Method for detecting content of steel slag asphalt mixture based on dynamic temperature response

Abstract

The invention relates to a steel slag asphalt mixture content detection method based on dynamic temperature response, and belongs to the technical field of road engineering material detection. According to the method, dynamic heating and cooling circulation are carried out on an asphalt sample, thermal infrared imaging equipment is used for monitoring the surface temperature change of a material in real time, and a temperature response curve in the heating and cooling processes is obtained. As the steel slag has lower heat conductivity coefficient and higher heat capacity, the steel slag shows a slower thermal response behavior in the heating and cooling processes, and the thermal response of the steel slag is different from that of the conventional asphalt aggregate. The mixing amount of the steel slag in the asphalt mixture can be accurately evaluated by analyzing a temperature-time area (TTA) and a correction coefficient and combining a weight function of a temperature section. The method has the advantages of being rapid in detection, accurate, non-destructive and the like, and is suitable for steel slag asphalt mixture evaluation in road construction and maintenance and material evaluation of laboratory formula research and development.

Description

Technical Field

[0001] The invention relates to the technical field of road engineering material detection, in particular to a quantitative detection method for detecting the amount of steel slag in asphalt mixture through a dynamic temperature response test and an area quantitative analysis method. Background Art

[0002] In recent years, with the efficient use of resources and the improvement of environmental protection requirements, steel slag, as an industrial waste, has been widely used in the field of road construction. Steel slag not only has high strength and wear resistance, but also can replace traditional aggregates, reduce the consumption of natural mineral resources, and realize the recycling of waste. Introducing steel slag into asphalt mixture not only helps to improve the performance of road structure, but also meets the requirements of green environmental protection and circular economy.

[0003] However, due to the physical properties of steel slag (such as lower thermal conductivity and higher heat capacity) being different from those of traditional aggregates (such as basalt, limestone, etc.), the amount of steel slag added to asphalt mixtures will have a significant impact on the mechanical properties, temperature response characteristics, and long-term durability of the mixture. Therefore, accurate detection of the amount of steel slag added to asphalt mixtures is crucial to ensure the performance stability and construction quality of pavement materials.

[0004] Existing technologies for detecting the content of steel slag asphalt mixtures mostly rely on chemical and physical property tests, which are complex and usually need to be performed in the laboratory, making them difficult to quickly apply to construction sites. Traditional thermal imaging technology is generally only performed at a stable temperature, which fails to fully reflect the true thermal response behavior of steel slag materials in dynamic temperature changes.

[0005] The present invention combines dynamic temperature response with area quantitative analysis and further introduces a correction coefficient and a temperature weight formula, which can more accurately evaluate the steel slag content in the steel slag asphalt mixture, overcomes the shortcomings of the prior art, and realizes rapid and non-destructive on-site detection. Summary of the invention

[0006] In order to solve the technical problems existing in the background technology, the present invention provides a method for detecting the content of steel slag asphalt mixture based on dynamic temperature response. By analyzing the temperature-time area (TTA) and the correction coefficient, combined with the weight function of the temperature section, the amount of steel slag in the asphalt mixture can be accurately evaluated. This method has the advantages of fast, accurate and non-destructive detection, and is suitable for the evaluation of steel slag asphalt mixture in road construction and maintenance, as well as material evaluation in laboratory formula development.

[0007] The present invention adopts the following technical scheme: a method for detecting the content of steel slag asphalt mixture based on dynamic temperature response, comprising the following steps:

[0008] Step 1: Performing a dynamic temperature response test on the steel slag asphalt mixture sample, wherein the dynamic temperature response test comprises: monitoring the temperature change on the surface of the sample when the sample is subjected to a heating and cooling cycle;

[0009] Step 2: Using a thermal infrared imaging device to obtain the temperature response curve of the sample during the heating and cooling process;

[0010] Step 3: Calculate the temperature-time area (TTA) of the temperature response curve, and calculate the R(TTA) ratio through the correction formula of material properties and temperature weight. Considering the influence of material properties on thermal response, a correction factor is introduced in the calculation process of R(TTA);

[0011] Step 4: Calculate the amount of steel slag based on the temperature-time area ratio R (TTA) to quantitatively characterize the proportion of steel slag in the asphalt mixture.

