Water conservancy project building full life cycle management method based on BIM technology analysis

Abstract

The invention discloses a water conservancy project building full life cycle management method based on BIM technical analysis, and relates to the technical field of building full life cycle management. And respectively constructing three structural indexes, namely a microcrack expansion rate index F1, a damp-heat permeation combined degradation index F2 and a dynamic stiffness phase deviation index F3. The multi-dimensional feature system not only covers key risk sources such as material microcosmic degradation, environmental coupling effect and mechanical response mutation, but also breaks through a traditional extensive method which depends on single sensing data to perform health judgment, and provides a digital expression model with physical interpretation force for structural risks. According to the method, a unified health data standard system is established, and the health data standard system can be used as a core data source of upper-layer applications such as dynamic structure evaluation, an early warning model and visual mapping.

Description

Technical Field

[0001] The present invention relates to the technical field of building life cycle management, and specifically to a full life cycle management method for water conservancy project buildings based on BIM technology analysis. Background Art

[0002] Against the backdrop of increasing complexity and extended service life for water conservancy infrastructure, the traditional segmented design-construction-operation-maintenance management model has gradually exposed bottlenecks such as a lack of data continuity and status awareness. Building dynamic information fusion models that synchronize virtual and real data, enabling real-time perception of structural status, trend assessment, and response control, has become a core approach to improving project safety and extending project lifespans.

[0003] Currently, the prevailing maintenance mechanism relies primarily on periodic inspections and on-site manual assessments, lacking a real-time data-driven status recognition and trend warning system. On the one hand, most BIM models remain at the design stage, preventing the dynamic embedding of data from the operation and maintenance phase, rendering the models "static model archives." On the other hand, while the various sensors deployed can collect data on structural displacement, crack expansion, humidity, and temperature, they lack effective structural semantics and health trend analysis mechanisms. This prevents monitoring data from being logically linked to components, creating a "data island" phenomenon.

[0004] The above situation is mainly due to two aspects: first, the traditional BIM model lacks a real-time data docking mechanism, and the model is disconnected from the on-site structural status, resulting in "inconsistency between the picture and the model"; second, the current monitoring system has not formed a unified collaborative perception framework of structure-environment-operation, resulting in the fragmentation of perception data dimensions and the inability to integrate modeling. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a full life cycle management method for water conservancy project buildings based on BIM technology analysis, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a full life cycle management method for water conservancy project buildings based on BIM technology analysis, comprising the following steps: S1. Integrate the water conservancy project design model, construction records, normalized component parameters, and historical operation and maintenance logs into the BIM platform to build a BIM structural semantic framework; S2. Based on the BIM structural semantic framework, various sensors are deployed in the component nodes of the water conservancy project building to collect the structural health parameters of the components, and pre-process them to fit them into the health data set KW; S3, mapping the data in the health dataset KW to the corresponding BIM structure semantic framework in a time series manner to build a dynamic BIM model; S4. By using the dynamic BIM model, feature extraction is performed on the acquired health data set KW and normalized structural parameters, structural indicators are constructed, and the comprehensive structural dynamic health index SDH is calculated; S5. Perform risk analysis on the obtained comprehensive structural dynamic health index SDH, evaluate future risk trends in combination with structural indicators, and calculate the trend response deviation prediction value Prisk; S6. Comprehensively analyze the obtained trend response offset prediction value Prisk. When the trend response offset prediction value Prisk is abnormal, mark it as a red high-risk component, dispatch maintenance resources, generate inspection work orders, and trigger multi-source video surveillance.

[0007] Preferably, S1 comprises the following steps: S11. Convert the 2D CAD drawings and 3D models in the water conservancy project design phase into recognizable BIM structural entities and obtain normalized component parameters; S12. Synchronously connect the construction records, including construction sequence, process method and pouring temperature curve, to the BIM platform; S13, inputting the normalized component parameters, including material density Pm, microstructure elastic modulus Em, and water vapor partial pressure Pv, into the BIM platform; S14. Map the historical operation and maintenance logs to the corresponding components in chronological order, and connect them to the BIM platform to build a BIM structural semantic framework.

[0008] Preferably, S2 includes S21 and S22; S21. By deploying multiple sensors in component nodes, including laser displacement sensors, fiber-optic distributed humidity sensors, fiber-optic distributed temperature sensors, piezoelectric ceramic sensor coupling devices, and fiber-optic strain gauges, component structural health parameters are collected, including microscale crack propagation velocity Vc, component internal relative humidity RH, component internal temperature RT, component dynamic stiffness degradation rate KG, and displacement-strain coupling phase offset angle Hea, and fit them into the original data set W; Among them, the microscale crack expansion velocity Vc is acquired by laser displacement sensor: The microscale crack propagation velocity Vc is obtained by the following formula: ; Where Lc(t) represents the length of microscale cracks at time t, Lc(t+Δt) represents the length of microscale cracks at time (t+Δt), and Δt represents the time interval; The relative humidity RH inside the component is collected and obtained through optical fiber distributed humidity sensors; The internal temperature RT of the component is acquired through optical fiber distributed temperature sensors; The dynamic stiffness degradation rate KG of the component is acquired through the piezoelectric ceramic sensor coupling device; The dynamic stiffness degradation rate KG of the component is obtained by the following formula: ; Where K(t) represents the equivalent stiffness of the component at time t, and K(t-Δt) represents the equivalent stiffness of the component at time (t-Δt); The displacement-strain coupling phase offset angle Hea is acquired by optical fiber strain gauge and laser displacement sensor.

