Bridge full life circle asset value index calculation method and system

Abstract

The invention relates to the field of bridge full-life-cycle asset value evaluation, in particular to a bridge full-life-cycle asset value index calculation method and system. The method comprises the following steps: calculating a historical discount rate according to a historical social discount rate and a historical construction industry production price index; obtaining historical maintenance and reinforcement time, analyzing the influence of the construction cost and the operation and maintenance cost on the total cost of the bridge based on the historical discount rate and the historical maintenance and reinforcement time, and obtaining cost uncertainty probability distribution and cost statistical characteristics; predicting a future discount rate according to the historical discount rate to obtain a dynamic discount rate; according to the dynamic discount rate, the cost uncertainty probability distribution and the cost statistical characteristics, calculating a bridge full life cycle cost present value; and calculating a bridge full-life-cycle asset value index based on the bridge full-life-cycle cost present value. The method solves the problem that there is no asset value index calculation in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of bridge full life cycle asset value assessment, and in particular to a method and system for calculating bridge full life cycle asset value indicators. Background Art

[0002] As an important transportation infrastructure, a reasonable assessment of the asset value of bridges will inevitably improve the accuracy of the overall transportation infrastructure asset assessment and provide a basis for management decisions.

[0003] Infrastructure such as bridges are assets with unique economic attributes. Current assessments of their value are limited to their construction costs or their potential subsequent profitability, without considering the functional value of bridges, such as the socioeconomic benefits derived from their capacity and safety. Furthermore, bridges are subject to various environmental factors over their lifetime, such as climate, loads, and material aging, which can lead to gradual degradation of their performance and necessitate timely maintenance and repair. Different maintenance methods and costs inevitably impact the lifespan of the bridge. In particular, current lifecycle cost calculation methods use a fixed discount rate, while future discount rates are variable. Therefore, it is necessary to consider the factors influencing bridge asset value from a holistic and long-term perspective to rationally assess the asset value of bridges. Summary of the Invention

[0004] In response to the defects in the existing technology, the present invention provides a method and system for calculating the asset value index of the entire life cycle of a bridge, which solves the problem that there is no asset value index calculation in the existing technology.

[0005] In order to achieve the above-mentioned purpose, one aspect of the present invention provides a method for calculating the asset value index of a bridge over its entire life cycle, comprising: obtaining a historical social discount rate and a historical construction industry production price index, and calculating the historical discount rate based on the historical social discount rate and the historical construction industry production price index; obtaining a historical maintenance and reinforcement time, and analyzing the impact of construction costs and operation and maintenance costs on the total cost of the bridge based on the historical discount rate and the historical maintenance and reinforcement time, and obtaining a cost uncertainty probability distribution and cost statistical characteristics; predicting future discount rates based on the historical discount rate to obtain a dynamic discount rate; calculating the present value of the bridge's entire life cycle cost based on the dynamic discount rate, the cost uncertainty probability distribution and the cost statistical characteristics; and calculating the asset value index of the bridge over its entire life cycle based on the present value of the bridge's entire life cycle cost.

[0006] This method comprehensively considers historical social discount rates and the historical construction industry production price index to calculate a historical discount rate that is closer to reality. Combined with historical repair and reinforcement time, it conducts an in-depth analysis of the impact of construction and operation and maintenance costs on the total cost of a bridge, revealing the probability distribution and statistical characteristics of cost uncertainty, thereby providing a scientific basis for cost management throughout the bridge's life cycle. Dynamically predicting future discount rates based on historical discount rates breaks through the limitations of traditional fixed discount rates, making the discount of future costs more consistent with changes in the economic environment. By calculating the present value of the bridge's life cycle costs and, based on this, calculating the bridge's life cycle asset value index, it is possible to comprehensively and systematically assess the economic value of the bridge, improving the accuracy of the calculation of the life cycle asset value index.

[0007] Optionally, the historical discount rate satisfies the following formula: ,

[0008] in, For the The historical discount rate for the year, is the social discount rate, For the The construction industry production price index in 2018 For the The construction industry producer price index for 2018.

[0009] The formula of the present invention calculates the historical discount rate. By combining the social discount rate with the construction industry production price index, the discount rate can be dynamically adjusted according to price changes in different years. It can more accurately reflect the value changes of construction industry funds at different times, provide a more realistic discount basis for construction project investment evaluation, cost-benefit analysis, etc., and improve the scientific nature of economic evaluation.

[0010] Optionally, the analysis of the impact of construction costs and operation and maintenance costs on the total cost of the bridge to obtain the cost uncertainty probability distribution and cost statistical characteristics includes: setting the historical maintenance and reinforcement time, the historical discount rate and the cost list corresponding to the total cost of the bridge as random variables, and calculating the probability distribution of the random variables; setting the construction cost and the operation and maintenance cost as target variables; analyzing the relationship between the probability distribution of the random variables and the target variables, and constructing a transfer model using the relationship; according to the probability distribution of the random variables, using the Monte Carlo method to extract samples from the random variables; using the samples and the transfer model to calculate the value of the target variable to obtain a target variable value set; analyzing the target variable value set to obtain the cost uncertainty probability distribution and cost statistical characteristics.

