Concrete proportion optimization method and system based on environmental impact analysis

Abstract

The invention relates to the technical field of concrete proportion optimization, and provides a concrete proportion optimization method and system based on environmental impact analysis. The method comprises the following steps: acquiring environment characteristics, and obtaining a high-temperature environment characteristic information set, a moisture characteristic information set and a low-temperature environment characteristic information set; obtaining a concrete proportioning space; a concrete proportion optimization function is constructed with the purpose of minimizing the concrete damage at the high temperature and minimizing the concrete damage at the low temperature; on the basis of the concrete proportion optimization function, concrete proportion optimization is carried out, and optimal concrete proportion parameters are obtained; and outputting an optimal concrete proportion parameter to obtain a concrete proportion optimization result. According to the method, the technical problem of freeze-thaw damage caused by moisture movement change and icing expansion of the concrete in a low-temperature and high-temperature alternating environment is solved, and the effects of reducing concrete damage, improving the durability of the concrete and prolonging the service life of the concrete by optimizing concrete proportioning parameters and predicting moisture distribution in the concrete are achieved.

Description

Technical Field

[0001] The present application relates to the field of concrete technology, specifically to the field of concrete mix ratio optimization technology, and in particular to a concrete mix ratio optimization method and system based on environmental impact analysis. Background Art

[0002] With the intensification of global climate change and the increasing frequency of extreme weather phenomena, the construction engineering field is facing unprecedented challenges. Especially in areas with alternating cold and hot climates, concrete is the most basic and widely used material in construction, and its performance stability is directly related to the safety and durability of buildings. Freeze-thaw cycles at low temperatures and changes in moisture migration at high temperatures have become key factors affecting the performance of concrete. Under extremely high temperature conditions, the moisture inside the concrete will move and redistribute significantly. As the temperature rises, the capillaries and microcracks in the concrete become channels for moisture migration, causing moisture to diffuse from areas with high humidity to dry areas. This redistribution of moisture not only affects the humidity gradient of the concrete, but may also cause changes in internal stress, thereby affecting the strength and durability of the concrete. When the ambient temperature drops below freezing, the moisture inside the concrete will freeze and expand significantly in volume. This physical change generates huge internal stress inside the concrete, especially in areas with tiny cracks or defects, where stress concentration is more obvious. With the repeated freeze-thaw cycles, internal stress continues to accumulate, and once it exceeds the resistance strength of the concrete, it will lead to the destruction of the concrete structure. Freeze-thaw damage not only manifests itself as peeling of the concrete surface, but in severe cases it can also cause the internal structure to become loose and even expose stones, seriously affecting the safety and service life of the building. Summary of the invention

[0003] The present application provides a concrete mix optimization method and system based on environmental impact analysis, aiming to solve the technical problem of freeze-thaw damage of concrete caused by moisture movement and ice expansion in an alternating low and high temperature environment.

[0004] In view of the above problems, the present application provides a concrete mix optimization method and system based on environmental impact analysis.

[0005] The first aspect disclosed in the present application provides a concrete mix ratio optimization method based on environmental impact analysis, the method comprising: collecting environmental characteristics in a concrete working environment to be optimized, obtaining a high temperature environment characteristic information set, a moisture characteristic information set and a low temperature environment characteristic information set; obtaining a concrete mix ratio space; constructing a concrete mix ratio optimization function for the purpose of minimizing concrete damage at high temperature and minimizing concrete damage at low temperature; within the concrete mix ratio space, based on the concrete mix ratio optimization function, optimizing the concrete mix ratio to obtain optimal concrete mix ratio parameters, wherein, according to the high temperature environment characteristic information set and the moisture characteristic information set, predicting the moisture distribution in the concrete when entering a low temperature environment under the concrete mix ratio parameters, combining the low temperature environment characteristic information set, predicting the concrete damage at low temperature, and optimizing; outputting the optimal concrete mix ratio parameters to obtain a concrete mix ratio optimization result.

[0006] Another aspect disclosed in the present application provides a concrete mix ratio optimization system based on environmental impact analysis, the system comprising: an environmental feature acquisition module, the environmental feature acquisition module is used to collect environmental features in a concrete working environment to be optimized, and obtain a high temperature environment feature information set, a moisture feature information set and a low temperature environment feature information set; a mix space acquisition module, the mix space acquisition module is used to obtain a concrete mix space; an optimization function construction module, the optimization function construction module is used to construct a concrete mix ratio optimization function for the purpose of minimizing concrete damage at high temperature and minimizing concrete damage at low temperature; a mix ratio optimization module, the mix ratio optimization module is used to optimize the concrete mix ratio in the concrete mix space based on the concrete mix ratio optimization function, and obtain optimal concrete mix ratio parameters, wherein the moisture distribution in the concrete when entering a low temperature environment under the concrete mix ratio parameters is predicted according to the high temperature environment feature information set and the moisture feature information set, and the concrete damage at low temperature is predicted in combination with the low temperature environment feature information set, and optimization is performed; an optimization result acquisition module, the optimization result acquisition module is used to output the optimal concrete mix ratio parameters and obtain a concrete mix ratio optimization result.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The above concrete mix optimization method based on environmental impact analysis first collects the environmental characteristics of the working environment, including temperature characteristics and moisture characteristics in a high temperature environment, and temperature characteristics in a low temperature environment, and organizes the feature information into a high temperature environment feature information set, a moisture feature information set, and a low temperature environment feature information set. Subsequently, the spatial range of the concrete mix is ​​determined, and this space contains all possible concrete mix combinations. Then, with the goal that the concrete mix can minimize the concrete damage caused by moisture migration and temperature stress at high temperatures, and also minimize the concrete damage caused by moisture freezing and expansion at low temperatures, a concrete mix optimization function is constructed, which comprehensively considers the damage prediction under the above two extreme environments. After determining the concrete mix space and optimization function, the mix optimization is performed. In this process, the high temperature environment feature information set and the moisture feature information set are used to predict the internal moisture distribution of concrete when it enters a low temperature environment under different mix parameters. Afterwards, combined with the low temperature environment feature information set, the damage that these moisture may cause to the concrete by freezing and expanding at low temperatures is predicted. By continuously adjusting the mix parameters, the mix combination that minimizes the predicted damage, that is, the optimal concrete mix parameters, is found. Then, the optimal concrete mix parameters are output as the concrete mix optimization result, which can help produce more durable and safer concrete materials in complex working environments.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 The figure is a flow chart of a concrete mix ratio optimization method based on environmental impact analysis in one embodiment.

