A rapid method for detecting the moisture content of fine aggregates in concrete
By establishing dedicated and general prediction models based on resistivity testing, the problems of long detection cycle and insufficient stability of fine aggregate moisture content were solved, enabling rapid and accurate moisture content detection and meeting the real-time water usage adjustment needs of concrete production.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA RAILWAY BEIJING ENG GRP CO LTD
- Filing Date
- 2026-03-30
- Publication Date
- 2026-06-30
AI Technical Summary
Existing methods for detecting the moisture content of fine aggregates have long testing cycles, making it difficult to meet the real-time requirements during concrete production. Furthermore, the test results are easily affected by the type of fine aggregate, particle morphology, and environmental conditions, resulting in insufficient stability.
By establishing a dedicated and general prediction model based on resistivity testing, combined with constant pressure filling and compaction, and employing nonlinear fitting and machine learning algorithms, we can achieve rapid and non-destructive detection of the moisture content of fine aggregates and establish the correspondence between fine aggregate types, resistivity values and moisture content.
It enables rapid and accurate detection of the moisture content of different types of fine aggregates, supports real-time adjustment of water usage during concrete production, and ensures the quality of finished concrete products.
Smart Images
Figure CN122306889A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of building material testing technology, and in particular to a rapid method for detecting the moisture content of fine aggregates used in concrete. Background Technology
[0002] Fine aggregate is a crucial component of concrete, and its moisture content directly affects the determination of the actual water required during concrete mixing, thus influencing the actual water-cement ratio. The water-cement ratio is a key parameter determining the strength, durability, and workability of concrete. In engineering practice, even slight fluctuations in the moisture content of fine aggregate can cause significant changes in concrete performance. During concrete production, fine aggregate is typically stored and accessed through open-air stockpiling or belt conveyors. Its moisture content is easily affected by various factors such as rainfall, ambient humidity, temperature changes, and storage time, exhibiting characteristics of frequent changes and uneven spatial distribution. Therefore, accurately obtaining the real-time moisture content of fine aggregate before concrete mixing is a crucial prerequisite for ensuring the accuracy of the concrete mix design.
[0003] Currently, the moisture content of fine aggregates in engineering projects is mostly determined by sampling followed by drying. While this method offers high accuracy, it is time-consuming, typically taking several hours, making it unsuitable for the real-time moisture content requirements of continuous concrete production. In practice, mixing plants often rely on empirical estimations or historical data to adjust water usage, easily leading to deviations in the actual water-cement ratio from the design value. To improve testing efficiency, fine aggregate moisture content testing devices based on principles such as capacitance and microwave methods have been implemented. However, these methods are susceptible to the influence of fine aggregate type, particle morphology, gradation characteristics, and environmental conditions, resulting in insufficient stability and versatility of the test results, especially under conditions involving non-natural fine aggregates such as manufactured sand, where testing errors are more pronounced.
[0004] Therefore, there is an urgent need to propose a method for detecting the moisture content of fine aggregates that is simple to implement, fast to detect, and adaptable to different types of fine aggregates, so as to meet the engineering requirements for real-time and stable detection of the moisture content of fine aggregates in the concrete production process. Summary of the Invention
[0005] This invention provides a rapid method for detecting the moisture content of fine aggregates used in concrete. The purpose is to provide a rapid method for detecting the moisture content of fine aggregates by testing and analyzing the resistivity characteristics of the fine aggregates, thereby achieving rapid and non-destructive detection of the moisture content of the fine aggregates. This overcomes the problems of long detection cycles and insufficient adaptability of existing detection methods, and provides a reliable basis for the dynamic adjustment of water usage in the concrete production process.
[0006] This invention provides a rapid method for detecting the moisture content of fine aggregates used in concrete, comprising the following steps: Samples of different types of fine aggregates were collected. For each type of fine aggregate sample, multiple samples with different known moisture contents were prepared in the range from oven-dry to saturated surface-dry. The samples were placed in a standard container and filled and compacted under constant pressure. The resistivity value of each sample was measured using a resistivity testing device to form a dataset containing the type of fine aggregate, moisture content, and resistivity value. Based on the dataset, a specific correspondence model is established by fitting resistivity and moisture content data with a nonlinear function for the same type of fine aggregate. For multiple types of fine aggregate, a machine learning algorithm is used to train a general prediction model by using the physical properties of fine aggregate and resistivity as input features and moisture content as output label. Take a sample of fine aggregate to be tested, fill and compact it under the same constant pressure as when the model was established, and then measure the resistivity value of the sample. Select either a specific correspondence model or a general prediction model according to the type of fine aggregate sample. When a specific correspondence model is selected, input the measured resistivity value into the model to calculate the moisture content. When a general prediction model is selected, input the measured resistivity value and the physical property parameters of the fine aggregate to be tested into the model to calculate the moisture content, and output the current moisture content of the fine aggregate sample.