[0012] In a further embodiment, the heating temperature ranges from ambient temperature to 100° C., and the heating and cooling rate is 5° C. / min.

[0013] In a further embodiment, the temperature-time area (TTA) of the temperature response curve is calculated by the following steps:

[0014] Step 301: Integrate the temperature data recorded at each time point during the heating and cooling process:

[0015]

[0016] Where T(x) is the temperature response value at time x, and the integration interval is from t0 to t n , F(x) is the weight function of the temperature section, which is used to adjust the contribution of different temperature sections to the TTA value;

[0017] Step 302: correct the temperature weight in the integration process, and obtain the temperature-time area (TTA) through the integration result; the weight function adopts the sigmoid function form as follows:

[0018]

[0019] Among them, β controls the steepness of the weight change, and T0 is the median of the temperature range.

[0020] In a further embodiment, the amount of slag added is calculated by the following steps:

[0021] Calculate the temperature-time area ratio R (TTA), which is the ratio of the TTA values ​​of the test sample to the reference sample:

[0022]

[0023] Among them, TTA sample is the temperature-time area of ​​the steel slag asphalt mixture sample during the heating and cooling process, TTA reference Refers to the temperature-time area of ​​the reference sample;

[0024] The amount of steel slag is calculated by combining the R (TTA) value with the standard curve.

[0025] In a further embodiment, the correction coefficient is calculated as follows:

[0026] Step 303: Set a correction coefficient C1 according to the content of mineral powder in the sample. When the content of mineral powder is high, the heat capacity of the material increases, resulting in an enhanced temperature hysteresis effect. The calculation formula of the correction coefficient C1 is:

[0027] C1=1+α1×P mineral

[0028] Among them, P mineral is the mineral powder content, α1 is the fitting constant;

[0029] Step 304, introducing asphalt modification correction coefficient C2: If the sample uses modified asphalt, the influence of the modifier on the thermal response is considered. The calculation formula of the asphalt modification correction coefficient C2 is:

[0030] C2=1+α2×M modifier ;

[0031] Among them, M modifier is the mass percentage of the modifier, α2 is the fitting constant;

[0032] Step 305, introducing an aging correction coefficient C3: Taking thermal conductivity into consideration, the calculation formula of the aging correction coefficient C3 is as follows:

[0033] C3=1+α3×A aging ;

[0034] Among them, A aging It is an indicator of the degree of aging;

[0035] In summary, the expression of the correction coefficient C is:

[0036] C = C1 × C2 × C3;

[0037] The correction coefficient C is multiplied by the TTA ratio R(TTA) to obtain the corrected R(TTA).

[0038] In a further embodiment, the standard curve is constructed by testing TTA values ​​of different steel slag dosages and performing standard curve fitting to estimate the proportion of steel slag.

[0039] In further embodiments, the method is applicable to rapid testing of steel slag asphalt mixtures on site and evaluation of material formulations in the laboratory.

[0040] Beneficial effects of the invention: The invention provides a method for detecting the content of steel slag asphalt mixture based on dynamic temperature response and area quantitative analysis. Through the thermal response behavior of the material under dynamic temperature changes, combined with thermal infrared imaging technology, integral calculation and correction coefficient of material properties, the proportion of steel slag in the steel slag asphalt mixture can be accurately evaluated. This method can evaluate the steel slag content through non-destructive testing in a short time, and has high accuracy and wide applicability, and is suitable for material formula evaluation at construction sites and laboratories. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a detection flow chart.

[0042] Figure 2 It is a dynamic temperature response curve diagram of the heating and cooling process of steel slag asphalt mixture and ordinary (new) asphalt mixture.

[0043] Figure 3 It is a temperature-time area (TTA) analysis diagram. DETAILED DESCRIPTION

[0044] Due to its lower thermal conductivity and higher heat capacity, steel slag exhibits different thermal response behavior under dynamic temperature changes compared to conventional aggregates. By dynamically monitoring the surface temperature of the material during the heating and cooling cycle, the difference in thermal response between steel slag and other materials can be captured. By analyzing the temperature-time area (TTA) of the sample and combining the correction coefficient of the material properties (such as mineral powder content, asphalt modification and aging degree), the R (TTA) value is calculated, and then the steel slag content in the sample is calculated by standard curve fitting. This method can achieve rapid and accurate detection of the content of steel slag asphalt mixture.

