Road engineering survey data analysis method and system

Abstract

The invention relates to the technical field of road survey, and particularly discloses a road engineering survey data analysis method and system, and the method comprises the steps: obtaining multi-source survey data of a target road; obtaining a parameter uncertainty set according to the multi-source survey data; generating a parameter combination sample according to the parameter uncertainty set; obtaining performance evolution data according to the parameter combination sample; generating an index evaluation result about the key performance index according to the performance evolution data; generating a risk quantification result according to the index evaluation result and a preset risk threshold value; according to the method, acquisition of multi-source survey data is combined with parameter uncertainty quantification, road performance dynamic evolution simulation and risk quantitative evaluation based on risk probability; therefore, the problems of low multi-source heterogeneous survey data integration efficiency, insufficient parameter uncertainty quantization, inaccurate road performance dynamic evolution simulation and poor long-term risk assessment reliability in existing road engineering are solved.

Description

Technical Field

[0001] This application relates to the field of road surveying technology, and more specifically, to a method and system for analyzing road engineering surveying data. Background Technology

[0002] The road engineering field is moving towards refined design and full life-cycle management. With advancements in surveying technology and the increasing availability of multi-source data, including geological, hydrological, traffic, and meteorological information, a foundation has been laid for improving project quality and efficiency. By integrating this data, the industry can better optimize structural layer design, material selection, and cost control, driving the transformation of engineering practices towards data-driven approaches and contributing to the achievement of sustainable development goals.

[0003] However, current technologies face numerous challenges in processing and integrating these multi-source, heterogeneous survey data. In the planning, design, construction evaluation, and life-cycle management of road engineering projects, existing methods have significant limitations in quantifying the uncertainties of engineering parameters, making it difficult to comprehensively capture inherent fluctuations and measurement errors. Simultaneously, existing technologies struggle to accurately simulate the dynamic evolution of road structural performance over time, especially under the coupling of multiple factors, where performance deviations are difficult to effectively identify and predict, leading to insufficient stability in prediction results. These shortcomings further affect the accurate assessment and reliable decision-making regarding long-term road risks, creating bottlenecks in the precise prediction and quantitative risk management of road life-cycle performance.

[0004] There is currently no effective technical solution to the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for analyzing road engineering survey data, in order to solve the problems of low efficiency in integrating multi-source heterogeneous survey data, insufficient quantification of parameter uncertainty, inaccurate simulation of dynamic evolution of road performance, and poor reliability of long-term risk assessment in existing road engineering.

[0006] In a first aspect, this application provides a method for analyzing road engineering survey data, the method comprising the following steps: S1. Obtain multi-source survey data of the target road; S2. Based on the type and characteristics of the multi-source survey data, analyze the statistical distribution characteristics or uncertainty range of each parameter in the multi-source survey data to obtain the parameter uncertainty set; S3. Generate parameter combination samples based on the set of parameter uncertainties; S4. Calculate the mechanical response of the target road under different load and environmental conditions based on the parameter combination sample, and couple the mechanical response with time factors to simulate the change path of road structure performance over time, and obtain performance evolution data. S5. Generate evaluation results for key performance indicators based on the performance evolution data; S6. Based on the evaluation results of the indicators and the preset risk threshold, calculate the risk probability that the target road will fall below the risk threshold within the pre-input lifespan, and generate a risk quantification result based on the risk probability.

[0007] The road engineering survey data analysis method, wherein step S2 includes: S21. Based on the source, format, and spatiotemporal scale of the multi-source survey data, identify the data types to classify the multi-source survey data and obtain multiple data subsets; S22. Identify the engineering parameters reflected by each data subset according to the data type; S23. Perform statistical processing on the engineering parameters corresponding to the data subset based on spatial variability or temporal correlation to obtain statistical distribution characteristics, or determine the uncertainty range of the engineering parameters based on the measurement error or inherent fluctuation range of the engineering parameters. S24. Integrate the statistical distribution characteristics or uncertainty intervals corresponding to each data subset to obtain the parameter uncertainty set.

[0008] The road engineering survey data analysis method, wherein step S3 includes: S31. Identify the dependencies between various engineering parameters based on the statistical distribution characteristics or uncertainty intervals in the set of parameter uncertainties. S32. Determine the value range of each engineering parameter based on the distribution characteristics or uncertainty interval in the set of parameter uncertainties. S33. Perform stratified sampling on the value range of each engineering parameter to obtain stratified samples of each engineering parameter. S34. Based on the aforementioned dependency relationship, random sampling is used to randomly combine the stratified samples to generate parameter combination samples.

[0009] The road engineering survey data analysis method, wherein step S31 includes: S311. Based on the statistical distribution characteristics in the parameter uncertainty set, calculate the statistical correlation index between the engineering parameters, or, based on the uncertainty interval in the parameter uncertainty set, analyze the overlapping or synergistic change trend between the fluctuation ranges of the engineering parameters. S312. Identify the dependencies between the various engineering parameters based on the overlapping or synergistic change trends between the statistical correlation indicators or the fluctuation ranges.

[0010] The road engineering survey data analysis method, wherein step S4, the step of calculating the mechanical response of the target road under different load and environmental conditions based on the parameter combination sample, includes: S41. Based on the parameter combination sample, construct a structural mechanics analysis model for the target road. The structural mechanics analysis model determines the geometric parameters, material mechanics parameters, and interlayer connection characteristics of the road structure layers based on the parameter combination sample. S42. Based on different loads and environmental conditions, and using the principles of structural mechanics, calculate one or more of the stress, strain, displacement, and settlement of the structural mechanics analysis model, as the mechanical response under the corresponding load and environmental conditions.