[0007] Preferably, when collecting samples of different types of fine aggregates, the types of fine aggregates include river sand, manufactured sand and sea sand. The apparent density, fineness modulus and mud content of each type of fine aggregate sample are measured in advance and the measurement results are marked as auxiliary parameters in the dataset.
[0008] Preferably, the method of filling and compacting the sample in a standard container under constant pressure is to use a standard compactor to compact the fine aggregate sample in the standard container in three layers, with each layer being compacted 25 times, so that different batches of samples reach the same level of compaction.
[0009] Preferably, the resistivity value of each sample is measured using the four-electrode method. The four electrodes are inserted into the densely packed fine aggregate sample in a straight line with equal spacing. The two outer electrodes are used to apply current, and the two inner electrodes are used to measure voltage drop.
[0010] Preferably, a power function is used when establishing a specific correspondence model for the same type of fine aggregate. For resistivity With moisture content The data is fitted nonlinearly, where , , The fitting parameters are used, and a fitting correlation coefficient R² greater than 0.95 is used as the criterion for judging the effectiveness of the model.
[0011] Preferably, when establishing a specific correspondence model for the same type of fine aggregate, an exponential function or a polynomial function is used to perform nonlinear fitting on the resistivity and moisture content data, and the fitted function expression is stored in the testing equipment as the moisture content detection model for that type of fine aggregate.
[0012] Preferably, when establishing a general prediction model for multiple fine aggregates, the random forest algorithm is used to use the type code of fine aggregates, fineness modulus and measured resistivity value as input features, and moisture content as output label to train the prediction model, and cross-validation is used to optimize the model parameters.
[0013] Preferably, when establishing a general prediction model for various fine aggregates, a support vector machine or neural network algorithm is used to train the prediction model by taking the type of fine aggregate, particle size distribution and mineral composition as feature parameters, along with resistivity value, and moisture content as output.
[0014] Preferably, after outputting the current moisture content of the fine aggregate sample to be tested, the current moisture content data is automatically transmitted to the central control system of the concrete mixing plant via Bluetooth or Wi-Fi module. The central control system automatically calculates the amount of water to be deducted based on the preset concrete mix proportion and the current moisture content data, and controls the water scale to batch the materials according to the deducted amount of water.
[0015] Preferably, after outputting the current moisture content of the fine aggregate sample to be tested, the current moisture content data is transmitted in real time to the distributed control system of the mixing plant via an industrial bus. The distributed control system automatically adjusts the subsequent concrete mixing ratio according to the current moisture content data, thereby realizing the closed-loop operation of fine aggregate moisture content detection and concrete batching control.
[0016] The beneficial effects of this invention compared to existing technologies are as follows: This invention overcomes the technical defects of existing fine aggregate moisture content testing methods, such as the long testing cycle of the drying method, which cannot meet the real-time mix proportion control requirements of concrete production, and the insufficient stability and versatility of the capacitance method and microwave method due to the susceptibility of fine aggregate type, particle morphology, and gradation characteristics. By establishing a dual-model system based on a dedicated model and a general prediction model based on resistivity testing, and combining it with constant pressure filling compaction control, this invention achieves rapid, non-destructive, and stable detection of moisture content in different types of fine aggregates. It can provide accurate real-time moisture content data for concrete mixing plants, effectively ensuring the quality of finished concrete products.
[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.
[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a rapid detection method for the moisture content of fine aggregates used in concrete according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the portable detector in an embodiment of the present invention; Figure 3 This is a schematic diagram of the resistivity as a function of water content (ρ-w curve) in an embodiment of the present invention. Detailed Implementation
[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0021] Example 1: Reference Figure 1 , Figure 2 , Figure 3 This embodiment provides an application example of a rapid detection method for the moisture content of fine aggregates used in concrete at a commercial concrete mixing plant. This mixing plant primarily uses river sand from location A and manufactured sand from location B as fine aggregates.
[0022] In the sample modeling stage, river sand samples from location A and manufactured sand samples from location B were collected. The apparent density, fineness modulus, and mud content of each sand sample were pre-determined. After drying the collected sand samples to constant weight, fine aggregate samples with moisture contents of 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, and 10% were prepared by adding water in stages and mixing thoroughly. The prepared samples were placed in standard measuring containers and compacted in three layers using a standard compactor, with each layer compacted 25 times to achieve a uniformly dense state. Subsequently, a four-electrode resistivity meter was used to test the resistivity of each sample. The four electrodes were inserted into the compacted fine aggregate sample at equal intervals in a straight line. A constant current was applied to the two outer electrodes, and the voltage drop was measured on the two inner electrodes. The resistivity values of each sample were recorded, forming a dataset containing the fine aggregate type, moisture content, and resistivity values.
