Intelligent prediction method and system for life cycle of building material

Abstract

The invention discloses an intelligent prediction method and system for the life cycle of a building material, and the method comprises the steps: obtaining the failure data and state data of the building material, fitting the failure data to obtain the basic predicted life of the building material, calculating the comprehensive damage degree according to the state data of the building material, calculating the environmental impact factor according to the environmental parameter, and predicting the life cycle of the building material. And calculating a first predicted life, a life deviation coefficient, an environmental damage degree, a loss life and a second predicted life, constructing a building material life cycle intelligent prediction model according to a calculation result, and inputting failure data and state data of a to-be-predicted building material into the building material life cycle intelligent prediction model to obtain a predicted life and a risk factor. The method not only can improve the efficiency and accuracy of building material life cycle intelligent prediction, but also has good interpretability, and can be directly applied to a building material life cycle intelligent prediction system.

Description

Technical Field

[0001] The present invention relates to the field of life prediction, and in particular to an intelligent prediction method and system for the life cycle of building materials. Background Art

[0002] In the field of construction, building materials are widely used in engineering structures and exterior decoration. After the building materials are put into use, their performance gradually declines with the change of time, environment and use conditions. Their service life and performance status are directly related to the safety, durability and use function of the building. The building material life assessment and prediction technology aims to evaluate the service life of building materials and predict their future status through scientific means, so as to timely discover the degradation of materials and provide a basis for personnel maintenance and decision-making, thereby extending the service life of building materials and improving the reliability and economy of the overall project.

[0003] However, traditional building material life assessment methods usually have many shortcomings, such as the cumbersome and time-consuming assessment process, and may cause certain damage to building materials or structures. With the development of science and technology, the prediction of the life cycle of building materials is moving towards intelligence. By integrating smart sensors and Internet of Things technology, professional data analysis models and algorithms are used to achieve accurate and efficient prediction of the health status and remaining service life of building materials. Therefore, combining modern information technology, artificial intelligence technology and scientific knowledge of building materials, by collecting and analyzing information such as performance parameters, usage environment, historical data, etc. of building materials, an accurate life prediction model is established, and an intelligent prediction method and system for the life cycle of building materials is designed to overcome the shortcomings of existing evaluation models and achieve efficient, accurate and non-destructive prediction of the life cycle of building materials, which is of great significance to improving the economy of building operation and maintenance and ensuring the safety of building structures. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent prediction method and system for the life cycle of building materials.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] Acquire failure data and state data of building materials, fit the failure data to obtain shape parameters and scale parameters of a Weibull model for estimating the life of building materials, and obtain a basic predicted life of building materials according to the Weibull model;

[0008] The state data includes building material state data and environmental state data, a comprehensive damage degree is calculated according to the building material state data, a first predicted life is determined according to the comprehensive damage degree and the basic predicted life, and a deviation analysis is performed on the first predicted life to obtain a life deviation coefficient;

[0009] Analyze the correlation between the environmental status data and the comprehensive damage parameters to determine environmental indicators, screen and label abnormal values ​​of the environmental indicators, and construct environmental impact factors according to the environmental indicators;

[0010] Determine an environmental damage coefficient according to the comprehensive damage degree and the environmental impact factor, determine a lost life according to the environmental damage coefficient, and determine a second predicted life according to the first predicted life, the lost life, and the life deviation coefficient;

[0011] An intelligent prediction model for the life cycle of building materials is constructed based on the second predicted life, the environmental damage coefficient, the life deviation coefficient and the lost life, and the failure data and status data of the building materials to be predicted are input into the intelligent prediction model for the life cycle of building materials to obtain the predicted life and risk factors.

[0012] Furthermore, the method for obtaining the basic predicted life of the building material comprises:

[0013] The failure data consists of test failure data and simulation failure data;

[0014] Conduct geotechnical tests on material specimens under the same mix ratio, construction technology and curing conditions to obtain basic material parameters and test failure data;

[0015] Construct a finite element model of the material specimen based on the basic parameters of the material, and perform finite element analysis on the material specimen to obtain simulated failure data;

[0016] The failure data is fitted according to the three-parameter Weibull model to obtain the scale parameter and shape parameter. The basic predicted life of building materials is calculated by the three-parameter Weibull model. The expression of the three-parameter Weibull model is:

[0017] F(t)=1-exp{-[λH(t-k0λ -1 )] α}

[0018]

[0019] Where F(t) is the three-parameter Weibull distribution function, f(t) is the three-parameter Weibull density function, t is time, t0 = (t-k0λ -1 ) is the time threshold, λ is the scale parameter, k0 is the ratio parameter between the scale parameter and the time threshold, and β is the shape parameter;

[0020] The reliability function R(t) and the failure function s(t) are determined according to the distribution function F(t), and the median failure time corresponding to F(t)=0.632 is calculated. The median failure time is the basic predicted life T0.

