Multi-scale evaluation method and system for mechanical strength of construction waste mixture for road

Abstract

The invention discloses a multi-scale evaluation method and system for mechanical strength of a construction waste mixture for roads, and relates to the technical field of foundation construction.The multi-scale evaluation method comprises the steps that mechanical properties of a prepared test piece are tested; organizing and analyzing mechanical property data obtained by testing; establishing a mechanical strength evaluation model of the construction waste mixture for the road based on the test data, the arrangement and analysis result, and the mix proportion, the raw material property and the maintenance condition of the construction waste mixture; and performing multi-scale evaluation on the mechanical strength of the construction waste mixture for the road based on the mechanical strength evaluation model. According to the method, the autoregression integral moving average model is adopted, multiple independent variables are considered, the random forest algorithm is adopted to comprehensively predict the mechanical strength, the maintenance conditions are considered, scene mechanical strength prediction is carried out based on the maintenance conditions, and the accuracy of final mechanical strength multi-scale evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of foundation construction, and more specifically, to a multi-scale evaluation method and system for the mechanical strength of building waste mixtures for roads. Background Art

[0002] As an important renewable resource, building waste plays a key role in the process of urbanization. Generally, building waste includes soil, materials, muck, sludge and other waste generated during the construction, reconstruction or expansion of various buildings and structures. With the rapid development of urban construction, the quantity of building waste is increasing continuously, posing serious challenges to the environment.

[0003] According to relevant data, the amount of building waste generated globally every year has reached an astonishing figure, and most of it is landfilled or piled up in the open air, causing serious pollution to the environment and wasting resources. In this context, it has become particularly urgent to find efficient and sustainable methods for the reuse of building waste.

[0004] At present, the application of building waste in road engineering has received increasing attention. However, the evaluation methods for the mechanical strength of building waste mixtures have not been widely studied and applied. The complex composition of building waste and the non-uniformity of its mechanical properties make it somewhat uncertain to use building waste mixtures in road engineering.

[0005] Therefore, how to propose a multi-scale evaluation method and system for the mechanical strength of building waste mixtures for roads to effectively evaluate the mechanical properties of building waste mixtures in road engineering is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a multi-scale evaluation method and system for the mechanical strength of building waste mixtures for roads to effectively evaluate the mechanical properties of building waste mixtures in road engineering. To achieve the above object, the present invention adopts the following technical solutions:

[0007] A multi-scale evaluation method for the mechanical strength of building waste mixtures for roads includes:

[0008] Screen out aggregates from building waste and uniformly mix them with inorganic binders according to a preset mix ratio to prepare standard mechanical test specimens;

[0009] Test the mechanical properties of the prepared specimens;

[0010] Sort out and analyze the mechanical property data obtained from the tests;

[0011] Based on the test data, the results of sorting and analysis, as well as the mix proportion, raw material properties, and curing conditions of the construction waste mixture, establish a mechanical strength evaluation model for the construction waste mixture used in roads;

[0012] Conduct a multi-scale evaluation of the mechanical strength of the construction waste mixture used in roads based on the mechanical strength evaluation model.

[0013] Optionally, the aggregates include: granular materials processed from concrete, mortar, stones, bricks, and tiles in construction waste, and are subjected to crushing and screening to obtain particles of different particle sizes;

[0014] The inorganic binder includes: cement, lime, fly ash. The inorganic binder reacts chemically or physically with the aggregates to form a road structure layer.

[0015] Optionally, the testing of the mechanical properties of the prepared specimens includes: testing the compressive strength, flexural strength, and tensile strength of the prepared specimens.

[0016] Optionally, the sorting and analysis of the mechanical property data obtained from the testing include: sorting and analyzing the mechanical property data obtained from the mechanical property testing, and calculating the average value, standard deviation, and variance of each mechanical property using statistical methods.

