Full-life-cycle carbon emission prediction method for solid waste-based concrete structure

Abstract

The invention discloses a whole life cycle carbon emission prediction method for a solid waste-based concrete structure, and the method comprises the steps: extracting historical production process characteristics, calculating historical production process carbon emission, building a production process carbon emission prediction model, and obtaining a production process carbon emission prediction amount. The method comprises the following steps: constructing a solid waste cementing material service performance evolution function and a full-life-cycle material reliability model, determining aging repair times, calculating a service process carbon emission predicted quantity, extracting historical dismantling process characteristics, calculating historical dismantling process carbon emission, and constructing a dismantling process carbon emission prediction model to obtain a dismantling process carbon emission predicted quantity. And determining the full-life-cycle carbon emission predicted quantity of the solid waste-based concrete structure. According to the method, not only is the crossing from static evaluation to dynamic prediction and from segmented calculation to full-period integration realized, but also an accurate and reliable quantification tool and decision support are provided for low-carbon design, material optimization and sustainable operation and maintenance of the solid waste-based concrete structure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building material carbon emission prediction, and particularly relates to a full life cycle carbon emission prediction method for solid waste-based concrete structure. BACKGROUND

[0002] The building industry is a key field of global energy consumption and carbon emission, and its green and low-carbon transformation has become a key path to achieve the "double carbon" goal. In this context, the use of industrial solid waste to prepare cementitious materials and apply them to concrete structures not only can absorb a large amount of solid waste and reduce the consumption of natural resources, but also can significantly reduce the high carbon emissions generated by cement production, which has outstanding environmental and economic benefits.

[0003] However, to scientifically evaluate and compare the comprehensive low-carbon benefits of different solid waste-based concrete formulations, it is not enough to measure only the carbon footprint of the production stage. A full life cycle carbon emission evaluation system covering raw material acquisition, production, service maintenance, and demolition disposal must be established. Traditional concrete carbon emission evaluation focuses on the production stage, and lacks systematic and dynamic quantitative methods for carbon emissions generated during the long-term service process, maintenance and reinforcement activities, and building demolition stage, resulting in incomplete evaluation results and low prediction accuracy, which makes it difficult to support the low-carbon optimization design and operation and maintenance decision of solid waste-based concrete structures. Therefore, the present application proposes a full life cycle carbon emission prediction method for solid waste-based concrete structure. By principal component analysis and neural network, a dynamic prediction model for the production and demolition stages is constructed. Relying on accelerated aging test and reliability theory, a service performance evolution function and repair decision model are established. The model robustness is improved by introducing physical constraint loss, sparsity regularization and adaptive weight mechanism, thereby realizing high-precision and integrated prediction of full life cycle carbon emission, and providing scientific support for green design, life extension evaluation and carbon management decision of solid waste-based concrete structure. SUMMARY

[0004] The purpose of the present application is to provide a full life cycle carbon emission prediction method for solid waste-based concrete structure.

[0005] To achieve the above purpose, the present application is implemented according to the following technical solutions: The present application comprises the following steps: Obtain the historical production process data of different solid waste-based concretes, extract the historical production process features and corresponding carbon emission factors, calculate the historical production process carbon emission, construct a production process carbon emission prediction model to obtain the predicted solid waste-based concrete production process carbon emission prediction value; Construct a solid waste cementitious material service performance evolution function and a full life cycle material reliability model, identify the aging and repair time, and combine the service environment of the predicted solid waste-based concrete structure to predict the number of aging and repair times within the design life. extracting standard aging repair operation data and corresponding carbon emission factor to calculate single standard aging repair operation carbon emission, calculating service process carbon emission prediction by aging repair times and single standard aging repair operation carbon emission; acquiring historical demolition process data of different solid waste-based concrete, extracting historical demolition process features and corresponding carbon emission factor, calculating historical demolition process carbon emission, constructing demolition process carbon emission prediction model to obtain demolition process carbon emission prediction of the to-be-predicted solid waste-based concrete; determining the life cycle carbon emission prediction of the solid waste-based concrete structure from the production process carbon emission prediction, the service process carbon emission prediction, and the demolition process carbon emission prediction; The production process data includes raw material data, transportation data, and preparation data of the solid waste-based concrete; and the historical production process features include material features, transportation features, and preparation features. The carbon emission factor is obtained from a carbon emission factor database of a public database; The production process includes raw material acquisition, raw material transportation, and concrete preparation.