[0011] The road engineering survey data analysis method, wherein step S4, the step of coupling the mechanical response with a time factor to simulate the change path of road structure performance over time and obtaining performance evolution data, includes: S43. Identify time-related key performance indicators based on the mechanical response, wherein the key performance indicators include one or more of fatigue life, bearing capacity, and settlement. S44. For the key performance indicators, extract the change relationships from a preset change relationship library, which includes the change relationships of various key performance indicators with respect to time degradation. S45. Based on the aforementioned relationship, calculate the change path of the key performance indicators over time within the pre-input lifespan to obtain the performance evolution data.

[0012] The road engineering survey data analysis method, wherein step S5 includes: S51. Perform statistical analysis on the performance evolution data of key performance indicators to generate the probability density function or cumulative distribution function of the key performance indicators. S52. Determine the confidence interval of the key performance indicator based on the probability density function or cumulative distribution function of the key performance indicator and in combination with the preset confidence level. S53. Integrate the probability density function or cumulative distribution function of the key performance indicators and the corresponding confidence intervals to obtain the evaluation results of the indicators.

[0013] The road engineering survey data analysis method, wherein step S52 includes: S521. Determine the quantiles corresponding to the preset confidence level based on the probability density function or cumulative distribution function of the key performance indicators. S522. Use the quantiles as the boundaries of the confidence intervals for the key performance indicators to determine the confidence intervals.

[0014] The road engineering survey data analysis method, wherein the index evaluation results include the probability density function or cumulative distribution function of key performance indicators and the corresponding confidence intervals, step S6 includes: S61. Based on the probability density function or cumulative distribution function of the key performance indicator and the preset risk threshold, calculate the probability that the key performance indicator of the target road is lower than the risk threshold within the pre-input lifespan, and obtain the risk probability. S62. Based on the risk probability and the confidence interval, determine the confidence range of the risk probability, and generate the risk quantification result based on the risk probability and the corresponding confidence range.

[0015] Secondly, this application also provides a road engineering survey data analysis system, the system comprising: The acquisition module is used to acquire multi-source survey data of the target road; The analysis module is used to analyze the statistical distribution characteristics or uncertainty range of each parameter in the multi-source survey data according to the type and characteristics of the multi-source survey data, so as to obtain the parameter uncertainty set; The sample generation module is used to generate parameter combination samples based on the parameter uncertainty set; The evolution module is used to calculate the mechanical response of the target road under different load and environmental conditions based on the parameter combination sample, and to couple the mechanical response with time factors to simulate the change path of road structure performance over time, thereby obtaining performance evolution data. An evaluation module is used to generate evaluation results for key performance indicators based on the performance evolution data. The risk quantification module is used to calculate the risk probability that the target road will fall below the risk threshold within a pre-input lifespan based on the evaluation results of the indicators and the preset risk threshold, and to generate a risk quantification result based on the risk probability.

[0016] As can be seen from the above, this application provides a method and system for analyzing road engineering survey data. The method of this application combines the acquisition of multi-source survey data with parameter uncertainty quantification, dynamic evolution simulation of road performance, and risk quantification assessment based on risk probability. This solves the problems of low efficiency in integrating multi-source heterogeneous survey data, insufficient parameter uncertainty quantification, inaccurate dynamic evolution simulation of road performance, and poor reliability of long-term risk assessment in existing road engineering projects. It achieves the effects of robust performance assessment, risk quantification, and decision support throughout the entire life cycle of roads. Attached Figure Description

[0017] Figure 1 A flowchart of a road engineering survey data analysis method provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the road engineering survey data analysis system provided in an embodiment of this application.

[0019] Attached reference numerals: 201, Acquisition module; 202, Analysis module; 203, Sample generation module; 204, Evolution module; 205, Evaluation module; 206, Risk quantification module. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] Firstly, please refer to Figure 1 This application provides a method for analyzing road engineering survey data in some embodiments, the method including the following steps: S1. Obtain multi-source survey data of the target road; S2. Based on the type and characteristics of multi-source survey data, analyze the statistical distribution characteristics or uncertainty intervals of each parameter in the multi-source survey data to obtain the parameter uncertainty set; S3. Generate parameter combination samples based on the parameter uncertainty set; S4. Calculate the mechanical response of the target road under different load and environmental conditions based on the parameter combination sample, and couple the mechanical response with time factors to simulate the change path of road structure performance over time, and obtain performance evolution data. S5. Generate evaluation results for key performance indicators based on performance evolution data; S6. Based on the indicator evaluation results and the preset risk threshold, calculate the risk probability that the target road will fall below the risk threshold within the pre-input lifespan, and generate risk quantification results based on the risk probability.

[0023] Specifically, multi-source survey data refers to various original information about road engineering obtained from different sources and using different technical means. It may include, but is not limited to, geological exploration data, hydrological monitoring data, traffic flow data, meteorological and environmental data, remote sensing image data, structural health monitoring data, etc., to provide comprehensive and uncertain original input for subsequent analysis and solve the problem of single data acquisition in the current situation.

[0024] More specifically, the parameter uncertainty set refers to the set formed by quantifying the statistical distribution characteristics or uncertainty intervals of engineering parameters extracted from multi-source survey data through statistical analysis or error assessment. It can be obtained using probabilistic statistical methods (such as normal distribution, log-normal distribution, Weibull distribution), interval analysis methods (such as determination based on measurement error and empirical range), or fuzzy mathematics methods. It is used to uniformly quantify and represent the inherent uncertainty of data from different sources and of different properties, solve the problem of insufficient parameter uncertainty quantification, and improve data fusion efficiency.