[0011] The present invention sets the historical maintenance and reinforcement time, historical discount rate and total cost list as random variables and calculates the probability distribution, fully considering the randomness and variability of various uncertain factors in practice, making the analysis more in line with reality, and setting the construction cost and operation and maintenance cost as the target variables, focusing on key cost factors, and conducting targeted in-depth analysis. By constructing a transfer model to analyze the relationship between the target variable and the random variable, the inherent connection and mechanism of action between the costs can be clearly revealed, providing a scientific basis for cost control and management. The probability distribution of cost uncertainty and the statistical characteristics of cost help to accurately assess the uncertainty range and characteristics of bridge costs, thereby improving the accuracy of uncertainty analysis.

[0012] Optionally, predicting the future discount rate based on the historical discount rate to obtain a dynamic discount rate includes: constructing a discount rate prediction model based on a support vector machine, and using the discount rate prediction model to predict based on the historical discount rate to obtain a dynamic discount rate.

[0013] The present invention constructs a discount rate prediction model through support vector machines. By utilizing the powerful nonlinear fitting ability of support vector machines, it can effectively mine the complex inherent laws in historical discount rate data, thereby accurately predicting future discount rates. Compared with traditional methods, the accuracy of the prediction is improved.

[0014] Optionally, the prediction based on the historical discount rate using the discount rate prediction model to obtain a dynamic discount rate includes: dividing the historical discount rate into delay steps to obtain multiple historical discount rate combinations; normalizing the historical discount rate combinations to obtain sample data; using the sample data to train and evaluate the discount rate prediction model; and using the evaluated discount rate prediction model to predict to obtain a dynamic discount rate.

[0015] This method divides and combines historical discount rates into delayed steps, effectively capturing the dynamic characteristics and trends of discount rates over time and exploring the potential relationships between discount rates at different time points. Normalizing the historical discount rate combinations eliminates the impact of dimensional differences between data, making the data more comparable and standardized, improving the efficiency and stability of discount rate prediction model training, and thereby enhancing prediction accuracy. Prediction using the evaluated discount rate prediction model further improves prediction accuracy.

[0016] Optionally, the present value of the bridge life cycle cost satisfies the following formula: ,

[0017] in, is the present value of the life cycle cost of the bridge, is the initial construction cost, is the total number of years in the life cycle of the bridge, is the expected value of the detection cost, is the expected value of preventive maintenance cost, is the expected cost of subsequent maintenance and reinforcement, For the The dynamic discount rate for the year, is the failure probability of the bridge, is the expected value of failure cost.

[0018] The formula of the present invention covers multiple key links such as initial construction cost, inspection, maintenance and repair, later reinforcement and failure cost. It can fully reflect the cost of the entire process from bridge construction to scrapping, avoid omissions in cost accounting, take into account the dynamic discount rate, and incorporate the time value of money, so that costs incurred at different times are comparable, making cost calculation more scientific and reasonable. The introduction of failure probability and failure cost helps to evaluate the risk cost during the operation of the bridge and improves the accuracy of the present value of the bridge's entire life cycle cost.

[0019] Optionally, the calculation of the bridge's full life cycle asset value index based on the present value of the bridge's full life cycle cost includes: obtaining relevant historical data of the bridge, and calculating the bridge asset value adjustment coefficient using the relevant historical data; and calculating the bridge's full life cycle asset value index based on the bridge asset value adjustment coefficient and the present value of the bridge's full life cycle cost.

[0020] This invention calculates the bridge asset value adjustment coefficient by acquiring historical data related to the bridge, fully tapping into the potential value of historical information. This historical data covers various details during the construction and use of the bridge, reflecting the actual condition and characteristics of the bridge, making the adjustment coefficient more realistic and laying the foundation for accurate asset value assessment. Determining the asset value index based on the asset value adjustment coefficient and the present value of the entire life cycle cost comprehensively considers cost inputs and adjustment factors related to the bridge's own condition. It not only considers the economic costs of construction and maintenance, but also adjusts the value based on the actual condition of the bridge, avoiding the one-sidedness of measuring asset value solely by cost, and improving the scientific and reasonableness of calculating asset value indicators.

[0021] Optionally, the bridge asset value adjustment coefficient satisfies the following formula: , , , , , ,

[0022] in, is the bridge asset value adjustment coefficient, is the risk cost adjustment coefficient, is the environmental cost adjustment coefficient, is the social cost adjustment coefficient, is the maintenance and reinforcement cost adjustment coefficient, Adjust the user value. is the total number of years of the relevant historical data, is the risk probability, For risk impact, For the year, For natural resource consumption, For noise pollution, For greenhouse gas emissions, is the gross regional product of the bridge site, is the consumer price index for the bridge site area, The accident cost, For repair and reinforcement costs, For maintenance costs, For preventive maintenance costs, is the average travel time, For driver comfort, is the average fuel consumption, For fuel costs.