[0006] Further, the method for constructing the production process carbon emission prediction model comprises: acquiring historical production process data of different solid waste-based concrete, extracting carbon emission factors of raw material production, transportation, power grid power generation, and mechanical operation from a public database, extracting historical production process features from the historical production process data by principal component analysis, and calculating production process carbon emission, including raw material carbon emission, transportation carbon emission, and preparation carbon emission, according to the historical production process data and the corresponding carbon emission factors; training the production process carbon emission prediction model by using the production process carbon emission and the historical production process features; the production process carbon emission prediction model comprises an input layer, a feature enhancement layer, a prediction regression layer, and a strategy output layer; The feature enhancement layer uses a fully connected neural network to perform high-dimensional mapping on the historical production process features; The prediction regression layer predicts the raw material carbon emission, the transportation carbon emission, and the preparation carbon emission by a raw material prediction head, a transportation prediction head, and a preparation prediction head, respectively; The strategy output layer adds up the outputs of the three prediction heads by a fully connected network to obtain the production process carbon emission prediction; The production process carbon emission prediction model adjusts the model prediction accuracy by a prediction loss, a physical constraint loss, and a sparsity loss, and the expression is: ; ; ; ; in For the total loss function, To predict losses, For physical constraint loss, For sparsity loss, , , For weight hyperparameters, The number of training samples. , , , To predict weights, , For the first The actual and predicted values ​​of raw material carbon emissions for the sample group. , For the actual and predicted values ​​of carbon emissions from transportation, , To prepare true and predicted values ​​of carbon emissions, , This represents the actual and predicted values ​​of carbon emissions during the production process. The output of the feature enhancement layer represents the production process feature dimension. For the first The weight parameters of each production process feature in the prediction head. This is the task weight index.

[0007] Furthermore, the method for constructing the service performance evolution function and the full life cycle material reliability model of solid waste cementitious materials includes: Accelerated aging tests were conducted on the solid waste-based concrete to be predicted. The inherent aging coefficient was determined by fitting the accelerated aging test results. The environmental impact coefficient was calculated based on the service environment parameters of the solid waste-based concrete structure to be predicted. An initial service performance evolution function for the solid waste-based concrete was constructed from the inherent aging coefficient and the environmental impact coefficient, with the following expression: ; ; in for The service performance of solid waste-based concrete. According to monthly statistics For the initial service performance of solid waste-based concrete, The inherent aging coefficient, for Environmental impact coefficient at any time for Cumulative number of months at any time For cumulative month index, For the first Monthly time interval, , The orthogonal influence coefficient is... The chloride ion concentration in the service environment. The service environment temperature; Based on the service performance evolution function of solid waste-based concrete Construct a life-cycle material reliability model, the expression of which is:

[0008] in for Reliability of material properties at all times This is the performance failure threshold. , for The mean and standard deviation of the difference between the service performance and the performance failure threshold of solid waste-based concrete within the sliding window at any given time; The aging repair of solid waste-based concrete is assessed based on the reliability of material properties. After aging repair, the repair effect coefficient is determined based on the interfacial bond strength of the repair materials. A service performance evolution function for solid waste-based concrete is then constructed, expressed as: ; ; in For the first The repair efficiency coefficient of secondary aging repair For the first Performance recovery coefficient after secondary aging repair This represents the total number of aging repair cycles. For correction factor, For the first Secondary aging repair interface adhesion strength This represents the initial tensile strength of solid waste-based concrete.

[0009] Furthermore, the method for predicting the number of aging repairs within the design life includes: Starting from the service life of the solid waste-based concrete structure, the reliability of the solid waste-based concrete material performance is calculated monthly using the initial solid waste-based concrete service performance evolution function and the full life cycle material reliability model. When the reliability of the solid waste-based concrete material performance is less than the reliability warning threshold, the first aging repair is carried out and the corresponding aging repair time is extracted. The solid waste-based concrete material performance reliability is calculated monthly by using the solid waste-based concrete service performance evolution function and the whole life cycle material reliability model, the next aging repair time is determined, the iterative calculation is performed until the aging repair time is greater than the structural design service life, the iteration is stopped, and the aging repair number corresponding to the aging repair time is taken as the aging repair number within the solid waste-based concrete design life.

[0010] Further, the method for constructing the demolition process carbon emission prediction model comprises: Obtaining historical demolition process data of different solid waste-based concretes, extracting carbon emission factors corresponding to blasting raw material production, blasting process, mechanical operation, excavator operation and transportation according to a public database, extracting historical demolition process features by using principal component analysis on the historical demolition process data, and calculating historical demolition process carbon emissions according to the historical demolition process data and the corresponding carbon emission factors; Training the demolition process carbon emission prediction model by using the historical demolition process features and the historical demolition process carbon emissions; the demolition process carbon emission prediction model comprises an input layer, a feature processing layer, a prediction layer and an output layer; The feature processing layer calculates a demolition difficulty influence factor according to the demolition process features, and obtains enhanced features according to the demolition process features and the demolition difficulty influence factor; the expression of the demolition difficulty influence factor is:

[0011] wherein is the demolition difficulty influence factor, , , is a demolition difficulty fusion weight, is a building type influence function, is a building type, , is a building concrete consumption and a reference consumption, , is a solid waste-based concrete compressive strength and a reference compressive strength, , is a solid waste-based concrete elastic modulus and a reference elastic modulus, is a concrete performance sensitive coefficient; The prediction layer captures the nonlinear relationship between the enhanced features, the demolition process features and the demolition process carbon emissions by using a three-layer BP neural network, and obtains a demolition process carbon emission prediction value; the three-layer BP neural network comprises an input layer, a hidden layer and an output layer; the hidden layer adopts an activation function; The demolition process carbon emission prediction model adopts an adaptive weight loss function to improve the model prediction accuracy; the adaptive weight loss function is determined according to the demolition difficulty influence factor, and the expression is: ; wherein is an adaptive weight loss function, is the number of training samples, is an adaptive weight, is the deconstruction difficulty influence factor of the first group of samples, is the deconstruction difficulty influence factor mean value, is the deconstruction difficulty influence factor standard deviation, is the actual carbon emission amount of the deconstruction process, is the carbon emission prediction amount of the deconstruction process, is a regularization coefficient, is a elastic net mixing parameter, is a model weight matrix; The deconstruction process carbon emission prediction model is inputted with the to-be-predicted solid waste-based concrete deconstruction process characteristics to obtain the carbon emission prediction amount of the deconstruction process.