[0021] Furthermore, the method for calculating the comprehensive damage degree according to the building material status data includes:

[0022] The building material status data includes the cross-sectional area and porosity of the building material;

[0023] Clean the aging and peeling parts of the surface of the building material to be predicted, measure the cross-sectional area of ​​the building material, and calculate the peeling damage parameter, which is expressed as:

[0024]

[0025] Where w1 is the peeling damage parameter, m0 is the initial mass of the building material, S0 is the initial cross-sectional area of ​​the building material, S S is the cross-sectional area of ​​the building material after stripping, a1 and a2 are mass constants;

[0026] The internal porosity of the building material to be predicted is measured by an ultrasonic detector, and the elastic modulus damage parameter is calculated. The expression is:

[0027] w2=[E0(1-P) / (1+P)-a3] / a4

[0028] Where w2 is the elastic modulus damage parameter, E0 is the initial elastic modulus of the building material, P is the porosity, and a3 and a4 are elastic modulus constants;

[0029] The comprehensive damage degree is determined according to the peeling damage parameter and the elastic modulus damage parameter, and the expression is:

[0030] w A =lg|Aw1 2 ·Bw2 2 |

[0031]

[0032] where w A is the comprehensive damage degree, A is the effective damage coefficient of the peeling damage parameter w1, and B is the effective damage coefficient of the elastic modulus damage parameter w2.

[0033] Furthermore, the method for obtaining the life deviation coefficient includes:

[0034] The comprehensive damage degree and basic predicted life are input into the first life prediction function to obtain the first predicted life, the expression is:

[0035]

[0036] Where T1 is the first predicted life, b1, b2, b3 are the first life prediction function model parameters obtained by fitting the failure data, and w A ′=dw A / dt is the relative change rate of comprehensive damage degree, σ p is the peak value of fatigue stress intensity;

[0037] The life deviation field is established based on the basic predicted life and the first predicted life, and the expression is:

[0038] f(m,n)=T1(m,n)-T0(m,n)

[0039] Where f(m, n) is the predicted life deviation field, T1(m, n) is the first predicted life, T0(m, n) is the basic predicted life, m is the predicted life mean and also represents the number in the x direction, and n is the predicted life standard deviation and also represents the number in the y direction;

[0040] The life deviation field is transformed by 2D-DCT positive term to obtain the life deviation modal function, which is expressed as:

[0041]

[0042] Where M is the total number of sampling points in the x direction, N is the total number of sampling points in the y direction, C(μ, v) is the orthogonal transformation coefficient matrix, μ and v are frequency values, and c(μ) and c(v) are normalization coefficients;

[0043] The orthogonal transformation matrix is ​​obtained by performing key mode identification on the life deviation mode function. At the same time, Perform inverse DCT transform to obtain the reconstructed lifetime deviation field Calculate the relative reconstruction error to get the lifetime deviation coefficient:

[0044]

[0045] where δ 2 To reconstruct the lifetime deviation field The relative reconstruction error of , and the lifetime deviation coefficient is δ.

[0046] Furthermore, the method for constructing environmental impact factors according to the environmental parameters includes:

[0047] Perform clustering operation on environmental status data to obtain peeling damage environmental index and elastic modulus damage environmental index, calculate the correlation coefficient between peeling damage environmental index and peeling damage parameter, calculate the correlation coefficient between elastic modulus damage environmental index and elastic modulus damage parameter, and select peeling damage environmental index and elastic modulus damage environmental index with higher correlation as main environmental index;

[0048] The hypersphere distribution model is used to screen out abnormalities in the main environmental indicators, and the main indicators outside the hypersphere are marked as abnormal data and labeled;

[0049] Input the main environmental indicators into the environmental impact function to obtain the environmental impact factor, the expression is:

[0050]

[0051] where w B is the environmental impact factor, c1, c2, c3, c4, c5 are environmental impact weight coefficients, C SO2 (t) is the atmospheric SO2 concentration at time t, C CO2 (t) is the atmospheric CO2 concentration at time t, t s is the starting time of atmospheric monitoring, t f is the end time of atmospheric monitoring, N max is the maximum noise intensity, V max is the maximum vibration intensity, ΔT is the temperature difference during the monitoring period, T s is the standard curing temperature, ΔH is the humidity difference during the monitoring period, H s is the standard maintenance humidity, R UV It is the external UV index.