[0025] More specifically, parameter combination samples refer to a series of datasets representing possible combinations of values ​​in the parameter uncertainty space, generated by sampling methods based on the statistical distribution characteristics or uncertainty intervals of each parameter in the parameter uncertainty set. These datasets can be implemented using random or quasi-random sampling methods such as Monte Carlo sampling, Latin hypercube sampling, and importance sampling. They are used to comprehensively simulate the actual variability of engineering parameters, provide diverse input scenarios for mechanical response calculation, capture performance deviations under the coupling effect of multiple factors, and overcome the limitations of insufficient stability of prediction results.

[0026] More specifically, performance evolution data refers to the trajectory or dataset of road structure performance changes over time by simulating the mechanical response of the road structure under different loads and environmental conditions with time factors. It can use physical models (such as fatigue damage models, creep models), empirical models, or data-driven models (such as machine learning models) to simulate the changes in performance over time, and is used to predict the dynamic changes that reflect the long-term performance of the road structure, solve the problem of not being able to simulate the dynamic evolution of road performance over time, and provide a dynamic basis for risk assessment.

[0027] More specifically, risk probability refers to the likelihood that the key performance indicators of the target road will fall below a preset risk threshold within a preset lifespan, and is used to directly quantify the potential risk of road failure or substandard performance.

[0028] More specifically, risk quantification results refer to the quantitative information about risk provided based on the calculated risk probability. This information may include the risk probability itself and its confidence range. It can be expressed as the numerical value of the risk probability, the risk level, the position in the risk matrix, or a confidence interval containing the risk probability. It is used to provide clear and quantitative risk information to support decision-makers in making auxiliary decisions and improve the accuracy of risk assessment and the reliability of decisions.

[0029] Specifically, this method achieves robust assessment and risk quantification of road performance throughout its entire life cycle through a systematic approach. First, step S1 acquires various multi-source survey data of the target road, which forms the foundation for subsequent analysis. Next, step S2 conducts in-depth analysis of the engineering parameters contained in these multi-source data, identifying their statistical distribution characteristics or uncertainty intervals, thereby constructing a parameter uncertainty set. This ensures a comprehensive capture of the inherent uncertainties in the data. Step S3, based on this parameter uncertainty set, generates a series of parameter combination samples. These samples cover the potential variation space of the parameters, providing diverse input scenarios for subsequent simulations. Subsequently, step S4 uses these parameter combination samples to calculate the mechanical response of the target road under different loads and environmental conditions, and couples these mechanical responses with time factors to simulate the dynamic path of road structural performance changes over time, generating performance evolution data. This data reflects the performance degradation trend of the road throughout its entire lifespan. Step S5, based on the performance evolution data, generates evaluation results for key performance indicators, refining complex dynamic information into quantifiable performance states. Finally, step S6 combines the evaluation results of these indicators with the preset risk threshold to calculate the probability that the target road will fall below the risk threshold within the pre-input lifespan, and generates a risk quantification result based on this probability. The entire process forms a closed loop, starting with the quantification of uncertainty in the raw data, going through dynamic performance simulation, and finally outputting quantified risk information, providing data-driven decision support for the planning, design, construction, and management of road engineering projects.

[0030] Through the above steps, the method of this application effectively solves the problems of low efficiency in integrating multi-source heterogeneous survey data, insufficient quantification of parameter uncertainty, inaccurate simulation of dynamic evolution of road performance, and poor reliability of long-term risk assessment in existing road engineering. The method of this application can efficiently integrate multi-source survey data from different sources, formats, and spatiotemporal scales. By quantifying parameter uncertainty, it improves the accuracy of data fusion. Simultaneously, by coupling mechanical response with time factors, it achieves dynamic simulation of road structural performance changes over time, more accurately capturing performance deviations under the coupling effect of multiple factors. Finally, by calculating risk probability and generating risk quantification results, it provides clear and quantified risk information, significantly improving the accuracy of risk assessment and the reliability of decision-making, thereby achieving robust assessment, risk quantification, and decision support for the entire life cycle performance of roads.

[0031] The method in this application combines the acquisition of multi-source survey data with parameter uncertainty quantification, dynamic evolution simulation of road performance, and risk quantification assessment based on risk probability. This solves the problems of low efficiency in integrating multi-source heterogeneous survey data, insufficient parameter uncertainty quantification, inaccurate dynamic evolution simulation of road performance, and poor reliability of long-term risk assessment in existing road engineering. It achieves the effect of robust assessment of road performance throughout its entire life cycle, risk quantification, and decision support.

[0032] In some preferred embodiments, step S2 includes: S21. Based on the source, format, and spatiotemporal scale of the multi-source survey data, identify the data types to classify the multi-source survey data and obtain multiple data subsets; S22. Identify the engineering parameters reflected in each data subset based on the data type; S23. Perform statistical processing on the engineering parameters corresponding to the data subset based on spatial variability or temporal correlation to obtain statistical distribution characteristics, or determine the uncertainty range of the engineering parameters based on the measurement error or inherent fluctuation range of the engineering parameters. S24. Integrate the statistical distribution characteristics or uncertainty intervals corresponding to each data subset to obtain the parameter uncertainty set.