[0011] Figure 2 The following is an architecture diagram of a concrete mix optimization system based on environmental impact analysis in one embodiment.

[0012] Explanation of the accompanying drawings: environmental feature collection module 1, ratio space acquisition module 2, optimization function construction module 3, ratio optimization module 4, optimization result acquisition module 5. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a concrete mix optimization method and system based on environmental impact analysis to solve the technical problem of freeze-thaw damage of concrete caused by moisture movement and ice expansion in an alternating low and high temperature environment.

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0015] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0016] Embodiment 1, as Figure 1 As shown, the present application provides a concrete mix ratio optimization method based on environmental impact analysis, the method comprising: The environmental characteristics of the concrete working environment to be optimized are collected to obtain a high temperature environment characteristic information set, a moisture characteristic information set and a low temperature environment characteristic information set.

[0017] Concrete, as an indispensable cornerstone of modern architecture, is directly affected by environmental conditions. During the day when the temperature is high, the high temperature causes the water inside the concrete to migrate, which may cause the formation and expansion of micro cracks, thereby reducing the strength and durability of the concrete. When the temperature is low at night, this water will quickly freeze at low temperatures, expand several times in volume, and exert tremendous pressure on the internal structure of the concrete. This repeated freeze-thaw cycle will not only cause the concrete surface to peel off, but in severe cases it will also deeply damage its internal structure and even cause the concrete to lose its bearing capacity.

[0018] In the embodiment of the present application, when preparing to optimize the concrete mix ratio to adapt to a specific working environment, the system terminal first collects environmental characteristics in the working area. These characteristics are divided into high temperature environment characteristic information set, moisture characteristic information set and low temperature environment characteristic information set. The high temperature environment characteristic information set mainly focuses on those periods where the daytime temperature rises significantly, especially in areas with a large temperature difference between day and night. In this set, the maximum daytime temperature at multiple time points is recorded in detail. These data are crucial for evaluating the impact of high temperature on the performance of concrete materials, because high temperature may accelerate the evaporation of water inside the concrete, leading to the formation and expansion of microcracks, thereby affecting the strength and durability of the concrete. The moisture characteristic information set mainly focuses on the moisture conditions in the working area, especially the rainfall conditions that are closely related to the performance of concrete. By recording the frequency, intensity and soil moisture changes after rainfall, it is possible to understand the moisture environment that concrete may face during the construction process and subsequent use stages. This information is of great significance for predicting the distribution, migration and possible freeze-thaw cycle effects of moisture inside the concrete. The low temperature environment characteristic information set is also aimed at areas with a large temperature difference between day and night. The low temperature environment characteristic information set records the specific temperature values ​​of these low temperature periods in detail, as well as the duration of the low temperature. These data are crucial for evaluating the freezing expansion potential of internal moisture in concrete at low temperatures, as freezing expansion is one of the main causes of freeze-thaw damage to concrete.

[0019] Furthermore, the present application provides a method for collecting environmental characteristics in a concrete working environment to be optimized, and also includes: Determine the concrete working environment for which mix ratio optimization is to be performed; and collect a high temperature set, a low temperature set, and a rainfall set according to meteorological data records of alternating high and low temperatures and rainfall in the working environment as the high temperature environment characteristic information set, the moisture characteristic information set, and the low temperature environment characteristic information set.

[0020] Preferably, the system terminal first clarifies the specific working environment of the concrete to be optimized. This environment is a specific area of ​​a certain geographical location, and the climatic conditions in this area have a significant impact on the performance of concrete. For this working environment, relevant meteorological data records are collected and sorted. These data include high and low temperature alternating temperature information and rainfall conditions. Specifically, the system terminal collects temperature data of high temperature periods to form a high temperature temperature set, which reflects the temperature change of the working environment under high temperature conditions; at the same time, the temperature data of low temperature periods are collected to form a low temperature temperature set to understand the temperature characteristics of the working environment under low temperature conditions. In addition, since moisture has an important influence on the performance of concrete, especially in an environment of alternating low and high temperatures, the presence and change of moisture will aggravate the damage of concrete. Therefore, the system terminal collects rainfall data and organizes it into a rainfall set. This set, as part of the moisture feature information set, helps us understand the moisture status in the working environment. The system terminal constructs a high temperature environment feature information set, a moisture feature information set, and a low temperature environment feature information set by collecting and analyzing the high and low temperature alternating temperature data and rainfall data in the working environment. These three sets together constitute a comprehensive description of the characteristics of the working environment, providing data support for subsequent concrete mix optimization.

[0021] Get concrete mix space.

[0022] In one embodiment, during the process of optimizing the concrete mix ratio, the system terminal understands the properties of the various raw materials used, including the strength grade and setting time of cement, the grading, fineness modulus, mud content of sand, the grading, mud content, crushing index of crushed stone or pebbles, and the properties of possible admixtures (such as water reducers and waterproofing agents) and admixtures (such as fly ash and slag powder). These properties will directly affect the mix ratio selection and performance of concrete. Subsequently, based on historical experience, technical specifications and professional knowledge, the ratio range of each component is determined to form a feasible ratio space, which contains the possible different combination ratios of various components in concrete.