[0052] Further, the method for determining the environmental damage coefficient, the lost life and the second predicted life includes:

[0053] According to the comprehensive damage degree w A and the environmental impact factor w B Determine the environmental damage coefficient, the expression is:

[0054]

[0055] where w C is the environmental damage coefficient, d1 is the weight of the environmental damage coefficient;

[0056] The basic predicted life T0, the first predicted life T1 and the comprehensive damage degree w A Input the loss life function to obtain the loss life, the expression is:

[0057]

[0058] Where T loss is the life lost, d2 is the first predicted life weight, is the maximum environmental damage coefficient, β is the loss life coefficient;

[0059] The second predicted life T2 is determined based on the basic predicted life, the first predicted life, the lost life and the life deviation coefficient; the second predicted life is negatively correlated with the lost life; the second predicted life is positively correlated with the first predicted life; the second predicted life is positively correlated with the product of the life deviation coefficient and the basic predicted life.

[0060] Furthermore, the method for obtaining the predicted life span and risk factors comprises:

[0061] The second predicted life, the lost life, the life deviation coefficient and the environmental damage coefficient are combined into a comprehensive data set, and the comprehensive data set is divided into a training set and a test set;

[0062] Construct an intelligent prediction model for the life cycle of building materials. The specific structure includes input layer, base model layer, strategy layer and output layer;

[0063] The input layer is connected to the machine model layer, and the preprocessed training set data is input into the machine model layer. The base model layer connects the random forest regression model, support vector machine model and linear regression model in parallel to make predictions and output the prediction results to the strategy layer. The strategy layer uses a hybrid method to train the meta-learner with a new holdout set to synthesize the final predicted lifespan and input it into the output layer. The two fully connected layers of the input layer generate prediction results and extract labels respectively, and output predicted lifespan and risk factors.

[0064] In the support vector machine base model, the hinge loss function is used to ensure the accuracy of classification, and the sequential minimum optimization algorithm is used to optimize the model weights; in the linear regression base model, the least squares method is used as the loss function to calculate the difference between the predicted value and the true value, and the stochastic gradient descent algorithm is used to improve the computational efficiency and convergence speed;

[0065] Use the test set to evaluate the model and output the intelligent prediction model of the building material life cycle;

[0066] The failure data and status data of the building materials to be predicted are input into the building materials life cycle intelligent prediction model to obtain the predicted life span and risk factors.

[0067] In the second aspect, an intelligent prediction system for the life cycle of building materials includes:

[0068] Simulation module: used to perform finite element simulation of building materials according to the geotechnical test results to obtain building material failure data;

[0069] Data acquisition module: used to obtain building material failure data and status data, and pre-process the building material failure data and status data;

[0070] Data processing module: used to calculate the basic predicted life of building materials according to the failure data, used to calculate the comprehensive damage degree according to the building material status data, used to calculate the environmental impact factor according to the environmental parameters, used to calculate the first predicted life, the life deviation coefficient, the environmental damage degree, the lost life and the second predicted life, and used to transmit the calculation results to the prediction model module and the intelligent supervision module;

[0071] Prediction model module: used for constructing an intelligent prediction model of the life cycle of building materials according to the second predicted life, the environmental damage degree, the life deviation coefficient and the lost life, and inputting the failure data and the state data of the building material to be predicted into the intelligent prediction model of the life cycle of building materials to obtain the predicted life and risk factors;

[0072] Intelligent supervision module: used to store, view and manage the failure data, the status data, the predicted life and the risk factors, visualize the predicted life and risk factors of building materials to users, and recommend maintenance measures based on risk factor matching.

[0073] The beneficial effects of the present invention are:

[0074] The present invention is an intelligent prediction method and system for the life cycle of building materials. Compared with the prior art, the present invention has the following technical effects:

[0075] The present invention can improve the data preprocessing capabilities and enhance the model adaptability in the intelligent prediction of the life cycle of building materials through finite element simulation, parameter fitting, correlation analysis, deviation modal analysis, calculation of environmental damage coefficient, calculation of lost life, calculation of second predicted life and model construction steps, thereby improving the efficiency and accuracy of the intelligent prediction of the life cycle of building materials, optimizing the intelligent prediction technology of the life cycle of building materials, greatly saving resources, and improving work efficiency. It can realize the prediction of the life cycle of building materials, provide a scientific basis for predicting the life cycle of building materials, and is of great significance to the safety of building structures. It can adapt to the prediction needs of intelligent prediction systems for the life cycles of different building materials and intelligent prediction systems for the life cycles of building materials of different users, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 The present invention is a flowchart of the steps of an intelligent prediction method for the life cycle of building materials. DETAILED DESCRIPTION

[0077] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0078] The present invention provides an intelligent prediction method and system for the life cycle of building materials, comprising the following steps:

[0079] like Figure 1 As shown, in this embodiment, the following steps are included:

[0080] Acquire failure data and state data of building materials, fit the failure data to obtain shape parameters and scale parameters of a Weibull model for estimating the life of building materials, and obtain a basic predicted life of building materials according to the Weibull model;