[0012] Further, the solid waste-based concrete formula comprises: solid waste cementitious material, coarse aggregate, fine aggregate and alkali activator; the cementitious material comprises metakaolin and granulated blast furnace slag; the coarse aggregate is commercial crushed stone; the fine aggregate is machine-made sand; and the alkali activator is prepared from water, sodium hydroxide solid and sodium silicate solution.

[0013] The present application has the following beneficial effects: Compared with the prior art, the present application has the following technical effects: The dynamic integrated prediction of the whole life cycle carbon emission is realized: all stages of raw material production, transportation, preparation, service maintenance and deconstruction disposal are covered, and the maintenance activities in the service stage are dynamically associated with carbon emission through the performance degradation model, providing more complete and more real carbon footprint evaluation results; The service carbon emission prediction mechanism based on performance reliability is innovated: by constructing the service performance evolution function of solid waste cementitious material and the whole life cycle material reliability model, the performance attenuation process of the material under the action of the environment can be simulated scientifically, and the repair and reinforcement opportunity can be accurately identified according to the reliability index, so as to dynamically predict the aging repair times and the corresponding carbon emission amount within the design life, and change the service stage carbon emission prediction from experience estimation to performance degradation-based scientific calculation; The accuracy and scientificity of carbon emission prediction in the demolition stage are improved: in view of the complexity of the demolition process, the demolition difficulty influence factor is provided, the comprehensive influence of building types, concrete consumption and material mechanical properties on demolition energy consumption is quantified, and the intelligent prediction model fusing the factor is used for regression prediction, so that the nonlinear relationship between various factors and carbon emission is effectively captured, and the accuracy and adaptability of demolition carbon emission prediction are significantly improved. It has good engineering application value and low-carbon guiding significance: the prediction method provided by the application can quickly predict and compare the full-life carbon footprint of solid waste-based concrete with different formulations, different structural forms and different environments, thereby providing key data support for the research and development of green building materials, the design of low-carbon structures and the optimization of operation and maintenance schemes, and effectively promoting energy saving and emission reduction and solid waste resource utilization in the building industry. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The steps of the full-life cycle carbon emission prediction method of the solid waste-based concrete structure of the application are shown in the flowchart. DETAILED DESCRIPTION

[0015] The application will be further described below through specific embodiments, and the illustrative embodiments of the application and the description are used to explain the application, but not as a limitation of the application.

[0016] The full-life cycle carbon emission prediction method of the solid waste-based concrete structure of the application includes the following steps: As shown in the figure, in the embodiment, the following steps are included: Figure 1 Obtain historical production process data of different solid waste-based concrete, extract historical production process features and corresponding carbon emission factors, calculate historical production process carbon emission, build a production process carbon emission prediction model to obtain a production process carbon emission prediction value of the solid waste-based concrete to be predicted; Build a service performance evolution function of solid waste cementitious material and a full-life cycle material reliability model, identify the aging repair time, and combine the service environment of the solid waste-based concrete structure to be predicted to predict the number of aging repairs within the design life; Extract standard aging repair operation data and corresponding carbon emission factors to calculate the carbon emission of a single standard aging repair operation, and calculate the service process carbon emission prediction value from the number of aging repairs and the carbon emission of a single standard aging repair operation; Obtain historical demolition process data of different solid waste-based concrete, extract historical demolition process features and corresponding carbon emission factors, calculate historical demolition process carbon emission, build a demolition process carbon emission prediction model to obtain a demolition process carbon emission prediction value of the solid waste-based concrete to be predicted; ​The full life cycle carbon emission prediction of the solid waste-based concrete structure is determined by the production process carbon emission prediction, the service process carbon emission prediction and the demolition process carbon emission prediction; The production process data includes raw material data, transportation data and preparation data of the solid waste-based concrete; and the historical production process features include material features, transportation features and preparation features. The carbon emission factor is obtained from a carbon emission factor database of a public database; The production process includes raw material acquisition, raw material transportation and concrete preparation.