[0023] With the purpose of minimizing concrete damage at high temperature and at low temperature, a concrete mix optimization function is constructed.

[0024] In one embodiment, in order to minimize concrete damage at high temperatures and concrete damage at low temperatures, the system terminal constructs a concrete mix ratio optimization function. During the construction process, the system terminal specifies the optimization goal, which is to reduce cracks and damage caused by water evaporation in concrete under high temperature conditions, and to reduce spalling and stress concentration caused by freezing and expansion of water inside concrete under low temperature conditions. The collected high temperature environment feature information set, water feature information set, and low temperature environment feature information set are randomly combined, and the weights and damage parameters corresponding to high and low temperatures are combined to construct a concrete mix ratio optimization function.

[0025] Furthermore, the present application provides a method for constructing a concrete mix optimization function for the purpose of minimizing concrete damage at high temperatures and minimizing concrete damage at low temperatures, including: The high temperature environment characteristic information set, the moisture characteristic information set and the low temperature environment characteristic information set are randomly combined to obtain M working scenes of concrete work, where M is an integer greater than 1; according to the M working scenes, a concrete mix ratio optimization function is constructed for the purpose of minimizing concrete damage at high temperature and minimizing concrete damage at low temperature, as shown in the following formula: .

[0026] Preferably, in order to more comprehensively consider the performance of concrete under different working environments, the system terminal randomly combines the high temperature environment feature information set, the moisture feature information set and the low temperature environment feature information set to generate M different concrete working scenes, where M is an integer greater than 1, representing the number of different environmental conditions simulated. Subsequently, in order to find the concrete mix that performs best under these variable working scenes, the system terminal constructs a concrete mix optimization function. The goal of this concrete mix optimization function is to minimize the damage of concrete at high temperatures and the damage of concrete at low temperatures, while giving these two goals different weights to reflect their relative importance. The concrete mix optimization function is as follows: ; Among them, OMP is the mix adaptability, which is used to measure the comprehensive performance of a certain concrete mix in all working scenarios. is the high temperature weight, which is used to adjust the importance of high temperature damage in the optimization function. The larger the value is, the more attention is paid to the damage of concrete under high temperature. is the low temperature weight, which is used to adjust the weight coefficient of the importance of low temperature damage. The larger the value of , the more attention is paid to the damage of concrete under low temperature. M is the number of working scenarios of the combination of high temperature environment feature information set, moisture feature information set and low temperature environment feature information set. e is the natural logarithm. is the high temperature damage parameter of concrete in the i-th working scenario under the concrete mix ratio parameter, which reflects the damage of the mix ratio in a high temperature environment. is the low-temperature damage parameter of concrete in the i-th working scene under the concrete mix ratio parameter, which reflects the damage of the mix ratio in a low-temperature environment.

[0027] In the concrete mix space, based on the concrete mix optimization function, the concrete mix optimization is performed to obtain the optimal concrete mix parameters, wherein the moisture distribution in the concrete when entering the low temperature environment under the concrete mix parameters is predicted according to the high temperature environment characteristic information set and the moisture characteristic information set, and the concrete damage under low temperature is predicted in combination with the low temperature environment characteristic information set for optimization.

[0028] In one embodiment, during the optimization of concrete mix ratio, the system terminal uses the concrete mix ratio optimization function to find the optimal concrete mix ratio parameters based on the acquired concrete mix ratio space. During the optimization process, the system terminal pays attention to the impact of high and low temperature environments on concrete performance. High temperature environment will cause the water in the concrete to evaporate, which will not only cause crack damage inside the concrete, but also change the moisture distribution inside the concrete. For example, the moisture on the surface of the concrete may evaporate quickly, while the deep area may still contain more moisture. This change in moisture distribution will further affect the performance of the concrete in a low temperature environment. Therefore, the system terminal will perform high temperature damage prediction based on M high temperature environment feature information of M working scenes to obtain M high temperature damage parameters. When concrete enters an environment below freezing, the moisture inside it will freeze and expand in volume. Since the high temperature has changed the moisture distribution inside the concrete, the stress generated by freezing and expansion at low temperatures may be more concentrated, resulting in greater stress concentration and spalling damage inside the concrete. In order to accurately predict the spalling damage under such low temperature, the system terminal combines the high temperature environment characteristic information set and the moisture characteristic information set to predict the moisture distribution in the concrete when entering the low temperature environment under the concrete mix parameters, and then combines the M low temperature environment characteristic information to predict the low temperature damage and obtain M low temperature damage parameters. Subsequently, the system terminal combines the constructed concrete mix optimization function to calculate the fitness of the M high temperature damage parameters and low temperature damage parameters to obtain the mix fitness of the current concrete mix parameters. After that, the calculated mix fitness is compared with the previously calculated mix fitness, and the concrete mix parameters with larger mix fitness are retained as temporary concrete mix parameters. Then, this process is repeated until convergence, and the concrete mix parameters stored in the temporary concrete mix parameters are output to obtain the optimal concrete mix parameters.

[0029] Furthermore, the present application provides a method for optimizing concrete mix ratio within the concrete mix ratio space based on the concrete mix ratio optimization function, including: In the concrete mix ratio space, a first concrete mix ratio parameter is randomly generated; according to the first concrete mix ratio parameter and M high temperature environment characteristic information of the M working scenes, high temperature damage prediction is performed to obtain M first high temperature damage parameters; according to the first concrete mix ratio parameter and M high temperature environment characteristic information and M moisture characteristic information of the M working scenes, M first moisture distributions in the concrete when entering a low temperature environment under the first concrete mix ratio parameter are predicted, and low temperature damage prediction is performed in combination with the M low temperature environment characteristic information to obtain M first low temperature damage parameters.