[0081] The state data includes building material state data and environmental state data, a comprehensive damage degree is calculated according to the building material state data, a first predicted life is determined according to the comprehensive damage degree and the basic predicted life, and a deviation analysis is performed on the first predicted life to obtain a life deviation coefficient;

[0082] Analyze the correlation between the environmental status data and the comprehensive damage parameters to determine environmental indicators, screen and label abnormal values ​​of the environmental indicators, and construct environmental impact factors according to the environmental indicators;

[0083] Determine an environmental damage coefficient according to the comprehensive damage degree and the environmental impact factor, determine a lost life according to the environmental damage coefficient, and determine a second predicted life according to the first predicted life, the lost life, and the life deviation coefficient;

[0084] An intelligent prediction model for the life cycle of building materials is constructed based on the second predicted life, the environmental damage coefficient, the life deviation coefficient and the lost life, and the failure data and status data of the building materials to be predicted are input into the intelligent prediction model for the life cycle of building materials to obtain the predicted life and risk factors.

[0085] In this embodiment, the method for obtaining the basic predicted life of the building material includes:

[0086] The failure data consists of test failure data and simulation failure data;

[0087] Conduct geotechnical tests on material specimens under the same mix ratio, construction technology and curing conditions to obtain basic material parameters and test failure data;

[0088] Construct a finite element model of the material specimen based on the basic parameters of the material, and perform finite element analysis on the material specimen to obtain simulated failure data;

[0089] The failure data is fitted according to the three-parameter Weibull model to obtain the scale parameter and shape parameter. The basic predicted life of building materials is calculated by the three-parameter Weibull model. The expression of the three-parameter Weibull model is:

[0090] F(t)=1-exp{-[λH(t-k0λ -1 )] α}

[0091]

[0092] Where F(t) is the three-parameter Weibull distribution function, f(t) is the three-parameter Weibull density function, t is time, t0 = (t-k0λ -1 ) is the time threshold, λ is the scale parameter, k0 is the ratio parameter between the scale parameter and the time threshold, and α is the shape parameter;

[0093] Determine the reliability function R(t) and the failure function s(t) according to the distribution function F(t), and calculate the median failure time corresponding to F(t)=0.632, wherein the median failure time is the basic predicted life T0;

[0094] In the actual evaluation, taking the cable tower of a cable-stayed bridge with an operation period of 5 years as an example, geotechnical tests were carried out on material specimens with the same mix ratio and curing conditions, and the following results were obtained: tensile strength 3.45MPa, compressive strength 40.33MPa, elastic modulus 30GPa, elongation at break 0.22%;

[0095] According to the results of geotechnical tests, a finite element model of the material specimen was constructed. The environmental conditions and load conditions were set to carry out finite element simulation of the material specimen destruction to obtain simulated failure data. The three-parameter Weibull model was used to fit the failure data to obtain the scale parameter α=2.5 and the shape parameter λ=130. According to the three-parameter Weibull model, the basic expected life under a stress level of 90MPa is predicted to be 39.6 years.

[0096] In this embodiment, the method for calculating the comprehensive damage degree according to the building material status data includes:

[0097] The building material status data includes the cross-sectional area and porosity of the building material;

[0098] Clean the aging and peeling parts of the surface of the building material to be predicted, measure the cross-sectional area of ​​the building material, and calculate the peeling damage parameter, which is expressed as:

[0099]

[0100] Where w1 is the peeling damage parameter, m0 is the initial mass of the building material, S0 is the initial cross-sectional area of ​​the building material, S Sis the cross-sectional area of ​​the building material after stripping, a1 and a2 are mass constants;

[0101] The internal porosity of the building material to be predicted is measured by an ultrasonic detector, and the elastic modulus damage parameter is calculated. The expression is:

[0102] w2=[E0(1-P) / (1+P)-a3] / a4

[0103] Where w2 is the elastic modulus damage parameter, E0 is the initial elastic modulus of the building material, P is the porosity, and a3 and a4 are elastic modulus constants;

[0104] The comprehensive damage degree is determined according to the peeling damage parameter and the elastic modulus damage parameter, and the expression is:

[0105] w A =lg|Aw1 2 ·Bw2 2 |

[0106]

[0107] where w A is the comprehensive damage degree, A is the effective damage coefficient of the peeling damage parameter w1, and B is the effective damage coefficient of the elastic modulus damage parameter w2;

[0108] In the actual evaluation, non-destructive measurement was performed on the tower of a cable-stayed bridge with an operation period of 5 years to obtain the following: the cross-sectional area S after the building material was stripped off S =1.43m 2 、Initial cross-sectional area S0=1.5m 2 , porosity P = 0.7%, initial mass m0 = 0.0024 g / cm 3 , the mass constants are a1=0.95, a2=0.05, the elastic modulus constants are a3=0.6, a4=0.4, the calculated peeling damage parameter is -18.9977, the elastic modulus damage parameter is 72.4572, and the comprehensive damage degree is 2.5574.