[0009] Preferably, S22, preprocessing the obtained original data set W, including noise filtering, missing interpolation and normalization, to obtain a healthy data set KW; Noise filtering is performed by using the median filter method to remove the noise in the original data set W; Missing data interpolation is performed by using linear interpolation to fill in the missing data in the original dataset W; Normalization processing is performed by using the Min-Max normalization method to process the data in the original data set W to obtain the healthy data set KW; The health data set KW is obtained by the following formula: ; Where KWa represents the a-th data item in the healthy dataset KW, Wa represents the a-th data item in the original dataset W, minWa represents the valley value of the a-th data item in the original dataset W, and maxWa represents the peak value of the a-th data item in the original dataset W.

[0010] Preferably, S3, a unique identifier is set for the component node in the BIM structure semantic framework: component number ID; and it is bound to the health data set KW to establish a mapping relationship: ; Where, IDi,t represents the i-th component number at time t, which is the unique identifier in the BIM model, such as gate 1 and dam block A2, and KWj(t) represents the j-th sample data in the health dataset KW at time t; Perform formal transformation on the health map of each component node: ; Where Mi(t) represents the state tuple of the i-th component at time t, IDi represents the i-th component number at time t, and (Xi, Yi, Zi) represents the spatial coordinates of the i-th component in the model; A health visualization engine is embedded in the BIM interface to read the status tuple Mi(t) of the i-th component at time t and perform model rendering. Dynamically control the color, brightness, and transparency of components based on the combined value of the jth sample data KWj(t) in the health dataset KW at time t; and construct a dynamic BIM model. Control the component color through the color mapping function: ; Where Ci(t) represents the RGB color value vector of the i-th component at time t, and Si(t) represents the comprehensive risk status score of the i-th component at time t; The comprehensive risk status score Si(t) of the i-th component at time t is obtained by the following formula: ; Where, Represents the preset weight value of the j-th sample data in the health data set KW. All values ​​of the comprehensive risk status score Si(t) of the i-th component at time t are in the range of [0,1]. The larger the value, the higher the risk.

[0011] Preferably, S4 includes S41 and S42; S41. Feature extraction of the healthy dataset KW and normalized structural parameters is performed using a dynamic BIM model to construct structural indices, including the microcrack growth rate index F1, the moisture-heat penetration combined degradation index F2, and the dynamic stiffness phase shift index F3. Among them, the microcrack growth rate index F1 represents the rate of crack growth under unit structural strength; the larger the value, the easier the crack is to grow and the greater the structural fragility. The microcrack growth rate index F1 is obtained by the following formula: ; Where Vc represents the microscale crack propagation velocity, Em represents the microstructural elastic modulus, and Pm represents the material density; The combined moisture-heat penetration degradation index F2 reflects the risk of water penetration, salt precipitation, and expansion of the material's internal microstructure caused by a moist heat environment. The combined moisture and heat penetration degradation index F2 is obtained by the following formula: ; Where RH represents the relative humidity inside the component, RT represents the internal temperature of the component, ln represents the natural logarithm with constant e as the base, Pv represents the water vapor partial pressure, and Ps represents the saturated water vapor pressure; The dynamic stiffness phase shift index F3 represents the frequency and response angle of the component stiffness change. A larger shift angle indicates inconsistent structural force-deformation response, indicating potential stiffness degradation or fatigue accumulation. The dynamic stiffness phase shift index F3 is obtained by the following formula: ; Where KG(t) represents the dynamic stiffness vector of the component, KG(t-Δt) represents the dynamic stiffness vector of the component at time (t-Δt), and arccos represents the inverse cosine function.

[0012] Preferably, S42, the obtained microcrack growth rate index F1, the moisture-heat penetration combined degradation index F2 and the dynamic stiffness phase shift index F3 are integrated to calculate and obtain the comprehensive structural dynamic health index SDH; The comprehensive structural dynamic health index SDH is obtained by the following formula: ; Where tan() represents the tangent function.

[0013] Preferably, S5 includes S51 and S52; S51. Collect the health index sequence {SDHi(t1), SDHi(t2), ..., SDHi(tn)} of each component at multiple consecutive time points; and calculate the trend derivative in combination with the microcrack growth rate index F1 and the moisture-heat penetration combined degradation index F2 to obtain a first-order evolution trend image of the structural state; this is used to predict the direction and speed of change in the next period; The component health value change trend slope xSDH is calculated by using the comprehensive structural dynamic health index SDH at multiple time points; The component health value change trend slope xSDH is obtained by the following formula: ; Where SDHi(t) represents the comprehensive structural dynamic health index of the i-th component at time t, SDHi(t-Δt) represents the comprehensive structural dynamic health index of the i-th component at time (t-Δt), and d represents the derivative symbol; The microcrack growth rate index change rate ΔF1 is obtained by calculating the trend derivative of the microcrack growth rate index F1; The microcrack growth rate exponential change rate ΔF1 is obtained by the following formula: ; Where, F1(t) represents the microcrack growth rate index at time t, and F1(t-Δt) represents the microcrack growth rate index at time (t-Δt); The trend derivative of the moisture-heat-penetration combined degradation index F2 is calculated to obtain the moisture-heat-penetration combined degradation index change rate ΔF2; The change rate of the combined moisture and heat penetration degradation index ΔF2 is obtained by the following formula: ; Where F2(t) represents the combined degradation index of moisture and heat penetration at time t, and F2(t-Δt) represents the combined degradation index of moisture and heat penetration at time (t-Δt).

