CN-122021306-A - External wall external heat insulation integrated stress state monitoring method and system

CN122021306ACN 122021306 ACN122021306 ACN 122021306ACN-122021306-A

Abstract

The invention relates to an external wall external heat insulation integrated stress state monitoring method and system, and belongs to the technical field of building structure health monitoring. The method comprises the steps of constructing a distributed topology monitoring network, conducting self-organizing network checking on state monitoring nodes in the distributed topology monitoring network, calculating thermal stress curvature gradients of an insulation layer to generate integrated stress parameters of an outer wall, conducting inversion constraint on stress response values of a thermal insulation wall structure layer to obtain a layered composite stress distribution map, conducting multi-scale abnormal detection on stress distribution change rates of different levels, conducting priority ranking on stress fluctuation values in the stress state evolution matrix, conducting hierarchical identification on monitoring weights of an outer wall thermal insulation layer area, writing thermal insulation wall state monitoring data into a structure stress state quantification system, conducting dynamic adjustment on the monitoring parameters in the outer wall thermal insulation layer area according to real-time stress monitoring feedback, and synchronously updating the structure stress state quantification system.

Inventors

Zhong Caimin
SANG LIJUAN
HAN HUIJUN

Assignees

上海中森建筑与工程设计顾问有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (12)

1. The method for monitoring the stress state of the external heat insulation integrated type of the external wall is characterized by comprising the following steps of: S1, acquiring thermal insulation wall state monitoring data, constructing a distributed topology monitoring network according to the thermal insulation wall state monitoring data, performing self-networking verification on state monitoring nodes in the distributed topology monitoring network, calculating thermal stress curvature gradient of a thermal insulation layer based on an elastic physical inversion algorithm, and generating an external wall integrated stress parameter; S2, constructing a layered composite stress mapping model, wherein the layered composite stress mapping model is based on a stress field reconstruction algorithm, performs inversion constraint on stress response values of a thermal insulation wall structure layer, and maps the integrated stress parameters of the outer wall into stress contribution variables to obtain a layered composite stress distribution diagram; S3, in the layered composite stress distribution diagram, multi-scale anomaly detection is carried out on stress distribution change rates of different levels according to time sequence evolution characteristics of stress contribution variables in a monitoring process, a stress state evolution matrix is constructed, and stress fluctuation values in the stress state evolution matrix are prioritized by combining a preset local stress failure reference, so that an external thermal insulation layer stress monitoring scheme is generated; And S4, executing the external heat-insulating layer stress monitoring scheme, carrying out grading identification on the monitoring weight of the external wall heat-insulating layer area, writing heat-insulating wall state monitoring data into a structure stress state quantization system, dynamically adjusting the monitoring parameters in the external wall heat-insulating layer area according to real-time stress monitoring feedback, and synchronously updating the structure stress state quantization system.
2. The method of claim 1, wherein the method for constructing the distributed topology monitoring network comprises the steps of mapping heat preservation wall state monitoring data into state monitoring nodes in the distributed topology monitoring network, wherein the heat preservation wall state monitoring data comprises node space coordinate information and strain response characteristics, establishing networking connection relations among the state monitoring nodes based on local adjacent evolution rules, automatically eliminating abnormal nodes responding to distortion according to data redundancy among the nodes, and obtaining the distributed topology monitoring network matched with the stress state of the outer wall.
3. The method of claim 1, wherein the method for verifying the ad hoc network is characterized by calculating a strain response value between a target state monitoring node and a neighbor state monitoring node according to strain transmission continuity of adjacent state monitoring nodes under the same environmental influence condition, carrying out stress transmission judgment on the strain response value, and dynamically adjusting connection weights of the state monitoring nodes in the ad hoc network when a nonlinear mutation is detected in the coupling relation between the physical response characteristics of the target node and the neighbor nodes.
4. The method of claim 1, wherein the method for generating the external wall integrated stress parameter comprises the steps of performing time synchronization on state monitoring nodes after verification of an ad hoc network to construct a multi-source heterogeneous monitoring space, introducing a temperature gradient driving item and a structural constraint item into the multi-source heterogeneous monitoring space, wherein the stress gradient driving item is a strain force variation obtained by gradient solution between monitoring nodes, the structural constraint item is a stress constraint relation constructed by combining lamellar structure characteristics and interlayer continuity conditions, and calculating strain force distribution and space curvature variation characteristics of the state monitoring nodes based on an elastic physical inversion algorithm to generate the external wall integrated stress parameter.
5. The method according to claim 1, wherein the layered composite stress mapping model is constructed by dividing a composite stress distribution structure into a load mapping functional layer and a structure constraint layer; The load mapping functional layer establishes a stress transmission relation of the load in the plane direction of the outer wall and in a local area according to the spatial distribution characteristics caused by external environmental factors, and carries out filtering correction on the environmental stress load to obtain boundary stress mapping parameters with the spatial distribution characteristics; The structural constraint layer takes rigidity constraint and displacement constraint as basic constraint inversion factors, takes stress response formed under a load environment as inversion boundary conditions, and limits the mapping range of the stress state of the load mapping functional layer; And establishing interlayer stress association between the load mapping functional layer and the structure constraint layer, fusing external load and the structure constraint condition to the same stress mapping frame, and constructing a layered composite stress mapping model with the stress characteristics of the multilayer structure.