[0023] The formula of the present invention comprehensively considers multiple factors such as risk, environment, society, maintenance and reinforcement, and user use. It can fully reflect the factors affecting the value of bridge assets and avoid one-sided evaluation. By calculating the average of each factor by year, it smoothes data fluctuations, makes the adjustment coefficient more stable and reliable, and can effectively reflect long-term trends. At the same time, each parameter in the formula is closely related to the actual operation of the bridge and the surrounding environment, and can be dynamically adjusted according to actual data, making the asset value assessment more in line with reality, providing a scientific and accurate quantitative basis for bridge asset management, investment decision-making, etc., and improving the scientificity and rationality of calculating the bridge asset value adjustment coefficient.

[0024] Optionally, the asset value index of the bridge throughout its life cycle satisfies the following formula: ,

[0025] in, is the asset value indicator of the bridge throughout its life cycle, is the bridge asset value adjustment coefficient, is the present value of the life cycle cost of the bridge.

[0026] Another aspect of the present invention provides a system for calculating the asset value index of a bridge throughout its life cycle, comprising: a processor, an input device, an output device, and a memory, wherein the processor, the input device, the output device, and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a method for calculating the asset value index of a bridge throughout its life cycle as described in any one of the previous aspects of the present invention.

[0027] The present invention provides a system for calculating the asset value index of a bridge throughout its entire life cycle, which has a compact structure, stable performance, high integration and simple composition. It can stably execute the method for calculating the asset value index of a bridge throughout its entire life cycle provided in the previous aspect of the present invention, further enhancing the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for calculating the asset value index of a bridge throughout its life cycle according to an embodiment of the present invention; Figure 2 The figure is a schematic diagram of the structure of a bridge life cycle asset value index calculation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0030] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0031] In an alternative embodiment, see Figure 1 ,like Figure 1This is a flow chart of a method for calculating the asset value index of a bridge throughout its life cycle according to an embodiment of the present invention, comprising the following steps: Step S1, obtaining a historical social discount rate and a historical construction industry production price index, and calculating a historical discount rate based on the historical social discount rate and the historical construction industry production price index.

[0032] In this example, the historical social discount rate reflects society's expectations of the time value of money and embodies the differences in the value of money at different points in time, influenced by various factors such as the macroeconomic situation and monetary policy. The historical construction industry producer price index reflects the price fluctuations of various input factors in the construction production process, such as fluctuations in the prices of building materials and labor costs.

[0033] The historical discount rate satisfies the following formula: ,

[0034] in, For the The historical discount rate for the year, is the social discount rate, For the The construction industry production price index in 2018 For the The construction industry producer price index for 2018.

[0035] Step S2: Obtain historical repair and reinforcement time, analyze the impact of construction cost and operation and maintenance cost on the total cost of the bridge based on the historical discount rate and the historical repair and reinforcement time, and obtain the cost uncertainty probability distribution and cost statistical characteristics.

[0036] In this example, we first study the bridge cost content at each stage and divide the cost components of the bridge's entire life cycle. The cost boundaries are divided according to different analysis objectives, and there are mainly the following division systems: Classification by bridge impact objects: financial cost, environmental cost and social cost of bridge structure.

[0037] Classification by subject: owner cost, user cost, and vulnerability cost.

[0038] Classification by stakeholders: owner cost, user cost, and social payment cost.

[0039] Classification by cost components: full life cycle cost of materials, full life cycle cost of equipment, full life cycle cost of human resources, and full life cycle management cost.

[0040] Divided by life cycle stage: pre-construction cost, construction cost, operation and maintenance cost, and demolition and recovery cost.

[0041] In this example, bridge costs are broken down by stage. Pre-construction costs cover planning and surveying expenses, construction costs include materials and labor, operating and maintenance costs cover daily upkeep and repairs, and demolition and recovery costs cover the costs associated with decommissioning the bridge. This allows for precise cost calculations at each stage, avoiding omissions or double-counting, and making cost accounting more scientific and accurate.

[0042] At the same time, each stage presents different risks, such as delays during construction and safety risks during operations and maintenance. By calculating asset value indicators at each stage, we can promptly identify risk points, formulate countermeasures in advance, mitigate risk losses, and ensure the smooth operation of the bridge's entire life cycle.

[0043] From a comprehensive perspective, this approach encompasses the entire bridge construction process, from conception to completion. Pre-construction costs include investments in preliminary work such as project planning, land acquisition, and feasibility studies. Construction costs encompass material procurement, labor, and equipment rental. Operational and maintenance costs cover expenses such as routine inspections, repairs, and defect management. Demolition and recycling costs encompass the cost of dismantling the bridge upon decommissioning and the value of recyclable materials. This comprehensive approach provides a clear understanding of the flow and overall scale of funds.