[0014] Preferably, S52, according to the obtained microcrack growth rate index change rate ΔF1 and the moisture-heat penetration combined degradation index change rate ΔF2, combined with the comprehensive structural dynamic health index SDH, calculate and obtain the trend response offset prediction value Prisk; The trend response offset prediction value Prisk is obtained by the following formula: ; Where, and They respectively represent the preset weight values ​​of the microcrack growth rate index change rate ΔF1 and the moisture-heat penetration combined degradation index change rate ΔF2.

[0015] Preferably, S6, comparing the obtained trend response deviation prediction value Prisk with a preset response deviation threshold Trs to obtain a risk level of the trend response deviation prediction value Prisk; The risk level of the trend response deviation prediction value Prisk is obtained by matching in the following way: When 0 < Prisk < Trs*0.5, the response offset threshold, indicating the first risk level, indicates that the component is in a stable structural health state. The dynamic health index changes steadily, indicating a low future risk trend. No maintenance intervention is required, and the standard plan can be implemented according to periodic inspections. When the response offset threshold Trs*0.5 ≤ the trend response offset prediction value Prisk ≤ the response offset threshold Trs, it indicates the second risk level and the component is in a normal state. The trend of health indicator change is accelerating, but has not yet reached the warning level. It is recommended to place the component in the "observation queue" and enter the short-term retest list in the next cycle. When the response offset threshold Trs is less than the trend response offset prediction value Prisk and less than the response offset threshold Trs*1.5, it indicates the third risk level and the component is in an abnormal state. The health change rate and risk response rate both increase, and the system enters a moderate alert state. Manual inspections must be prioritized during the next maintenance cycle, and local monitoring or increased deployment of inspection points should be carried out if necessary. When the response offset threshold Trs*1.5≤the trend response offset prediction value Prisk≤the response offset threshold Trs*2, it indicates the fourth risk level, and the component is in a high-risk state; the health indicators are seriously abnormal or the trend surges in a short period of time, and there is a potential risk of structural failure. Emergency intervention measures should be taken immediately, including temporary closure, structural reinforcement, special assessment, etc. This is a serious structural warning; When the trend response deviation prediction value Prisk is at the fourth level, the component is marked as red high risk in the BIM model, and maintenance resources are dispatched, inspection work orders are generated, and multi-source video surveillance is triggered; Among them, the inspection work order includes the component number ID, status details and priority level.

[0016] The present invention provides a full life cycle management method for water conservancy project buildings based on BIM technology analysis, which has the following beneficial effects: (1) By deeply integrating the health data extracted from the dynamic BIM model with the normalized structural parameters, three structural indicators were constructed: the microcrack growth rate index F1, the moisture-heat penetration combined degradation index F2, and the dynamic stiffness phase shift index F3. This multidimensional feature system not only covers key risk sources such as material microdegradation, environmental coupling effects, and mechanical response mutations, but also breaks the traditional extensive method of relying on single sensor data for health judgment, providing a digital expression model with physical interpretation for structural risks, and significantly enhancing the scientific nature and controllability of water conservancy project health assessment.

[0017] This solution is no longer limited to the analysis of a single structural parameter. Instead, it takes into account the temporal correlation and nonlinear response characteristics between the actual service environment of the structure and the dynamic response of the structure through the construction of the indicators of moisture and heat penetration combined degradation index F2 and dynamic stiffness phase shift index F3, truly embodying the three-dimensional collaborative perception idea of ​​"structure-environment-operation".

[0018] (2) By setting a unique identifier ID for each component node and binding it to the corresponding monitoring data in the health data set KW, a mapping relationship between the component and the data is established. This binding mechanism makes each piece of sensor data no longer an unstructured "floating value", but an "entity health label" with clear structural semantics. This improvement breaks through the limitations of the traditional BIM model and monitoring system being separated and the data being unable to be aligned, and realizes the integrated management of structure and data, providing precise support for advanced functions such as health trend tracking and state evolution analysis. By introducing the form of state tuples to express the three-dimensional spatial position and health status of components at any time, and embedding a health visualization engine to control its rendering of color, brightness, and transparency, the component realizes the dynamic state expression over time in the BIM interface. This innovation realizes the transformation from static drawings to dynamic health images, enabling maintenance personnel to intuitively identify risk components and perceive structural change trends, greatly improving the response speed and predictability of operation and maintenance work.

[0019] (3) By introducing data processing strategies such as median filtering, linear interpolation, and Min-Max normalization, the original sensor data set W is preprocessed to form a health data set KW. This processing flow significantly improves the stability, integrity, and comparability of the data, avoids the abnormal effects of factors such as sensor fluctuations, acquisition delays, and missing measurements in field data, and lays a solid foundation for subsequent data analysis and trend modeling. Compared with the common problems of data clutter and difficulty in modeling in traditional monitoring systems, this method establishes a unified health data standard system that can serve as the core data source for upper-level applications such as dynamic structure assessment, early warning models, and visual mapping.

[0020] (4) Compared with traditional delayed responsive maintenance, this mechanism has the ability to intervene in advance, which is a key shift from "passive maintenance" to "active early warning". In S6, the system automatically marks high-risk components and generates inspection work orders, dispatches maintenance resources, and even links video surveillance based on the level judgment results of the trend response deviation prediction value Prisk, realizing automatic closed-loop management of the entire process from monitoring-analysis-response-intervention. This mechanism significantly reduces human dependence and response delays, improves the efficiency of water conservancy project structural risk response, and has the practicality and scalability to be deployed in large and complex engineering systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the steps of the whole life cycle management method of water conservancy project buildings based on BIM technology analysis of the present invention; Figure 2 A schematic diagram of a process for obtaining a trend response offset prediction value according to the present invention; Figure 3 It is a line graph of the comprehensive structural dynamic health index of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Example