[0033] Specifically, step S21 addresses the inherent significant differences in source, format, and spatiotemporal scale of multi-source survey data by identifying and classifying data types, decomposing the original heterogeneous data into multiple data subsets with similar characteristics. This classification process forms the basis for subsequent precise analysis, avoiding information confusion and processing difficulties that may result from directly handling complex heterogeneous data. Subsequently, step S22, based on the classified data subsets, explicitly identifies the specific engineering parameters reflected in each subset, ensuring that subsequent uncertainty quantification accurately applies to the corresponding engineering attributes and avoids confusion or analytical bias between parameters. Next, step S23 performs refined uncertainty quantification on the engineering parameters corresponding to each data subset, including two parallel or optional methods: first, statistical processing based on the spatial variability or temporal correlation of the engineering parameters to capture their randomness and dynamic change patterns, thereby obtaining statistical distribution characteristics that conform to reality; second, directly determining the uncertainty interval based on the measurement error or inherent fluctuation range of the engineering parameters to cover the inaccuracies of data acquisition and the natural fluctuations of the parameters themselves. These two methods together ensure a comprehensive characterization of the uncertainty of the engineering parameters. Finally, step S24 integrates the statistical distribution characteristics or uncertainty intervals obtained from all data subsets to form a complete and structured set of parameter uncertainties. This set contains quantitative uncertainty information for all relevant engineering parameters, providing high-quality input for subsequent generation of parameter combination samples, calculation of mechanical response, and risk quantification.

[0034] The method presented in this application, through data classification and targeted analysis, ensures a more accurate and comprehensive acquisition of the statistical distribution characteristics or uncertainty ranges of various engineering parameters. Consequently, the constructed parameter uncertainty set possesses higher precision and completeness, significantly improving the accuracy and reliability of subsequent road life-cycle performance assessments and risk quantification results, effectively solving the problem of accurately quantifying the uncertainty of complex multi-source data in existing technologies.

[0035] In some preferred embodiments, step S3 includes: S31. Identify the dependencies between various engineering parameters based on the statistical distribution characteristics or uncertainty intervals in the parameter uncertainty set. S32. Determine the value range of each engineering parameter based on the distribution characteristics or uncertainty interval in the parameter uncertainty set; S33. Perform stratified sampling on the value range of each engineering parameter to obtain stratified samples of each engineering parameter. S34. Based on the dependency relationship, random sampling is used to randomly combine the stratified samples to generate parameter combination samples.

[0036] Specifically, dependency refers to the interrelationship or influence between different parameters in road engineering. It can be manifested as statistical correlation, such as positive or negative correlation, or as overlapping or synergistic change trends in parameter fluctuation ranges.

[0037] More specifically, stratified sampling is a sampling method that divides the range of values ​​of a population into several sub-intervals or "strata", and then draws samples independently from each sub-interval.

[0038] Specifically, the operational logic of this scheme is as follows: First, by identifying the dependencies between engineering parameters, the authenticity of subsequent sample combinations is ensured. Next, the value range of each parameter is defined, setting boundaries for sampling. Then, stratified sampling is performed for each parameter's value range to ensure comprehensive coverage of individual parameter samples. Finally, considering the identified dependencies, the stratified samples are randomly combined to generate parameter combination samples that accurately reflect the parameter uncertainty space and its inherent relationships.

[0039] More specifically, before generating parameter combination samples, it is necessary to identify potential dependencies between engineering parameters based on the existing statistical distribution characteristics or uncertainty intervals in the parameter uncertainty set. These dependencies reflect the interactions between parameters, such as the potential positive correlation between material strength and density. Accurately identifying these relationships is fundamental to generating parameter combination samples that conform to reality. Simultaneously, based on the distribution characteristics or uncertainty intervals in the parameter uncertainty set, the value range of each engineering parameter is determined. After determining the value range, stratified sampling is performed on the value range of each engineering parameter. Stratified sampling divides the value range of each parameter into multiple sub-intervals and draws samples from each sub-interval, ensuring that the entire value range of the parameter, including its extreme and intermediate values, is adequately covered with a limited sample size. This is crucial for capturing the impact of parameters on road performance under different values. Finally, based on the previously identified dependencies, random sampling is used to randomly combine these stratified samples. This combination is not a simple unconditional random pairing, but rather conditionally considers the correlation between parameters. For example, if two parameters are positively correlated, their sample values ​​will tend to change in the same direction when combined. This dependency-based combination method ensures that the generated parameter combination samples not only cover the uncertainty range of individual parameters, but more importantly, they reflect the real coupling effect and synergistic change trend between parameters.

[0040] By generating high-quality parameter combination samples, this approach provides a robust and reliable data foundation for subsequent analysis. This refined handling of parameter uncertainties and their dependencies enables subsequent mechanical response calculations and performance evolution simulations to accurately capture performance deviations under the coupling effects of multiple factors, thereby improving the accuracy and reliability of performance prediction and risk quantification management throughout the entire road lifecycle. This approach overcomes the problems of existing technologies where samples cannot comprehensively and accurately cover the uncertainty space and are difficult to capture performance deviations under the coupling effects of multiple factors, providing a solid foundation for robust assessment and risk quantification in road engineering.

[0041] In some preferred embodiments, step S31 includes: S311. Based on the statistical distribution characteristics in the parameter uncertainty set, calculate the statistical correlation index between each engineering parameter, or, based on the uncertainty interval in the parameter uncertainty set, analyze the overlapping or synergistic change trend between the fluctuation ranges of each engineering parameter. S312. Identify the dependencies between various engineering parameters based on the overlapping or synergistic change trends of statistical correlation indicators or fluctuation ranges.

