridge pier structure and the various physical entities in the deep foundation pit construction area are consistent. The geometric twin layer is discretized into a finite element mesh, dividing it into several computational units, and each computational unit is assigned a unique spatial coordinate and number. Multi-field time-varying constitutive models of existing bridge piers, support structures, and geological bodies are constructed respectively, and the multi-field time-varying constitutive models are embedded into the computational units of the corresponding physical entity types to form an initial physical twin layer; Based on the effective stress principle, a control equation is established that couples the stress field, displacement field, and seepage field. The control equation is then coupled and embedded into the initial physical twin layer to form a physical twin layer. The physical twin layer and the geometric twin layer are mapped and fused to generate a digital twin model; Multiphysics coupling simulation analysis is performed based on the updated digital twin model, and the time-varying multi-field distribution prediction results under the current preset construction step are output, including: The inverted geological parameters of the current preset construction step are subjected to db4 wavelet threshold denoising and wavelet packet energy entropy extraction to obtain the energy entropy of the current preset construction step. The time calculation step size of the updated digital twin model is determined based on the energy entropy of the current preset construction step. Based on the time calculation step, an iterative method is used to perform multiphysics coupling simulation analysis on the updated digital twin model to obtain the time-varying multi-field distribution prediction results under the current preset construction step; the time-varying multi-field distribution prediction results include stress field distribution, displacement field distribution and seepage field distribution; Based on the time-varying multi-field distribution prediction results under the current preset construction step, determine multiple safety status assessment indicators for the current preset construction step. The joint failure probability is calculated based on multiple safety status assessment indicators to obtain the joint failure probability, and the construction safety status of the current preset construction step is assessed based on the joint failure probability.

2. The method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion according to claim 1, characterized in that, The multi-source monitoring data includes the load on the existing bridge piers, the distance from the center of the existing bridge piers to the preset monitoring points, the foundation depth of the existing bridge piers, and the on-site settlement of the bridge pier foundations; the geological parameters of the deep foundation pit construction area include the elastic modulus and Poisson's ratio of the soil. The step of performing inversion analysis on the geological parameters of the deep foundation pit construction area based on the multi-source monitoring data of the current preset construction step to obtain the inverted geological parameters of the current preset construction step includes: The Mindlin elasticity solution was used, combined with the load on the existing pier, the distance from the center of the existing pier to the preset monitoring point, the foundation depth of the existing pier, and geological parameters, to determine the calculated settlement of the pier foundation. With the optimization objective of minimizing the difference between the calculated settlement of the bridge pier foundation and the on-site settlement of the bridge pier foundation, the geological parameters that minimize the difference between the calculated settlement of the bridge pier foundation and the on-site settlement of the bridge pier foundation are obtained by inversion using the Newton iteration method and are used as the inversion geological parameters for the current preset construction step.

3. The method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion according to claim 1, characterized in that, The step of synchronously mapping the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step into the digital twin model, and updating the digital twin model, includes: The inverted geological parameters replace the parameters of the corresponding calculation units of the geological bodies in the digital twin model, and multi-source monitoring data are applied as boundary conditions to the digital twin model to update the physical state of the digital twin model.

4. The method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion according to claim 1, characterized in that, The safety status assessment indicators include pier foundation settlement displacement, pier body strain, support structure displacement, support structure strain, and geological volume strain. The step of determining multiple safety status assessment indicators for the current preset construction step based on the time-varying multi-field distribution prediction results under the current preset construction step includes: The settlement displacement of the bridge pier foundation is determined based on the maximum vertical displacement of all calculation units corresponding to the existing bridge pier in the displacement field distribution. The strain of the pier body is determined based on the maximum axial strain of all calculation units corresponding to the existing pier in the stress field distribution. The displacement of the support structure is determined based on the maximum horizontal displacement of all calculation units corresponding to the support structure in the displacement field distribution. The strain of the support structure is determined based on the maximum axial strain of all calculation units corresponding to the support structure in the stress field distribution. Based on the seepage field distribution, determine the maximum head height of all calculation units corresponding to the geological body, determine the pore water pressure corresponding to the geological body, and determine the volumetric strain of the geological body based on the pore water pressure and the maximum stress of the geological body.