[0030] Optionally, in the process of optimizing the concrete mix ratio, the system terminal randomly generates a set of initial concrete mix ratio parameters as the first concrete mix ratio parameters in the concrete mix ratio space. This set of parameters represents the initial proportions of various raw materials in the concrete. In order to evaluate the performance of the first concrete mix ratio parameters under different working environments, the system terminal uses the high temperature environment characteristic information in the known M working scenes to predict high temperature damage. For each working scene, the system terminal predicts the damage of the concrete under high temperature through a pre-built high temperature damage predictor based on the first concrete mix ratio parameters and the corresponding high temperature environment characteristic information, thereby obtaining M first high temperature damage parameters. These parameters reflect the degree of damage of the concrete under different high temperature environments. Similarly, the system terminal uses a pre-built moisture distribution predictor to predict M high temperature environment characteristic information and M moisture characteristic information, and obtains M first moisture distributions in the concrete when entering a low temperature environment under the first concrete mix ratio parameters. Then, the low temperature damage predictor is used to predict the low temperature damage of the M first moisture distributions and the M low temperature environment characteristic information to obtain M first low temperature damage parameters. In summary, the whole process includes randomly generating initial concrete mix parameters, predicting high temperature damage using high temperature environment characteristic information, predicting moisture distribution before low temperature by combining high temperature and moisture characteristic information, and predicting low temperature damage using low temperature environment characteristic information. Through these steps, the performance of the first concrete mix parameters under different working environments can be comprehensively evaluated.

[0031] According to the M first high-temperature damage parameters and the M first low-temperature damage parameters, based on the concrete mix ratio optimization function, a first mix ratio fitness is obtained; the second concrete mix ratio parameters are continuously randomly generated, M second high-temperature damage parameters and M second low-temperature damage parameters are predicted and obtained, and the second mix ratio fitness is calculated and obtained; and the concrete mix ratio optimization is continued until convergence, and the final concrete mix ratio parameters are output to obtain the optimal concrete mix ratio parameters.

[0032] Optionally, after obtaining the M first high temperature damage parameters and M first low temperature damage parameters corresponding to the first concrete mix ratio parameter, the system terminal uses the previously constructed concrete mix ratio optimization function to calculate the first mix ratio fitness of this group of mix ratio parameters. This fitness is an indicator that combines high temperature and low temperature damage parameters, and is used to evaluate the overall performance of the mix ratio parameter in all working scenarios. Subsequently, the mix ratio space is continuously explored, and a second group of concrete mix ratio parameters, i.e., the second concrete mix ratio parameters, are randomly generated. For this new group of mix ratio parameters, the system terminal repeats the previous steps, performs high temperature and low temperature damage prediction, obtains M second high temperature damage parameters and M second low temperature damage parameters respectively, and then uses the concrete mix ratio optimization function to calculate the second mix ratio fitness of the second concrete mix ratio parameter. Afterwards, the system terminal compares the size of the first mix ratio fitness and the second mix ratio fitness, and determines which group of mix ratio parameters performs better overall, i.e., which group of mix ratio fitness is higher. Based on this comparison, the system terminal retains the concrete mix ratio parameters with higher fitness as temporary concrete mix ratio parameters. Then, repeat the above process, randomly generate concrete mix parameters from the concrete mix space, calculate the mix fitness of this concrete mix parameter, and compare it with the mix fitness of the temporary concrete mix parameter, and retain the better one. This concrete mix optimization process will continue until the convergence condition is met, that is, the improvement of fitness in multiple consecutive iterations is very small, or the preset maximum number of iterations is reached. Once any of the convergence conditions is met, the system terminal determines that the optimal or near-optimal concrete mix parameter has been found, and outputs the final mix parameter as the optimal concrete mix parameter.

[0033] Furthermore, the present application provides high temperature damage prediction according to the first concrete mix ratio parameter and the M high temperature environment characteristic information of the M working scenes, including: According to the high-temperature damage test data of concrete, a set of sample concrete mix ratio parameters and a set of sample high-temperature environment characteristic information are collected, and according to the scale of concrete cracks under different sample high-temperature environment characteristic information and sample concrete mix ratio parameters, a set of sample high-temperature damage parameters is labeled to obtain; the set of sample concrete mix ratio parameters, the set of sample high-temperature environment characteristic information and the set of sample high-temperature damage parameters are used to train a high-temperature damage predictor; the high-temperature damage predictor is used to perform high-temperature damage prediction on the first concrete mix ratio parameter and the M high-temperature environment characteristic information to obtain M first high-temperature damage parameters.