[0017] Optionally, establishing a mechanical strength evaluation model for the construction waste mixture used in roads based on the test data, the results of sorting and analysis, as well as the mix proportion, raw material properties, and curing conditions of the construction waste mixture includes:

[0018] Preprocess the test data, analysis results, as well as the mix proportion, raw material properties, and curing condition data of the construction waste mixture;

[0019] Use an autoregressive integrated moving average model for mechanical strength prediction;

[0020] Consider multiple independent variables and use the random forest algorithm to predict the mechanical strength, and combine with the autoregressive integrated moving average model to form the final mechanical strength prediction result;

[0021] Consider the curing conditions and select the curing conditions for scenario mechanical strength prediction.

[0022] Optionally, using the random forest algorithm to predict the mechanical strength includes:

[0023] Take the mechanical strength as the dependent variable, and the test data, analysis results, as well as the mix proportion and raw material properties of the construction waste mixture as independent variables, and input them into the random forest model;

[0024] Set the number of random forest trees \(K\), the training ratio, use MSE as the node splitting evaluation criterion, set it to sampling with replacement, set the maximum depth of the tree to \(a\), the maximum number of leaf nodes to \(b\), and the threshold of node division impurity to \(c\);

[0025] After the random forest model for prediction is run, adjust the parameters and retrain the random forest model according to whether the observation evaluation criterion is reasonable;

[0026] Input the independent variable data to be predicted into the random forest model to obtain the prediction result.

[0027] Optionally, it further includes: verifying the estimation accuracy of the random forest model, including verifying with measured sample points. The measured sample point verification data comes from the monitoring data at different time points. The accuracy verification indicators include the coefficient of determination \(R\) 2 , the root mean square error \(RMSE\) and the mean relative error \(MRE\). The higher the \(R\) 2 , the smaller the \(RMSE\) and \(MRE\), indicating the better performance of the random forest model.

[0028] Optionally, considering the maintenance conditions and selecting the maintenance conditions for scenario mechanical strength prediction includes:

[0029] Considering the maintenance conditions, predict the scenario mechanical strength according to the preset quarter. Collect the number of historical high-temperature weather days, the average monthly maximum temperature, the number of non-rainy days, and the number of rainy days in the preset quarter according to the preset quarter;

[0030] Normalize the climate characteristics of the preset quarter and conduct a comprehensive score;

[0031] Determine the climate characteristics of the scenario plan, and use the determined climate characteristics as the search condition to find the similar days of the day to be predicted. Weight the mechanical strength prediction results of the similar days and the final mechanical strength prediction results as the predicted mechanical strength of the prediction day in the preset quarter.

[0032] Optionally, the weighting of the mechanical strength prediction results of the similar days and the final mechanical strength prediction results further includes calculating the mechanical strength prediction results of the similar days and the final mechanical strength prediction results through the Euclidean distance, and performing weighted calculation when the calculation threshold is met.

[0033] Optionally, a multi-scale evaluation system for the mechanical strength of road construction waste mixture includes:

[0034] Mixing ratio module: used to screen out aggregates from construction waste and uniformly mix them with inorganic binders according to the preset mixing ratio to prepare standard mechanical test specimens;

[0035] Testing module: used to test the mechanical properties of the prepared specimens;

[0036] Sorting module: used to sort and analyze the mechanical property data obtained from tests;

[0037] Model construction module: used to establish a mechanical strength evaluation model for building waste mixture for road use based on the test data, the results of sorting and analysis, as well as the mix ratio, raw material properties, and curing conditions of the building waste mixture;

[0038] Evaluation module: used to conduct multi-scale evaluation of the mechanical strength of building waste mixture for road use based on the mechanical strength evaluation model.