[0109] In this embodiment, the method for obtaining the life deviation coefficient includes:

[0110] The comprehensive damage degree and basic predicted life are input into the first life prediction function to obtain the first predicted life, the expression is:

[0111]

[0112] Where T1 is the first predicted life, b1, b2, b3 are the first life prediction function model parameters obtained by fitting the failure data, and w A ′=dw A / dt is the relative change rate of comprehensive damage degree, σ p is the peak value of fatigue stress intensity;

[0113] The life deviation field is established based on the basic predicted life and the first predicted life, and the expression is:

[0114] f(m,n)=T1(m,n)-T0(m,n)

[0115] Where f(m, n) is the predicted life deviation field, T1(m, n) is the first predicted life, T0(m, n) is the basic predicted life, m is the predicted life mean and also represents the number in the x direction, and n is the predicted life standard deviation and also represents the number in the y direction;

[0116] The life deviation field is transformed by 2D-DCT positive term to obtain the life deviation modal function, which is expressed as:

[0117]

[0118] Where M is the total number of sampling points in the x direction, N is the total number of sampling points in the y direction, C(μ, v) is the orthogonal transformation coefficient matrix, μ and v are frequency values, and c(μ) and c(v) are normalization coefficients;

[0119] The orthogonal transformation matrix is ​​obtained by performing key mode identification on the life deviation mode function. At the same time, Perform inverse DCT transform to obtain the reconstructed lifetime deviation field Calculate the relative reconstruction error to get the lifetime deviation coefficient:

[0120]

[0121] where δ 2 To reconstruct the lifetime deviation field The relative reconstruction error, the life deviation coefficient is δ;

[0122] In the actual evaluation, the life deviation coefficient of the tower of a cable-stayed bridge with an operating period of 5 years was calculated to be 0.699.

[0123] In this embodiment, the method for constructing the environmental impact factor according to the environmental parameters includes:

[0124] Perform clustering operation on environmental status data to obtain peeling damage environmental index and elastic modulus damage environmental index, calculate the correlation coefficient between peeling damage environmental index and peeling damage parameter, calculate the correlation coefficient between elastic modulus damage environmental index and elastic modulus damage parameter, and select peeling damage environmental index and elastic modulus damage environmental index with higher correlation as main environmental index;

[0125] The hypersphere distribution model is used to screen out abnormalities in the main environmental indicators, and the main indicators outside the hypersphere are marked as abnormal data and labeled;

[0126] In the actual assessment, the main environmental indicators of the towers of a cable-stayed bridge in this group with an operating period of 5 years were screened for abnormalities. The UV index and atmospheric SO2 concentration data were outside the hypersphere, which were marked as risk factors;

[0127] Input the main environmental indicators into the environmental impact function to obtain the environmental impact factor, the expression is:

[0128]

[0129] where w B is the environmental impact factor, c1, c2, c3, c4, c5 are environmental impact weight coefficients, C SO2 (t) is the atmospheric SO2 concentration at time t, C CO2 (t) is the atmospheric CO2 concentration at time t, t s is the starting time of atmospheric monitoring, t f is the end time of atmospheric monitoring, N max is the maximum noise intensity, V max is the maximum vibration intensity, ΔT is the temperature difference during the monitoring period, T s is the standard curing temperature, ΔH is the humidity difference during the monitoring period, H s is the standard maintenance humidity, R UV is the external UV index;

[0130] In the actual evaluation, the main environmental indicators of the cable tower of a cable-stayed bridge with an operation period of 5 years are: atmospheric SO2 concentration 15ppb, atmospheric CO2 concentration 450ppm, maximum noise intensity 70dB, maximum vibration intensity 0.15m / s 2 , the temperature difference during the monitoring period is 12℃, the humidity difference during the monitoring period is 5%, the external ultraviolet index is 4, the environmental impact weight coefficients are c1=0.2, c2=0.4, c3=0.1, c4=0.1, c5=0.2, and the environmental impact factor is 1.10695 when substituting into the environmental impact function.