[0017] In this embodiment, the method for constructing the production process carbon emission prediction model comprises: The historical production process data of different solid waste-based concretes is obtained, the carbon emission factors corresponding to raw material production, transportation, power grid power generation and mechanical operation are extracted from a public database, the historical production process data is extracted by principal component analysis to obtain historical production process features, and the production process carbon emission is calculated according to the historical production process data and the corresponding carbon emission factors, including raw material carbon emission, transportation carbon emission and preparation carbon emission. The production process carbon emission prediction model is trained by the production process carbon emission and the historical production process features; the production process carbon emission prediction model comprises an input layer, a feature enhancement layer, a prediction regression layer and a strategy output layer. The feature enhancement layer uses a fully connected neural network to perform high-dimensional mapping on the historical production process features. The prediction regression layer predicts the raw material carbon emission, the transportation carbon emission and the preparation carbon emission by a raw material prediction head, a transportation prediction head and a preparation prediction head, respectively. The strategy output layer adds the outputs of the three prediction heads by a fully connected network to obtain the production process carbon emission prediction. The production process carbon emission prediction model adjusts the model prediction accuracy by a prediction loss, a physical constraint loss and a sparsity loss, and the expression is: ; ; ; ; wherein is a total loss function, is a prediction loss, is a physical constraint loss, is a sparsity loss, , , is a weight hyperparameter, is a number of training samples, 、 、 、 is a prediction weight, 、 is a true value and a predicted value of raw material carbon emission of the first group of samples, 、 is a true value and a predicted value of transportation carbon emission, 、 is a true value and a predicted value of preparation carbon emission, 、 is a true value and a predicted value of production process carbon emission, is a production process feature dimension output by a feature enhancement layer, is a weight parameter of the first production process feature in the prediction head, is a task weight index; In actual evaluation, historical production process data of different solid waste-based concretes is obtained; the production process data includes raw material data, transportation data and preparation data of the solid waste-based concrete; the raw material data includes material proportioning of the solid waste-based concrete; the transportation data includes solid waste origin, construction site, carrier type and transportation material; the preparation data includes preparation procedure, equipment used, working time of each procedure and power of each procedure; According to a public database, carbon emission factors corresponding to raw material production, transportation, power grid generation and mechanical operation are extracted, and historical production process features are obtained by principal component analysis on the historical production process data; The historical production process features include material features, transportation features and preparation features; the material features include solid waste type, solid waste content and concrete material performance; the transportation features include transportation distance of each raw material, solid waste content and concrete material performance; the preparation features include preparation procedure, solid waste content and concrete material performance; The concrete material performance is obtained through material property experiments and includes concrete strength grade and elastic modulus; the concrete material performance is affected by material proportioning (coarse / fine aggregate and cement content) and thus has an indirect relationship with raw material carbon emission, transportation carbon emission (how much work the transportation tool does) and preparation carbon emission (how much work the equipment screening / cement mixing does); Production process carbon emission is calculated according to the historical production process data and corresponding carbon emission factors, in particular as follows: Raw material carbon emission: the mass content of each raw material in unit mass of solid waste-based concrete is determined according to the material proportioning, the product of the mass content of each raw material and the corresponding carbon emission factor is calculated to obtain the carbon emission of each raw material in unit mass of solid waste-based concrete, and the cumulative product is multiplied by the density of the solid waste-based concrete to obtain the raw material carbon emission per unit volume of the solid waste-based concrete; Transportation carbon emissions: (1) Determine the transportation distance of solid waste materials according to the geographic coding of the production place and the construction place (divided into land transportation distance and water transportation distance according to the transportation tool, the same material may be transferred by multiple transportation tools, the transportation carbon emissions are calculated segmentally and then accumulated), determine the transportation distance of sand and stone raw materials according to the nearest sand and stone mining area directly searched from the construction place (also may contain land transportation distance and water transportation distance), and the transportation distance of the remaining raw materials is 50km, and the transportation mode is land transportation; (2) Calculate the product of the carbon emission factor of the transportation tool and the transportation distance, and then divide by the single raw material load of the transportation tool to obtain the transportation carbon emission per unit mass of raw material; (3) Calculate the product of the transportation carbon emission per unit mass of raw material and the mass content, and accumulate to obtain the transportation carbon emission per unit mass of solid waste-based concrete, and multiply by the density of solid waste-based concrete to obtain the transportation carbon emission per unit volume of solid waste-based concrete; Preparation carbon emissions: Calculate the product of the process power (device power), device working time and power grid power generation carbon emission factor of each process in turn according to the preparation process, and accumulate to obtain the power consumption carbon emission, calculate the product of the device working time and the device mechanical carbon emission factor of each process in turn according to the preparation process, and accumulate to obtain the mechanical carbon emission, and take the sum of the power consumption carbon emission and the mechanical carbon emission of the preparation per unit volume of solid waste-based concrete structure as the preparation carbon emission per unit volume of solid waste-based concrete; In the production process carbon emission prediction model: The input layer needs to preprocess the production process characteristics (standardization, unique coding) and convert them into fixed-dimensional vectors; The feature enhancement layer adopts two layers of fully connected neural networks to map the production process characteristics (the original feature dimension is 1 / 4d) to high dimensions, wherein the first layer expands the dimension to 1 / 2d through the ReLU activation function, and the second layer expands the dimension to d through the ReLU activation function; In the prediction regression layer, the raw material prediction head captures the linear relationship between material characteristics and raw material carbon emissions through a linear layer to predict the raw material carbon emissions; the transportation prediction head captures the nonlinear relationship between transportation characteristics and transportation carbon emissions through a BP neural network to predict the transportation carbon emissions; the preparation prediction head adopts a recurrent neural network to capture the time nonlinear relationship between preparation characteristics and preparation carbon emissions to predict the preparation carbon emissions; The strategy output layer integrates the outputs of the three prediction heads through a Sigmoid activation function to obtain the production process carbon emission prediction (per 1m 3 Solid waste-based concrete is the unit); Based on The sample data is grouped, the production process carbon emission prediction model is trained according to the total loss function, and the weight hyperparameters , , are taken as 0.5 / 0.3 / 0.2, and the prediction weight , , , It is 0.25 / 0.25 / 0.25 / 0.25.