[0042] Specifically, statistical correlation indicators refer to numerical values ​​that quantify the strength of the statistical association between two or more engineering parameters. These can be calculated using Pearson correlation coefficient, Spearman rank correlation coefficient, Kendall rank correlation coefficient, etc. Overlapping or synergistic trends in fluctuation ranges refer to the degree of numerical intersection of the uncertainty ranges of different engineering parameters, or the consistent or opposite directions of change they exhibit over time or under different conditions. This can be analyzed using methods such as interval intersection calculation, time series analysis, and scatter plot trend analysis. Identifying the dependencies between various engineering parameters can be achieved using methods such as setting thresholds, cluster analysis, and graph theory modeling.

[0043] Specifically, step S311 provides two parallel and complementary approaches to obtain quantitative information on the correlation between parameters based on the data form in the parameter uncertainty set. When parameters exist in the form of statistical distribution characteristics, the system calculates the statistical correlation index between them. When parameters exist in the form of uncertainty intervals, the system analyzes whether the fluctuation ranges of these parameters overlap or exhibit a coordinated changing trend, such as observing whether the value intervals of two parameters frequently increase or decrease simultaneously. These calculations and analyses provide an objective data foundation for subsequent dependency identification. Subsequently, step S312 analyzes these quantitative results to determine whether there is a dependency between parameters, the strength of the dependency, and the type of dependency, avoiding bias caused by subjective judgment and ensuring that the identified dependency relationships are more accurate and reliable.

[0044] The method presented in this application provides two detailed identification approaches: calculation based on statistical correlation indicators and trend analysis of fluctuation ranges. This ensures that information reflecting the interactions between parameters can be effectively extracted regardless of the form in which the engineering parameter data exists. This results in more accurate and comprehensive identification of dependencies, avoiding biases caused by subjective judgment. The accurately identified dependencies enable the subsequently generated parameter combination samples to more realistically reflect the multi-factor interactions of various parameters in road engineering, thereby significantly improving the accuracy and stability of the results for the entire road lifecycle performance assessment, mechanical response calculation, performance evolution simulation, and risk quantification.

[0045] In some preferred embodiments, step S4, which involves calculating the mechanical response of the target road under different load and environmental conditions based on the parameter combination sample, includes: S41. Based on the parameter combination sample, construct the structural mechanics analysis model of the target road. The structural mechanics analysis model determines the geometric parameters, material mechanics parameters and interlayer connection characteristics of the road structure layer based on the parameter combination sample. S42. Based on different loads and environmental conditions, and using the principles of structural mechanics, calculate one or more of the stress, strain, displacement, and settlement of the structural mechanics analysis model as the mechanical response under the corresponding load and environmental conditions.

[0046] Specifically, a structural mechanics analysis model refers to a mathematical or physical model used to simulate the mechanical behavior of road structures under external loads and environmental effects. It can be implemented using finite element models, discrete element models, or multi-layer elastic system models.

[0047] More specifically, geometric parameters refer to parameters describing the dimensions and shape of road structural layers, and may include layer thickness, width, slope, or cross-sectional shape. Material mechanics parameters refer to parameters describing the mechanical properties of the materials used in road structural layers, and may include elastic modulus, Poisson's ratio, compressive strength, tensile strength, or fatigue characteristics. Interlayer bonding characteristics refer to parameters describing the interaction and force transmission mechanisms between road structural layers, and may include interfacial friction coefficient, bond strength, or shear stiffness. Structural mechanics principles refer to the scientific theories and calculation methods used to analyze the deformation, stress, strain, and other responses of structures under load.

[0048] Specifically, step S41 uses the parameter combination samples containing uncertainty information obtained in the previous steps as the basis for constructing the target road structure mechanical analysis model. This means that the model is no longer based on a single deterministic value, but can reflect the possible variability of the geometric parameters, material mechanical parameters, and interlayer connection characteristics of the road structure layers. In this way, abstract parameter information is concretely transformed into a structural model that can be mechanically analyzed, ensuring that the model can fully reflect the various variability and uncertainties that may exist in actual engineering. Step S42, based on the structural mechanical analysis model constructed in step S41, uses the principles of structural mechanics to calculate the mechanical response of the road under different loads and environmental conditions. Load conditions can include traffic loads and self-weight loads, and environmental conditions can include temperature and humidity, thus comprehensively considering the complex factors that the road may face in actual operation. By calculating one or more of stress, strain, displacement, and settlement, this scheme can capture the road mechanical behavior under the coupled effects of multiple factors.

[0049] This application's method solves the problem of how to transform abstract parameter information into a concrete model capable of mechanical analysis by explicitly converting parameter combination samples containing uncertain information into a specific structural mechanics analysis model. By determining the geometric parameters, material mechanics parameters, and interlayer connection characteristics of the road structure layers based on parameter combination samples, it ensures that the constructed model can fully reflect the various variability and uncertainties that roads may encounter in actual engineering, making the mechanical response calculation more closely resemble reality. Furthermore, this application's method solves the problem of how to comprehensively and accurately calculate the road's mechanical response under multi-factor coupling by explicitly calculating mechanical responses such as stress, strain, displacement, and settlement under various load and environmental conditions using structural mechanics principles. This allows the calculation to fully consider the various complex factors that roads may face in actual operation, thus more accurately capturing the road's mechanical behavior under multi-factor coupling. Therefore, this application's method improves the accuracy of mechanical response calculation, fully reflecting the real behavior of roads under complex load and environmental conditions, thereby providing accurate and reliable input data for subsequent performance evolution simulation and risk assessment, improving the accuracy and reliability of the assessment.