[0131] In this embodiment, the method for determining the environmental damage coefficient, the lost life and the second predicted life includes:

[0132] According to the comprehensive damage degree w A and the environmental impact factor w B Determine the environmental damage coefficient, the expression is:

[0133]

[0134] where wC is the environmental damage coefficient, d1 is the weight of the environmental damage coefficient;

[0135] The basic predicted life T0, the first predicted life T1 and the comprehensive damage degree w A Input the loss life function to obtain the loss life, the expression is:

[0136]

[0137] Where T loss is the life lost, d2 is the first predicted life weight, is the maximum environmental damage coefficient, β is the loss life coefficient;

[0138] Determine the second predicted life T2 according to the basic predicted life, the first predicted life, the lost life and the life deviation coefficient; the second predicted life is negatively correlated with the lost life; the second predicted life is positively correlated with the first predicted life; the second predicted life is positively correlated with the product of the life deviation coefficient and the basic predicted life;

[0139] In the actual assessment, the environmental damage coefficient weight d1=0.5 is taken, and the environmental damage coefficient is calculated to be 1.5414 based on the above comprehensive damage degree and environmental impact factors. The first predicted life weight d2=2 and the loss life coefficient β=0.5 are taken, and the loss life is calculated to be 4.6124 years.

[0140] In this embodiment, the method for obtaining the predicted life span and risk factors includes:

[0141] The second predicted life, the lost life, the life deviation coefficient and the environmental damage coefficient are combined into a comprehensive data set, and the comprehensive data set is divided into a training set and a test set;

[0142] Construct an intelligent prediction model for the life cycle of building materials. The specific structure includes input layer, base model layer, strategy layer and output layer;

[0143] The input layer is connected to the machine model layer, and the preprocessed training set data is input into the machine model layer. The base model layer connects the random forest regression model, support vector machine model and linear regression model in parallel to make predictions and output the prediction results to the strategy layer. The strategy layer uses a hybrid method to train the meta-learner with a new holdout set to synthesize the final predicted lifespan and input it into the output layer. The two fully connected layers of the input layer generate prediction results and extract labels respectively, and output predicted lifespan and risk factors.

[0144] In the support vector machine base model, the hinge loss function is used to ensure the accuracy of classification, and the sequential minimum optimization algorithm is used to optimize the model weights; in the linear regression base model, the least squares method is used as the loss function to calculate the difference between the predicted value and the true value, and the stochastic gradient descent algorithm is used to improve the computational efficiency and convergence speed;

[0145] Use the test set to evaluate the model and output the intelligent prediction model of the building material life cycle;

[0146] Inputting the failure data and status data of the building materials to be predicted into the building materials life cycle intelligent prediction model to obtain the predicted life span and risk factors;

[0147] In the actual evaluation, a data set consisting of multiple groups of second predicted life, loss life, life deviation coefficient and environmental damage coefficient was used to train the model to obtain an intelligent prediction model for the life cycle of building materials. The failure data and status data of the tower of a cable-stayed bridge with an operation period of 5 years in this group were input into the intelligent prediction model for the life cycle of building materials. The predicted life of the tower of a cable-stayed bridge with an operation period of 5 years was 37.8206 years, and the risk factors were the ultraviolet index and the atmospheric SO2 concentration. Therefore, the remaining predicted life of the tower of the cable-stayed bridge with an operation period of 5 years was 32.8206 years. At the same time, maintenance measures can be taken according to the risk factor labels to enhance the durability and UV resistance of building materials.

[0148] In the second aspect, an intelligent prediction system for the life cycle of building materials includes:

[0149] Simulation module: used to perform finite element simulation of building materials according to the geotechnical test results to obtain building material failure data;

[0150] Data acquisition module: used to obtain building material failure data and status data, and pre-process the building material failure data and status data;

[0151] Data processing module: used to calculate the basic predicted life of building materials according to the failure data, used to calculate the comprehensive damage degree according to the building material status data, used to calculate the environmental impact factor according to the environmental parameters, used to calculate the first predicted life, the life deviation coefficient, the environmental damage degree, the lost life and the second predicted life, and used to transmit the calculation results to the prediction model module and the intelligent supervision module;

[0152] Prediction model module: used for constructing an intelligent prediction model of the life cycle of building materials according to the second predicted life, the environmental damage degree, the life deviation coefficient and the lost life, and inputting the failure data and the state data of the building material to be predicted into the intelligent prediction model of the life cycle of building materials to obtain the predicted life and risk factors;

[0153] Intelligent supervision module: used to store, view and manage the failure data, the status data, the predicted life and the risk factors, visualize the predicted life and risk factors of building materials to users, and recommend maintenance measures based on risk factor matching.

[0154] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent prediction method for the life cycle of building materials, characterized in that: The following steps are involved: S1. Obtaining failure data and state data of building materials, fitting the failure data to obtain shape parameters and scale parameters of a Weibull model for estimating the life of building materials, and obtaining a basic predicted life of building materials according to the Weibull model; S2, the state data includes building material state data and environmental state data, calculating the comprehensive damage degree according to the building material state data, determining the first predicted life according to the comprehensive damage degree and the basic predicted life, and performing deviation analysis on the first predicted life to obtain a life deviation coefficient; S3, analyzing the correlation between the environmental status data and the comprehensive damage parameters to determine environmental indicators, screening abnormal values ​​of the environmental indicators and labeling them, and constructing environmental impact factors according to the environmental indicators; S4, determining an environmental damage coefficient according to the comprehensive damage degree and the environmental impact factor, determining a lost life according to the environmental damage coefficient, and determining a second predicted life according to the first predicted life, the lost life and the life deviation coefficient; S5. Construct an intelligent prediction model for the life cycle of building materials based on the second predicted life, the environmental damage coefficient, the life deviation coefficient and the lost life, and input the failure data and status data of the building materials to be predicted into the intelligent prediction model for the life cycle of building materials to obtain the predicted life and risk factors.