[0023] Based on the above dataset, a nonlinear fitting was performed on the relationship between resistivity and moisture content of river sand in area A to establish a specific correspondence model. The model expression is as follows: The same processing was applied to the data of manufactured sand from location B to obtain a dedicated model. The above model is stored in the database of a portable fine aggregate moisture content meter.
[0024] During the rapid on-site testing phase, operators took samples from the river sand stockpile at location A. Following the same method used in the modeling phase, the samples were compacted into a measuring cup, and the resistivity was measured using a four-electrode resistivity testing device of the same specifications. The measured resistivity value was 1500 Ω·m. The operator selected "river sand from location A" as the material type on the testing instrument, and the instrument automatically called the corresponding dedicated model to perform calculations, obtaining a fine aggregate moisture content of 4.2%, which was displayed on the testing instrument screen.
[0025] During the concrete mix design phase, operators automatically transmit the current moisture content data to the central control system of the concrete mixing plant via Bluetooth. The central control system automatically calculates the amount of water introduced by the fine aggregate based on the preset fine aggregate content in the concrete mix design. After deducting the introduced water from the total designed water consumption, it obtains the actual amount of water to be added and controls the water scale to dispense the materials according to the required amount of water.
[0026] Example 2: Based on Example 1, this example provides an application case of a rapid detection method for the moisture content of fine aggregates in concrete in a large-scale intelligent concrete mixing plant. The fine aggregates used in this mixing plant have complex sources, involving various sources and types of fine aggregates such as river sand, manufactured sand, and sea sand.
[0027] During the sample modeling phase, various fine aggregate samples were collected. The apparent density, fineness modulus, and mud content of each fine aggregate sample were measured, and the mineral composition characteristics were recorded. After drying each sand sample, multiple samples with different moisture content gradients ranging from oven-dry to saturated surface-dry were prepared. The samples were placed in standard measuring containers and compacted under constant pressure to achieve a uniform density. The resistivity values of each sample were measured using a four-electrode resistivity meter, and the fine aggregate type, fineness modulus, resistivity value, and moisture content of each sample were recorded to form a dataset.
[0028] Based on the aforementioned dataset, a general prediction model was established using the random forest algorithm. The type code of fine aggregate, fineness modulus, and measured resistivity value were used as input features, and moisture content was used as the output label for training. Cross-validation was employed to optimize the model parameters. After training, the general prediction model was stored in the data processing unit of the online detection system. When establishing the general prediction model, the random forest algorithm was used, with 100 decision trees, a maximum depth of 10, and a minimum number of sample splits of 5. Five-fold cross-validation was used to optimize the model parameters. Input features included the type code of fine aggregate, fineness modulus, and measured resistivity value; the output was moisture content. The trained model was stored in the detection equipment.
[0029] During the rapid on-site testing phase, an automatic sampling device and a resistivity measuring device are integrated and installed at the material discharge position of the sand belt scale. The system automatically acquires fine aggregate samples according to a preset cycle, compacts them under constant pressure, and simultaneously completes resistivity measurement. At the same time, it retrieves the type and fineness modulus of the current batch of fine aggregate from the material management system. The above characteristic data are input into a trained random forest model, and the model outputs the moisture content result of the fine aggregate in real time.
[0030] During the concrete mix proportion control stage, moisture content data is transmitted in real time to the distributed control system of the mixing plant via an industrial bus. The distributed control system automatically calculates the amount of water to be deducted based on the current moisture content data and adjusts the subsequent concrete mixing proportions, achieving closed-loop operation of fine aggregate moisture content detection and concrete batching control.
[0031] Example 3: Based on Example 1, this example provides an application case of a rapid detection method for the moisture content of fine aggregates in concrete under mixed fine aggregate conditions. A certain engineering site uses fine aggregates made from a mixture of river sand and manufactured sand in a specific ratio.
[0032] During the sample modeling stage, river sand and manufactured sand samples used in the project were collected respectively, and river sand-specific models and manufactured sand-specific models were established respectively according to the method in Example 1, and stored in a portable detector.
[0033] During the rapid on-site testing phase, operators took samples from the mixture pile, compacted it under constant pressure, and measured the resistivity. Since the sample was a mixture, the operators selected the general prediction mode on the testing instrument. The instrument's built-in random forest general prediction model used the measured resistivity value and the average fineness modulus of the mixture as input to calculate the overall moisture content. To verify accuracy, the operators simultaneously separated the mixture using a sieving method and measured the moisture content separately, calculating the overall moisture content using a weighted average. The two results showed a good agreement.