[0050] In some preferred embodiments, step S4, which couples the mechanical response with a time factor to simulate the change path of road structure performance over time and obtain performance evolution data, includes: S43. Identify time-related key performance indicators based on mechanical response. Key performance indicators include one or more of fatigue life, bearing capacity, and settlement. S44. For key performance indicators, extract change relationships from a preset change relationship library. The change relationship library includes the change relationships of various key performance indicators with respect to time degradation. S45. Based on the changing relationships, calculate the change path of key performance indicators over time within the pre-input lifespan to obtain performance evolution data.

[0051] Specifically, the pre-defined change relationship database refers to a collection that stores the degradation patterns of various engineering performance parameters over time. It includes mathematical models, empirical curves, or data tables showing the changes in key performance indicators (such as fatigue life, load-bearing capacity, and settlement) of different types of road materials and structures under different environmental conditions. This database can be implemented using databases, lookup tables, or sets of algorithms, and these relationships can be established based on historical monitoring data, laboratory accelerated aging test results, or theoretical derivation models.

[0052] Specifically, the above steps ensure the accuracy and reliability of performance evolution data by identifying key performance indicators, utilizing a pre-established change relationship database, and calculating detailed evolution paths. Step S43 uses the mechanical response calculated in the previous step to identify key performance indicators strongly correlated with time. This identification process is a targeted identification driven by the actual mechanical behavior of the road structure, focusing on indicators such as fatigue life, bearing capacity, or settlement. Step S44 extracts specific change relationships corresponding to the identified key performance indicators by accessing a pre-established change relationship database, ensuring that the simulation process is based on existing engineering knowledge, empirical data, or theoretical models. Step S45 transforms the abstract degradation relationships into specific, quantified performance data time series. The output is not a general trend, but a detailed evolution curve or dataset, showing the dynamic changes in road performance throughout its service life.

[0053] Through the above processing, the method of this application uses the extracted relationships to calculate the precise path of key performance indicators changing over time within a preset lifespan, generating performance evolution data with detailed and quantitative characteristics, accurately capturing the dynamic changes in road performance, effectively improving the precision and accuracy of performance evolution data, and being able to capture performance deviations under the coupling effect of multiple factors, thereby improving the stability of prediction results.

[0054] In some preferred embodiments, step S5 includes: S51. Perform statistical analysis on the performance evolution data of key performance indicators to generate the probability density function or cumulative distribution function of key performance indicators. S52. Determine the confidence interval of the key performance indicators based on the probability density function or cumulative distribution function of the key performance indicators and in combination with the preset confidence level. S53. Integrate the probability density function or cumulative distribution function of the key performance indicators and the corresponding confidence intervals to obtain the indicator evaluation results.

[0055] Specifically, the probability density function describes the probability of a continuous random variable taking a value near a specific point, while the cumulative distribution function describes the probability of a random variable taking a value less than or equal to a specific value. The confidence level indicates the reliability to which the corresponding interval contains the true parameter value, and can be set as a percentage.

[0056] Specifically, step S51, through statistical analysis of performance evolution data, transcends the evaluation of a single numerical value, generating probability density functions or cumulative distribution functions for key performance indicators (KPIs). This comprehensively describes the statistical distribution characteristics of KPIs, revealing the range of possible values ​​and the probability of each value, thus fully quantifying performance uncertainty and laying the foundation for robust evaluation. Step S52 utilizes the probability density function or cumulative distribution function, combined with a pre-set confidence level, to determine the confidence interval of the KPIs. The confidence interval provides a clear range, indicating the interval within which the true value of the KPI might fall at a given confidence level. This directly addresses the problem of insufficient stability in the evaluation results. By quantifying the reliability range of the evaluation results, the prediction of road performance becomes more reliable and convincing, avoiding the bias that may arise from a single predicted value. Finally, step S53 organically combines the probability distribution information obtained in S51 with the confidence interval determined in S52 to form the final KPI evaluation result. This comprehensive evaluation result can more accurately reflect the performance fluctuations and deviations under the coupling effect of multiple factors, providing a more accurate and robust input for risk probability calculation and risk quantification in the subsequent step S6.

[0057] In some preferred embodiments, step S52 includes: S521. Determine the quantiles corresponding to the preset confidence level based on the probability density function or cumulative distribution function of the key performance indicators. S522. Use quantiles as the boundaries of the confidence intervals for key performance indicators to determine the confidence intervals.

[0058] Specifically, quantiles refer to the numerical points that divide data into a specific proportion in a probability distribution.

[0059] Specifically, step S521 uses the generated probability density function or cumulative distribution function of the key performance indicator (KPI) and a pre-set confidence level to calculate the corresponding quantiles. The probability density function or cumulative distribution function provides complete information about the distribution of KPI values, while the confidence level defines the probability that the expected confidence interval contains the true value. Using this information, the numerical point corresponding to a specific cumulative probability, i.e., the quantile, can be accurately located from the probability distribution. Subsequently, step S522 uses the quantiles determined in step S521 directly as the boundaries of the KPI confidence interval, forming a complete performance evaluation chain. In this way, this solution solves the problem of accurately defining the boundaries of the KPI confidence interval in the context of complex engineering data, making the entire evaluation process more robust and reliable.