[0034] Optionally, based on the damage test data of concrete in a high temperature environment, the system terminal collects a series of sample data sets, which include different sample concrete mix parameter sets, sample high temperature environment characteristic information sets (such as temperature, duration, etc.), and the scale of concrete cracks under these specific mix ratios and environmental conditions. The system terminal uses this information, combines historical experience and professional knowledge to annotate the high temperature damage parameters of each sample, and generates a sample high temperature damage parameter set. Subsequently, the sample concrete mix parameter set, the sample high temperature environment characteristic information set, and the sample high temperature damage parameter set are divided into a training set, a validation set, and a test set, and a multi-layer perceptron (MLP) model is constructed using a deep learning framework. The structure and parameters of the input layer, hidden layer, and output layer of the model are determined according to actual needs and data characteristics, and ReLU is used as the activation function and the mean square error MSE is used as the loss function. Before starting training, all parameters in the MLP model, including weights and biases, are initialized using random numbers. After initialization, the system terminal passes the concrete mix parameters and high temperature environment characteristic information in the training set as input data to the input layer. The input data is forward propagated through the neurons of the hidden layer. In each hidden layer, the input data is multiplied by the weight matrix, the bias term is added, and then processed by the activation function ReLU to obtain the output of the layer. This process is repeated until the output layer is reached. The neurons in the output layer receive the output from the last hidden layer and produce the final prediction result, that is, the high temperature damage parameter. After that, the difference between the high temperature damage parameter and the sample high temperature damage parameter is calculated using the loss function mean square error MSE. The loss function measures the accuracy of the model prediction, and the smaller its value, the more accurate the model prediction. Then, the system terminal backpropagates the gradient of the loss function from the output layer to the input layer according to the chain rule. During the backpropagation process, the gradient of each parameter to the loss function is calculated. The optimization algorithm Adam is used to update the parameters of the model according to the gradient. The goal of the optimization algorithm is to minimize the loss function and reduce the prediction error by adjusting the parameters. Repeat the steps of forward propagation, calculating the loss, backpropagation, and updating the parameters until the preset number of iterations is reached. During the training process, the loss and performance indicators such as accuracy on the training set and the validation set are regularly monitored to detect overfitting or underfitting problems in a timely manner. After the training is completed, the current model is evaluated using the test set to measure the performance of the model in actual applications, and the prediction accuracy, error rate and other indicators of the model are checked and compared with the actual needs. If the actual needs are met, the current model is output to obtain the high temperature damage predictor. Otherwise, the learning rate, batch size, number of hidden layers, number of neurons and other parameters are tuned to find the best model configuration.After the high temperature damage predictor is constructed, the system terminal inputs the first concrete mix parameter and M high temperature environment characteristic information into the high temperature damage predictor to predict the M first high temperature damage parameters of concrete under this set of mix parameters and environmental conditions. These prediction results are helpful to evaluate the performance of different concrete mixes in high temperature environments, thereby guiding the design and optimization of concrete mixes.

[0035] Further, the present application provides a method for predicting M first moisture distributions in concrete when entering a low temperature environment under the first concrete mix ratio parameter according to the first concrete mix ratio parameter and the M high temperature environment characteristic information and the M moisture characteristic information of the M working scenes, and performing low temperature damage prediction in combination with the M low temperature environment characteristic information, including: According to the low-temperature damage test data of concrete, a sample high-temperature environment characteristic information set and a sample moisture characteristic information set are collected, and the distribution depth of moisture in the concrete under different sample high-temperature environment characteristic information and moisture characteristic information is obtained to obtain a sample moisture distribution set; the high-temperature environment characteristic information set, the sample moisture characteristic information set and the sample moisture distribution set are used to train a moisture distribution predictor, and the M high-temperature environment characteristic information and the M moisture characteristic information are predicted to obtain M first moisture distributions; low-temperature damage prediction is performed in combination with the M first moisture distributions and the M low-temperature environment characteristic information to obtain M first low-temperature damage parameters, wherein each first low-temperature damage parameter includes a concrete spalling scale.

[0036] Optionally, the system terminal collects sample information under different high temperature environments and moisture conditions based on the low temperature damage test data of concrete. This information includes a sample high temperature environment feature information set, a sample moisture feature information set (such as initial moisture content, moisture source, etc.), and the actual distribution depth of moisture in the concrete under these conditions. The system terminal uses this information, combined with historical experience and professional knowledge to annotate the moisture distribution of each sample, and generates a sample moisture distribution set. Subsequently, using the same construction method as above, the moisture distribution predictor is trained using the collected high temperature environment feature information, moisture feature information and moisture distribution data. The purpose of this predictor is to predict the distribution depth of moisture in concrete when any new high temperature environment feature and moisture feature information are given. Similarly, the system terminal collects a sample low temperature environment feature information set (such as the duration of low temperature, temperature range, etc.) from the low temperature damage test data, and uses the same method to combine the sample low temperature environment feature information set and the sample moisture distribution set for annotation to obtain a sample low temperature damage parameter set, and then uses the same construction method to train a low temperature damage predictor. Afterwards, the system terminal uses the trained moisture distribution predictor to predict M high temperature environment features and moisture feature information to obtain M predicted first moisture distributions. Then, the M first moisture distributions and M low-temperature environment characteristic information are input into the low-temperature damage predictor for low-temperature damage prediction, and M first low-temperature damage parameters are obtained. Each first low-temperature damage parameter specifically describes the scale of spalling that may occur in concrete under different conditions.

[0037] Furthermore, the present application provides a method for distinguishing and retaining the second concrete mix ratio parameter and the first concrete mix ratio parameter, including: Determine whether the second mix fitness is greater than the first mix fitness, and if so, retain the second concrete mix parameter; if not, obtain the appearance ratio of the M working scenes in the working environment, and assign M scene weights; according to the M first high temperature damage parameters and the M second high temperature damage parameters, as well as the M first low temperature damage parameters and the M second low temperature damage parameters, perform weighted calculation and statistics according to the M scene weights to obtain a first score and a second score, and construct a retention probability distribution, wherein the retention probability distribution includes a first probability interval and a second probability interval; randomly generate a random number between 0 and 1, determine the probability interval it falls into, and retain the second concrete mix parameter or the first concrete mix parameter.