[0045] The present invention adopts the following technical solutions:

[0046] (1) Sample preparation and equipment configuration

[0047] Take samples from asphalt pavement to obtain mixture samples containing steel slag. Ensure that the samples may contain different proportions of steel slag.

[0048] Thermal infrared imaging equipment and dynamic heating devices are configured to ensure rapid temperature rise and fall and precise control. The temperature setting range is usually from ambient temperature to 100°C, and the temperature rise and fall rate is 5°C / min.

[0049] (2) Dynamic temperature response test

[0050] By heating and cooling the steel slag asphalt mixture sample, the temperature change on the material surface is monitored in real time. Due to the low thermal conductivity of steel slag, it exhibits a slow temperature response during the heating and cooling process. The temperature response curve of the sample is recorded by thermal infrared imaging equipment to capture its thermal response effect.

[0051] The steps of dynamic temperature response test are as follows:

[0052] Heating process: gradually heat the sample from ambient temperature to 100°C, set the heating rate to 5°C / min, and record the temperature change of the sample during the entire heating process.

[0053] Cooling process: Stop heating, allow the sample to cool naturally to ambient temperature, and continue to record the temperature response during the cooling process.

[0054] Through dynamic temperature response testing, the complete temperature response curve of the sample during heating and cooling is obtained, providing a basis for subsequent quantitative analysis.

[0055] (3) Temperature-time area (TTA) calculation

[0056] Based on the temperature response curve, the temperature-time area (TTA) of the sample is calculated. TTA is the integrated area of ​​temperature change over time, which represents the thermal response behavior of the sample in dynamic temperature change. The specific calculation formula is:

[0057]

[0058] Among them, T(x) is the temperature value at time x, t0 is the heating start time, t n The TTA value is the cooling end time. The TTA value can reflect the thermal response speed and temperature hysteresis characteristics of the material.

[0059] (4) Area quantitative analysis and standard curve construction

[0060] In order to quantitatively evaluate the amount of steel slag added, the construction of a standard curve is crucial. By conducting dynamic temperature response tests on steel slag asphalt mixtures with different amounts of steel slag added, the TTA value of each sample is calculated and compared with the TTA value of the sample without steel slag to obtain the TTA ratio:

[0061]

[0062] Among them, TTA sample is the temperature-time area of ​​the steel slag asphalt mixture sample during the heating and cooling process, TTA reference Refers to the temperature-time area of ​​a reference sample, usually an asphalt mixture without slag or a standard recipe mixture.

[0063] By fitting the R(TTA) of different dosages, a standard curve is established. The standard curve is used to establish a corresponding relationship between the R(TTA) of the unknown dosage sample and its actual slag dosage.

[0064] (5) Introduction of TTA correction factor

[0065] In order to further improve the detection accuracy, TTA is calculated in an integral form and the weight of the temperature range is taken into account. The specific formula is:

[0066]

[0067] Among them, F(x) is the weight function of the temperature range, which is used to adjust the contribution of different temperature ranges to the TTA value. In order to make the weight reflect the temperature change characteristics of the material, the weight function can be in the form of a sigmoid function:

[0068]

[0069] Among them, β controls the steepness of the weight change, and T0 is the median of the temperature range.

[0070] By introducing a dynamic weight function, TTA calculation is more accurate and can better reflect the thermal response behavior of materials in different temperature ranges.

[0071] (6) Introduction of R(TTA) correction factor

[0072] In order to improve the calculation accuracy, the calculation of R(TTA) also needs to consider the influence of material properties on thermal response, especially factors such as mineral powder content, asphalt modification and asphalt aging degree. The specific correction coefficient calculation method is as follows:

[0073] 1. Mineral powder content correction coefficient C1 sets the correction coefficient according to the mineral powder content in the sample. When the mineral powder content is high, the heat capacity of the material increases, resulting in an enhanced temperature hysteresis effect. The calculation formula is:

[0074] C1=1+α1×P mineral

[0075] Among them, P mineral is the mineral powder content, and α1 is the fitting constant.

