# Reinforcement Model of Green Building Materials Based on Grey Neural Networks

Authors: Dengdeng Zheng; Guojie Wang; Yongjin Li
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the rapid growth of China’s economy, all kinds of large or super-large buildings are constantly emerging, people pay more and more attention to building safety and environmental protection, and the reinforcement technology of green building materials plays an increasingly important role. The design involves a wide range of reinforcement. In the concrete design, it is necessary to comprehensively consider various factors, improve the safety of material structure through effective measures and ways, and realize the stable development of the construction industry. This paper analyzes and discusses the reinforcement design of green building materials. Grey model and NN model are two commonly used models in building prediction. This paper applies it to the reinforcement of green building materials and constructs a combined grey NN model. It combines the advantages of grey theory model and artificial NN model. Through the organic combination of grey theory and NN model, complex uncertain problems can be solved. It will be used in data processing of reinforcement monitoring and can get ideal calculation results. The reinforcement design in this paper can improve the use safety of buildings and realize the stability and sustainable development of the industry.


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## Body

## 1. Introduction

With the rapid development of social economy, China’s construction industry has also made great progress. In this context, people pay more attention to the safety of construction projects [1]. At the same time, the resource consumption brought by architecture has become an important factor restricting economic development [2]. At present, under the double constraints of resources and environment, and the challenge of construction efficiency, the construction industry is facing a green and energy-efficient technological change. Although the development situation of green buildings is good, there are still many problems [3]. For example, lack of perfect laws and regulations, neglect of technology integration, complete technical system, and standard system have not yet been formed, green building technology integration system for regional and functional applicability has not yet been established, the scale of promotion and demonstration of green buildings is still small, and extensive display and leading effect has not yet been exerted. The building should not only meet the needs of building use but also further enhance the safety and stability of the overall structure of the building and prolong the service life of the building [4]. In order to avoid the potential safety hazard caused by external factors, it is necessary to strengthen the structural reinforcement design. Through integrated design, process production, and modular assembly of prefabricated components, assembled buildings can achieve quick loading, quick unloading, and reuse of green building wall materials [5]. Its outer wall panels are mostly designed to be movable in order to achieve better shock resistance. After construction, however, due to internal reaction and moisture reduction, the precast fiberized cement slab will shrink significantly over time, resulting in expansion of expansion joints [6]. The constructed concrete exterior wall panel is currently a porous material that has been in a hydration reflection state for a long time and cannot be effectively bonded, causing the green assembly building to lose its firmness. To improve the safety of building materials and structures, and to promote the development of the construction industry, comprehensive consideration of various factors and effective reinforcement design measures are required in concrete design [7].The application of green buildings in China has made some achievements, the proportion of energy-saving buildings has increased year by year, and the concept of green buildings has been generally recognized and achieved certain results. Prefabricated green building is a building made up of prefabricated components by integrated production and quick assembly [8]. The internal and external wall panels, beams and columns, stairs, etc. are mainly assembled by flow method, and because of the heterogeneity of the spliced joints and the mobility of the wall panels, the demand for reinforcement is increased. For the reinforcement of existing building materials, there are currently some methods such as adding buttresses, adding steel mesh-mortar layer, and outsourcing reinforced concrete [9]. It is more practical to adopt jacket reinforcement technology, which is to add precast wallboard system to two original building structures to build balconies, and at the same time, to reinforce the original masonry structure and improve its antipressure and antiseismic capacity [10]. Prestress is used to reinforce the structure by prestressed steel or steel tie rods, which can improve the stability and bearing capacity of the structure. With the double force of load and prestress, the building bearing capacity and bending resistance can be improved, thus, effectively preventing the structure from deforming and improving the bearing capacity [11]. It is of theoretical and practical significance to adopt an economic and effective reinforcement scheme to reinforce the existing prefabricated building exterior wall materials. However, because of the different structural characteristics and reinforcement requirements of reinforced buildings, it is necessary to construct the reinforcement component library with the help of technology, and flexibly allocate and design reinforcement models according to the requirements to meet the reinforcement requirements of different buildings. In this paper, the reinforcement model of green building materials is studied based on neural network (NN) [12].The structure of building materials is closely related to the property and life safety of users. If the service life of the building is long, its structure will easily lead to various factors and potential safety hazards [13]. In order to meet the actual needs of users, construction enterprises need to flexibly select reinforcement technology, grasp the technical points and key links of reinforcement construction, effectively improve the quality of reinforcement construction, and ensure the stability and reliability of building structure [14]. Through scientific reinforcement design, the stability and safety of the building can be improved. This paper analyzes and discusses the reinforcement design of green building materials structure and explains the main reinforcement design methods combined with the reinforcement principle of building materials structure. This paper expounds on how to build the reinforcement model of green building materials with the help of model building, model simulation, and model application technologies widely used in the field of scientific research. This paper discusses the significance and function of the reinforcement model of green building materials. On this basis, it focuses on the composition, construction steps, and methods of the model and analyzes and perfects the combination form of grey model and NN model. Through calculation and analysis with examples, it is found that the prediction accuracy of this grey NN combination model is better than that of grey model and NN. This shows that the model in this paper has absorbed the advantages of the two single models and has obvious advantages in practical application. I hope this study can give some reference to China’s construction industry.