6. The method of claim 1, wherein the stress field reconstruction algorithm performs inversion constraint on stress response values by mapping stress gradient driving items and strain response characteristics to stress nodes of each structural layer, calculating stress response values of stress nodes of each structural layer based on the stress field reconstruction algorithm, correlating node stresses of adjacent structural layers, mapping the stress response values to a uniform global constraint stress space, and performing inversion constraint on nonlinear changes of local stress response values by combining stress gradient consistency thresholds preset in the global constraint stress space.
7. The method of claim 1, wherein the layered composite stress distribution map uses a structure level as a longitudinal dimension and uses an outer wall plane space coordinate as a transverse dimension, a multi-dimensional stress display frame is constructed, the outer wall integrated stress parameters are decoupled and distributed according to the structure level and the space position, stress components of the outer wall plane coordinate position are extracted, a stress contribution mapping function is introduced to convert the stress components into stress contribution variables, and the contribution proportion of adjacent structure layers in the whole stress state is reflected, so that the layered composite stress distribution map is obtained.
8. The method of claim 1, wherein the stress state evolution matrix is constructed by hierarchically reconstructing stress response data according to a time sequence and a structure level, quantifying stress change rates and accumulation effects of stress nodes in a continuous monitoring period, generating stress fluctuation stability indexes, and constructing stress state evolution matrices by jointly restricting time sequence responses of stress nodes of adjacent structure layers based on an interlayer time sequence association constraint mechanism.
9. The method of claim 1, wherein the method for generating the external thermal insulation layer stress monitoring scheme is characterized by comprising the steps of carrying out time sequence analysis on stress fluctuation values based on the stress fluctuation stability index, extracting stress evolution characteristics of high-priority environmental areas, calculating stress failure risk probability of each area by combining a preset local stress failure reference to generate a monitoring priority queue, and formulating an adjustment strategy of a targeted monitoring path and sampling frequency according to the monitoring priority queue to generate the external thermal insulation layer stress monitoring scheme.
10. The method according to claim 1, wherein the structure stress state quantization system comprises a data processing layer and a decision support layer; The data processing layer is used for aggregating node stress indexes in the same structural layer according to interlayer continuity constraint and nonlinear coupling relation to form a hierarchical stress characteristic vector, and carrying out standardized quantification on strain response of each state monitoring node by combining external environment factors and structure constraint conditions; And the decision support layer is used for calculating stress risk indexes of all monitoring areas by combining coupling constraint among structural layers and dynamically adjusting the monitoring parameters in the outer wall heat insulation layer areas according to the monitoring priority and real-time stress state feedback.
11. The method of claim 2, wherein the local adjacent evolution rule establishes the networking connection relationship between the state monitoring nodes by calculating a strain transfer coefficient between adjacent nodes with each state monitoring node in a local neighborhood search space as a center, and identifying initial connection weights of the adjacent nodes according to node space coordinates and local density characteristics to establish the networking connection relationship.
12. An integrated exterior wall insulation stress state monitoring system for performing the method of any of claims 1-11, comprising: The distributed topology construction module is used for acquiring thermal insulation wall state monitoring data, constructing a distributed topology monitoring network according to the thermal insulation wall state monitoring data, performing self-networking verification on state monitoring nodes in the distributed topology monitoring network, calculating thermal stress curvature gradient of a thermal insulation layer based on an elastic physical inversion algorithm, and generating an external wall integrated stress parameter; The layered composite stress mapping module is used for constructing a layered composite stress mapping model, carrying out inversion constraint on stress response values of the heat preservation wall structure layer based on a stress field reconstruction algorithm, and mapping the integrated stress parameters of the outer wall into stress contribution variables to obtain a layered composite stress distribution diagram; the stress state evolution module is used for carrying out multi-scale abnormal detection on stress distribution change rates of different levels in the layered composite stress distribution diagram according to time sequence evolution characteristics of stress contribution variables in a monitoring process to construct a stress state evolution matrix, and carrying out priority ranking on stress fluctuation values in the stress state evolution matrix by combining a preset local stress failure reference to generate an external thermal insulation layer stress monitoring scheme; And the structure stress quantification module is used for executing the external heat-insulating layer stress monitoring scheme, carrying out hierarchical identification on the monitoring weight of the external wall heat-insulating layer area, writing the heat-insulating wall state monitoring data into the structure stress state quantification system, dynamically adjusting the monitoring parameters in the external wall heat-insulating layer area according to real-time stress monitoring feedback, and synchronously updating the structure stress state quantification system.