[0076] 2. Asphalt modification correction factor C2: If the sample uses modified asphalt (such as SBS modified asphalt), the effect of the modifier on the thermal response needs to be considered. The calculation formula is:

[0077] C2=1+α2×M modifier

[0078] Among them, M modifier is the mass percentage of the modifier, and α2 is the fitting constant.

[0079] 3. Aging correction factor C3: The aging degree of asphalt will also affect its thermal conductivity. The correction factor is determined by aging indicators (such as needle penetration):

[0080] C3=1+α3×A aging

[0081] Among them, A aging An indicator of aging degree.

[0082] The expression of comprehensive correction coefficient C is:

[0083] C=C1×C2×C3

[0084] The correction coefficient C is multiplied by the TTA ratio R(TTA) to obtain the corrected R(TTA).

[0085] In order to better explain the method for detecting the content of steel slag asphalt mixture based on dynamic temperature response provided by the present invention, the technical solution of an embodiment of the present invention will be described below.

[0086] Example 1

[0087] Application scenario: Testing of steel slag asphalt mixture for municipal roads. Experimental purpose: At a municipal road construction site, quantitatively test the amount of steel slag in the steel slag asphalt mixture to evaluate whether its actual amount meets the design standards.

[0088] 1.1 Sample collection and preparation

[0089] (1) Random samples were taken from three different construction sections of the municipal road. The weight of each sample was approximately 500 g, ensuring that it contained evenly distributed steel slag and conventional aggregate asphalt mixture.

[0090] (2) The samples are numbered Sample A, Sample B, and Sample C.

[0091] 1.2 Dynamic temperature response test

[0092] (1) Heating process: Each sample was heated using a heating device, with the temperature range set at 20°C to 100°C and the heating rate at 5°C / min. The heating process for each sample lasted for about 30 minutes.

[0093] (2) Cooling process: After stopping heating, the sample is cooled naturally, and the gradual drop in temperature during the cooling process is recorded until the temperature returns to ambient temperature (about 20°C).

[0094] 1.3 Thermal imaging equipment and data acquisition

[0095] (1) Use high-resolution thermal infrared imaging equipment to record the surface temperature changes of the sample during heating and cooling. The imaging frequency is 10 frames per second to ensure that the details of the temperature changes can be captured.

[0096] (2) Import the temperature change data into the processing software to generate the temperature response curves for heating and cooling.

[0097] 1.4TTA calculation and RTTA correction

[0098] (1) Temperature-time area (TTA) calculation: Integrate the temperature response curve of each sample and calculate the temperature-time area (TTA) of the heating and cooling stages. According to the integral formula:

[0099]

[0100] Where T(x) is the temperature at time x, and the integration interval is from t0 to t n The time for the entire heating and cooling process.

[0101] (2) Calculation of correction coefficient:

[0102] Mineral powder content C1: Through laboratory analysis, the mineral powder contents of Samples A, B, and C are 7%, 9%, and 8%, respectively.

[0103] Asphalt modification C2: Sample B used SBS modified asphalt, and its correction factor C2 = 1.15.

[0104] Aging degree C3: The needle penetration test shows that Sample A has a higher degree of aging, with a correction factor C3 = 1.10, and the correction factor C3 of Samples B and C is 1.05.

[0105] The comprehensive correction coefficient C is calculated as:

[0106] C=C1×C2×C3

[0107] 1.5 Standard curve and calculation of slag dosage

[0108] According to the calculated R (TTA) value, the actual amount of steel slag is calculated through the standard curve. The R (TTA) values ​​of Sample A, B, and C are 1.2, 1.35, and 1.3, respectively, and the corresponding steel slag amounts are 28%, 35%, and 32%, respectively.

[0109] 1.6 Results and Discussion

[0110] The municipal road construction standard requires that the steel slag content should not exceed 30%. The steel slag content of Sample B exceeded the standard requirement, so the construction company adjusted the proportion of the mixture to ensure that it met the design specifications.