[0038] Optionally, after obtaining the first and second ratio fitness, the system terminal compares the first and second ratio fitness. If the second ratio fitness is greater than the first ratio fitness, the system terminal stores the second concrete ratio parameter corresponding to the second ratio fitness as a temporary concrete ratio parameter for subsequent comparison. If the second ratio fitness is less than the first ratio fitness, the system terminal compares the damage parameters under high temperature and low temperature environments. Specifically, the system terminal counts M working scenes, obtains the number of occurrences of each scene in the working environment, and then calculates the ratio with the total number of times to obtain the occurrence ratio of each scene, and uses this occurrence ratio as the scene weight of the scene, so that the system terminal can obtain M scene weights. For each working scene, the high temperature damage parameter and the low temperature damage parameter of the first concrete ratio parameter and the second concrete ratio parameter are compared respectively. If the damage parameter of the second concrete ratio parameter at high temperature or low temperature is better than that of the first concrete ratio parameter, that is, the damage parameter is smaller, then a score value is added to the basic score of the second concrete ratio parameter. The basic score is initially 0. After each score increment calculation is completed, the basic score will be replaced with the calculation result. The score increment is set according to actual needs and is 1 by default. In this way, the system terminal obtains two sets of score sets, namely the first high temperature / low temperature score set and the second high temperature / low temperature score set. Subsequently, the above score sets are weighted according to the scene weights set for each work scene to obtain the first score and the second score. For example, if there are three work scenes and the corresponding scene weights are 0.3, 0.4, and 0.3 respectively, then the score of each scene is multiplied by the corresponding weight and then summed to obtain the total score of the first ratio and the second ratio, that is, the first score and the second score. After that, a retention probability distribution is constructed based on the total score of the two ratios. This distribution is divided into a first probability interval and a second probability interval. The interval division is based on the relative size of the two scores. For example, if the first score is 0.6 and the second score is 0.4, then the first probability interval is set to (0-0.6) and the second probability interval is set to (0.6-1). Then, a number between 0 and 1 is generated randomly to determine which probability interval the number falls into. If the random number falls within the first probability interval, the first concrete mix parameter is retained as a temporary concrete mix parameter; if it falls within the second probability interval, the second concrete mix parameter is retained. This method introduces randomness, while maintaining a certain optimization direction, it also increases the flexibility of decision-making and avoids entering a local optimal solution.

[0039] The optimal concrete mix ratio parameters are output to obtain a concrete mix ratio optimization result.

[0040] In one embodiment, after completing the damage assessment of multiple concrete mix parameters under high and low temperature environments and going through a series of comparisons, weighted calculations, and random selection processes, the system terminal determines the optimal concrete mix parameters, and the system terminal outputs the optimized and screened optimal concrete mix parameters as the concrete mix optimization result. The concrete mix optimization result represents the concrete mix scheme that is expected to exhibit the best performance under the current conditions.

[0041] In summary, the embodiments of the present application have at least the following technical effects: The embodiment of the present application first collects environmental characteristic information in the concrete working environment to obtain a set of high temperature environment characteristics, moisture characteristics and low temperature environment characteristics. Subsequently, the concrete ratio space is obtained, and the concrete ratio optimization function is constructed with the goal of minimizing concrete damage at high and low temperatures. In the concrete ratio space, the ratio optimization is performed through the optimization function to obtain the optimal concrete ratio parameters. According to the high temperature and moisture characteristic information, the moisture distribution in the concrete under the low temperature environment is predicted, and the concrete damage at low temperature is predicted in combination with the low temperature characteristic information, and the concrete ratio parameters are further optimized. Finally, the optimal ratio parameters are output to obtain the optimization results. In the specific implementation process, the working environment of the concrete is first determined, and according to the meteorological data records, the high temperature temperature, low temperature temperature and rainfall set are collected as characteristic information. The optimization function obtains different working scenes by randomly combining these characteristic information sets, and constructs the optimization function with the goal of minimizing damage. In the ratio optimization process, the initial ratio parameters are randomly generated, and the ratio parameters are gradually optimized by predicting high temperature and low temperature damage until convergence, and the final optimal ratio parameters are output. These technical effects jointly solve the technical problem of freeze-thaw damage of concrete caused by moisture movement and ice expansion in alternating low and high temperature environments, and achieve the effect of reducing concrete damage and improving the durability and service life of concrete by optimizing concrete mix parameters and predicting moisture distribution in concrete.

[0042] Embodiment 2 is based on the same inventive concept as the concrete mix ratio optimization method based on environmental impact analysis in the previous embodiment. Figure 2As shown, the present application provides a concrete mix ratio optimization system based on environmental impact analysis, and the system includes: an environmental feature acquisition module 1: the environmental feature acquisition module 1 is used to collect environmental features in the concrete working environment to be optimized, and obtain a high temperature environment feature information set, a moisture feature information set and a low temperature environment feature information set; a mix space acquisition module 2: the mix space acquisition module 2 is used to obtain a concrete mix space; an optimization function construction module 3: the optimization function construction module 3 is used to construct a concrete mix ratio optimization function for the purpose of minimizing concrete damage at high temperature and minimizing concrete damage at low temperature; a mix ratio optimization module 4: the mix ratio optimization module 4 is used to optimize the concrete mix ratio in the concrete mix space based on the concrete mix ratio optimization function to obtain the optimal concrete mix ratio parameters, wherein, according to the high temperature environment feature information set and the moisture feature information set, the moisture distribution in the concrete when entering the low temperature environment under the concrete mix ratio parameters is predicted, and combined with the low temperature environment feature information set, the concrete damage at low temperature is predicted and optimized; an optimization result acquisition module 5: the optimization result acquisition module 5 is used to output the optimal concrete mix ratio parameters and obtain the concrete mix ratio optimization result.

[0043] Furthermore, the environmental feature acquisition module 1 is also used to execute the following method: Determine the concrete working environment for which mix ratio optimization is to be performed; and collect a high temperature set, a low temperature set, and a rainfall set according to meteorological data records of alternating high and low temperatures and rainfall in the working environment as the high temperature environment characteristic information set, the moisture characteristic information set, and the low temperature environment characteristic information set.