## 2. Related Work

Based on the quality-benefit game, literature [15] proposes a cost-control method for bonding and strengthening the performance of green building materials for wall repair. In literature [16], the constraint parameter model of cost control of bonding and strengthening performance of green building materials is constructed in order to achieve cost prediction control. Artificial NN is introduced in the literature [17]. The learning algorithm of NN is described in detail, as well as the shortcomings and methods for improving NN. The main architectural design methods are explained in combination with architectural structure design principles in literature [18]. According to literature [19], deformation occurs during the construction and operation of engineering buildings due to the influence of many factors. If the deformation exceeds the prescribed limit, it will have an impact on the normal use of buildings, and in severe cases, it will jeopardize building safety and result in significant societal and human losses. The key technologies of building energy conservation, building model system, and database construction are discussed in literature [20]. The related technical systems and their relationships are discussed as a whole, and the key technologies of each part of the model system are discussed on this basis. From a technical standpoint, try to clarify the key technologies of building energy conservation, building model system, and database construction. Literature [21] focuses on the composition, construction steps, and methods of green building model system. Literature [22] holds that the development of building energy conservation and green building in China will inevitably go through a process from local to global, from primary to advanced. In this process, there are problems of understanding and development of the construction industry and the whole society, as well as many specific policy, technical, and economic problems. These are inevitable in the process of green building development. How to seize the opportunity requires deeper thinking, and more practical actions by all parties. Literature [23] introduces the modeling principle and method of NN model and uses this model to process and analyze the actual monitoring data.In order to ensure the safety and stability of the building structure, it is necessary to start from the perspective of the whole building structure, flexibly select the reinforcement technology according to the actual building material structure situation, and supervise and control the technology, personnel, and key links in the actual reinforcement construction, so as to effectively improve the reinforcement effect of the building structure and enhance the overall safety and reliability of the building. In this paper, grey model and NN are applied to the reinforcement of green building materials, and a combined grey NN model is constructed. By using the method of grouping regression test and analysis, the reinforcement performance control of green building materials is carried out, and the benefit test of bonding reinforcement of green building materials is carried out. Combining with the method of fuzzy decision-making, the reinforcement performance control optimization of green building materials is realized. It is known that the reinforcement effect of green building materials by the method in this paper is better, and the reinforcement benefit of buildings is improved.


## 3. Methodology

### 3.1. Grey NN

Grey system theory is a new interdisciplinary subject that focuses on extracting valuable information from a small amount of known data through generation and development, as well as controlling system changes [24]. Grey theoretical model is a nonlinear extrapolation prediction method that has been widely used in the fields of social science and natural science due to its advantages of less sample data, flexible and convenient modeling, high prediction accuracy, testability, and so on. Grey theory focuses on the problem of “clear extension and unclear connotation,” and one of its main theoretical features is “poor information modeling,” which uses sequence operators to reveal the problem’s inherent law [25]. Grey system theory is a combination of automatic control theory and operational research theory, and it is one of the most effective mathematical methods for solving uncertain information.GM(1,l) model uses the new data generated by accumulation to model, which weakens the randomness of the original data to a certain extent, makes it easy to find out the data change rule, and has the advantage of less samples for modeling [26]. When establishing dynamic models with grey theory, continuous differential equations representing time series are often used, and the dynamic analysis of discrete and continuous systems is used as a means to reflect and control the dynamics of the system. The use of grey theory can truly reflect the law of movement of things and also provide the necessary conditions for the control research of the system.Artificial NN [27] refers to a network structure composed of many similar basic units, which can respond to external stimuli-like biological meridian system. NN will be followed by a loss function layer to calculate the loss and gradient according to different tasks, so as to update the network weight through the back propagation of gradient descent method and chain rule of compound function gradient. Artificial NN is an abstraction, simplification, and simulation of the nervous system of human brain, which is not a true description. It has its own characteristics in structure, performance, and ability. In terms of structure, NN has the characteristics of parallelism of information processing, distribution of information storage, interconnection of information processing units, and plasticity of structure [28]. In terms of performance, it has the characteristics of high nonlinearity, good fault tolerance, and inaccurate calculation. In terms of ability, it has the characteristics of self-study, self-organization, and self-adaptability. The basic unit of NN is called “neuron,” as shown in Figure 1 below.Figure 1
Schematic diagram of neuron.The advantage of the NN model is that it has a high short-term prediction accuracy, has local teleproximity network characteristics, and can approximate any nonlinear function. The grey network model [29] is an organic blend of grey theory and the NN model that can solve complex uncertain problems. The NN model is made up of a large number of complex neurons. To realize the storage and processing of complex information in the NN model, we used an interconnection structure, large-scale parallel interconnection, and nonlinear processing. Each neuron in NN must be connected according to certain rules, which determine how the connection weight changes. Parallel computing, distributed information storage, strong fault tolerance, adaptive learning function, and other advantages of the NN model make it extremely superior in dealing with complex artificial intelligence and nonlinear problems [30].