[0039] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method and system for multi-scale evaluation of the mechanical strength of building waste mixture for road use, and has the following beneficial effects:

[0040] The present invention proposes a method for multi-scale evaluation of the mechanical strength of building waste mixture for road use, including: screening out aggregates from the building waste, and uniformly mixing them with inorganic binders according to a preset mix ratio to prepare standard mechanical test specimens; conducting mechanical property tests on the prepared specimens; sorting and analyzing the mechanical property data obtained from the tests; establishing a mechanical strength evaluation model for building waste mixture for road use based on the test data, the results of sorting and analysis, as well as the mix ratio, raw material properties, and curing conditions of the building waste mixture; conducting multi-scale evaluation of the mechanical strength of building waste mixture for road use based on the mechanical strength evaluation model. The present invention discloses using an autoregressive integrated moving average model for mechanical strength prediction; considering multiple independent variables and using a random forest algorithm to predict the mechanical strength, and combining with the autoregressive integrated moving average model to form the final mechanical strength prediction result; considering the curing conditions, selecting the curing conditions for scenario mechanical strength prediction. The present invention uses an autoregressive integrated moving average model for mechanical strength prediction, combines with a random forest algorithm, improves the accuracy of the final mechanical strength prediction result, through considering the climate conditions, conducts multi-scenario mechanical strength prediction analysis based on quarters, effectively responds to the influence of curing conditions on the road, and realizes the effective evaluation of the mechanical properties of building waste mixture in road engineering. Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0042] Figure 1Schematic flow chart of a multi-scale evaluation method for the mechanical strength of a building waste mixture for road use provided by the present invention.

[0043] Figure 2 Structural framework diagram of a multi-scale evaluation system for the mechanical strength of a building waste mixture for road use provided by the present invention. Detailed implementation manners

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] An embodiment of the present invention discloses a multi-scale evaluation method for the mechanical strength of a building waste mixture for road use. As Figure 1 shown, it includes:

[0046] Screen out aggregates from the building waste, and uniformly mix them with inorganic binders according to a preset mix ratio to prepare standard mechanical test specimens;

[0047] Test the mechanical properties of the prepared specimens;

[0048] Sort out and analyze the mechanical property data obtained from the tests;

[0049] Based on the test data, the results of sorting out and analyzing, as well as the mix ratio, raw material properties, and curing conditions of the building waste mixture, establish a mechanical strength evaluation model for the building waste mixture for road use;

[0050] Based on the mechanical strength evaluation model, conduct multi-scale evaluation of the mechanical strength of the building waste mixture for road use.

[0051] Furthermore, the aggregates include: granular materials processed from concrete, mortar, stones, and bricks and tiles in the building waste, and are subjected to crushing and screening treatments to obtain particles of different particle sizes;

[0052] The inorganic binders include: cement, lime, and fly ash. The inorganic binders react chemically or physically with the aggregates to form a road structure layer with certain strength and stability.

[0053] Furthermore, the testing of the mechanical properties of the prepared specimens includes: testing the mechanical properties of compressive strength, flexural strength, and tensile strength of the prepared mechanical test specimens.

[0054] In the detailed implementation manners, the testing of the mechanical properties includes:

[0055] S1: Take representative aggregates and determine their air-dried moisture content.

[0056] S2: Weigh the mass of the test cylinder itself (m1), fix the test cylinder on the bottom plate, place the spacer block inside the cylinder, put a filter paper on the spacer block, and install the collar.

[0057] S3: Take the prepared test specimens and pour them into the cylinder in 3 layers and compact them layer by layer.

[0058] S4: For every 3 cylinders of compacted specimens, take representative specimens for moisture content test.

[0059] S5: Remove the collar, use a straight scraper to level the compacted specimens along the top of the test cylinder, repair the uneven surface with fine materials, take out the spacer block, and weigh the mass of the test cylinder and the specimens (m2).

[0060] S6: When the CBR specimen is prepared by static pressure molding, calculate the required amount of specimen according to the determined compaction degree and perform one-time static pressure molding.

[0061] S7: Soak in water to measure the swelling amount.

[0062] S8: Penetration test.

[0063] Furthermore, in S1, prepare 3 specimens according to the optimum moisture content. After adding water and thoroughly mixing the aggregates, load them into a sealed container or plastic bag for infiltration. The infiltration time for cohesive soil shall not be less than 24h, for silty soil it can be shortened to 12h, for sandy soil it can be shortened to 6h, and for natural gravel it can be shortened to about 2h; if necessary, prepare specimens with three dry densities, and control the dry density of the specimens between 90% and 100% of the maximum dry density; if 3 specimens are made for each dry density, a total of 9 specimens will be made, and about 55kg of specimens will be required in total for the 9 specimens; when using impact molding to make specimens, the number of impacts per layer is generally 30 times, 50 times, and 98 times respectively; when using static pressure molding to make specimens, calculate the required amount of specimen according to the determined compaction degree and perform one-time static pressure molding.