[0044] Furthermore, the optimization function construction module 3 is also used to execute the following method: The high temperature environment characteristic information set, the moisture characteristic information set and the low temperature environment characteristic information set are randomly combined to obtain M working scenes of concrete work, where M is an integer greater than 1; according to the M working scenes, a concrete mix ratio optimization function is constructed for the purpose of minimizing concrete damage at high temperature and minimizing concrete damage at low temperature, as shown in the following formula: ; Among them, OMP is the fitness ratio, is the high temperature weight, is the low temperature weight, M is the number of working scenarios that combine the high temperature environment feature information set, the moisture feature information set, and the low temperature environment feature information set, e is the natural logarithm, is the high temperature damage parameter of concrete in the i-th working scene under the concrete mix parameters, is the low-temperature damage parameter of concrete in the i-th working scene under the concrete mix parameters.

[0045] Furthermore, the ratio optimization module 4 is also used to execute the following method: In the concrete mix ratio space, a first concrete mix ratio parameter is randomly generated; according to the first concrete mix ratio parameter and the M high temperature environment characteristic information of the M working scenes, high temperature damage prediction is performed to obtain M first high temperature damage parameters; according to the first concrete mix ratio parameter and the M high temperature environment characteristic information and M moisture characteristic information of the M working scenes, M first moisture distributions in the concrete when entering a low temperature environment under the first concrete mix ratio parameter are predicted, and low temperature damage prediction is performed in combination with the M low temperature environment characteristic information to obtain M first low temperature damage parameters; according to the M first high temperature damage parameters and the M first low temperature damage parameters, based on the concrete mix ratio optimization function, a first mix ratio fitness is obtained; the second concrete mix ratio parameter is continuously randomly generated, M second high temperature damage parameters and M second low temperature damage parameters are predicted, and the second mix ratio fitness is calculated; and the concrete mix ratio optimization is continuously performed until convergence, and the final concrete mix ratio parameters are output to obtain the optimal concrete mix ratio parameters.

[0046] Furthermore, the ratio optimization module 4 is also used to execute the following method: According to the high-temperature damage test data of concrete, a set of sample concrete mix ratio parameters and a set of sample high-temperature environment characteristic information are collected, and according to the scale of concrete cracks under different sample high-temperature environment characteristic information and sample concrete mix ratio parameters, a set of sample high-temperature damage parameters is labeled to obtain; the set of sample concrete mix ratio parameters, the set of sample high-temperature environment characteristic information and the set of sample high-temperature damage parameters are used to train a high-temperature damage predictor; the high-temperature damage predictor is used to perform high-temperature damage prediction on the first concrete mix ratio parameter and the M high-temperature environment characteristic information to obtain M first high-temperature damage parameters.

[0047] Furthermore, the ratio optimization module 4 is also used to execute the following method: According to the low-temperature damage test data of concrete, a sample high-temperature environment characteristic information set and a sample moisture characteristic information set are collected, and the distribution depth of moisture in the concrete under different sample high-temperature environment characteristic information and moisture characteristic information is obtained to obtain a sample moisture distribution set; the high-temperature environment characteristic information set, the sample moisture characteristic information set and the sample moisture distribution set are used to train a moisture distribution predictor, and the M high-temperature environment characteristic information and the M moisture characteristic information are predicted to obtain M first moisture distributions; low-temperature damage prediction is performed in combination with the M first moisture distributions and the M low-temperature environment characteristic information to obtain M first low-temperature damage parameters, wherein each first low-temperature damage parameter includes a concrete spalling scale.

[0048] Furthermore, the ratio optimization module 4 is also used to execute the following method: Determine whether the second mix fitness is greater than the first mix fitness, and if so, retain the second concrete mix parameter; if not, obtain the appearance ratio of the M working scenes in the working environment, and assign M scene weights; according to the M first high temperature damage parameters and the M second high temperature damage parameters, as well as the M first low temperature damage parameters and the M second low temperature damage parameters, perform weighted calculation and statistics according to the M scene weights to obtain a first score and a second score, and construct a retention probability distribution, wherein the retention probability distribution includes a first probability interval and a second probability interval; randomly generate a random number between 0 and 1, determine the probability interval it falls into, and retain the second concrete mix parameter or the first concrete mix parameter.

[0049] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0051] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A concrete mix optimization method based on environmental impact analysis, characterized in that: The method comprises: Collect environmental characteristics in the concrete working environment to be optimized, and obtain a high temperature environment characteristic information set, a moisture characteristic information set, and a low temperature environment characteristic information set; Get concrete mix space; To minimize concrete damage at high temperature and at low temperature, a concrete mix optimization function is constructed. In the concrete mix ratio space, based on the concrete mix ratio optimization function, the concrete mix ratio is optimized to obtain the optimal concrete mix ratio parameters, wherein the moisture distribution in the concrete when entering the low temperature environment under the concrete mix ratio parameters is predicted according to the high temperature environment characteristic information set and the moisture characteristic information set, and the concrete damage under low temperature is predicted in combination with the low temperature environment characteristic information set for optimization; The optimal concrete mix ratio parameters are output to obtain a concrete mix ratio optimization result.

2. The method for optimizing concrete mix ratio based on environmental impact analysis according to claim 1, characterized in that: Collect environmental characteristics of the concrete working environment to be optimized, including: Determine the concrete working environment for mix optimization; According to the meteorological data records of alternating high and low temperatures and rainfall in the working environment, a high temperature set, a low temperature set and a rainfall set are collected as the high temperature environment characteristic information set, the moisture characteristic information set and the low temperature environment characteristic information set.