[0044] In terms of decision support, cost data from each stage provides a solid foundation for scientific decision-making. In the early stages of construction, cost estimates can be used to determine project feasibility. During construction, comparing budgeted and actual costs allows for timely optimization of construction plans. During the operation and maintenance phase, rational maintenance plans are arranged to ensure bridge safety and performance. During the demolition and reclamation phase, decisions on whether to demolish or retain the bridge are made based on cost-benefit analysis.

[0045] From the perspective of asset value assessment, this method can comprehensively consider the changes in costs and asset values at each stage, accurately reflect the true value of the bridge, provide a reliable value reference for economic activities such as asset transactions and mortgages, and avoid value deviations caused by one-sided assessments.

[0046] Analyzing the impact of construction and operation and maintenance costs on the total cost of a bridge and obtaining the probability distribution of cost uncertainty and cost statistical characteristics specifically includes the following sub-steps: Step S201: setting the cost list corresponding to the historical repair and reinforcement time, the historical discount rate, and the total cost of the bridge as random variables, and calculating the probability distribution of the random variables.

[0047] In this example, the historical maintenance and reinforcement time reflects the timing of repairs to maintain the bridge's performance during its lifetime. The maintenance and reinforcement timeframes in different years are uncertain and are affected by factors such as the bridge's operational status and environmental factors. The historical discount rate is also uncertain due to factors such as the economic environment and policies. Fluctuations in the economic landscape over time can cause the discount rate to fluctuate, which is crucial for assessing the time value of money in bridge cost calculations.

[0048] When calculating the probability distribution of the random variables, the frequency of each random variable is analyzed. When the amount of data meets a certain amount, the frequency is equivalent to the probability.

[0049] Taking the cost list corresponding to the total cost of a bridge as an example, we collected the total cost list for the bridge at different time periods and subdivided the various costs in the total cost list by expense category, such as material costs, labor costs, and equipment costs. We then compiled the values of each cost category at different time points or projects, creating a detailed data table to facilitate subsequent analysis. For each cost category, we divided the values into appropriate ranges and counted the number of times the cost data appeared within each range, i.e., the frequency. Dividing the frequency by the total number of data points yielded the frequency of each range. The frequency approximated the probability of the cost occurring within that range.

[0050] Based on the calculated frequency distribution, try to select a suitable probability distribution function for fitting, determine the parameters of the distribution function through mathematical methods, make the fitting curve as close as possible to the actual frequency distribution, and test the fitted probability distribution to evaluate its rationality. If the test results show that the fitting effect is not good, it is necessary to reselect the distribution function or adjust the parameters until a probability model that can accurately describe the distribution of cost data is determined.

[0051] Step S202: setting the construction cost and the operation and maintenance cost as target variables.

[0052] Setting construction and operation and maintenance costs as target variables lays a key foundation for a comprehensive assessment of the full lifecycle costs of a bridge. Construction costs encompass all expenses from project preparation to completion and delivery, including material procurement, labor salaries, and machinery rentals. These expenses are significantly affected by the project scale, construction technology, and market price fluctuations. Operation and maintenance costs, which include routine inspections, repairs and reinforcement, and equipment upgrades, depend on the bridge's usage, life cycle, and technical standards. By setting construction and operation and maintenance costs as target variables, we focus on key cost factors and conduct in-depth, targeted analysis.

[0053] Step S203: Analyze the relationship between the probability distribution of the random variable and the target variable, and construct a transfer model using the relationship.

[0054] In this embodiment, the transfer model is a key tool for revealing the intrinsic connection between random variables and target variables, and realizing uncertainty transfer and target variable calculation.

[0055] The construction of a transfer model requires the comprehensive application of multiple technologies and methods. First, we must deeply analyze the probability distribution of random variables such as historical maintenance and reinforcement time, historical discount rates, and total bridge cost lists, and collect a large amount of relevant historical data. On the one hand, regression analysis can be used. Based on the data characteristics, the linear or nonlinear relationship between variables can be determined through methods such as least squares to construct a corresponding transfer model. On the other hand, machine learning algorithms can be selected, such as neural networks. The structure of the network, such as the number of nodes in the input layer, hidden layer, and output layer, must be determined. A large amount of data is used for training, and the network weights and biases are adjusted to allow it to learn the complex relationship between random variables and target variables, achieve accurate fitting of the variable relationship, and successfully construct a transfer model.

[0056] Step S204: extracting samples from the random variable using the Monte Carlo method according to the probability distribution of the random variable.

[0057] In this embodiment, the Monte Carlo method, also known as the statistical simulation method, is a numerical calculation method guided by probability statistics theory. Its principle is based on the law of large numbers. When the number of experiments is large enough, the frequency of an event will approach its probability.