[0064] Furthermore, in S3, about 1500 - 1750g of specimen is required for each layer, and the amount should make the compacted specimen 1 - 2mm higher than 1 / 3 of the cylinder height; after compacting the large test cylinder, the specimen should not be more than 10mm higher than the cylinder height.

[0065] Furthermore, the specific steps of S7 are as follows:

[0066] S71. After the specimen is made, remove the damaged filter paper on the top surface of the specimen, place a good filter paper, and install a perforated plate with an adjusting rod on it, and add 4 load plates on the perforated plate.

[0067] S72. Put the test cylinder and the perforated plate together into the tank (without water first), tighten the mold with a pull rod, install a dial gauge, and read the initial reading.

[0068] S73. Fill the water tank with water until the water level is above the top of the test cylinder. During the soaking period, the water level in the tank should be maintained at about 25 mm above the top surface of the test cylinder. Usually, the specimen needs to be soaked for 4 days and nights.

[0069] S74. At the end of the soaking, read the final reading of the dial gauge on the specimen and calculate the expansion rate using the formula:

[0070]

[0071] In the formula: δ e - The expansion rate of the specimen after soaking, calculated to 0.1%;

[0072] H1 - The height (mm) of the specimen at the end of soaking;

[0073] H0 - The initial height (mm) of the specimen;

[0074] S75. Take out the specimen from the water tank, pour out the water on the top surface of the specimen, let it stand for 15 minutes to drain, then remove the additional load, perforated plate, bottom plate and filter paper, and weigh (m3) to calculate the changes in the humidity and density of the specimen.

[0075] Furthermore, the specific steps of S8 are as follows:

[0076] S81. A force measuring ring with an appropriate tonnage should be selected. At the end of the penetration, the reading of the force measuring ring should preferably be more than 1 / 3 of its range.

[0077] S82. Level the soaked specimen and make the penetration rod in full contact with the top surface of the specimen. Place 4 load plates around the penetration rod.

[0078] S83. Apply a little load on the penetration rod first to make the specimen and the soil sample in close contact, then adjust the pointers of the dial gauges for measuring force and deformation to integers and record the initial readings.

[0079] S84. Apply load to make the penetration rod press into the specimen at a speed of 1 - 1.25 mm / min, and simultaneously measure and record the readings of the three dial gauges; record the penetration amounts at some integer readings (such as 20, 40, 60) of the dial gauge in the force measuring device, and note that when the penetration amount is 250×10 -2 mm, there should be more than 5 readings; therefore, the first reading in the force measuring device should be when the penetration amount is about 30×10 -2 mm.

[0080] Furthermore, in the result collation, with the unit pressure (p) as the abscissa and the penetration amount (l) as the ordinate, draw the p-l relationship curve as shown in Figure (1). Curve 1 on the figure is appropriate. The start section of Curve 2 is a concave curve and needs to be corrected. When correcting, draw a tangent line at the point of variable curvature and intersect it with the ordinate at point O'. O' is the corrected origin.

[0081] Furthermore, in the result collation, the California Bearing Ratio (CBR) is calculated according to the formula when the penetration is 2.5 mm and 5 mm respectively;

[0082]

[0083] In the formula, CBR is the California Bearing Ratio, calculated to 0.1%, and p is the unit pressure (kPa).

[0084] Furthermore, in the result collation, the wet density of the specimen is calculated as follows:

[0085]

[0086] In the formula, ρ is the wet density of the specimen, calculated to 0.01 g / cm 3 , m 2 - The combined mass of the test cylinder and the specimen (g), m 1 - The mass of the test cylinder (g), 2177 - The volume of the test cylinder (cm 3 ).