[0018] In this embodiment, the method for constructing the service performance evolution function and the full life cycle material reliability model of solid waste cementitious materials includes: Accelerated aging tests were conducted on the solid waste-based concrete to be predicted. The inherent aging coefficient was determined by fitting the accelerated aging test results. The environmental impact coefficient was calculated based on the service environment parameters of the solid waste-based concrete structure to be predicted. An initial service performance evolution function for the solid waste-based concrete was constructed from the inherent aging coefficient and the environmental impact coefficient, with the following expression: ; ; in for The service performance of solid waste-based concrete. According to monthly statistics For the initial service performance of solid waste-based concrete, The inherent aging coefficient, for Environmental impact coefficient at any time for Cumulative number of months at any time For cumulative month index, For the first Monthly time interval, , The orthogonal influence coefficient is... The chloride ion concentration in the service environment. The service environment temperature; Based on the service performance evolution function of solid waste-based concrete Construct a life-cycle material reliability model, the expression of which is:

[0019] in for Reliability of material properties at all times This is the performance failure threshold. , for The mean and standard deviation of the difference between the service performance and the performance failure threshold of solid waste-based concrete within the sliding window at any given time; The aging repair of solid waste-based concrete is assessed based on the reliability of material properties. After aging repair, the repair effect coefficient is determined based on the interfacial bond strength of the repair materials. A service performance evolution function for solid waste-based concrete is then constructed, expressed as: ; ; in For the first The repair efficiency coefficient of secondary aging repair For the first Performance recovery coefficient after secondary aging repair This represents the total number of aging repair cycles. For correction factor, For the first Secondary aging repair interface adhesion strength This represents the initial tensile strength of solid waste-based concrete.

[0020] 4. The method for predicting the carbon emissions of a solid waste-based concrete structure throughout its entire life cycle according to claim 1, characterized in that the method for predicting the number of aging repairs within the design life includes: Starting from the service life of the solid waste-based concrete structure, the reliability of the solid waste-based concrete material performance is calculated monthly using the initial solid waste-based concrete service performance evolution function and the full life cycle material reliability model. When the reliability of the solid waste-based concrete material performance is less than the reliability warning threshold, the first aging repair is carried out and the corresponding aging repair time is extracted. The service performance evolution function of solid waste-based concrete and the material reliability model of the whole life cycle are used to calculate the material performance reliability of solid waste-based concrete on a monthly basis to determine the next aging repair time. The calculation is iterated until the aging repair time is greater than the structural design service life, at which point the iteration stops and the number of aging repairs corresponding to the previous aging repair time is taken as the number of aging repairs within the design service life of solid waste-based concrete. In the actual evaluation, accelerated aging tests (300 freeze-thaw cycles) were conducted, and the test data were analyzed using an exponential decay model. The inherent aging coefficient was obtained by fitting. The orthogonal influence coefficients were calibrated through orthogonal experiments. , The environmental impact factor is updated monthly based on the historical monthly average service environment temperature and annual average service environment chloride ion concentration. ; The service performance (specifically, the compressive strength of solid waste-based concrete) of the concrete was calculated monthly using an initial solid waste-based concrete service performance evolution function. The material reliability of the solid waste-based concrete was also calculated monthly using a life-cycle material reliability model. When the concrete material reliability fell below a reliability warning threshold, the first aging repair was performed, and the corresponding aging repair time was extracted. In the life-cycle material reliability model, the performance failure threshold... Taking 70% of the initial service performance, a reliability warning threshold is set based on a structural failure probability of 5% to 0.1%. is 2.33~3.09; After the first aging repair, the repair effect coefficient is introduced and the performance recovery coefficient Update the initial solid waste-based concrete service performance evolution function to obtain the solid waste-based concrete service performance evolution function. Continue to calculate the solid waste-based concrete material performance reliability monthly in combination with the whole life cycle material reliability model. Extract the next aging repair time. Iterative calculation is performed until the aging repair time is greater than the structural design service life of 40 years. The aging repair number corresponding to the last calculated aging repair time is selected as the aging repair number within the design life of the solid waste-based concrete. The correction coefficient is 0.8~1.2, the performance recovery coefficient is 0.3~0.8, and decreases with the increase of the repair number. A net paste with a higher net paste strength than the current solid waste-based concrete net paste is used for aging repair, and the bonding strength of the aging repair interface is a constant value. The mean and standard deviation of the difference between the solid waste-based concrete service performance and the performance failure threshold value reflect the fluctuation of the solid waste-based concrete service performance caused by the temperature in the sliding window before the time point; When aging repair is performed, the surface of the structure is polished and thinned, and then new reinforcing materials are poured at the polishing interface. Taking the standard beam structure aging repair as an example, the reinforcing replacement rate of single aging repair is determined to be 5%. The reinforcing material carbon emission factor is extracted, the reinforcing material replacement volume of unit volume of solid waste-based concrete beam structure of single aging repair (unit volume*reinforcing replacement rate) is calculated, and (reinforcing material carbon emission factor*reinforcing material replacement volume) is calculated to obtain the material carbon emission of single aging repair. The polishing time is obtained from (reinforcing material replacement volume / polishing machine efficiency). The polishing machine mechanical carbon emission factor and the power grid power generation carbon emission factor are extracted, and the sum of (polishing machine mechanical carbon emission factor*polishing time) and (polishing time*polishing machine power*power grid power generation carbon emission factor) is calculated to obtain the polishing carbon emission of single aging repair. The welding time is a fixed value. The welding mechanical carbon emission factor is extracted, and the sum of (welding time*welding mechanical carbon emission factor) and (welding time*welding power*power grid power generation carbon emission factor) is calculated to obtain the welding carbon emission of single aging repair. The temporary power consumption carbon emission of single aging repair is calculated from (remaining temporary power consumption power*temporary power consumption time*power grid power generation carbon emission factor). The sum of the material carbon emission, the polishing carbon emission, the welding carbon emission and the temporary power consumption carbon emission of single aging repair is taken as the operation carbon emission of single standard aging repair. The service process carbon emission prediction is calculated from the aging repair number and the operation carbon emission of single standard aging repair.