[0058] When sampling, random variables such as historical repair and reinforcement times, historical discount rates, and the total cost list for bridges were set. Their probability distributions reflect the uncertainty inherent in real-world scenarios. For example, the timing of historical repair and reinforcement is affected by factors such as the frequency of bridge use and the degree of environmental erosion, resulting in multiple possible occurrences. This uncertainty is reflected in the probability distribution. Monte Carlo, a method for numerical calculations using random sampling, simulates real-world uncertainty in this application scenario based on a large number of repeated random experiments.

[0059] Based on the probability distribution of random variables, the probability distribution type of each random variable is determined, and corresponding sampling algorithms are used for different distribution types.

[0060] In an optional embodiment, the random variable satisfies the normal distribution, and a random number that conforms to its distribution characteristics is generated using methods such as the Box-Muller transformation. Then, two independent uniformly distributed random numbers are used, and after a specific mathematical transformation, to generate a sample value that conforms to the normal distribution characteristics of the random variable.

[0061] For each cost item in the total bridge cost list, if its probability distribution is approximated by frequency statistics, sampling can be performed using the cumulative distribution function. This involves sorting each cost item in the total cost list from smallest to largest, calculating the cumulative frequency, and generating a uniformly distributed random number between 0 and 1. The corresponding cost sample value can then be determined by searching the cumulative frequency table.

[0062] Finally, through repeated sampling, a large number of random variable sample combinations are obtained. These sample combinations fully account for the uncertainty of random variables and cover a wide range of possible scenarios. Each set of samples represents a possible real-world scenario. This set provides a rich data foundation for subsequent analysis of the probability distribution of cost uncertainty and cost statistics, helping to more accurately assess the uncertainty and variation patterns of bridge life cycle costs.

[0063] Step S205 : Calculate the value of the target variable using the sample and the transfer model to obtain a target variable value set.

[0064] In this embodiment, the transfer model serves as the medium connecting the sample and the target variable (construction and operation and maintenance costs). This transfer model is constructed based on an in-depth analysis of the complex relationship between random variables and the target variable. Samples are sequentially fed into the transfer model for calculation. If the transfer model is a linear model based on regression analysis, the random variable values in the sample are calculated according to the linear relationship defined by the model, resulting in the corresponding predicted value of the target variable, i.e., the target variable value. If the transfer model is based on a neural network, the sample data is transferred and processed between nodes in each layer of the neural network. Through neuron activation and weight adjustment, the predicted value of the target variable, i.e., the target variable value, is ultimately output.

[0065] As a large number of samples are continuously input and calculated, a series of target variable calculation results are obtained, which together form the target variable value set. This set includes the possible values of the target variable under various combinations of random factors, and fully reflects the uncertainty of construction and operation and maintenance costs.

[0066] Step S206: Analyze the target variable value set to obtain cost uncertainty probability distribution and cost statistical characteristics.

[0067] In this embodiment, the cost statistical characteristics include, but are not limited to, an expected value of detection cost, an expected value of preventive maintenance and repair cost, an expected value of subsequent maintenance and reinforcement cost, and an expected value of failure cost.

[0068] After obtaining the target variable value set, we first organize all the calculated results for construction and operation and maintenance costs. By counting the frequency of data occurrence within different cost intervals and calculating their frequencies, we can approximate the probability of the cost occurring in each interval. Using this data, we can plot a probability distribution histogram or fit a probability distribution curve to visually present the uncertainty distribution of the costs.

[0069] For the expected values of inspection cost, preventive maintenance cost, post-maintenance and reinforcement cost, and failure cost, corresponding data subsets were extracted from the target variable value set. Taking inspection cost as an example, the inspection cost values calculated for each calculation were summarized. By counting the frequency of data within different cost intervals, the probability of cost occurrence in each interval was used to plot a probability distribution histogram and fit a probability distribution curve. The statistical characteristics of the probability distribution were obtained, and the mathematical expectation of the probability distribution was calculated based on the mathematical definition of the expected value, which is the sum of the products of all possible values and their corresponding probabilities. Calculating the expected values of other costs using similar methods can clearly understand the average level of each cost over the entire life cycle of the bridge.

[0070] Step S3: predicting the future discount rate based on the historical discount rate to obtain a dynamic discount rate.

[0071] The process of predicting the future discount rate based on the historical discount rate to obtain a dynamic discount rate specifically includes the following steps: Step S301: construct a discount rate prediction model based on a support vector machine, and use the discount rate prediction model to perform prediction according to the historical discount rate to obtain a dynamic discount rate.

[0072] In this embodiment, the support vector machine is a supervised machine learning algorithm that is widely used in classification and regression analysis. Its core idea is to determine an optimal hyperplane that can separate data points of different categories as much as possible, so that the distance between the two categories of data points and the hyperplane is maximized. This distance is called the margin, and the hyperplane with the largest margin is the optimal hyperplane.