[0087] The dry density of the specimen is calculated as follows:

[0088]

[0089] In the formula, ρ d - The dry density of the specimen, calculated to 0.01 g / cm 3 ; w - The water content of the specimen (%).

[0090] The water absorption of the specimen after soaking is calculated as follows:

[0091] w a = m3 - m2;

[0092] In the formula, w a - The water absorption of the specimen after soaking (g), m3 - The combined mass of the test cylinder and the specimen after soaking (g), m2 - The combined mass of the test cylinder and the specimen (g).

[0093] Furthermore, the collation and analysis of the mechanical property data obtained from the test include: collating and analyzing the mechanical property data obtained from the mechanical property test, and calculating the mean value, standard deviation and variance of each mechanical property by using statistical methods.

[0094] Specifically, (1) Calculate the mean value (Mean): The mean value is the sum of all data points in the data set divided by the number of data points: Where: is the mean value, n is the number of data points, x iis the i-th data point; (2) Calculate the variance: The variance is the average of the squares of the differences between the data points and the mean value, and is used to measure the degree of dispersion of the data: where: σ 2 is the variance, is the mean value, n is the number of data points, and x i is the i-th data point; (3) Calculate the standard deviation: The standard deviation is the square root of the variance and is used to measure the degree of dispersion of the data, with the unit being the same as the original data: where: σ is the standard deviation, σ 2 is the variance, is the mean value, n is the number of data points, and x i is the i-th data point.

[0095] Furthermore, the mechanical strength evaluation model of the construction waste mixture for road use established based on the test data, sorting and analysis results, as well as the mix ratio, raw material properties, and curing conditions of the construction waste mixture includes:

[0096] Preprocess the test data, analysis results, as well as the mix ratio, raw material properties, and curing condition data of the construction waste mixture;

[0097] Use the autoregressive integrated moving average model for mechanical strength prediction;

[0098] Consider multiple independent variables and use the random forest algorithm to predict the mechanical strength, and combine it with the autoregressive integrated moving average model to form the final mechanical strength prediction result;

[0099] Consider the curing conditions and select the curing conditions for scenario mechanical strength prediction.

[0100] In the specific implementation manner, the preprocessing includes outlier processing and normalization processing:

[0101] For the outlier processing of the test data, analysis results, as well as the mix ratio, raw material properties, and curing condition data of the construction waste mixture, use the Lagrange interpolation method, and the specific operation is as follows:

[0102] Input the time points X and the corresponding data Y of the time series;

[0103] Calculate the basis function L of the n-th order Lagrange algorithm i (x);

[0104]

[0105] where L i (x) is the basis function of the interpolation polynomial, and x is the basis function L iThe unknown on (x), x i and x j are respectively the i-th and j-th interpolations between two normal points, where i = 0, 1, 2...n, j = 0, 1, 2...n, and n is the number of difference times between the two points, being an integer; is the successive multiplication operation, that is, multiplying from j = 0 to j = n.

[0106] Calculate the interpolation function L n (x) of the n-th order Lagrange interpolation algorithm;

[0107] The interpolation function L n (x) has the calculation expression as follows:

[0108]

[0109] where y i is the data corresponding to the interpolation point, L n (x) is the n-th order interpolation polynomial of y i , and L i (x) is the basis function of the interpolation polynomial, that is, adding L i (x)y i from i = 0 to i = n.

[0110] Input the interpolation points and obtain the corresponding data interpolation through the n-th order Lagrange formula.

[0111] The normalization method adopted is the maximum normalization method, and the calculation expression is:

[0112]

[0113] The ARIMA model, whose full name is Autoregressive Integrated Moving Average Model (simply noted as ARIMA), is a famous time-series prediction method proposed by Box and Jenkins in the early 1970s. Therefore, it is also known as the Box-Jenkins model or the Box-Jenkins method. Among them, ARIMA(p, d, q) is called the differential autoregressive moving average model. AR is autoregression, and p is the number of autoregressive terms; MA is moving average, q is the number of moving average terms, and d is the number of times of differencing when the time series becomes stationary. The so-called ARIMA model refers to a model established by transforming a non-stationary time series into a stationary time series and then regressing the dependent variable only on its lagged values and the present and lagged values of the random error term. The ARIMA model includes the moving average process (MA), autoregressive process (AR), autoregressive moving average process (ARMA), and ARIMA process according to whether the original sequence is stationary and the differences in the parts included in the regression.