[0111] Example 2

[0112] Application scenario: Testing of steel slag asphalt mixture on highways. Experimental purpose: To conduct quantitative testing on the steel slag asphalt mixture during the construction of a highway to ensure that the amount of steel slag meets the design requirements of the highway.

[0113] 2.1 Sample preparation

[0114] (1) Samples were taken from different construction sections of the highway paving site. The weight of each sample was 750 g and the samples were numbered Sample D, E, and F.

[0115] (2) The samples contained different proportions of steel slag and modified asphalt was used.

[0116] 2.2 Dynamic temperature response test

[0117] (1) Heating and cooling conditions: The temperature range was set at 20°C to 100°C, and the heating rate was 4°C / min. The cooling process was natural cooling, and the temperature response data was collected by thermal infrared imaging equipment.

[0118] (2) The thermal imaging frequency is 12 frames per second to ensure high-precision recording.

[0119] 2.3TTA calculation

[0120] The TTA was calculated for each sample during the heating and cooling process, and the calculation formula was the same as that in Example 1. The TTA values ​​of Samples D, E, and F were 3500, 4100, and 3800, respectively.

[0121] 2.4 Correction coefficient and R(TTA) calculation

[0122] (1) Mineral powder content C1: The mineral powder contents of the three groups of samples were 6%, 8%, and 7%.

[0123] (2) Modified asphalt C2: The usage of SBS modifier is 5%, so the correction factor C2 = 1.15.

[0124] (3) Aging degree C3: Through aging tests, the aging correction factors of the samples were determined to be 1.05, 1.10, and 1.08, respectively.

[0125] The comprehensive correction coefficient is calculated as: C = C1 × C2 × C3

[0126] The corrected R(TTA) values ​​are 1.3, 1.45, and 1.4, respectively.

[0127] 2.5 Evaluation of slag dosage

[0128] According to the standard curve, the slag content of Sample D, E, and F is 30%, 38%, and 35% respectively. The content of Sample E exceeds the design requirements (no more than 35%), and the construction unit needs to adjust the proportion according to the test results.

[0129] 2.6 Results and Recommendations

[0130] During construction, attention should be paid to the uniform distribution of steel slag to ensure that the dosage is within the design standard range. This method effectively monitors the use of steel slag during construction and provides data support for subsequent projects.

[0131] Example 3

[0132] Application scenario: Laboratory optimization of steel slag asphalt formula Experiment purpose: Through dynamic temperature response test, optimize the asphalt mixture formula under different steel slag dosages to ensure that its thermal response performance and durability meet the road design requirements.

[0133] 3.1 Sample preparation

[0134] Four groups of asphalt mixture samples with different steel slag content were prepared, namely 10%, 30%, 50% and 70%, with each group of samples weighing 500 grams and numbered as Sample G, H, I and J.

[0135] 3.2 Dynamic temperature response test

[0136] (1) Heating and cooling conditions: The samples were heated from 20°C to 100°C at a heating rate of 5°C / min. The cooling process was natural cooling, and the temperature response curve of each group of samples was tested.

[0137] (2) Use thermal infrared imaging equipment to record temperature data with an imaging frequency of 15 frames per second.

[0138] 3.3 Calculation of TTA and R(TTA)

[0139] (1) Integrate the temperature response curve of each group of samples and calculate its TTA value.

[0140] (2) Correction coefficients C1, C2, and C3 are introduced to consider the mineral powder content, modifier usage, and aging degree, respectively, to obtain the corrected RTTA value.

[0141] 3.4 Relationship between slag dosage and thermal response performance

[0142] (1) Through the R (TTA) value analysis, there is a linear relationship between the amount of steel slag added and the thermal response performance of the material. As the amount of steel slag added increases, the thermal response speed of the material increases and the temperature response characteristics improve.

[0143] (2) The linear relationship between the TTA ratio R(TTA) and the slag dosage is well fitted and can be used for the subsequent formulation design optimization.

[0144] 3.5 Optimized recipe suggestions

[0145] Based on the effect of different slag dosages on thermal response, the laboratory determined that the best formula is a mixture with 30% slag dosage, which can improve the utilization rate of slag while ensuring durability.