[0073] Support vector machines have strong generalization capabilities. Due to their characteristic of maximizing margins, support vector machines have good generalization performance when processing new data and are less prone to overfitting. Furthermore, support vector machines are suitable for learning with small samples and, with the help of their kernel functions, can effectively process high-dimensional data.

[0074] Wherein, performing prediction based on the historical discount rate using the discount rate prediction model to obtain a dynamic discount rate includes: Step S30101: divide the historical discount rate into delay steps to obtain multiple historical discount rate combinations.

[0075] In this embodiment, dividing the delay step size refers to the operation of dividing the historical discount rate according to certain time intervals in the time series. This can capture the dynamic characteristics of the discount rate over time. Because the discount rate is affected to varying degrees by factors such as the economic environment and policy adjustments under different time spans, the longer the time interval, the more the historical discount rate combination can reflect the impact of the macroeconomic cycle on the historical discount rate. The shorter the time interval, the more subtle changes caused by short-term market fluctuations can be reflected in the historical discount rate combination.

[0076] Step S30102: normalize the historical discount rate combination to obtain sample data.

[0077] In this example, the essence of normalization is to scale the historical discount rate combination so that it falls within a specific range. Given the inherent characteristics of the historical discount rate combination, normalization can eliminate differences in dimensionality and numerical range between different features, ensuring that each feature has a relatively equal weight in the model, thereby improving the quality of the sample data.

[0078] Step S30103: Use the sample data to train and evaluate the discount rate prediction model.

[0079] The sample data is divided into a training set and a test set in a ratio of 8:2. The training set is used to train the discount rate prediction model, and the test set is used to evaluate the discount rate prediction model. The randomness and representativeness of the data must be ensured during the division to avoid the impact of data bias on the performance of the discount rate prediction model.

[0080] The training set discount rate prediction model is put into the training process. During the training process, the discount rate prediction model will determine the optimal hyperplane to fit the relationship between the discount rate in the training set and related influencing factors by adjusting the parameters of the kernel function and the penalty factor of the model according to the algorithm principle of the support vector machine. This process is continuously iterated until the discount rate prediction model converges, that is, the performance of the discount rate prediction model is no longer significantly improved.

[0081] After training the discount rate prediction model, independently evaluate it using the test set. This includes calculating the root mean square error, mean error, and coefficient of determination between the predicted results and the true values. Because the test set was not used in the model training process, the test set evaluation results better reflect the model's generalization ability and prediction accuracy in practical applications. If the evaluation metrics on the test set differ significantly from those on the training set, there may be an overfitting issue, requiring model adjustment or optimization. In this case, you can try adjusting the discount rate prediction model penalty factor or changing the kernel function.

[0082] The mean absolute error satisfies the following formula: ,

[0083] in, represents the mean absolute error, Indicates the number of samples, The true value of the historical discount rate of samples, Indicates the The historical discount rate forecast value of the sample.

[0084] The root mean square error satisfies the following formula: ,

[0085] in, represents the root mean square error, represents the number of samples, Indicates the The true value of the historical discount rate of samples, Indicates the The historical discount rate forecast value of the sample.

[0086] The coefficient of determination satisfies the following formula: ,

[0087] in, represents the coefficient of determination, represents the number of samples, Indicates the The true value of the historical discount rate of samples, Indicates the The historical discount rate forecast value of samples, Represents the average of the true values of historical discount rates.

[0088] Step S30104: Use the evaluated discount rate prediction model to make predictions to obtain a dynamic discount rate.

[0089] In this embodiment, the discount rate data of a time interval pushed forward from the current time is used as the input number. After the input data is normalized, it is input into the discount rate prediction model, and the discount rate prediction model outputs the prediction result. This is a dynamic acquisition process, which will change with the change of the input data, thereby obtaining a dynamic discount rate.

[0090] Step S4, calculating the present value of the bridge life cycle cost based on the dynamic discount rate, the cost uncertainty probability distribution and the cost statistical characteristics.

[0091] In this example, the dynamic discount rate reflects the changing time value of money. Discount rates vary across time periods, significantly impacting the present value of costs. The cost uncertainty probability distribution encompasses the probabilities of construction and operation and maintenance costs under various scenarios, such as fluctuations in construction material prices and the probability of equipment failure.

[0092] Cost statistical characteristics include but are not limited to expected detection costs, expected preventive maintenance and repair costs, expected subsequent repair and reinforcement costs, and expected failure costs.

[0093] The present value of the bridge's life cycle cost satisfies the following formula: ,

[0094] in, is the present value of the life cycle cost of the bridge, is the initial construction cost, is the total number of years in the life cycle of the bridge, is the expected value of the detection cost, is the expected value of preventive maintenance cost, is the expected cost of subsequent maintenance and reinforcement, For the The dynamic discount rate for the year, is the failure probability of the bridge, is the expected value of failure cost.