[0114] Furthermore, the use of the autoregressive integrated moving average model for mechanical strength prediction includes using the ARIMA model for mechanical strength prediction:

[0115] Construct a mechanical strength time series matrix b;

[0116] Based on the ARIMA model for mechanical strength prediction, it includes:

[0117] For the dimensionality-reduced mechanical strength time series b k , predict the mechanical strength value at a future time based on the ARIMA model. The ARIMA(p, d, q) model is:

[0118]

[0119] Among them, p is the order of the autoregressive model, d is the number of times of differencing to make it a stationary sequence, q is the order of the moving average model, O is the lag operator, and the random variable β k (n) is white noise, t i is the coefficient of the AR model part, b i is the coefficient of the MA model part.

[0120] Specifically, it also includes performing a stationarity test and d-order differencing until the mechanical strength sequence is stationary.

[0121] In the specific implementation manner, the following stability test is performed: the ADF verification method is used to perform a stability test on the original sequence. If the sequence does not meet the stability condition, a difference transformation is performed to convert the non-stationary time series into a stationary time series.

[0122] Furthermore, determine the order p + q of the ARIMA model. By analyzing the partial autocorrelation function (PACF) and the autocorrelation function (ACF), identify the parameters of the ARIMA model. Based on the obtained parameters, construct the ARIMA model. Online model parameter estimation:

[0123] The model of the mechanical strength time series at time n is

[0124] Predict the mechanical strength value at the future time n + t from the mechanical strength time series matrix at time n, that is,

[0125] where d kh represents the h-th parameter of the EMA model. The mechanical strength of the road construction waste mixture is evaluated under the time scale by predicting the mechanical strength through the ARIMA model.

[0126] Furthermore, using the random forest algorithm to predict the mechanical strength includes:

[0127] Taking the mechanical strength as the dependent variable, and the test data, analysis results, mix proportion of the construction waste mixture, and properties of the raw materials as independent variables, and input them into the random forest model;

[0128] Set the number of random forest trees K, the training proportion, use the mean squared error (MSE) as the node splitting evaluation criterion, set it to sampling with replacement, set the maximum depth of the tree to a, the maximum number of leaf nodes to b, and the threshold of node division impurity to c;

[0129] After running the predicted random forest model, adjust the parameters according to whether the observation evaluation criterion is reasonable and retrain the random forest model;

[0130] Input the independent variable data to be predicted into the random forest model to obtain the prediction result. In this embodiment, by taking the mechanical strength as the dependent variable, and the test data, analysis results, mix proportion of the construction waste mixture, and properties of the raw materials as independent variables, and inputting them into the random forest algorithm to predict the mechanical strength, the mechanical strength evaluation of the road construction waste mixture at the micro scale is realized.

[0131] Furthermore, it also includes: verifying the estimation accuracy of the random forest model, including verifying with measured sample points. The verification data of the measured sample points comes from the monitoring data at different time points. The accuracy verification index includes the coefficient of determination R 2, Root Mean Square Error (RMSE) and Mean Relative Error (MRE), R 2 The higher the value, the smaller the RMSE and MRE, indicating a better performance of the random forest model.

[0132] Furthermore, considering the curing conditions and selecting the curing conditions for scenario mechanical strength prediction includes:

[0133] Considering the curing conditions, predicting the scenario mechanical strength according to the preset quarter, and collecting the number of historical high-temperature days, the average monthly maximum temperature, the number of non-rainy days, and the number of rainy days in the preset quarter according to the preset quarter;

[0134] Normalize the climate characteristics of the preset quarter and conduct a comprehensive score;

[0135] Determine the climate characteristics of the scenario plan, and use the determined climate characteristics as search conditions to find similar days of the day to be predicted, and weight the mechanical strength prediction results of the similar days and the final mechanical strength prediction results as the predicted mechanical strength of the prediction day in the preset quarter.