5. The method for joint safety monitoring of existing bridge piers and deep foundation pits based on multi-source data fusion according to claim 4, characterized in that, The step of calculating the joint failure probability based on multiple safety status assessment indicators to obtain the joint failure probability, and assessing the construction safety status of the current preset construction step based on the joint failure probability, includes: Each of the safety status assessment indicators is input into the limit state equation to determine the limit state value of each of the safety status assessment indicators; Based on the limit state values ​​of each of the aforementioned safety status assessment indicators, multiple safety status assessment indicators that exceed the limit state are selected as failure mode indicators. The subjective and objective weights of each failure mode index are determined by the AHP analytic hierarchy process and the entropy weight method, respectively. The subjective and objective weights of each failure mode index are then weighted and fused to obtain the combined weight of each failure mode index. The sampling ratio of each failure mode index is allocated according to the combined weight, and samples of each failure mode index are drawn from the normal probability distribution according to the allocated sampling ratio. The samples of each failure mode index are substituted into the limit state equation, and the failure samples are identified according to the limit state of the samples of each failure mode index, thus forming a failure sample set of each failure mode index. The number of failure samples in the failure sample set of each failure mode index and the total number of samples extracted for each failure mode index are counted, and the local failure probability of each failure mode index is calculated. The joint failure probability is obtained by weighting the local failure probability of each failure mode index with the combined weight. The combined failure probability is compared with a preset combined failure probability threshold. If the combined failure probability is greater than the preset combined failure probability threshold, the construction safety status of the current preset construction step is determined to be unsafe.

6. A safety monitoring system for the linkage between existing bridge piers and deep foundation pits based on multi-source data fusion, characterized in that, include: The geological parameter inversion module is used to acquire multi-source monitoring data of the existing bridge pier structure and the deep foundation pit construction area under the current preset construction step, and to perform inversion analysis on the geological parameters of the deep foundation pit construction area based on the multi-source monitoring data of the current preset construction step to obtain the inverted geological parameters of the current preset construction step. The digital twin model update module is used to construct a digital twin model including existing bridge piers, deep foundation pits, support structures, and geological bodies. It synchronously maps the inverted geological parameters of the current preset construction step and the multi-source monitoring data of the current preset construction step into the digital twin model, updating the digital twin model. The construction of the digital twin model including existing bridge piers, deep foundation pits, support structures, and geological bodies includes: Using BIM technology, a geometric twin layer is constructed that includes existing bridge piers, deep foundation pits, support structures, and geological bodies; wherein the spatial positional relationship and geometric characteristics of the geometric twin layer with the existing bridge pier structure and the various physical entities in the deep foundation pit construction area are consistent. The geometric twin layer is discretized into a finite element mesh, dividing it into several computational units, and each computational unit is assigned a unique spatial coordinate and number. Multi-field time-varying constitutive models of existing bridge piers, support structures, and geological bodies are constructed respectively, and the multi-field time-varying constitutive models are embedded into the computational units of the corresponding physical entity types to form an initial physical twin layer; Based on the effective stress principle, a control equation is established that couples the stress field, displacement field, and seepage field. The control equation is then coupled and embedded into the initial physical twin layer to form a physical twin layer. The physical twin layer and the geometric twin layer are mapped and fused to generate a digital twin model; The simulation response prediction module is used to perform multiphysics coupled simulation analysis based on the updated digital twin model, and output the time-varying multi-field distribution prediction results under the current preset construction step; the multiphysics coupled simulation analysis based on the updated digital twin model, and output the time-varying multi-field distribution prediction results under the current preset construction step, includes: The inverted geological parameters of the current preset construction step are subjected to db4 wavelet threshold denoising and wavelet packet energy entropy extraction to obtain the energy entropy of the current preset construction step. The time calculation step size of the updated digital twin model is determined based on the energy entropy of the current preset construction step. Based on the time calculation step, an iterative method is used to perform multiphysics coupling simulation analysis on the updated digital twin model to obtain the time-varying multi-field distribution prediction results under the current preset construction step; the time-varying multi-field distribution prediction results include stress field distribution, displacement field distribution and seepage field distribution; The evaluation index determination module is used to determine multiple safety status evaluation indicators for the current preset construction step based on the time-varying multi-field distribution prediction results under the current preset construction step. The construction safety assessment module is used to calculate the joint failure probability based on multiple safety status assessment indicators, obtain the joint failure probability, and assess the construction safety status of the current preset construction step based on the joint failure probability.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the safety monitoring method for the linkage between existing bridge piers and deep foundation pits based on multi-source data fusion as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the steps of the safety monitoring method for the linkage between existing bridge piers and deep foundation pits based on multi-source data fusion as described in any one of claims 1-5.

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