3. The method for optimizing concrete mix ratio based on environmental impact analysis according to claim 1, characterized in that: To minimize the concrete damage at high temperature and at low temperature, a concrete mix optimization function is constructed, including: Randomly combining the high temperature environment feature information set, the moisture feature information set, and the low temperature environment feature information set to obtain M working scenes of concrete work, where M is an integer greater than 1; According to the M working scenarios, a concrete mix optimization function is constructed for the purpose of minimizing concrete damage at high temperature and minimizing concrete damage at low temperature, as shown in the following formula: ； Among them, OMP is the ratio fitness, is the high temperature weight, is the low temperature weight, M is the number of working scenarios that combine the high temperature environment feature information set, the moisture feature information set, and the low temperature environment feature information set, e is the natural logarithm, is the high temperature damage parameter of concrete in the i-th working scene under the concrete mix parameters, is the low-temperature damage parameter of concrete in the i-th working scene under the concrete mix parameters.

4. The method for optimizing concrete mix ratio based on environmental impact analysis according to claim 3 is characterized in that: In the concrete mix ratio space, based on the concrete mix ratio optimization function, concrete mix ratio optimization is performed, including: In the concrete mix ratio space, randomly generating a first concrete mix ratio parameter; According to the first concrete mix ratio parameter and the M high temperature environment characteristic information of the M working scenes, high temperature damage prediction is performed to obtain M first high temperature damage parameters; According to the first concrete mix ratio parameter and the M high temperature environment characteristic information and the M moisture characteristic information of the M working scenes, predict M first moisture distributions in the concrete when entering a low temperature environment under the first concrete mix ratio parameter, and perform low temperature damage prediction in combination with the M low temperature environment characteristic information to obtain M first low temperature damage parameters; According to the M first high-temperature damage parameters and the M first low-temperature damage parameters, based on the concrete mix ratio optimization function, a first mix ratio fitness is obtained; Continue to randomly generate the second concrete mix parameters, predict and obtain M second high temperature damage parameters and M second low temperature damage parameters, and calculate and obtain the second mix fitness; And continue to optimize the concrete mix ratio until convergence, output the final concrete mix ratio parameters, and obtain the optimal concrete mix ratio parameters.

5. The method for optimizing concrete mix ratio based on environmental impact analysis according to claim 4, characterized in that: According to the first concrete mix ratio parameter and the M high temperature environment characteristic information of the M working scenes, high temperature damage prediction is performed, including: According to the high temperature damage test data of concrete, a set of sample concrete mix parameters and a set of sample high temperature environment characteristic information are collected, and the scale of concrete cracks generated under different sample high temperature environment characteristic information and sample concrete mix parameters are annotated to obtain a set of sample high temperature damage parameters; Using the sample concrete mix parameter set, the sample high temperature environment characteristic information set and the sample high temperature damage parameter set to train a high temperature damage predictor; The high temperature damage predictor is used to perform high temperature damage prediction on the first concrete mix ratio parameter and the M high temperature environment characteristic information to obtain M first high temperature damage parameters.

6. The method for optimizing concrete mix ratio based on environmental impact analysis according to claim 4, characterized in that: According to the first concrete mix ratio parameter and the M high temperature environment characteristic information and the M moisture characteristic information of the M working scenes, M first moisture distributions in the concrete when entering a low temperature environment under the first concrete mix ratio parameter are predicted, and low temperature damage prediction is performed in combination with the M low temperature environment characteristic information, including: According to the low-temperature damage test data of concrete, a sample high-temperature environment characteristic information set and a sample moisture characteristic information set are collected, and the distribution depth of moisture in the concrete under different sample high-temperature environment characteristic information and moisture characteristic information is obtained to obtain a sample moisture distribution set; Using the high temperature environment characteristic information set, the sample moisture characteristic information set and the sample moisture distribution set, training a moisture distribution predictor, predicting the M high temperature environment characteristic information and the M moisture characteristic information, and obtaining M first moisture distributions; Combined with the M first moisture distributions and the M low-temperature environment characteristic information, low-temperature damage prediction is performed to obtain M first low-temperature damage parameters, wherein each first low-temperature damage parameter includes a concrete spalling scale.

7. The method for optimizing concrete mix ratio based on environmental impact analysis according to claim 4, characterized in that: The second concrete mix ratio parameter and the first concrete mix ratio parameter are judged and retained, including: Determine whether the second mix ratio fitness is greater than the first mix ratio fitness, and if so, retain the second concrete mix ratio parameter; If not, obtaining the occurrence ratios of the M working scenes in the working environment and assigning M scene weights; According to the M first high temperature injury parameters and the M second high temperature injury parameters, and the M first low temperature injury parameters and the M second low temperature injury parameters, a first score and a second score are obtained by weighted calculation according to the M scene weights, and a retained probability distribution is constructed, where the retained probability distribution includes a first probability interval and a second probability interval; A random number between 0 and 1 is randomly generated, and the probability interval into which the random number falls is determined, and the second concrete mix ratio parameter or the first concrete mix ratio parameter is retained.

8. The concrete mix optimization system based on environmental impact analysis is characterized by: The steps for implementing the concrete mix ratio optimization method based on environmental impact analysis as described in any one of claims 1 to 7 include: Environmental feature collection module: collects environmental features in the concrete working environment to be optimized, and obtains a high-temperature environment feature information set, a moisture feature information set, and a low-temperature environment feature information set; Mixing space acquisition module: obtain concrete mixing space; Optimization function construction module: Construct concrete mix optimization function with the purpose of minimizing concrete damage at high temperature and minimizing concrete damage at low temperature; A mix ratio optimization module: in the concrete mix ratio space, based on the concrete mix ratio optimization function, optimizes the concrete mix ratio to obtain the optimal concrete mix ratio parameters, wherein the moisture distribution in the concrete when entering the low temperature environment under the concrete mix ratio parameters is predicted according to the high temperature environment characteristic information set and the moisture characteristic information set, and the concrete damage under low temperature is predicted in combination with the low temperature environment characteristic information set for optimization; Optimization result acquisition module: outputs the optimal concrete mix ratio parameters to obtain the concrete mix ratio optimization result.

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