[0023] This invention provides a full life cycle management method for water conservancy project buildings based on BIM technology analysis. Figures 1 to 3 , including the following steps: S1. Integrate the water conservancy project design model, construction records, normalized component parameters, and historical operation and maintenance logs into the BIM platform to build a BIM structural semantic framework; S2. Based on the BIM structural semantic framework, various sensors are deployed in the component nodes of the water conservancy project building to collect the structural health parameters of the components, and pre-process them to fit them into the health data set KW; S3, mapping the data in the health dataset KW to the corresponding BIM structure semantic framework in a time series manner to build a dynamic BIM model; S4. By using the dynamic BIM model, feature extraction is performed on the acquired health data set KW and normalized structural parameters, structural indicators are constructed, and the comprehensive structural dynamic health index SDH is calculated; S5. Perform risk analysis on the obtained comprehensive structural dynamic health index SDH, evaluate future risk trends in combination with structural indicators, and calculate the trend response deviation prediction value Prisk; S6. Comprehensively analyze the obtained trend response offset prediction value Prisk. When the trend response offset prediction value Prisk is abnormal, mark it as a red high-risk component, dispatch maintenance resources, generate inspection work orders, and trigger multi-source video surveillance.

[0024] In this example, by integrating and embedding the design model, construction records, component parameters, and operation and maintenance logs into the BIM platform in step S1, this approach achieves the first integrated fusion of cross-phase information for water conservancy project construction, building a structural framework with semantic recognition capabilities. Compared to traditional segmented management models, this approach connects the data chain from construction to use, laying a complete information foundation for subsequent dynamic monitoring and intelligent assessment, and avoiding data breakpoints and data distortion.

[0025] Through the monitoring data collection and BIM mapping process from S2 to S3, the dynamic binding and visual mapping of sensor data and structural components are achieved, forming a true "structure-data fusion model". This dynamic BIM model can present the evolution of the structural state in real time, realizing the upgrade from a static design model to a dynamic operation and maintenance model, and effectively solving the problem of "demolding operation" of the existing BIM model during the operation and maintenance stage. In step S4, a structural indicator system is constructed based on component health data and normalized structural parameters, and a comprehensive structural dynamic health index SDH is generated, breaking through the limitations of traditional reliance on manual inspections or single-point monitoring. This method can comprehensively reflect micro-scale degradation trends such as microcrack extension, permeability degradation, and stiffness changes, thereby improving the sensitivity and foresight of structural risk identification.

[0026] In S5, by evaluating the temporal changes of structural indicators and constructing the trend response offset prediction value Prisk, a quantitative judgment of the future healthy evolution direction of the structure is achieved, and the ability to warn of trends and predict development trends is provided. Compared with traditional delayed responsive maintenance, this mechanism has the ability to intervene in advance, which is a key shift from "passive maintenance" to "active warning". In S6, based on the level determination results of the trend response offset prediction value Prisk, the system automatically marks high-risk components and generates inspection work orders, dispatches maintenance resources, and even links video surveillance, realizing automatic closed-loop management of the entire process from monitoring-analysis-response-intervention. This mechanism significantly reduces human dependence and response delays, improves the efficiency of risk response of water conservancy project structures, and has the practicality and scalability to be deployed in large and complex engineering systems. Example

[0027] This embodiment is explained in Example 1, please refer to Figure 1 Specifically, S1 includes the following steps: S11. Convert the 2D CAD drawings and 3D models in the water conservancy project design phase into recognizable BIM structural entities and obtain normalized component parameters; S12. Synchronously connect the construction records, including construction sequence, process method and pouring temperature curve, to the BIM platform; S13, inputting the normalized component parameters, including material density Pm, microstructure elastic modulus Em, and water vapor partial pressure Pv, into the BIM platform; S14. Map the historical operation and maintenance logs to the corresponding components in chronological order, and connect them to the BIM platform to build a BIM structural semantic framework.

[0028] S2 includes S21 and S22; S21. By deploying multiple sensors in component nodes, including laser displacement sensors, fiber-optic distributed humidity sensors, fiber-optic distributed temperature sensors, piezoelectric ceramic sensor coupling devices, and fiber-optic strain gauges, component structural health parameters are collected, including microscale crack propagation velocity Vc, component internal relative humidity RH, component internal temperature RT, component dynamic stiffness degradation rate KG, and displacement-strain coupling phase offset angle Hea, and fit them into the original data set W; Among them, the microscale crack expansion velocity Vc is acquired by laser displacement sensor: The microscale crack propagation velocity Vc is obtained by the following formula: ; Where Lc(t) represents the length of microscale cracks at time t, Lc(t+Δt) represents the length of microscale cracks at time (t+Δt), and Δt represents the time interval; The relative humidity RH inside the component is collected and obtained through optical fiber distributed humidity sensors; The internal temperature RT of the component is acquired through optical fiber distributed temperature sensors; The dynamic stiffness degradation rate KG of the component is acquired through the piezoelectric ceramic sensor coupling device; The dynamic stiffness degradation rate KG of the component is obtained by the following formula: ; Where K(t) represents the equivalent stiffness of the component at time t, and K(t-Δt) represents the equivalent stiffness of the component at time (t-Δt); The displacement-strain coupling phase offset angle Hea is acquired by optical fiber strain gauge and laser displacement sensor.

[0029] S22, preprocessing the obtained original data set W, including noise filtering, missing interpolation and normalization, to obtain a healthy data set KW; Noise filtering is performed by using the median filter method to remove the noise in the original data set W; Missing data interpolation is performed by using linear interpolation to fill in the missing data in the original dataset W; Normalization processing is performed by using the Min-Max normalization method to process the data in the original data set W to obtain the healthy data set KW; The health data set KW is obtained by the following formula: ; Where KWa represents the a-th data item in the healthy dataset KW, Wa represents the a-th data item in the original dataset W, minWa represents the valley value of the a-th data item in the original dataset W, and maxWa represents the peak value of the a-th data item in the original dataset W.