### 3.2. Strengthening Design of Green Building Materials

With the development of green buildings, people pay great attention to the research on the reinforcement technology of green building materials, and the model of green building materials reinforcement is needed and applied to the construction of green building materials reinforcement. While improving the reinforcement performance of green building materials, reducing the cost, and improving the quality of green buildings.Before reinforcing the building material structure, we should start from the overall perspective of the building engineering, that is, the requirements of the overall stability of the building on the building materials. Before determining the reinforcement scheme, we should make an overall analysis of the building material structure and then formulate the reinforcement optimization scheme according to the possible defects of the material structure. In the later construction, we should not arbitrarily change the scheme content to avoid the influence of design problems on the performance of the building structure and even secondary damage to the building structure. At the same time, in the design of the reinforcement scheme, the integrity of the building structure is comprehensively considered to avoid the negative impact of the reinforcement construction on the overall stability of the structure. The seismic performance and structural load value of the building structure should be calculated and analyzed, and the final reinforcement scheme determined based on the given data, so that the designed building structure can be uniformly stressed and the structure’s stability can be further ensured. To ensure an effective connection between the original structure and the reinforcement parts, the reinforcement materials used in the structural reinforcement design should be as similar as possible to the building structural materials. If the original structural materials are difficult to come by, it is necessary to evaluate their performance and choose materials that are similar. The construction process of green building material index system is shown in Figure2.Figure 2
Construction process of green building material index system.Before the reinforcement design, in order to ensure the effectiveness of the reinforcement design scheme, it is necessary to organize relevant personnel to carry out detailed inspection of the building structure, analyze the existing quality defects in the building structure and the places that need to be reinforced and optimized, and then carry out the reinforcement design work in strict accordance with the design standards and reinforcement construction specifications. And sampling according to the standards and specifications, monitoring the samples, and taking the inspection report as an important basis for strengthening design.In the reinforcement design and construction, some reinforcement techniques and methods have high requirements for technicians, and the construction unit must have a construction team with strong comprehensive quality to complete the structural reinforcement work with good quality and quantity, otherwise, it cannot completely follow the reinforcement design scheme and achieve ideal reinforcement results. Therefore, the construction unit needs to set up a strong and experienced construction team. Before reinforcement construction, professional training should be given to constructors, and the quality of construction materials should be checked. For welding work, it is necessary to ensure that constructors have certificates and the quality of reinforcement construction.In the long-term operation of buildings, steel bars will be corroded by many external factors. If the corrosion and rust of the steel structure in the building are not found before the reinforcement design, it will affect the later reinforcement construction. Therefore, if there is an old steel structure in the building, it is necessary to organize personnel to carry out derusting operations, remove the rust on the surface of the old steel, comprehensively analyze the basic performance and damage degree of the structure, and design a reasonable and scientific reinforcement scheme through accurate calculation and demonstration. The reinforcement construction must be done in accordance with the original building’s overall bearing capacity and the current site situation, with special attention paid to site quality management to ensure that all reinforcement construction operations follow the original technical specifications. After each construction process is completed, strict quality acceptance is required to ensure that the quality is qualified before the next process can begin.