[0146] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0147] The meaning of "and / or" described in this application means that the situations where each exists alone or both exist at the same time are included.

[0148] The term “connection” as used in this application may mean a direct connection between components or an indirect connection between components via other components.

[0149] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for detecting the content of steel slag asphalt mixture based on dynamic temperature response, characterized in that: The following steps are involved: Step 1: Performing a dynamic temperature response test on the steel slag asphalt mixture sample, wherein the dynamic temperature response test includes: monitoring the temperature change on the surface of the sample when the sample is subjected to a heating and cooling cycle; Step 2: Using a thermal infrared imaging device to obtain the temperature response curve of the sample during the heating and cooling process; Step 3: Calculate the temperature-time area (TTA) of the temperature response curve, and calculate the R(TTA) ratio through the correction formula of material properties and temperature weight. Considering the influence of material properties on thermal response, a correction factor is introduced in the calculation process of R(TTA); Step 4: Calculate the amount of steel slag based on the temperature-time area ratio R (TTQ) to quantitatively characterize the proportion of steel slag in the asphalt mixture.

2. The method for detecting the content of steel slag asphalt mixture based on dynamic temperature response according to claim 1 is characterized in that: The heating temperature range is from ambient temperature to 100° C., and the heating and cooling rate is 5° C. / min.

3. The method for detecting the content of steel slag asphalt mixture based on dynamic temperature response according to claim 1, characterized in that: The temperature-time area (TTA) of the temperature response curve is calculated by the following steps: Step 301: Integrate the temperature data recorded at each time point during the heating and cooling process: Wherein, T(x) is the temperature response value at time x, the integration interval is from t0 to tn, and F(x) is the weight function of the temperature section, which is used to adjust the contribution of different temperature sections to the TTA value; Step 302: correct the temperature weight in the integration process, and obtain the temperature-time area (TTA) through the integration result; the weight function adopts the sigmoid function form as follows: Among them, β controls the steepness of the weight change, and T0 is the median of the temperature range.

4. The method for detecting the content of steel slag asphalt mixture based on dynamic temperature response according to claim 1, characterized in that: The amount of slag added is calculated by the following steps: Calculate the temperature-time area ratio R(TTA), which is the ratio of the TTA values ​​of the test sample to the reference sample: Among them, TTA sample is the temperature-time area of ​​the steel slag asphalt mixture sample during the heating and cooling process, TTA reference Refers to the temperature-time area of ​​the reference sample; The amount of steel slag is calculated by combining the R (TTA) value with the standard curve.

5. The method for detecting the content of steel slag asphalt mixture based on dynamic temperature response according to claim 1, characterized in that: The correction factor is calculated as follows: Step 303, setting a correction coefficient C1 according to the content of mineral powder in the sample. When the content of mineral powder is high, the heat capacity of the material increases, resulting in an enhanced temperature hysteresis effect; The calculation formula of the correction coefficient C1 is: C1=1+α1×P mineral Among them, P mineral is the mineral powder content, α1 is the fitting constant; Step 304, introducing asphalt modification correction coefficient C2: If the sample uses modified asphalt, the influence of the modifier on the thermal response is considered. The calculation formula of the asphalt modification correction coefficient C2 is: C2=1+α2×M modifier ; Among them, M modifier is the mass percentage of the modifier, α2 is the fitting constant; Step 305, introducing an aging correction coefficient C3: Taking thermal conductivity into consideration, the calculation formula of the aging correction coefficient C3 is as follows: C3=1+α3×A aging ; Among them, A aging It is an indicator of the degree of aging; In summary, the expression of the correction coefficient C is: C = C1 × C2 × C3; The correction coefficient C is multiplied by the TTA ratio R(TTA) to obtain the corrected R(TTA).

6. The method for detecting the content of steel slag asphalt mixture based on dynamic temperature response according to claim 1, characterized in that: The standard curve is constructed by testing the TTA values ​​of different steel slag dosages and performing standard curve fitting to estimate the proportion of steel slag.

7. The method for detecting the content of steel slag asphalt mixture based on dynamic temperature response according to claim 1, characterized in that: The method is suitable for rapid detection of steel slag asphalt mixture on site and evaluation of material formulation in laboratory.

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