[0060] In some preferred embodiments, step S6 includes: S61. Based on the probability density function or cumulative distribution function of the key performance indicators and the preset risk threshold, calculate the probability that the key performance indicators of the target road are lower than the risk threshold within the pre-input lifespan, and obtain the risk probability. S62. Based on the risk probability and confidence interval, determine the confidence range of the risk probability, and generate the risk quantification result based on the risk probability and the corresponding confidence range.

[0061] Specifically, the confidence range of risk probability refers to the interval within which the calculated true value of risk probability may fall under a given confidence level.

[0062] Specifically, step S61 calculates the probability that the key performance indicators of the target road will fall below the risk threshold within the pre-input lifespan, thus obtaining the risk probability. This step utilizes the complete probability distribution information of the performance indicators, rather than a single deterministic value, enabling the calculated risk probability to more accurately reflect the true risk level under uncertainty conditions. Further, step S62, based on the confidence intervals of the key performance indicators already included in the indicator evaluation results, combines them with the risk probability calculated in S61 to derive the confidence range of the risk probability itself. This means that instead of providing a single risk probability point estimate, a range encompassing the possible fluctuation range of that risk probability is provided. Finally, this confidence range of the risk probability is combined with the risk probability itself to generate the final risk quantification result. In this way, this scheme effectively transmits and quantifies the uncertainty of key performance indicators into the risk probability, ensuring that the final risk quantification result includes not only the possibility of risk occurrence but also the reliability or fluctuation range of this possibility estimate. This allows decision-makers to have a more comprehensive understanding of potential risk changes when making auxiliary decisions, thereby making more robust and reliable decisions.

[0063] Through the above design, the method of this application can effectively transfer the uncertainty of key performance indicators to the quantification of risk probability, so that the final risk quantification result is no longer a single point estimate, but includes the confidence range of risk probability, comprehensively reflecting the confidence level and fluctuation range of risk probability, and significantly improving the robustness and reliability of risk assessment results.

[0064] In some preferred embodiments, step S62 includes: S621. Determine the boundary values ​​of key performance indicators based on the confidence intervals of the key performance indicators. S622. Calculate the confidence range of risk probability based on the boundary values ​​of key performance indicators and preset risk thresholds; S623, combine the risk probabilities and corresponding confidence ranges to obtain the risk quantification results.

[0065] Specifically, the boundary values ​​of key performance indicators refer to the upper and lower limits of the confidence interval of key performance indicators.

[0066] Specifically, step S621 determines the boundary values ​​of key performance indicators (KPIs) based on their confidence intervals, providing clear input for subsequent calculations of the risk probability confidence range and laying the foundation for assessing the fluctuation range of risk probability, ensuring a comprehensive consideration of performance uncertainty. Secondly, step S622 calculates the risk probability confidence range based on the boundary values ​​of the KPIs and a preset risk threshold, directly mapping the uncertainty of the KPIs to the uncertainty of the risk probability, thus forming a risk probability confidence range with upper and lower limits. Finally, step S623 combines the risk probability and the corresponding confidence range to form a more comprehensive and insightful risk quantification result.

[0067] Through the above design, the method of this application combines risk probability with confidence range, thereby effectively improving the accuracy and robustness of risk quantification results, enabling more accurate assessment and quantification of risks, and thus improving the credibility of the final risk quantification results.

[0068] Secondly, please refer to Figure 2 Some embodiments of this application also provide a road engineering survey data analysis system, the system comprising: The acquisition module is used to acquire multi-source survey data of the target road; The analysis module is used to analyze the statistical distribution characteristics or uncertainty range of each parameter in the multi-source survey data according to the type and characteristics of the multi-source survey data, so as to obtain the parameter uncertainty set; The sample generation module is used to generate parameter combination samples based on the set of parameter uncertainties. The evolution module is used to calculate the mechanical response of the target road under different load and environmental conditions based on the parameter combination sample. It couples the mechanical response with time factors to simulate the change path of road structure performance over time and obtain performance evolution data. The evaluation module is used to generate evaluation results for key performance indicators based on performance evolution data. The risk quantification module is used to calculate the probability that the target road will fall below the risk threshold within a pre-inputted lifespan, based on the indicator evaluation results and the preset risk threshold, and to generate risk quantification results based on the risk probability.

[0069] The system in this application combines the acquisition of multi-source survey data with parameter uncertainty quantification, dynamic evolution simulation of road performance, and risk quantification assessment based on risk probability. This solves the problems of low efficiency in integrating multi-source heterogeneous survey data, insufficient parameter uncertainty quantification, inaccurate dynamic evolution simulation of road performance, and poor reliability of long-term risk assessment in existing road engineering. It achieves the effect of robust assessment of road performance throughout its entire life cycle, risk quantification, and decision support.

[0070] Furthermore, 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.

[0071] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0072] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0073] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for analyzing road engineering survey data, characterized in that, The method includes the following steps: S1. Obtain multi-source survey data of the target road; S2. Based on the type and characteristics of the multi-source survey data, analyze the statistical distribution characteristics or uncertainty range of each parameter in the multi-source survey data to obtain the parameter uncertainty set; S3. Generate parameter combination samples based on the set of parameter uncertainties; S4. Calculate the mechanical response of the target road under different load and environmental conditions based on the parameter combination sample, and couple the mechanical response with time factors to simulate the change path of road structure performance over time, and obtain performance evolution data. S5. Generate evaluation results for key performance indicators based on the performance evolution data; S6. Based on the evaluation results of the indicators and the preset risk threshold, calculate the risk probability that the target road will fall below the risk threshold within the pre-input lifespan, and generate a risk quantification result based on the risk probability.