[0136] In the specific implementation manner, considering the curing conditions and selecting the curing conditions for scenario mechanical strength prediction includes:

[0137] Considering the curing conditions, select the third quarter to predict the scenario mechanical strength according to the quarter, and separately count the number of historical high-temperature days, the average monthly maximum temperature, the number of non-rainy days, and the number of rainy days in the third quarter;

[0138] Normalize the climate characteristics of the third quarter and conduct a comprehensive score;

[0139] Determine the climate characteristics of the scenario plan, including: the rapid growth situation of curing condition requirements and the slow growth situation of curing condition requirements. Among them, for the high-speed growth plan of curing condition requirements: take the climate conditions of the month with the highest historical annual score;

[0140] For the medium-speed growth plan of curing condition requirements: take the climate conditions of the month with the median historical annual score;

[0141] According to the determined climate characteristics, as search conditions, find similar days of the day to be predicted, and weight the mechanical strength prediction results of the similar days and the final mechanical strength prediction results as the predicted mechanical strength of the prediction day in the third quarter.

[0142] Furthermore, weighting the mechanical strength prediction results of the similar days and the final mechanical strength prediction results also includes calculating the mechanical strength prediction results of the similar days and the final mechanical strength prediction results through the Euclidean distance, and performing the weighting calculation when the calculation threshold is met.

[0143] In a specific embodiment, a multi-scale evaluation system for the mechanical strength of a building waste mixture for roads is as follows Figure 2 shown, including:

[0144] Mix ratio module: used to screen out aggregates from building waste and uniformly mix them with inorganic binders according to a preset mix ratio to prepare standard mechanical test specimens;

[0145] Testing module: used to test the mechanical properties of the prepared specimens;

[0146] Sorting module: used to sort and analyze the mechanical property data obtained from the tests;

[0147] Model construction module: used to establish a mechanical strength evaluation model for the building waste mixture for roads based on the test data, sorting and analysis results, as well as the mix ratio, raw material properties, and curing conditions of the building waste mixture;

[0148] Evaluation module: used to perform multi-scale evaluation of the mechanical strength of the building waste mixture for roads based on the mechanical strength evaluation model.

[0149] In a specific embodiment, it further includes a verification and optimization module, which is used to apply the evaluation model to actual road projects, verify and optimize the evaluation model by comparing the actual use effect with the prediction results. At the same time, according to the feedback in engineering practice, continuously improve and adjust the evaluation methods and processes.

[0150] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0151] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-scale evaluation method for mechanical strength of road construction waste mixture, characterized in that: include: Aggregates are screened from construction waste and uniformly mixed with inorganic binders according to a preset mix ratio to prepare standard mechanical test specimens; Test the mechanical properties of the prepared specimens; Organize and analyze the mechanical properties data obtained from the test; Based on the test data and analysis results, as well as the mix ratio, raw material properties and maintenance conditions of the construction waste mixture, a mechanical strength evaluation model for road construction waste mixture was established; A multi-scale evaluation of the mechanical strength of road construction waste mixture is performed based on the mechanical strength evaluation model.

2. The multi-scale evaluation method for mechanical strength of road construction waste mixture according to claim 1 is characterized in that: The aggregate includes: granular materials processed from concrete, mortar, stone, bricks and tiles in construction waste, which are crushed and sieved to obtain particles of different particle sizes; The inorganic binder includes cement, lime and fly ash. The inorganic binder reacts chemically or physically with aggregate to form a road structure layer.

3. The multi-scale evaluation method for mechanical strength of road construction waste mixture according to claim 1, characterized in that: The testing of mechanical properties of the prepared specimens includes: testing the compressive strength, flexural strength and tensile strength mechanical properties of the prepared specimens.