[0095] In this example, the bridge failure probability is based on in-depth research and analysis of various aspects of the bridge structure, including its mechanical properties, material characteristics, environmental factors, and load effects. Advanced numerical simulation methods, such as finite element analysis, accurately simulate the stress state of the bridge under various operating conditions and thus predict its likely failure modes. Furthermore, combined with on-site monitoring data, real-time information on the bridge's actual operating status is obtained, further improving the accuracy of the failure probability assessment.

[0096] Step S5: Calculate the asset value index of the bridge throughout its life cycle based on the present value of the bridge's life cycle cost.

[0097] Calculating the asset value index of the bridge's entire life cycle based on the present value of the bridge's entire life cycle cost specifically includes the following sub-steps: Step S501: Obtain relevant historical data of the bridge, and calculate the bridge asset value adjustment coefficient using the relevant historical data.

[0098] In this embodiment, the relevant historical data includes but is not limited to risk probability, risk impact, natural resource consumption, noise pollution, greenhouse gas emissions, GDP of the bridge site area, consumer price index of the bridge site area, accident costs, repair and reinforcement costs, maintenance costs, preventive maintenance costs, average driving time, driver comfort, average fuel consumption and fuel costs.

[0099] The bridge asset value adjustment coefficient satisfies the following formula: , , , , , ,

[0100] in, is the bridge asset value adjustment coefficient, is the risk cost adjustment coefficient, is the environmental cost adjustment coefficient, is the social cost adjustment coefficient, is the maintenance and reinforcement cost adjustment coefficient, Adjust the user value. is the total number of years of the relevant historical data, is the risk probability, For risk impact, For the year, For natural resource consumption, For noise pollution, For greenhouse gas emissions, is the gross regional product of the bridge site, is the consumer price index for the bridge site area, The accident cost, For repair and reinforcement costs, For maintenance costs, For preventive maintenance costs, is the average travel time, For driver comfort, is the average fuel consumption, For fuel costs.

[0101] In this embodiment, while existing technologies may only consider a few factors when evaluating bridge asset value, the formula in this invention comprehensively considers multiple factors, including risk, environment, society, maintenance and reinforcement, and user value. For example, it not only focuses on maintenance and reinforcement costs, but also incorporates preventive maintenance costs; it also considers not only economic benefits but also environmental costs such as natural resource consumption and noise pollution, making the assessment more comprehensive and objective.

[0102] Secondly, using year as a variable and combining it with historical data can reflect changes in bridge asset value at different points in time. Compared to fixed parameter assessments, this approach is more adaptable to the dynamic changes in various factors throughout a bridge's lifecycle, providing an accurate basis for management decisions at different stages.

[0103] Finally, each influencing factor is quantified through a clear mathematical formula and reflected in the calculation in the form of coefficients, avoiding subjective judgment and fuzzy evaluation, and improving the accuracy and credibility of the evaluation results.

[0104] Step S502 , calculating a bridge life cycle asset value index based on the beam asset value adjustment coefficient and the present value of the bridge life cycle cost.

[0105] In this example, the bridge asset value adjustment coefficient encompasses multiple factors, including risk, environment, and society, reflecting the value impact of bridges in different dimensions. The present value of a bridge's lifecycle costs, on the other hand, considers the time value of money across various stages of construction, maintenance, and repair. Combining these two factors, multiplying the bridge asset value adjustment coefficient by the present value of the lifecycle costs, yields a more realistic indicator of the bridge's lifecycle asset value. This indicator not only reflects cost inputs but also considers the impact of various factors on value. It can be used to assess the comprehensive benefits of bridge assets, providing a scientific basis for decisions regarding bridge planning, operation, maintenance, and asset disposal.

[0106] The asset value index of the bridge throughout its life cycle satisfies the following formula: ,

[0107] in, is the asset value indicator of the bridge throughout its life cycle, is the bridge asset value adjustment coefficient, is the present value of the life cycle cost of the bridge.

[0108] like Figure 2As shown, another aspect of the present invention also provides a system for calculating the asset value index of a bridge throughout its life cycle, comprising: a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute relevant steps of a relevant embodiment of a method for calculating the asset value index of a bridge throughout its life cycle of the present invention.