### 3.3. Building Material Reinforcement Model

Through the continuous training of samples, the change of NN connection weights and its topological structure can be realized, so as to achieve the goal that the network output gradually approaches the expected output. We call this process a learning and training process, and its main function is to dynamically adjust the connection weights. The performance of NN information processing is determined by the learning method of NN, and the learning rules or training algorithms are rules for changing the adjustment weights. BPNN is a multilayer feedforward network, and the transformation function of its neurons is an S-type function, so the output is a continuous quantity between 0 and 1, which can realize any nonlinear mapping from input to output, and the weights are adjusted by back propagation learning algorithm.The BP algorithm has two parts: forward information transmission and backward error propagation. The input information is given, processed layer by layer by the hidden layer through the input layer, and the actual output value of each unit is calculated in the forward transmission process. If the expected output value cannot be found in the output layer, the difference between the actual output and the expected output is calculated recursively layer by layer, and the weight is adjusted accordingly. After determining the structure of the BPNN, the input-output sample set is used to train it, and the network’s weights and values are studied and adjusted so that the network can realize the given input-output mapping relationship. The trained BPNN can also give appropriate output for the input that is not in the sample set, but this sample set must contain information similar to that of the training sample set, otherwise, it is necessary to retrain to find appropriate weights in order to make accurate forecast. The constraint parameters of the reinforcement performance of green building materials are recorded as BU and BV, BU and BV, and the control model of the reinforcement performance of green building materials can be expressed as follows:(1)BU=∑j=1MMaxj∈NjBjk,(2)BV=∑j=1MMinj∈NjBjk.In the construction of green building materials reinforcement project, the grouping detection design is carried out in the construction stage and reinforcement stage. In information theory, entropy is a measure of uncertainty. The uncertainty and entropy decrease as the amount of information increases. The greater the uncertainty and entropy, the less information there is. The degree of randomness and disorder of an event can be judged by calculating entropy value, and the degree of dispersion of an index can also be judged by entropy value, according to the characteristics of entropy. The greater an index’s degree of dispersion, the greater its influence on a comprehensive evaluation. The cost control method is used to control the green building materials reinforcement, and the cost prediction and engineering cost control of the green building materials reinforcement are carried out by combining the cost control and the related constraint parameter analysis method.Family is a collection of similar building engineering components. By constructing the strengthening design family library, it can be fully used in different building areas. Construction of external wall panel family: the direction of external wall and balcony board of green prefabricated building can be divided into south side and north side, and the lettersS and N can be used to divide the family reservoir area. If the layout is not in the north-south side, the logo can be appropriately adjusted. GM(1,1) model is used to calculate the residual sequence and normalize it. The normalized result is used as input data, the predicted value is calculated by BPNN, and finally, the reinforced predicted value is obtained. The number of neurons in BPNN input layer is 4, the number of neurons in hidden layer is 8, and the number of neurons in output layer is 1. GM(1,1) model and network model are realized by VC++ programming. Enter the family library design interface, stretch the entity according to the reference plane, and lock the reference plane, so as to associate the wall with the reference plane, which is convenient to directly adjust and control the position of the reference plane and change the parameters of the wall when building the reinforcement model. Let the initial value of the model be as follows.
(3)x01+αnkn+αn−1kn−1+⋯+α1k+α0.The initial value is substituted into the response of GM(1,1) model.(4)x∧1k+1=x01+∑i=0nαik+1i−uae−ak+ua.Let us taken=1, then,
(5)x∧1k=x01+α1k+α0−uae−ak+ua.Define the following objective function.(6)J=∑k=1nx∧1k−x1k2=∑k=1nx01+α1k+α0−uae−ak−1+ua−x1k2.The model includes functions such as energy consumption analysis and simulation based on model, energy consumption prediction, analysis, simulation, and early warning, comparison of simulation data and actual measurement data, automatic energy consumption optimization based on model simulation, and three-dimensional visual simulation based on geospatial data, allowing for intuitive and vivid energy consumption simulation of regional energy consumption density. During continuous optimization operations, guide the strengthening of green energy-saving building design and construction, as well as energy management. The mode of automatically generating components between two points loaded into the project can be used in conjunction with the corresponding family parameter formula to adjust component connectors to component sizes, automatically determine the number and spacing, and improve model strengthening efficiency.In the process of building reinforcement, the analysis and calculation of the structure is the key. The response function regards these discrete data as continuous changes, they all conform to a certain function rule, and they all change continuously. Using these continuous, regular, and limited data, we can fit a differential equation, find out its changing rule, and then predict the development trend of the data. We can better fit the parameters of the differential equation by using the time response function to model NN, and the fitting result is more reasonable. The model precision calculation formula is(7)m=∑i=1nei2n−11/2.The absolute error calculation formula is(8)MAE=1n∑i=1nyi−y^i,where ei is the residual error and n is the corresponding number of samples.Visual simulation is a new type of simulation technology formed by the combination of computer visualization technology and system modeling technology. Its essence is to use graphics or images to track, control, and postprocess the simulation calculation process, and at the same time, realize the visualization of the simulation software interface, which has the modeling characteristics of speed, efficiency, intuition, and image. Building 3D visual simulation technology, which integrates the design process and operation process of green building materials reinforcement, will make the visualization of green building materials reinforcement closer to details, more visual, and dynamic through 3D technology. The model system is gradually optimized due to accumulation through repeated application and then has a direct positive effect on the building’s strengthening effect. It has great guiding significance for the overall strengthening design of the planned new building, not only because it can dig deep into the existing building’s potential for strengthening but also because it can dig deep into the existing building’s potential for strengthening. The object set of reinforcement of green building materials is reasonably determined in this chapter, and the analysis model of reinforcement performance of green building materials based on grey NN is constructed to improve the reinforcement ability of green building materials.