2. According to claim 1, a method for intelligent prediction of the life cycle of building materials is characterized in that: The method for obtaining the basic predicted life of the building material comprises: The failure data consists of test failure data and simulation failure data; Conduct geotechnical tests on material specimens under the same mix ratio, construction technology and curing conditions to obtain basic material parameters and test failure data; Construct a finite element model of the material specimen based on the basic parameters of the material, and perform finite element analysis on the material specimen to obtain simulated failure data; The failure data is fitted according to the three-parameter Weibull model to obtain the scale parameter and shape parameter. The basic predicted life of building materials is calculated by the three-parameter Weibull model. The expression of the three-parameter Weibull model is: F(t)=1-exp{-[λH(t-k0λ -1 )] α } Where F(t) is the three-parameter Weibull distribution function, f(t) is the three-parameter Weibull density function, t is time, t0 = (t-k0λ -1 ) is the time threshold, λ is the scale parameter, k0 is the ratio parameter between the scale parameter and the time threshold, and α is the shape parameter; The reliability function R(t) and the failure function s(t) are determined according to the distribution function F(t), and the median failure time corresponding to F(t)=0.632 is calculated. The median failure time is the basic predicted life T0.

3. According to claim 1, a method for intelligent prediction of the life cycle of building materials is characterized in that: The method for calculating the comprehensive damage degree according to the building material status data comprises: The building material status data includes the cross-sectional area and porosity of the building material; Clean the aging and peeling parts of the surface of the building material to be predicted, measure the cross-sectional area of ​​the building material, and calculate the peeling damage parameter, which is expressed as: Where w1 is the peeling damage parameter, m0 is the initial mass of the building material, S0 is the initial cross-sectional area of ​​the building material, S S is the cross-sectional area of ​​the building material after stripping, a1 and a2 are mass constants; The internal porosity of the building material to be predicted is measured by an ultrasonic detector, and the elastic modulus damage parameter is calculated. The expression is: w2=[E0(1-P) / (1+P)-a3] / a4 Where w2 is the elastic modulus damage parameter, E0 is the initial elastic modulus of the building material, P is the porosity, and a3 and a4 are elastic modulus constants; The comprehensive damage degree is determined according to the peeling damage parameter and the elastic modulus damage parameter, and the expression is: w A =lg|Aw1 2 ·Bw2 2 | where w A is the comprehensive damage degree, A is the effective damage coefficient of the peeling damage parameter w1, and B is the effective damage coefficient of the elastic modulus damage parameter w2.

4. According to claim 1, a method for intelligent prediction of the life cycle of building materials is characterized in that: The method for obtaining the life deviation coefficient comprises: The comprehensive damage degree and basic predicted life are input into the first life prediction function to obtain the first predicted life, the expression is: Where T1 is the first predicted life, b1, b2, b3 are the first life prediction function model parameters obtained by fitting the failure data, and w A ′ =dw A / dt is the relative change rate of comprehensive damage degree, σ p is the peak value of fatigue stress intensity; The life deviation field is established based on the basic predicted life and the first predicted life, and the expression is: f(m,n)=T1(m,n)-T0(m,n) Where f(m, n) is the predicted life deviation field, T1(m, n) is the first predicted life, T0(m, n) is the basic predicted life, m is the predicted life mean and also represents the number in the x direction, and n is the predicted life standard deviation and also represents the number in the y direction; The life deviation field is transformed by 2D-DCT positive term to obtain the life deviation modal function, which is expressed as: Where M is the total number of sampling points in the x direction, N is the total number of sampling points in the y direction, C(μ, v) is the orthogonal transformation coefficient matrix, μ and v are frequency values, and c(μ) and c(v) are normalization coefficients; The orthogonal transformation matrix is ​​obtained by performing key mode identification on the life deviation mode function. At the same time, Perform inverse DCT transform to obtain the reconstructed lifetime deviation field Calculate the relative reconstruction error to get the lifetime deviation coefficient: where δ 2 To reconstruct the lifetime deviation field The relative reconstruction error of , and the lifetime deviation coefficient is δ.