[0109] The present invention provides a bridge lifecycle asset value index calculation system. Each functional component can be integrated into a single processing unit, each component can exist physically separately, or two or more components can be integrated into a single unit. These integrated components can be implemented as either hardware or software functions, further enhancing the overall applicability and practical application capabilities of the present invention.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for calculating the asset value index of a bridge throughout its life cycle, characterized in that: The method comprises: Obtaining a historical social discount rate and a historical construction industry production price index, and calculating a historical discount rate based on the historical social discount rate and the historical construction industry production price index; Obtaining historical repair and reinforcement time, and based on the historical discount rate and the historical repair and reinforcement time, analyzing the impact of construction costs and operation and maintenance costs on the total cost of the bridge, and obtaining a probability distribution of cost uncertainty and cost statistical characteristics; Predicting future discount rates based on the historical discount rates to obtain a dynamic discount rate; Calculating the present value of the bridge's full life cycle cost based on the dynamic discount rate, the cost uncertainty probability distribution, and the cost statistical characteristics; The life cycle asset value index of the bridge is calculated based on the present value of the life cycle cost of the bridge.

2. The method for calculating the asset value index of a bridge throughout its life cycle according to claim 1 is characterized in that: The historical discount rate satisfies the following formula: ， in, For the The historical discount rate for the year, is the social discount rate, For the The construction industry production price index in 2018 For the The construction industry producer price index for 2018.

3. The method for calculating the asset value index of a bridge throughout its life cycle according to claim 1 is characterized in that: The analysis of the impact of construction costs and operation and maintenance costs on the total cost of the bridge and the acquisition of cost uncertainty probability distribution and cost statistical characteristics include: Setting the cost list corresponding to the historical repair and reinforcement time, the historical discount rate, and the total bridge cost as random variables, and calculating the probability distribution of the random variables; Setting the construction cost and the operation and maintenance cost as target variables; Analyzing the relationship between the probability distribution of the random variable and the target variable, and constructing a transfer model using the relationship; According to the probability distribution of the random variable, a sample is drawn from the random variable using a Monte Carlo method; Calculating the value of the target variable using the sample and the transfer model to obtain a target variable value set; The target variable value set is analyzed to obtain the cost uncertainty probability distribution and cost statistical characteristics.

4. The method for calculating the asset value index of a bridge throughout its life cycle according to claim 1 is characterized in that: The predicting of the future discount rate based on the historical discount rate to obtain a dynamic discount rate includes: A discount rate prediction model is constructed based on a support vector machine, and the discount rate prediction model is used to perform prediction according to the historical discount rate to obtain a dynamic discount rate.

5. The method for calculating the asset value index of a bridge throughout its life cycle according to claim 4 is characterized in that: The step of performing prediction based on the historical discount rate using the discount rate prediction model to obtain a dynamic discount rate includes: Dividing the historical discount rate into delay steps to obtain multiple historical discount rate combinations; Normalizing the historical discount rate combination to obtain sample data; Using the sample data to train and evaluate the discount rate prediction model; The evaluated discount rate prediction model is used to make predictions to obtain a dynamic discount rate.

6. The method for calculating the asset value index of a bridge throughout its life cycle according to claim 1 is characterized in that: The present value of the bridge's life cycle cost satisfies the following formula: ， in, is the present value of the life cycle cost of the bridge, is the initial construction cost, is the total number of years in the life cycle of the bridge, is the expected value of the detection cost, is the expected value of preventive maintenance cost, is the expected cost of subsequent maintenance and reinforcement, For the The dynamic discount rate for the year, is the failure probability of the bridge, is the expected value of failure cost.

7. The method for calculating the asset value index of a bridge throughout its life cycle according to claim 1 is characterized in that: The calculation of the bridge life cycle asset value index based on the present value of the bridge life cycle cost includes: Obtaining relevant historical data of the bridge, and calculating a bridge asset value adjustment coefficient using the relevant historical data; The asset value index of the bridge throughout its life cycle is calculated based on the beam asset value adjustment coefficient and the present value of the bridge throughout its life cycle cost.

8. The method for calculating the asset value index of a bridge throughout its life cycle according to claim 7 is characterized in that: The bridge asset value adjustment coefficient satisfies the following formula: ， ， ， ， ， ， in, is the bridge asset value adjustment coefficient, is the risk cost adjustment coefficient, is the environmental cost adjustment coefficient, is the social cost adjustment coefficient, is the maintenance and reinforcement cost adjustment coefficient, Adjust the user value. is the total number of years of the relevant historical data, is the risk probability, To respond to risks, For the year, For natural resource consumption, For noise pollution, For greenhouse gas emissions, is the gross regional product of the bridge site, is the consumer price index for the bridge site area, The accident cost, For repair and reinforcement costs, For maintenance costs, For preventive maintenance costs, is the average travel time, For driver comfort, is the average fuel consumption, For fuel costs.

9. The method for calculating the asset value index of a bridge throughout its life cycle according to claim 7 is characterized in that: The asset value index of the bridge throughout its life cycle satisfies the following formula: ， in, is the asset value indicator of the bridge throughout its life cycle, is the bridge asset value adjustment coefficient, is the present value of the life cycle cost of the bridge.

10. A bridge life cycle asset value index calculation system, characterized in that: include: A processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a method for calculating the asset value index of the entire life cycle of a bridge as described in any one of claims 1 to 9.

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