## 3.1. Grey NN

Grey system theory is a new interdisciplinary subject that focuses on extracting valuable information from a small amount of known data through generation and development, as well as controlling system changes [24]. Grey theoretical model is a nonlinear extrapolation prediction method that has been widely used in the fields of social science and natural science due to its advantages of less sample data, flexible and convenient modeling, high prediction accuracy, testability, and so on. Grey theory focuses on the problem of “clear extension and unclear connotation,” and one of its main theoretical features is “poor information modeling,” which uses sequence operators to reveal the problem’s inherent law [25]. Grey system theory is a combination of automatic control theory and operational research theory, and it is one of the most effective mathematical methods for solving uncertain information.GM(1,l) model uses the new data generated by accumulation to model, which weakens the randomness of the original data to a certain extent, makes it easy to find out the data change rule, and has the advantage of less samples for modeling [26]. When establishing dynamic models with grey theory, continuous differential equations representing time series are often used, and the dynamic analysis of discrete and continuous systems is used as a means to reflect and control the dynamics of the system. The use of grey theory can truly reflect the law of movement of things and also provide the necessary conditions for the control research of the system.Artificial NN [27] refers to a network structure composed of many similar basic units, which can respond to external stimuli-like biological meridian system. NN will be followed by a loss function layer to calculate the loss and gradient according to different tasks, so as to update the network weight through the back propagation of gradient descent method and chain rule of compound function gradient. Artificial NN is an abstraction, simplification, and simulation of the nervous system of human brain, which is not a true description. It has its own characteristics in structure, performance, and ability. In terms of structure, NN has the characteristics of parallelism of information processing, distribution of information storage, interconnection of information processing units, and plasticity of structure [28]. In terms of performance, it has the characteristics of high nonlinearity, good fault tolerance, and inaccurate calculation. In terms of ability, it has the characteristics of self-study, self-organization, and self-adaptability. The basic unit of NN is called “neuron,” as shown in Figure 1 below.Figure 1
Schematic diagram of neuron.The advantage of the NN model is that it has a high short-term prediction accuracy, has local teleproximity network characteristics, and can approximate any nonlinear function. The grey network model [29] is an organic blend of grey theory and the NN model that can solve complex uncertain problems. The NN model is made up of a large number of complex neurons. To realize the storage and processing of complex information in the NN model, we used an interconnection structure, large-scale parallel interconnection, and nonlinear processing. Each neuron in NN must be connected according to certain rules, which determine how the connection weight changes. Parallel computing, distributed information storage, strong fault tolerance, adaptive learning function, and other advantages of the NN model make it extremely superior in dealing with complex artificial intelligence and nonlinear problems [30].