[0030] In this embodiment, through S11 to S14 in step S1, the present invention effectively integrates the design model, including 2D CAD and 3D models, construction process information, including construction sequence, process method and pouring temperature, materials and physical parameters, such as material density Pm, elastic modulus Em, and water vapor partial pressure Pv, and historical operation and maintenance data, and uniformly connects them to the BIM platform. This mechanism not only breaks the barriers of traditional information being scattered across multiple systems and formats, but also achieves component-level lifecycle data integration by building a structural semantic framework, providing a high-precision anchor point for binding subsequent monitoring data to the structural model, significantly improving the "expression depth" of the model and the integrity of engineering information.

[0031] Through S21 in step S2, the system deploys a variety of highly sensitive sensing devices at component nodes, including laser displacement sensors, fiber-optic distributed humidity and temperature sensors, piezoelectric ceramic responders, and fiber-optic strain gauges. These devices can cover health indicators in multiple dimensions, ranging from microscale crack propagation velocity Vc, component internal relative humidity RH, component internal temperature RT, component dynamic stiffness degradation rate KG, to displacement-strain coupling phase offset angle Hea. This deployment method comprehensively overcomes the shortcomings of traditional inspections or single physical quantity monitoring methods in acquiring in-depth information, achieving "quasi-real-time, multi-parameter, and high spatial resolution" perception coverage of the entire process of structural performance degradation, providing more comprehensive physical evidence support for structural safety assessments.

[0032] By introducing data processing strategies such as median filtering, linear interpolation, and Min-Max normalization, the raw sensor dataset W is preprocessed to form a healthy dataset KW. This process significantly improves data stability, integrity, and comparability, avoiding anomalies in field data caused by sensor fluctuations, acquisition delays, and missed measurements, laying a solid foundation for subsequent data analysis and trend modeling. Compared to the data clutter and modeling difficulties common in traditional monitoring systems, this method establishes a unified health data standard system that can serve as a core data source for higher-level applications such as dynamic structural assessment, early warning models, and visualization mapping. Example

[0033] This embodiment is explained in Example 2, please refer to Figure 1 Specifically: S3. Set a unique identifier for the component node in the BIM structure semantic framework: component number ID; and bind it with the health data set KW to establish a mapping relationship: ; Where IDi,t represents the i-th component number at time t, KWj(t) represents the j-th sample data in the health data set KW at time t; Perform formal transformation on the health map of each component node: ; Where Mi(t) represents the state tuple of the i-th component at time t, IDi represents the i-th component number at time t, and (Xi, Yi, Zi) represents the spatial coordinates of the i-th component in the model; A health visualization engine is embedded in the BIM interface to read the status tuple Mi(t) of the i-th component at time t and perform model rendering. Dynamically control the color, brightness, and transparency of components based on the combined value of the jth sample data KWj(t) in the health dataset KW at time t; and construct a dynamic BIM model. Control the component color through the color mapping function: ; Where Ci(t) represents the RGB color value vector of the i-th component at time t, and Si(t) represents the comprehensive risk status score of the i-th component at time t; The comprehensive risk status score Si(t) of the i-th component at time t is obtained by the following formula: ; Where, Represents the preset weight value of the j-th sample data in the health dataset KW.

[0034] In this embodiment, a mapping relationship between components and data is established by setting a unique identifier ID for each component node and binding it to the corresponding monitoring data in the health dataset KW. This binding mechanism eliminates the need for each piece of sensor data to be an unstructured "floating value" and instead represents an "entity health label" with clear structural semantics. This improvement overcomes the limitations of traditional BIM models, which are separated from monitoring systems and cannot align data, and achieves integrated structure-data management, providing precise support for advanced functions such as health trend tracking and state evolution analysis. By introducing the form of a state tuple to express the three-dimensional spatial position and health status of a component at any time, and embedding a health visualization engine to control its rendering color, brightness, and transparency, the component's state is dynamically expressed over time in the BIM interface. This innovation achieves a transition from static drawings to dynamic health images, enabling maintenance personnel to intuitively identify risky components and perceive structural change trends, greatly improving the responsiveness and predictability of operation and maintenance work.

[0035] In this embodiment, the system dynamically calculates a comprehensive component risk status score by assigning preset weights to various indicators in the health dataset. This score directly drives the RGB color values ​​of the visual rendering, allowing component colors to not only convey the risk level but also reflect nuances of risk. This adjustable weighted scoring mechanism is configurable and scalable, allowing adjustments based on component category, importance level, or operational requirements. This significantly enhances the system's sensitivity to multiple risk states and its ability to provide personalized representations. Example

[0036] This embodiment is explained in Example 3, please refer to Figure 1 and Figure 3 , specifically: S4 includes S41 and S42; S41. Feature extraction of the healthy dataset KW and normalized structural parameters is performed using a dynamic BIM model to construct structural indices, including the microcrack growth rate index F1, the moisture-heat penetration combined degradation index F2, and the dynamic stiffness phase shift index F3. Among them, the microcrack growth rate index F1 represents the rate of crack growth under unit structural strength; The microcrack growth rate index F1 is obtained by the following formula: ; Where Vc represents the microscale crack propagation velocity, Em represents the microstructural elastic modulus, and Pm represents the material density; The combined moisture-heat penetration degradation index F2 reflects the risk of water penetration, salt precipitation, and expansion of the material's internal microstructure caused by a moist heat environment. The combined moisture and heat penetration degradation index F2 is obtained by the following formula: ; Where RH represents the relative humidity inside the component, RT represents the internal temperature of the component, ln represents the natural logarithm with constant e as the base, Pv represents the water vapor partial pressure, and Ps represents the saturated water vapor pressure; The dynamic stiffness phase shift index F3 represents the frequency and response angle of capturing component stiffness changes; The dynamic stiffness phase shift index F3 is obtained by the following formula: ; Where KG(t) represents the dynamic stiffness vector of the component, KG(t-Δt) represents the dynamic stiffness vector of the component at time (t-Δt), and arccos represents the inverse cosine function.