Description

External wall external heat insulation integrated stress state monitoring method and system Technical Field The invention belongs to the technical field of building structure health monitoring, and particularly relates to an external heat preservation integrated stress state monitoring method and system for an external wall. Background Along with the continuous improvement of the requirements of building energy conservation and sustainable development, the external wall external heat insulation system is widely applied to modern buildings. However, the long-term exposure of the external wall insulation layer to environmental loads, such as wind load, temperature gradient, humidity change, self-weight of the structure, etc., can lead to complex stress distribution inside the insulation layer, thereby forming local thermal stress, shear stress and interlayer coupling effect. If the stress states cannot be monitored and evaluated in time, the heat preservation layer is easy to crack, fall off and even potential structural safety hazards are easily caused, so that the durability and the service performance of the building are affected. The traditional external wall monitoring method is mostly dependent on a single-point strain or temperature sensor, lacks multi-level and global quantitative analysis on the whole stress state of the heat preservation layer, and cannot realize accurate identification on local abnormal stress and long-term stress evolution. At present, some researches try to predict the stress of an outer wall heat preservation layer through finite element modeling, stress field simulation or thermal-force coupling analysis, but the methods have obvious limitations that on one hand, the model precision is highly dependent on structural parameters and boundary conditions, so that the diversity and complexity of an actual building are difficult to cover, and on the other hand, a dynamic feedback mechanism for monitoring data in real time is lacking, so that the method cannot respond to local stress mutation caused by sudden load, environmental change or material aging in time. In addition, the existing method is not systematic in the aspects of stress distribution decoupling, interlayer coupling constraint and stress evolution analysis of the multilayer structure, and closed-loop support of monitoring data to structural health management decisions cannot be realized. Therefore, there is an urgent need for an external wall insulation layer stress monitoring technology capable of combining real-time monitoring data and a physical inversion algorithm, which not only can obtain node-level stress response, but also can realize multidimensional quantification of an external wall stress state through level and global mapping, can dynamically identify local abnormality and evolution trend, and can provide closed-loop data support for monitoring weight classification, scheme generation and structure health evaluation, so that monitoring precision, data reliability and long-term safety of an external wall insulation layer are improved. The method lays a theoretical and technical foundation for developing an intelligent stress monitoring method for the outer wall insulation layer based on a distributed topology monitoring network, stress contribution mapping and closed-loop self-adaptive optimization. Disclosure of Invention In order to solve the problems in the prior art, the invention provides an external thermal insulation integrated stress state monitoring method for an external wall, The aim of the invention can be achieved by the following technical scheme: S1, acquiring thermal insulation wall state monitoring data, constructing a distributed topology monitoring network according to the thermal insulation wall state monitoring data, performing self-networking verification on state monitoring nodes in the distributed topology monitoring network, calculating thermal stress curvature gradient of a thermal insulation layer based on an elastic physical inversion algorithm, and generating an external wall integrated stress parameter; S2, constructing a layered composite stress mapping model, wherein the layered composite stress mapping model is based on a stress field reconstruction algorithm, performs inversion constraint on stress response values of a thermal insulation wall structure layer, and maps the integrated stress parameters of the outer wall into stress contribution variables to obtain a layered composite stress distribution diagram; S3, in the layered composite stress distribution diagram, multi-scale anomaly detection is carried out on stress distribution change rates of different levels according to time sequence evolution characteristics of stress contribution variables in a monitoring process, a stress state evolution matrix is constructed, and stress fluctuation values in the stress state evolution matrix are prioritized by combining a preset local stress failure reference, so that an externa