[0021] In the embodiment, the method for constructing the demolition process carbon emission prediction model comprises: obtain historical demolition process data of different solid waste-based concrete, extract carbon emission factors corresponding to blasting raw material production, blasting process, mechanical operation, excavator operation, and transportation from a public database, extract historical demolition process features by using principal component analysis on the historical demolition process data, and calculate historical demolition process carbon emissions according to the historical demolition process data and the corresponding carbon emission factors; train a demolition process carbon emission prediction model using the historical demolition process features and the historical demolition process carbon emissions; the demolition process carbon emission prediction model includes an input layer, a feature processing layer, a prediction layer, and an output layer; The feature processing layer calculates a demolition difficulty influence factor according to the demolition process features, and obtains enhanced features by enhancing the demolition process features according to the demolition difficulty influence factor; the expression of the demolition difficulty influence factor is:

[0022] wherein is the demolition difficulty influence factor, , , is a demolition difficulty fusion weight, is a building type influence function, is a building type, , is the concrete consumption of the building and the reference consumption, , is the compressive strength of the solid waste-based concrete and the reference compressive strength, , is the elastic modulus of the solid waste-based concrete and the reference elastic modulus, is a concrete performance sensitivity coefficient; The prediction layer captures the nonlinear relationship between the enhanced features, the demolition process features, and the demolition process carbon emissions by a three-layer BP neural network, and obtains demolition process carbon emission predictions; the three-layer BP neural network includes an input layer, a hidden layer, and an output layer; the hidden layer uses an activation function; The demolition process carbon emission prediction model uses an adaptive weight loss function to improve the prediction accuracy of the model; the adaptive weight loss function is determined according to the demolition difficulty influence factor, and the expression is: ; wherein is the adaptive weight loss function, is the number of training samples, is the adaptive weight, is the demolition difficulty influence factor of the i-th sample, is the average of the demolition difficulty influence factors, is the average of the demolition difficulty influence factors, a standard deviation of a demolition difficulty factor, a real carbon emission of a demolition process, a carbon emission prediction of a demolition process, a regularization coefficient, a elastic net mixing parameter, a model weight matrix; inputting demolition process features of a solid waste-based concrete to be predicted into a real carbon emission prediction model of a demolition process to obtain a carbon emission prediction of a demolition process; In the actual evaluation, the historical demolition process data of different solid waste-based concretes are obtained, specifically including building information (building type, concrete consumption), blasting material consumption, demolition equipment power and cumulative duration, cumulative working duration of excavator, construction site, and transportation frequency; the blasting carbon emission is calculated by multiplying the blasting material consumption by (blasting raw material production carbon emission factor + blasting process carbon emission factor); the mechanical demolition carbon emission is calculated by summing (demolition equipment cumulative duration * mechanical operation carbon emission factor) and (demolition equipment power * cumulative duration * power grid power generation carbon emission factor); the excavator demolition carbon emission is calculated by (cumulative working duration of excavator * excavator operation carbon emission factor); the building waste transportation distance is obtained by searching the nearest building waste treatment site according to the construction site, and the building waste transportation carbon emission is calculated by (transportation frequency * building waste transportation distance * transportation carbon emission factor); the building demolition carbon emission is obtained by summing the blasting carbon emission, the mechanical demolition carbon emission, the excavator demolition carbon emission and the building waste transportation carbon emission, and the demolition process carbon emission (per 1m 3 of solid waste-based concrete) is calculated by (building demolition carbon emission / building concrete consumption); The demolition process features include building waste transportation distance, transportation mode, building type, building concrete consumption and solid waste-based concrete material performance; the building concrete consumption is (building engineering volume * concrete proportioning * loss coefficient); In the demolition process carbon emission prediction model, the enhanced feature vector is ; the input layer of the three-layer BP neural network includes m neurons, the hidden layer includes 2m neurons, and the output layer includes m neurons; the regularization coefficient of the adaptive weight loss function is 0.01, the elastic net mixing parameter is 0.5; inputting demolition process features of a solid waste-based concrete to be predicted into a real carbon emission prediction model of a demolition process to obtain a carbon emission prediction of a demolition process; The production process carbon emission prediction, the service process carbon emission prediction and the demolition process carbon emission prediction of the solid waste-based concrete structure to be predicted are accumulated to obtain the life cycle carbon emission prediction of the solid waste-based concrete structure per unit volume.