## 3.2. Strengthening Design of Green Building Materials

With the development of green buildings, people pay great attention to the research on the reinforcement technology of green building materials, and the model of green building materials reinforcement is needed and applied to the construction of green building materials reinforcement. While improving the reinforcement performance of green building materials, reducing the cost, and improving the quality of green buildings.Before reinforcing the building material structure, we should start from the overall perspective of the building engineering, that is, the requirements of the overall stability of the building on the building materials. Before determining the reinforcement scheme, we should make an overall analysis of the building material structure and then formulate the reinforcement optimization scheme according to the possible defects of the material structure. In the later construction, we should not arbitrarily change the scheme content to avoid the influence of design problems on the performance of the building structure and even secondary damage to the building structure. At the same time, in the design of the reinforcement scheme, the integrity of the building structure is comprehensively considered to avoid the negative impact of the reinforcement construction on the overall stability of the structure. The seismic performance and structural load value of the building structure should be calculated and analyzed, and the final reinforcement scheme determined based on the given data, so that the designed building structure can be uniformly stressed and the structure’s stability can be further ensured. To ensure an effective connection between the original structure and the reinforcement parts, the reinforcement materials used in the structural reinforcement design should be as similar as possible to the building structural materials. If the original structural materials are difficult to come by, it is necessary to evaluate their performance and choose materials that are similar. The construction process of green building material index system is shown in Figure2.Figure 2
Construction process of green building material index system.Before the reinforcement design, in order to ensure the effectiveness of the reinforcement design scheme, it is necessary to organize relevant personnel to carry out detailed inspection of the building structure, analyze the existing quality defects in the building structure and the places that need to be reinforced and optimized, and then carry out the reinforcement design work in strict accordance with the design standards and reinforcement construction specifications. And sampling according to the standards and specifications, monitoring the samples, and taking the inspection report as an important basis for strengthening design.In the reinforcement design and construction, some reinforcement techniques and methods have high requirements for technicians, and the construction unit must have a construction team with strong comprehensive quality to complete the structural reinforcement work with good quality and quantity, otherwise, it cannot completely follow the reinforcement design scheme and achieve ideal reinforcement results. Therefore, the construction unit needs to set up a strong and experienced construction team. Before reinforcement construction, professional training should be given to constructors, and the quality of construction materials should be checked. For welding work, it is necessary to ensure that constructors have certificates and the quality of reinforcement construction.In the long-term operation of buildings, steel bars will be corroded by many external factors. If the corrosion and rust of the steel structure in the building are not found before the reinforcement design, it will affect the later reinforcement construction. Therefore, if there is an old steel structure in the building, it is necessary to organize personnel to carry out derusting operations, remove the rust on the surface of the old steel, comprehensively analyze the basic performance and damage degree of the structure, and design a reasonable and scientific reinforcement scheme through accurate calculation and demonstration. The reinforcement construction must be done in accordance with the original building’s overall bearing capacity and the current site situation, with special attention paid to site quality management to ensure that all reinforcement construction operations follow the original technical specifications. After each construction process is completed, strict quality acceptance is required to ensure that the quality is qualified before the next process can begin.


## 3.3. Building Material Reinforcement Model

Through the continuous training of samples, the change of NN connection weights and its topological structure can be realized, so as to achieve the goal that the network output gradually approaches the expected output. We call this process a learning and training process, and its main function is to dynamically adjust the connection weights. The performance of NN information processing is determined by the learning method of NN, and the learning rules or training algorithms are rules for changing the adjustment weights. BPNN is a multilayer feedforward network, and the transformation function of its neurons is an S-type function, so the output is a continuous quantity between 0 and 1, which can realize any nonlinear mapping from input to output, and the weights are adjusted by back propagation learning algorithm.The BP algorithm has two parts: forward information transmission and backward error propagation. The input information is given, processed layer by layer by the hidden layer through the input layer, and the actual output value of each unit is calculated in the forward transmission process. If the expected output value cannot be found in the output layer, the difference between the actual output and the expected output is calculated recursively layer by layer, and the weight is adjusted accordingly. After determining the structure of the BPNN, the input-output sample set is used to train it, and the network’s weights and values are studied and adjusted so that the network can realize the given