4. The multi-scale evaluation method for mechanical strength of road construction waste mixture according to claim 1, characterized in that: The arranging and analyzing the mechanical property data obtained from the test includes: arranging and analyzing the mechanical property data obtained from the mechanical property test, and using statistical methods to calculate the average value, standard deviation and variance of each mechanical property.

5. The multi-scale evaluation method for mechanical strength of road construction waste mixture according to claim 1, characterized in that: The mechanical strength evaluation model of road construction waste mixture is established based on the test data and the collated analysis results, as well as the mix ratio of the construction waste mixture, the properties of the raw materials, and the maintenance conditions, including: Pre-process the test data and analysis results, as well as the mix ratio of construction waste mixture, raw material properties, and maintenance conditions; The autoregressive integrated moving average model is used to predict mechanical strength; The random forest algorithm is used to predict the mechanical strength by considering multiple independent variables, and the final mechanical strength prediction result is formed by combining the autoregressive integral moving average model; Considering the maintenance conditions, the maintenance conditions are selected for scenario mechanical strength prediction.

6. A multi-scale evaluation method for mechanical strength of road construction waste mixture according to claim 5, characterized in that: The prediction of mechanical strength using random forest algorithm includes: The mechanical strength was used as the dependent variable, and the test data and analysis results, as well as the mix ratio and raw material properties of the construction waste mixture were used as independent variables and input into the random forest model; Set the number of random forest trees K, the training ratio, use MSE as the node splitting evaluation criterion, set sampling with replacement, set the maximum depth of the tree to a, set the maximum number of leaf nodes to b, and set the node partition impurity threshold to c; After the predicted random forest model is run, the parameters are adjusted and the random forest model is retrained according to whether the observed evaluation criteria are reasonable; Input the independent variable data to be predicted into the random forest model to obtain the prediction results.

7. A multi-scale evaluation method for mechanical strength of road construction waste mixture according to claim 6, characterized in that: Also includes: The estimation accuracy of the random forest model was verified, including the actual sample point verification. The actual sample point verification data came from the monitoring data at different time points. The accuracy verification indicators included the determination coefficient R 2 , root mean square error RMSE and mean relative error MRE, R 2 The higher it is, the smaller the RMSE and MRE are, which means the better the performance of the random forest model is.

8. The multi-scale evaluation method for mechanical strength of road construction waste mixture according to claim 5, characterized in that: The consideration of the maintenance conditions and the selection of the maintenance conditions for scene mechanical strength prediction include: Considering the maintenance conditions, the scenario mechanical strength prediction is carried out according to the preset quarter, and the number of historical high temperature days, the average monthly maximum temperature, the number of non-rainy days, and the number of rainy days in the preset quarter are collected according to the preset quarter; Normalize the preset seasonal climate characteristics and give a comprehensive score; Determine the climate characteristics of the scenario, and use the determined climate characteristics as search conditions to find similar days to the day to be predicted, and weight the mechanical strength prediction results of the similar days with the final mechanical strength prediction results as the predicted mechanical strength of the prediction day in the preset quarter.

9. The multi-scale evaluation method for mechanical strength of road construction waste mixture according to claim 8, characterized in that: The weighting of the mechanical strength prediction results of similar days and the final mechanical strength prediction results also includes calculating the mechanical strength prediction results of similar days and the final mechanical strength prediction results by Euclidean distance, and performing weighted calculation when a calculation threshold is met.

10. A multi-scale evaluation system for mechanical strength of road construction waste mixture, characterized in that: include: Proportioning module: used to select aggregates from construction waste and mix them evenly with inorganic binders according to the preset proportion to prepare standard mechanical test specimens; Test module: used to test the mechanical properties of the prepared specimens; Arrangement module: used to arrange and analyze the mechanical properties data obtained from the test; Model building module: used to establish a mechanical strength evaluation model for road construction waste mixture based on test data and collated analysis results, as well as the mix ratio, raw material properties, and maintenance conditions of the construction waste mixture; Evaluation module: used for multi-scale evaluation of the mechanical strength of road construction waste mixture based on the mechanical strength evaluation model.

Images