[0023] In the present embodiment, the solid waste-based concrete formulation comprises: solid waste cementitious material, coarse aggregate, fine aggregate, and alkali activator; the cementitious material comprises metakaolin and granulated blast furnace slag; the coarse aggregate is commercial crushed stone; the fine aggregate is machine-made sand; and the alkali activator is prepared from water, sodium hydroxide solid, and sodium silicate solution.

[0024] The above merely provides the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting the carbon emissions of solid waste-based concrete structures throughout their entire life cycle, characterized in that, Includes the following steps: S1. Obtain historical production process data of different solid waste-based concrete and extract the characteristics of historical production process and corresponding carbon emission factors. Calculate the carbon emission of historical production process and construct a carbon emission prediction model of production process to obtain the predicted carbon emission of solid waste-based concrete production process. S2. Construct the service performance evolution function of solid waste cementitious materials and the material reliability model for the whole life cycle, identify the aging repair time, and predict the number of aging repairs within the design life by combining the service environment of the solid waste-based concrete structure to be predicted. S3. Extract standard aging repair operation data and corresponding carbon emission factors to calculate the carbon emission of a single standard aging repair operation. Calculate the predicted carbon emission during service life based on the number of aging repairs and the carbon emission of a single standard aging repair operation. S4. Obtain historical demolition process data of different solid waste-based concrete, extract the characteristics of the historical demolition process and the corresponding carbon emission factors, calculate the carbon emission of the historical demolition process, and construct a carbon emission prediction model of the demolition process to obtain the predicted carbon emission of the solid waste-based concrete demolition process. S5. Determine the total life-cycle carbon emission forecast of solid waste-based concrete structures based on the predicted carbon emissions during the production process, the service process, and the demolition process. The production process data includes raw material data, transportation data, and preparation data for solid waste-based concrete; the historical production process characteristics include material characteristics, transportation characteristics, and preparation characteristics. The carbon emission factors are obtained by querying a publicly available carbon emission factor database. The production process includes raw material acquisition, raw material transportation, and concrete preparation.

2. The method for predicting the carbon emissions of solid waste-based concrete structures throughout their entire life cycle according to claim 1, characterized in that, The method for constructing a carbon emission prediction model for the production process includes: Historical production process data of different solid waste-based concrete were obtained. Carbon emission factors corresponding to raw material production, transportation, power generation, and mechanical operation were extracted from public databases. Principal component analysis was used to extract historical production process data to obtain historical production process characteristics. Based on historical production process data and corresponding carbon emission factors, the carbon emissions of the production process were calculated, including raw material carbon emissions, transportation carbon emissions, and preparation carbon emissions. A carbon emission prediction model for the production process is trained using the carbon emissions from the production process and historical production process characteristics. The carbon emission prediction model for the production process includes an input layer, a feature enhancement layer, a prediction regression layer, and a policy output layer. The feature enhancement layer uses a fully connected neural network to perform high-dimensional mapping of historical production process features; The prediction regression layer uses the raw material prediction head, transportation prediction head, and preparation prediction head to predict the carbon emissions of raw materials, carbon emissions of carbon transportation, and carbon emissions of preparation, respectively. The strategy output layer sums the outputs of the three prediction heads through a fully connected network to obtain the predicted carbon emissions of the production process. The carbon emission prediction model for the production process adjusts the prediction accuracy through prediction loss, physical constraint loss, and sparsity loss, as expressed in the following expression: ； ； ； ； in For the total loss function, To predict losses, For physical constraint loss, For sparsity loss, , , For weight hyperparameters, The number of training samples. , , , To predict weights, , For the first The actual and predicted values ​​of raw material carbon emissions for the sample group. , For the actual and predicted values ​​of carbon emissions from transportation, , To prepare true and predicted values ​​of carbon emissions, , This represents the actual and predicted values ​​of carbon emissions during the production process. The output of the feature enhancement layer represents the production process feature dimension. For the first The weight parameters of each production process feature in the prediction head. This is the task weight index.