input-output mapping relationship. The trained BPNN can also give appropriate output for the input that is not in the sample set, but this sample set must contain information similar to that of the training sample set, otherwise, it is necessary to retrain to find appropriate weights in order to make accurate forecast. The constraint parameters of the reinforcement performance of green building materials are recorded as BU and BV, BU and BV, and the control model of the reinforcement performance of green building materials can be expressed as follows:(1)BU=∑j=1MMaxj∈NjBjk,(2)BV=∑j=1MMinj∈NjBjk.In the construction of green building materials reinforcement project, the grouping detection design is carried out in the construction stage and reinforcement stage. In information theory, entropy is a measure of uncertainty. The uncertainty and entropy decrease as the amount of information increases. The greater the uncertainty and entropy, the less information there is. The degree of randomness and disorder of an event can be judged by calculating entropy value, and the degree of dispersion of an index can also be judged by entropy value, according to the characteristics of entropy. The greater an index’s degree of dispersion, the greater its influence on a comprehensive evaluation. The cost control method is used to control the green building materials reinforcement, and the cost prediction and engineering cost control of the green building materials reinforcement are carried out by combining the cost control and the related constraint parameter analysis method.Family is a collection of similar building engineering components. By constructing the strengthening design family library, it can be fully used in different building areas. Construction of external wall panel family: the direction of external wall and balcony board of green prefabricated building can be divided into south side and north side, and the lettersS and N can be used to divide the family reservoir area. If the layout is not in the north-south side, the logo can be appropriately adjusted. GM(1,1) model is used to calculate the residual sequence and normalize it. The normalized result is used as input data, the predicted value is calculated by BPNN, and finally, the reinforced predicted value is obtained. The number of neurons in BPNN input layer is 4, the number of neurons in hidden layer is 8, and the number of neurons in output layer is 1. GM(1,1) model and network model are realized by VC++ programming. Enter the family library design interface, stretch the entity according to the reference plane, and lock the reference plane, so as to associate the wall with the reference plane, which is convenient to directly adjust and control the position of the reference plane and change the parameters of the wall when building the reinforcement model. Let the initial value of the model be as follows.
(3)x01+αnkn+αn−1kn−1+⋯+α1k+α0.The initial value is substituted into the response of GM(1,1) model.(4)x∧1k+1=x01+∑i=0nαik+1i−uae−ak+ua.Let us taken=1, then,
(5)x∧1k=x01+α1k+α0−uae−ak+ua.Define the following objective function.(6)J=∑k=1nx∧1k−x1k2=∑k=1nx01+α1k+α0−uae−ak−1+ua−x1k2.The model includes functions such as energy consumption analysis and simulation based on model, energy consumption prediction, analysis, simulation, and early warning, comparison of simulation data and actual measurement data, automatic energy consumption optimization based on model simulation, and three-dimensional visual simulation based on geospatial data, allowing for intuitive and vivid energy consumption simulation of regional energy consumption density. During continuous optimization operations, guide the strengthening of green energy-saving building design and construction, as well as energy management. The mode of automatically generating components between two points loaded into the project can be used in conjunction with the corresponding family parameter formula to adjust component connectors to component sizes, automatically determine the number and spacing, and improve model strengthening efficiency.In the process of building reinforcement, the analysis and calculation of the structure is the key. The response function regards these discrete data as continuous changes, they all conform to a certain function rule, and they all change continuously. Using these continuous, regular, and limited data, we can fit a differential equation, find out its changing rule, and then predict the development trend of the data. We can better fit the parameters of the differential equation by using the time response function to model NN, and the fitting result is more reasonable. The model precision calculation formula is(7)m=∑i=1nei2n−11/2.The absolute error calculation formula is(8)MAE=1n∑i=1nyi−y^i,where ei is the residual error and n is the corresponding number of samples.Visual simulation is a new type of simulation technology formed by the combination of computer visualization technology and system modeling technology. Its essence is to use graphics or images to track, control, and postprocess the simulation calculation process, and at the same time, realize the visualization of the simulation software interface, which has the modeling characteristics of speed, efficiency, intuition, and image. Building 3D visual simulation technology, which integrates the design process and operation process of green building materials reinforcement, will make the visualization of green building materials reinforcement closer to details, more visual, and dynamic through 3D technology. The model system is gradually optimized due to accumulation through repeated application and then has a direct positive effect on the building’s strengthening effect. It has great guiding significance for the overall strengthening design of the planned new building, not only because it can dig deep into the existing building’s potential for strengthening but also because it can dig deep into the existing building’s potential for strengthening. The object set of reinforcement of green building materials is reasonably determined in this chapter, and the analysis model of reinforcement performance of green building materials based on grey NN is constructed to improve the reinforcement ability of green building materials.