[0037] S42, fusing the obtained microcrack growth rate index F1, the moisture-heat penetration combined degradation index F2, and the dynamic stiffness phase shift index F3 to calculate and obtain the comprehensive structural dynamic health index SDH; The comprehensive structural dynamic health index SDH is obtained by the following formula: ; Where tan() represents the tangent function.

[0038] Specific examples: Table 1: Structural index fusion and SDH calculation results; Group number Microcrack growth rate index F1 Moisture and heat penetration combined deterioration index F2 Dynamic stiffness phase shift index F3 Comprehensive structural dynamic health index SDH Group 1 0.2 0.25 0.1 1.071 Group 2 0.35 0.45 0.25 1.184 Group 3 0.55 0.6 0.4 1.331 Group 4 0.7 0.8 0.55 1.488 Group 5 0.9 0.95 0.7 1.718 In this example, health data extracted from the dynamic BIM model is deeply integrated with normalized structural parameters to construct three structural indicators: the microcrack growth rate index F1, the combined moisture-heat penetration degradation index F2, and the dynamic stiffness phase shift index F3. This multidimensional feature system not only covers key risk sources such as material microdegradation, environmental coupling effects, and sudden mechanical response, but also breaks away from the traditional, crude approach of relying on single sensor data for health assessment. It provides a digital representation model with physical interpretation for structural risks, significantly enhancing the scientific nature and controllability of water conservancy project health assessments.

[0039] This approach goes beyond analyzing a single structural parameter. Instead, by constructing the combined moisture-heat penetration degradation index F2 and the dynamic stiffness phase shift index F3, it considers the temporal correlation and nonlinear response characteristics between the structure's actual service environment and its dynamic response. This truly embodies the three-dimensional collaborative perception concept of "structure-environment-operation." This mechanism effectively identifies risk scenarios such as "non-destructive degradation," "environmentally induced anomalies," and "fatigue accumulation mutations," which are difficult to monitor using traditional technologies.

[0040] Through the nonlinear fusion of the microcrack growth rate index F1, the combined moisture-heat penetration degradation index F2, and the dynamic stiffness phase shift index F3 in step S42, a comprehensive health index (SDH) is formed that simultaneously reflects crack evolution, moisture penetration effects, and dynamic response anomalies. This index features continuous numerical expression, risk evolution trends, and controllable upper and lower limits, resolving the difficulty of establishing a unified judgment standard in traditional structural health assessments. Its output can be directly used for system alarm threshold setting, trend warning model input, and operation and maintenance resource scheduling, forming a key hub for the system's "quantified health perception → intelligent judgment → proactive response" approach. Example

[0041] This embodiment is explained in Example 4. Please refer to Figure 1 and Figure 2 ,Specifically: S5 includes S51 and S52; S51. Collect the health index sequence {SDHi(t1), SDHi(t2), ..., SDHi(tn)} of each component at multiple consecutive time points; and calculate the trend derivative in combination with the microcrack growth rate index F1 and the moisture-heat penetration combined degradation index F2 to obtain a first-order evolution trend image of the structural state; The component health value change trend slope xSDH is calculated by using the comprehensive structural dynamic health index SDH at multiple time points; The component health value change trend slope xSDH is obtained by the following formula: ; Where SDHi(t) represents the comprehensive structural dynamic health index of the i-th component at time t, SDHi(t-Δt) represents the comprehensive structural dynamic health index of the i-th component at time (t-Δt), and d represents the derivative symbol; The microcrack growth rate index change rate ΔF1 is obtained by calculating the trend derivative of the microcrack growth rate index F1; The microcrack growth rate exponential change rate ΔF1 is obtained by the following formula: ; Where, F1(t) represents the microcrack growth rate index at time t, and F1(t-Δt) represents the microcrack growth rate index at time (t-Δt); The trend derivative of the moisture-heat-penetration combined degradation index F2 is calculated to obtain the moisture-heat-penetration combined degradation index change rate ΔF2; The change rate of the combined moisture and heat penetration degradation index ΔF2 is obtained by the following formula: ; Where F2(t) represents the combined degradation index of moisture and heat penetration at time t, and F2(t-Δt) represents the combined degradation index of moisture and heat penetration at time (t-Δt).

[0042] S52, calculating and obtaining a trend response offset prediction value Prisk based on the obtained microcrack growth rate index change rate ΔF1 and the moisture-heat penetration combined degradation index change rate ΔF2 and the comprehensive structural dynamic health index SDH; The trend response offset prediction value Prisk is obtained by the following formula: ; Where, and They respectively represent the preset weight values ​​of the microcrack growth rate index change rate ΔF1 and the moisture-heat penetration combined degradation index change rate ΔF2.