3. The method for predicting the carbon emissions of solid waste-based concrete structures throughout their entire life cycle according to claim 1, characterized in that, The method for constructing the service performance evolution function and the full life cycle material reliability model of solid waste cementitious materials includes: Accelerated aging tests were conducted on the solid waste-based concrete to be predicted. The inherent aging coefficient was determined by fitting the accelerated aging test results. The environmental impact coefficient was calculated based on the service environment parameters of the solid waste-based concrete structure to be predicted. An initial service performance evolution function for the solid waste-based concrete was constructed from the inherent aging coefficient and the environmental impact coefficient, with the following expression: ； ； in for The service performance of solid waste-based concrete. According to monthly statistics For the initial service performance of solid waste-based concrete, The inherent aging coefficient, for Environmental impact coefficient at any time for Cumulative number of months at any time For cumulative month index, For the first Monthly time interval, , The orthogonal influence coefficient is... The chloride ion concentration in the service environment. The service environment temperature; Based on the service performance evolution function of solid waste-based concrete Construct a life-cycle material reliability model, the expression of which is: ； in for Reliability of material properties at all times This is the performance failure threshold. , for The mean and standard deviation of the difference between the service performance and the performance failure threshold of solid waste-based concrete within the sliding window at any given time; The aging repair of solid waste-based concrete is assessed based on the reliability of material properties. After aging repair, the repair effect coefficient is determined based on the interfacial bond strength of the repair materials. A service performance evolution function for solid waste-based concrete is then constructed, expressed as: ; ； in For the first The repair efficiency coefficient of secondary aging repair For the first Performance recovery coefficient after secondary aging repair This represents the total number of aging repair cycles. For correction factor, For the first Secondary aging repair interface adhesion strength This represents the initial tensile strength of solid waste-based concrete.

4. The method for predicting the carbon emissions of solid waste-based concrete structures throughout their entire life cycle according to claim 1, characterized in that, The method for predicting the number of aging repairs within the design life includes: Starting from the service life of the solid waste-based concrete structure, the reliability of the solid waste-based concrete material performance is calculated monthly using the initial solid waste-based concrete service performance evolution function and the full life cycle material reliability model. When the reliability of the solid waste-based concrete material performance is less than the reliability warning threshold, the first aging repair is carried out and the corresponding aging repair time is extracted. The reliability of solid waste-based concrete material performance is calculated monthly using the service performance evolution function of solid waste-based concrete and the full life cycle material reliability model. The next aging repair time is determined, and the calculation is iterated until the aging repair time is greater than the structural design service life. The iteration stops when the time is greater than the structural design service life. The number of aging repairs corresponding to the previous aging repair time is taken as the number of aging repairs within the design service life of solid waste-based concrete.

5. The method for predicting the carbon emissions of a solid waste-based concrete structure throughout its entire life cycle according to claim 1, characterized in that, The method for constructing a carbon emission prediction model for the demolition process includes: Historical demolition process data of different solid waste-based concrete were obtained. Carbon emission factors corresponding to blasting raw material production, blasting process, mechanical operation, excavator operation and transportation were extracted from public databases. Principal component analysis was used to extract historical demolition process data to obtain historical demolition process characteristics. Carbon emissions of historical demolition process were calculated based on historical demolition process data and corresponding carbon emission factors. A carbon emission prediction model for the demolition process is trained using historical demolition process characteristics and historical demolition process carbon emissions; the carbon emission prediction model for the demolition process includes an input layer, a feature processing layer, a prediction layer, and an output layer; The feature processing layer calculates a demolition difficulty influence factor based on the demolition process features, and enhances the demolition process features based on the demolition difficulty influence factor to obtain enhanced features; the expression for the demolition difficulty influence factor is: ； in Factors affecting the difficulty of demolition, , , To integrate the difficulty of dismantling, For the influence function of building type, For building type, , This provides the usage and reference dosage of concrete for construction. , The compressive strength and reference compressive strength of solid waste-based concrete. , The elastic modulus and reference elastic modulus of solid waste-based concrete are given. This is the sensitivity coefficient for concrete performance. The prediction layer captures the nonlinear relationship between enhancement features, dismantling process features, and carbon emissions during the dismantling process using a three-layer BP neural network to obtain the predicted carbon emissions during the dismantling process. The three-layer BP neural network includes an input layer, a hidden layer, and an output layer. The hidden layer uses an activation function. The carbon emission prediction model for the demolition process employs an adaptive weight loss function to improve prediction accuracy. This adaptive weight loss function is determined based on the demolition difficulty influencing factors, and its expression is: ； in For adaptive weight loss function, The number of training samples. For adaptive weights, For the first The difficulty of dismantling the sample group is an influencing factor. The average value of the factors affecting the difficulty of demolition. The standard deviation of the factor affecting the difficulty of demolition. This represents the actual carbon emissions during the demolition process. This is the predicted amount of carbon emissions during the demolition process. The regularization coefficient is . For the mixed parameters of the elastic network, This is the model weight matrix; Input the characteristics of the solid waste-based concrete demolition process to be predicted into the carbon emission prediction model of the demolition process to obtain the predicted carbon emission of the demolition process.

6. The method for predicting the carbon emissions of a solid waste-based concrete structure throughout its entire life cycle according to claim 1, characterized in that, The solid waste-based concrete formula includes: solid waste cementitious materials, coarse aggregate, fine aggregate, and alkali activator; the cementitious materials include metakaolin and granulated blast furnace slag; the coarse aggregate is commercial crushed stone; the fine aggregate is manufactured sand; and the alkali activator is prepared from water, solid sodium hydroxide, and sodium silicate solution.

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