## 4. Result Analysis and Discussion

The grey model combines the advantages of grey theory and NN model. It can simulate and forecast the reinforcement monitoring data accurately in the case of small sample, poor information, and fluctuation data series and has the advantages of simple model, no need to determine nonlinear function and convenient calculation, which can provide a better method for data processing of reinforcement monitoring. After the transformation, the grey differential equation is completely mapped to a NN. In the process of NN training, its weights are constantly revised, which means that the parameters of the grey differential equation are constantly whitening and accurate. Therefore, the prediction accuracy is also constantly strengthening.From the massive historical data of building energy consumption, through data classification and screening, various databases can be gradually formed. And directly promote the integral molding of the model system. The accuracy of grey model parameters corresponding to the network’s weights and thresholds has greatly improved after the network training is completed. Furthermore, because the network’s weights and thresholds are determined by differential equations’ parameters, they are not determined at random and are close to optimal values, reducing network training time and increasing efficiency. In general, there are two processing methods after the completion of the grey back propagation neural network (BPNN): one is to directly model and predicts the original data using the optimized NN; second, the optimal weights are obtained using the optimized NN, and then the optimal whitening values of grey differential equation parameters are reversely calculated using the optimal weights, resulting in a grey model with higher accuracy than the original model. The cost control and reinforcement benefit test of green building materials reinforcement are carried out from the perspective of the benefit game, and the optimal solution set of green building materials reinforcement is obtained. The comparison results of control accuracy obtained by different methods are shown in Figure3.Figure 3
Control error performance comparison.The analysis shows that the accuracy and confidence level of controlling the reinforcement performance of green building materials by the method in this paper are high. According to the established business framework, identify the data entities generated, controlled, and used by the business. According to the relationship of data entities, information resources are analyzed, screened, clustered, merged, etc., data classes are formed, and the relationship between subject databases and data tables contained in subject databases are determined. Use the improved method of initial value to forecast. Comparing the simulated values of the first 30 periods of GM(1,1) model data with the actual measured values, the residual values are obtained, and then the NN model is established by using the residual values of the 30 periods of data. The trained NN model is used to forecast the data from 30 to 38 periods. Finally, the predicted value of GM(1,1) model and the predicted value of NN are accumulated as the final predicted value. The prediction results of grey NN model and other two prediction models are obtained. In order to test the prediction accuracy of the grey prediction model and the grey NN model, we make an experimental comparison. The prediction accuracy of different models is compared. Figure4 shows the comparison result of square sum error (SSE). Figure 5 shows the comparison result of mean square error (MSE). Figure 6 is the comparison result of mean absolute error (MAE).Figure 4
SSE comparison results of different models.Figure 5
MSE comparison results of different models.Figure 6
MAE comparison results of different models.By comparison, it can be seen that the prediction results of this model are better than the other two models. Considering the rules of information application and the subject database determined in the data architecture, the logical relationship between the business lines and the subject database is emphatically analyzed, and the business lines are grouped and merged. All kinds of single prediction results can be regarded as fragments with different information contents. Combining these fragments with their own characteristics can greatly reduce the uncertainty of the whole system. At the same time, it can also reflect the real system in a more comprehensive and accurate way. Figure7 is a comparison between the fitting values of GM(1,1) model and grey NN model and the original values.Figure 7
Comparison between fitting values of different models and original values.The index system is a collection of quantitative descriptions of building features and green building material reinforcement features. The evaluation index system selects appropriate data indexes from the data index system for combination. Through the quantitative evaluation of the reinforcement data index system, it judges the comprehensive energy efficiency level of buildings and finds out the weak links in the reinforcement process of building green building materials from the evaluation results. The grey NN model combines the results of grey and NN prediction into a new NN, using the simulated and measured values of these two prediction models as network training samples. Finally, the combined model’s prediction results are input and output as the prediction results of the two prediction models. This method is a dynamic weight allocation method in the sense that the new NN is used to combine and output the two input prediction results using a weight allocation method trained by NN. Figure8 shows a comparison of the original values and the reinforced predicted values of the GM(1,1) and grey NN models.Figure 8
Comparison between the predicted values of different models and the original values.It can be concluded from Figure8 that the calculation accuracy of grey NN model is obviously higher than that of GM(1,1) model. GM(1,1) model is more suitable for the simulation and prediction of exponentially increasing data series, while grey NN model is more suitable for the simulation and prediction of data series with large fluctuation.For the same set of observation data, the grey model is used to forecast. Taking the simulated value of the model as the learning sample of NN and the measured value as the output target of NN, the network is trained. Then, the predicted value of the model is used as the input of the network, the trained NN is used for prediction, and the final output is the predicted value of the model. The experimental results show that the prediction accuracy of this model is the highest. In actual monitoring data, most data series are fluctuating due to the influence of errors. Therefore, the grey NN model has more practical value in the actual reinforcement monitoring data processing. Moreover, its establishment process is relatively simple, the model structure is relatively intuitive, and it has a good effect in the actual prediction process.


## 5. Conclusions

At present, people pay more and more attention to the reinforcement of buildings in the construction and operation stages and pay more and more attention to the application of green reinforcement materials for buildings. As an important component of green buildings, prefabricated buildings are fabricated by prefabricated production of wallboard, beams and columns, floors, etc., and assembled on site. However, due to the requirement of earthquake resistance, the wall can move during the splicing of prefabricated components, which leads to the problem of jointing. However, in the process of reinforcement and transformation, because the workflow and mode of the whole life cycle of prefabricated building materials have not yet formed a fixed building information model, and there is no ready-made reinforcement model for reference, which has become the main obstacle to the application and development of this kind of green building materials. In this paper, the theory and prediction principles of grey GM(1,1) model and BPNN model are discussed in detail, and the reinforcement model of green building materials based on grey NN is constructed. In this model, the simulated value and measured value of grey GM(1,1) model are used as training samples to establish NN, then the prediction of grey model is used as input, and the output of NN is the final predicted value. This paper realizes the construction of reinforcement design model and can automatically generate images, which can provide application support for the real-time application of jacket reinforcement technology of green assembled building exterior wall materials. Through the comparison of experiments, it is found that the accuracy of this method is better than that of GM (1,1) model and NN model. Due to the limited research time and method of the author, how to better combine the grey theory with NN, and how to choose the network parameters and determine the network structure need further research and practice.



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*Source: 4589401-2022-03-24.xml*