5. According to claim 1, the intelligent prediction method for the life cycle of building materials is characterized in that: The method for constructing an environmental impact factor according to the environmental parameters comprises: Clustering operation is performed on environmental parameters to obtain peeling damage environmental indicators and elastic modulus damage environmental indicators, and the correlation coefficients between the peeling damage environmental indicators and the peeling damage parameters, and the correlation coefficients between the elastic modulus damage environmental indicators and the elastic modulus damage parameters are calculated. The peeling damage environmental indicators and elastic modulus damage environmental indicators with higher correlation are selected as the main environmental indicators. The hypersphere distribution model is used to screen out abnormalities in the main environmental indicators, and the main indicators outside the hypersphere are marked as abnormal data and labeled; Input the main environmental indicators into the environmental impact function to obtain the environmental impact factor, the expression is: where w B is the environmental impact factor, c1, c2, c3, c4, c5 are environmental impact weight coefficients, C SO2 (t) is the atmospheric SO2 concentration at time t, C CO2 (t) is the atmospheric CO2 concentration at time t, t s is the start time of atmospheric monitoring, t f is the end time of atmospheric monitoring, N max is the maximum noise intensity, V max is the maximum vibration intensity, ΔT is the temperature difference during the monitoring period, T s is the standard curing temperature, ΔH is the humidity difference during the monitoring period, H s is the standard maintenance humidity, R UV It is the external UV index.

6. The intelligent prediction method for the life cycle of building materials according to claim 1 is characterized in that: The method for determining the environmental damage coefficient, the lost life and the second predicted life comprises: According to the comprehensive damage degree w A and the environmental impact factor w B Determine the environmental damage coefficient, the expression is: where w C is the environmental damage coefficient, d1 is the weight of the environmental damage coefficient; The basic predicted life T0, the first predicted life T1 and the comprehensive damage degree w A Input the loss life function to obtain the loss life, the expression is: Where T loss is the life lost, d2 is the first predicted life weight, is the maximum environmental damage coefficient, β is the loss life coefficient; The second predicted life T2 is determined based on the basic predicted life, the first predicted life, the lost life and the life deviation coefficient; the second predicted life is negatively correlated with the lost life; the second predicted life is positively correlated with the first predicted life; the second predicted life is positively correlated with the product of the life deviation coefficient and the basic predicted life.

7. The intelligent prediction method for the life cycle of building materials according to claim 1 is characterized in that: The method for obtaining the predicted life span and risk factors comprises: The second predicted life, the lost life, the life deviation coefficient and the environmental damage coefficient are combined into a comprehensive data set, and the comprehensive data set is divided into a training set and a test set; Construct an intelligent prediction model for the life cycle of building materials. The specific structure includes input layer, base model layer, strategy layer and output layer; The input layer is connected to the machine model layer, and the preprocessed training set data is input into the machine model layer. The base model layer connects the random forest regression model, support vector machine model and linear regression model in parallel to make predictions and output the prediction results to the strategy layer. The strategy layer uses a hybrid method to train the meta-learner with a new holdout set to synthesize the final predicted lifespan and input it into the output layer. The two fully connected layers of the input layer generate prediction results and extract labels respectively, and output predicted lifespan and risk factors. In the support vector machine base model, the hinge loss function is used to ensure the accuracy of classification, and the sequential minimum optimization algorithm is used to optimize the model weights; in the linear regression base model, the least squares method is used as the loss function to calculate the difference between the predicted value and the true value, and the stochastic gradient descent algorithm is used to improve the computational efficiency and convergence speed; Use the test set to evaluate the model and output the intelligent prediction model of the building material life cycle; The failure data and status data of the building materials to be predicted are input into the building materials life cycle intelligent prediction model to obtain the predicted life span and risk factors.

8. An intelligent prediction system for the life cycle of building materials, used to execute the method according to any one of claims 1 to 7, comprising: Simulation module: used to perform finite element simulation of building materials according to the geotechnical test results to obtain building material failure data; Data acquisition module: used to obtain building material failure data and status data, and pre-process the building material failure data and status data; Data processing module: used to calculate the basic predicted life of building materials according to the failure data, used to calculate the comprehensive damage degree according to the building material status data, used to calculate the environmental impact factor according to the environmental parameters, used to calculate the first predicted life, the life deviation coefficient, the environmental damage degree, the lost life and the second predicted life, and used to transmit the calculation results to the prediction model module and the intelligent supervision module; Prediction model module: used for constructing an intelligent prediction model of the life cycle of building materials according to the second predicted life, the environmental damage degree, the life deviation coefficient and the lost life, and inputting the failure data and the state data of the building material to be predicted into the intelligent prediction model of the life cycle of building materials to obtain the predicted life and risk factors; Intelligent supervision module: used to store, view and manage the failure data, the status data, the predicted life and the risk factors, visualize the predicted life and risk factors of building materials to users, and recommend maintenance measures based on risk factor matching.

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