[0034] During the concrete mix design control stage, operators input comprehensive moisture content data into the batching plant control system. The control system then adjusts the water usage accordingly to ensure concrete production quality. This embodiment demonstrates that the general predictive model can effectively address the moisture content detection problem of mixed fine aggregates, expanding the applicability of this invention.
[0035] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.
Claims
1. A rapid method for detecting the moisture content of fine aggregates used in concrete, characterized in that, Includes the following steps: Samples of different types of fine aggregates were collected. For each type of fine aggregate sample, multiple samples with different known moisture contents were prepared in the range from oven-dry to saturated surface-dry. The samples were placed in a standard container and filled and compacted under constant pressure. The resistivity value of each sample was measured using a resistivity testing device to form a dataset containing the type of fine aggregate, moisture content, and resistivity value. Based on the dataset, a specific correspondence model is established by fitting resistivity and moisture content data with a nonlinear function for the same type of fine aggregate. For multiple types of fine aggregate, a machine learning algorithm is used to train a general prediction model by using the physical properties of fine aggregate and resistivity as input features and moisture content as output label. Take a sample of fine aggregate to be tested, fill and compact it under the same constant pressure as when the model was established, and then measure the resistivity value of the sample. Select either a specific correspondence model or a general prediction model according to the type of fine aggregate sample. When a specific correspondence model is selected, input the measured resistivity value into the model to calculate the moisture content. When a general prediction model is selected, input the measured resistivity value and the physical property parameters of the fine aggregate to be tested into the model to calculate the moisture content, and output the current moisture content of the fine aggregate sample.
2. The method for rapid detection of moisture content in fine aggregates for concrete according to claim 1, characterized in that, When collecting samples of different types of fine aggregates, including river sand, manufactured sand and sea sand, the apparent density, fineness modulus and mud content of each type of fine aggregate sample were measured in advance, and the measurement results were marked as auxiliary parameters in the dataset.
3. The method for rapid detection of moisture content in fine aggregates for concrete according to claim 1, characterized in that, The method of filling and compacting the sample in a standard container under constant pressure is to use a standard compactor to compact the fine aggregate sample in the standard container in three layers, with each layer being compacted 25 times, so that different batches of samples reach the same level of compaction.
4. The method for rapid detection of moisture content in fine aggregates for concrete according to claim 1, characterized in that, The resistivity of each sample was measured using the four-electrode method. The four electrodes were inserted into the compacted fine aggregate sample in a straight line with equal spacing. The two outer electrodes were used to apply current, and the two inner electrodes were used to measure voltage drop.
5. The rapid detection method for moisture content of fine aggregates for concrete according to claim 1, characterized in that, When establishing a specific correspondence model for the same type of fine aggregate, a power function is used. For resistivity With moisture content The data is fitted nonlinearly, where , , The fitting parameters are used, and a fitting correlation coefficient R² greater than 0.95 is used as the criterion for judging the effectiveness of the model.
6. The method for rapid detection of moisture content in fine aggregates for concrete according to claim 1, characterized in that, When establishing a specific correspondence model for the same type of fine aggregate, an exponential function or a polynomial function is used to perform nonlinear fitting on the resistivity and moisture content data, and the fitted function expression is stored in the testing equipment as the moisture content detection model for that type of fine aggregate.
7. The method for rapid detection of moisture content in fine aggregates for concrete according to claim 1, characterized in that, When establishing a general prediction model for various fine aggregates, the random forest algorithm is used to train the prediction model by using the type code of fine aggregate, fineness modulus and measured resistivity value as input features and moisture content as output label. The model parameters are then optimized by cross-validation.
8. The method for rapid detection of moisture content in fine aggregates for concrete according to claim 1, characterized in that, When establishing a general prediction model for various fine aggregates, a support vector machine or neural network algorithm is used to train the prediction model by taking the type of fine aggregate, particle size distribution, and mineral composition as feature parameters, along with resistivity value, and moisture content as output.
9. The method for rapid detection of moisture content in fine aggregates for concrete according to claim 1, characterized in that, After outputting the current moisture content of the fine aggregate sample to be tested, the current moisture content data is automatically transmitted to the central control system of the concrete mixing plant via Bluetooth or Wi-Fi module. The central control system automatically calculates the amount of water to be deducted based on the preset concrete mix proportion and the current moisture content data, and controls the water scale to batch the materials according to the deducted amount of water.
10. The method for rapid detection of moisture content in fine aggregates for concrete according to claim 1, characterized in that, After outputting the current moisture content of the fine aggregate sample to be tested, the current moisture content data is transmitted in real time to the distributed control system of the mixing plant via the industrial bus. The distributed control system automatically adjusts the subsequent concrete mixing ratio according to the current moisture content data, realizing the closed-loop operation of fine aggregate moisture content detection and concrete batching control.