2. The road engineering survey data analysis method according to claim 1, characterized in that, Step S2 includes: S21. Based on the source, format, and spatiotemporal scale of the multi-source survey data, identify the data types to classify the multi-source survey data and obtain multiple data subsets; S22. Identify the engineering parameters reflected by each data subset according to the data type; S23. Perform statistical processing on the engineering parameters corresponding to the data subset based on spatial variability or temporal correlation to obtain statistical distribution characteristics, or determine the uncertainty range of the engineering parameters based on the measurement error or inherent fluctuation range of the engineering parameters. S24. Integrate the statistical distribution characteristics or uncertainty intervals corresponding to each data subset to obtain the parameter uncertainty set.

3. The road engineering survey data analysis method according to claim 1, characterized in that, Step S3 includes: S31. Identify the dependencies between various engineering parameters based on the statistical distribution characteristics or uncertainty intervals in the set of parameter uncertainties. S32. Determine the value range of each engineering parameter based on the distribution characteristics or uncertainty interval in the set of parameter uncertainties. S33. Perform stratified sampling on the value range of each engineering parameter to obtain stratified samples of each engineering parameter. S34. Based on the aforementioned dependency relationship, random sampling is used to randomly combine the stratified samples to generate parameter combination samples.

4. The road engineering survey data analysis method according to claim 3, characterized in that, Step S31 includes: S311. Based on the statistical distribution characteristics in the parameter uncertainty set, calculate the statistical correlation index between the engineering parameters, or, based on the uncertainty interval in the parameter uncertainty set, analyze the overlapping or synergistic change trend between the fluctuation ranges of the engineering parameters. S312. Identify the dependencies between the various engineering parameters based on the overlapping or synergistic change trends between the statistical correlation indicators or the fluctuation ranges.

5. The road engineering survey data analysis method according to claim 1, characterized in that, In step S4, the step of calculating the mechanical response of the target road under different loads and environmental conditions based on the parameter combination sample includes: S41. Based on the parameter combination sample, construct a structural mechanics analysis model for the target road. The structural mechanics analysis model determines the geometric parameters, material mechanics parameters, and interlayer connection characteristics of the road structure layers based on the parameter combination sample. S42. Based on different loads and environmental conditions, and using the principles of structural mechanics, calculate one or more of the stress, strain, displacement, and settlement of the structural mechanics analysis model, as the mechanical response under the corresponding load and environmental conditions.

6. The road engineering survey data analysis method according to claim 1, characterized in that, In step S4, the step of coupling the mechanical response with a time factor to simulate the change path of road structure performance over time and obtain performance evolution data includes: S43. Identify time-related key performance indicators based on the mechanical response, wherein the key performance indicators include one or more of fatigue life, bearing capacity, and settlement. S44. For the key performance indicators, extract the change relationships from a preset change relationship library, which includes the change relationships of various key performance indicators with respect to time degradation. S45. Based on the aforementioned relationship, calculate the change path of the key performance indicators over time within the pre-input lifespan to obtain the performance evolution data.

7. The road engineering survey data analysis method according to claim 1, characterized in that, Step S5 includes: S51. Perform statistical analysis on the performance evolution data of key performance indicators to generate the probability density function or cumulative distribution function of the key performance indicators. S52. Determine the confidence interval of the key performance indicator based on the probability density function or cumulative distribution function of the key performance indicator and in combination with the preset confidence level. S53. Integrate the probability density function or cumulative distribution function of the key performance indicators and the corresponding confidence intervals to obtain the evaluation results of the indicators.

8. The road engineering survey data analysis method according to claim 7, characterized in that, Step S52 includes: S521. Determine the quantiles corresponding to the preset confidence level based on the probability density function or cumulative distribution function of the key performance indicators. S522. Use the quantiles as the boundaries of the confidence intervals for the key performance indicators to determine the confidence intervals.

9. The road engineering survey data analysis method according to claim 1, characterized in that, The evaluation results of the indicators include the probability density function or cumulative distribution function of the key performance indicators and the corresponding confidence intervals. Step S6 includes: S61. Based on the probability density function or cumulative distribution function of the key performance indicator and the preset risk threshold, calculate the probability that the key performance indicator of the target road is lower than the risk threshold within the pre-input lifespan, and obtain the risk probability. S62. Based on the risk probability and the confidence interval, determine the confidence range of the risk probability, and generate the risk quantification result based on the risk probability and the corresponding confidence range.

10. A road engineering survey data analysis system, characterized in that, The system includes: The acquisition module is used to acquire multi-source survey data of the target road; The analysis module is used to analyze the statistical distribution characteristics or uncertainty range of each parameter in the multi-source survey data according to the type and characteristics of the multi-source survey data, so as to obtain the parameter uncertainty set; The sample generation module is used to generate parameter combination samples based on the parameter uncertainty set; The evolution module is used to calculate the mechanical response of the target road under different load and environmental conditions based on the parameter combination sample, and to couple the mechanical response with time factors to simulate the change path of road structure performance over time, thereby obtaining performance evolution data. An evaluation module is used to generate evaluation results for key performance indicators based on the performance evolution data. The risk quantification module is used to calculate the risk probability that the target road will fall below the risk threshold within a pre-input lifespan based on the evaluation results of the indicators and the preset risk threshold, and to generate a risk quantification result based on the risk probability.

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