[0043] S6. Compare the obtained trend response deviation prediction value Prisk with a preset response deviation threshold Trs to obtain a risk level of the trend response deviation prediction value Prisk; The risk level of the trend response deviation prediction value Prisk is obtained by matching in the following way: When 0 < trend response offset prediction value Prisk < response offset threshold Trs*0.5, it indicates the first risk level and the component is in a stable structural health state; When the response offset threshold Trs*0.5≤trend response offset prediction value Prisk≤response offset threshold Trs, it indicates the second risk level and the component is in a normal state; When the response offset threshold Trs < the trend response offset prediction value Prisk < the response offset threshold Trs*1.5, it indicates the third risk level and the component is in an abnormal state; When the response offset threshold Trs*1.5≤trend response offset prediction value Prisk≤response offset threshold Trs*2, it indicates the fourth risk level and the component is in a high-risk state; When the trend response deviation prediction value Prisk is at the fourth level, the component is marked as red high risk in the BIM model, and maintenance resources are dispatched, inspection work orders are generated, and multi-source video surveillance is triggered; Among them, the inspection work order includes the component number ID, status details and priority level.

[0044] In this embodiment, through health index sequence analysis and trend derivative calculation in S51, the system not only focuses on the static health score at a given moment, but also identifies the directionality and rate characteristics of structural performance changes. The introduction of the slope of the health value trend, the rate of change of the microcrack index, and the rate of change of the hygrothermal index enables the system to dynamically determine whether the structure is in a stage of accelerated degradation, providing a quantitative basis for early identification of hidden dangers. This mechanism overcomes the limitations of traditional "static judgment and critical alarm" methods, shifting risk assessment from a "results-oriented" to a "trend-driven" approach.

[0045] By combining the changing microcrack propagation rate and the trends of moisture-heat infiltration degradation, a trend response offset prediction value was constructed, forming a composite indicator with evolutionary predictive capabilities. This trend-centric modeling logic effectively identifies components in the structural operation state that have not yet reached the alarm threshold but have already shown risk propensity, enabling early quantification of potential risks. This mechanism can be widely applied to early warning systems for structures such as dams, gates, and diversion tunnels, enhancing predictive management and control capabilities during the service life of the structures.

[0046] This implementation scientifically categorizes risk into four levels by matching predicted trend response deviations with set thresholds. This system then integrates with the BIM system to visually mark risky components, automatically generate inspection work orders, intelligently schedule maintenance resources, and trigger the video surveillance system. This closed-loop response chain directly translates assessment results into operational and maintenance action instructions, significantly reducing human dependency and response delays, truly achieving an automated management closed loop from "identification" to "response."

[0047] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The full life cycle management method of water conservancy project buildings based on BIM technology analysis is characterized by: The following steps are involved: S1. Integrate the water conservancy project design model, construction records, normalized component parameters, and historical operation and maintenance logs into the BIM platform to build a BIM structural semantic framework; S2. Based on the BIM structural semantic framework, various sensors are deployed in the component nodes of the water conservancy project building to collect the structural health parameters of the components, and pre-process them to fit them into the health data set KW; S3, mapping the data in the health dataset KW to the corresponding BIM structure semantic framework in a time series manner to build a dynamic BIM model; S4. By using the dynamic BIM model, feature extraction is performed on the acquired health data set KW and normalized structural parameters, structural indicators are constructed, and the comprehensive structural dynamic health index SDH is calculated; S5. Perform risk analysis on the obtained comprehensive structural dynamic health index SDH, evaluate future risk trends in combination with structural indicators, and calculate the trend response deviation prediction value Prisk; S6. Comprehensively analyze the obtained trend response offset prediction value Prisk. When the trend response offset prediction value Prisk is abnormal, mark it as a red high-risk component, dispatch maintenance resources, generate inspection work orders, and trigger multi-source video surveillance.

2. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 1 is characterized in that: S1 includes the following steps: S11. Convert the 2D CAD drawings and 3D models in the water conservancy project design phase into recognizable BIM structural entities and obtain normalized component parameters; S12. Synchronously connect the construction records, including construction sequence, process method and pouring temperature curve, to the BIM platform; S13, inputting the normalized component parameters, including material density Pm, microstructure elastic modulus Em, and water vapor partial pressure Pv, into the BIM platform; S14. Map the historical operation and maintenance logs to the corresponding components in chronological order, and connect them to the BIM platform to build a BIM structural semantic framework.

3. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 2 is characterized by: S2 includes S21 and S22; S21. By deploying multiple sensors in component nodes, including laser displacement sensors, fiber-optic distributed humidity sensors, fiber-optic distributed temperature sensors, piezoelectric ceramic sensor coupling devices, and fiber-optic strain gauges, component structural health parameters are collected, including microscale crack propagation velocity Vc, component internal relative humidity RH, component internal temperature RT, component dynamic stiffness degradation rate KG, and displacement-strain coupling phase offset angle Hea, and fit them into the original data set W; Among them, the microscale crack expansion velocity Vc is acquired by laser displacement sensor: The relative humidity RH inside the component is collected and obtained through optical fiber distributed humidity sensors; The internal temperature RT of the component is acquired through optical fiber distributed temperature sensors; The dynamic stiffness degradation rate KG of the component is acquired through the piezoelectric ceramic sensor coupling device; The displacement-strain coupling phase offset angle Hea is acquired by optical fiber strain gauge and laser displacement sensor.

4. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 3 is characterized by: S22, preprocessing the obtained original data set W, including noise filtering, missing interpolation and normalization, to obtain a healthy data set KW; Noise filtering is performed by using the median filter method to remove the noise in the original data set W; Missing data interpolation is performed by using linear interpolation to fill in the missing data in the original dataset W; Normalization processing is performed by using the Min-Max normalization method to process the data in the original data set W to obtain the healthy data set KW; The health data set KW is obtained by the following formula: ； Where KWa represents the a-th data item in the healthy dataset KW, Wa represents the a-th data item in the original dataset W, minWa represents the valley value of the a-th data item in the original dataset W, and maxWa represents the peak value of the a-th data item in the original dataset W.

5. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 4 is characterized in that: S3. Set a unique identifier for the component node in the BIM structural semantic framework: component number ID; and bind it to the health dataset KW to establish a mapping relationship: ； Where IDi,t represents the i-th component number at time t, KWj(t) represents the j-th sample data in the health data set KW at time t; Perform formal transformation on the health map of each component node: ； Where Mi(t) represents the state tuple of the i-th component at time t, IDi represents the i-th component number at time t, and (Xi, Yi, Zi) represents the spatial coordinates of the i-th component in the model; A health visualization engine is embedded in the BIM interface to read the status tuple Mi(t) of the i-th component at time t and perform model rendering. Dynamically control the color, brightness, and transparency of components based on the combined value of the jth sample data KWj(t) in the health dataset KW at time t; and construct a dynamic BIM model. Control the component color through the color mapping function: ； Where Ci(t) represents the RGB color value vector of the i-th component at time t, and Si(t) represents the comprehensive risk status score of the i-th component at time t; The comprehensive risk status score Si(t) of the i-th component at time t is obtained by the following formula: ； Where, Represents the preset weight value of the j-th sample data in the health dataset KW.

6. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 5 is characterized by: S4 includes S41 and S42; S41. Feature extraction of the healthy dataset KW and normalized structural parameters is performed using a dynamic BIM model to construct structural indices, including the microcrack growth rate index F1, the moisture-heat penetration combined degradation index F2, and the dynamic stiffness phase shift index F3. Among them, the microcrack growth rate index F1 represents the rate of crack growth under unit structural strength; The microcrack growth rate index F1 is obtained by the following formula: ； Where Vc represents the microscale crack propagation velocity, Em represents the microstructural elastic modulus, and Pm represents the material density; The combined moisture-heat penetration degradation index F2 reflects the risk of water penetration, salt precipitation, and expansion of the material's internal microstructure caused by a moist heat environment. The combined moisture and heat penetration degradation index F2 is obtained by the following formula: ； Where RH represents the relative humidity inside the component, RT represents the internal temperature of the component, ln represents the natural logarithm with constant e as the base, Pv represents the water vapor partial pressure, and Ps represents the saturated water vapor pressure; The dynamic stiffness phase shift index F3 represents the frequency and response angle of capturing component stiffness changes; The dynamic stiffness phase shift index F3 is obtained by the following formula: ； Where KG(t) represents the dynamic stiffness vector of the component, KG(t-Δt) represents the dynamic stiffness vector of the component at time (t-Δt), arccos represents the inverse cosine function, and Δt represents the time interval.

7. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 6 is characterized in that: S42, fusing the obtained microcrack growth rate index F1, the moisture-heat penetration combined degradation index F2, and the dynamic stiffness phase shift index F3 to calculate and obtain the comprehensive structural dynamic health index SDH; The comprehensive structural dynamic health index SDH is obtained by the following formula: ； Where tan() represents the tangent function.

8. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 7 is characterized in that: S5 includes S51 and S52; S51. Collect the health index sequence {SDHi(t1), SDHi(t2), ..., SDHi(tn)} of each component at multiple consecutive time points; and calculate the trend derivative in combination with the microcrack growth rate index F1 and the moisture-heat penetration combined degradation index F2 to obtain a first-order evolution trend image of the structural state; The component health value change trend slope xSDH is calculated by using the comprehensive structural dynamic health index SDH at multiple time points; The microcrack growth rate index change rate ΔF1 is obtained by calculating the trend derivative of the microcrack growth rate index F1; The change rate ΔF2 of the moisture-heat-penetration combined degradation index is obtained by performing trend derivative calculation on the moisture-heat-penetration combined degradation index F2.

9. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 2 is characterized by: S52, calculating and obtaining a trend response offset prediction value Prisk based on the obtained microcrack growth rate index change rate ΔF1 and the moisture-heat penetration combined degradation index change rate ΔF2 and the comprehensive structural dynamic health index SDH; The trend response offset prediction value Prisk is obtained by the following formula: ； Where, and They respectively represent the preset weight values ​​of the microcrack growth rate index change rate ΔF1 and the moisture-heat penetration combined degradation index change rate ΔF2.

10. The method for managing the entire life cycle of a water conservancy project building based on BIM technology analysis according to claim 1 is characterized in that: S6. Compare the obtained trend response deviation prediction value Prisk with a preset response deviation threshold Trs to obtain a risk level of the trend response deviation prediction value Prisk; The risk level of the trend response deviation prediction value Prisk is obtained by matching in the following way: When 0 < trend response offset prediction value Prisk < response offset threshold Trs*0.5, it indicates the first risk level and the component is in a stable structural health state; When the response offset threshold Trs*0.5≤trend response offset prediction value Prisk≤response offset threshold Trs, it indicates the second risk level and the component is in a normal state; When the response offset threshold Trs < the trend response offset prediction value Prisk < the response offset threshold Trs*1.5, it indicates the third risk level and the component is in an abnormal state; When the response offset threshold Trs*1.5≤trend response offset prediction value Prisk≤response offset threshold Trs*2, it indicates the fourth risk level and the component is in a high-risk state; When the trend response deviation prediction value Prisk is at the fourth level, the component is marked as red high risk in the BIM model, and maintenance resources are dispatched, inspection work orders are generated, and multi-source video surveillance is triggered; Among them, the inspection work order includes the component number ID, status details and priority level.

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