Performance testing method and system for ultra-high performance concrete

Abstract

The present invention relates to the field of concrete performance testing, and specifically to a performance testing method and system for ultra-high performance concrete. The method first obtains the displacement data of the concrete samples of the test group within a preset time period, and the penetration resistance data of the concrete samples of the control group within the preset time period, and obtains the first initial setting time period and the initial setting time point of the control group, corrects the displacement data in each time sub-segment, obtains the optimized displacement data of the preset time period, selects the initial setting time of the test group according to the difference between the penetration resistance data on both sides of the initial setting time point and the optimized displacement data on both sides of the target time sub-segment, and obtains the early age autogenous shrinkage deformation coefficient of the concrete sample according to the difference between the optimized displacement data of the test group between the end time of the preset time period and the initial setting time. The present invention can improve the calculation accuracy of the early autogenous shrinkage deformation coefficient of concrete and improve the accuracy of concrete performance testing.

Description

Technical Field

[0001] The present invention relates to the field of concrete performance testing, and in particular to a performance testing method and system for ultra-high performance concrete. Background Art

[0002] Ultra-high performance concrete is a new type of cement composite material with ultra-high strength, ultra-high toughness, ultra-high durability and excellent workability. Among them, early age autogenous shrinkage deformation refers to the deformation caused by the shrinkage of concrete volume due to water evaporation and chemical reactions during the coagulation and hardening process of concrete. Early age autogenous shrinkage deformation is a key indicator for evaluating the performance of ultra-high performance concrete.

[0003] In the related art, sensors are usually used to detect the displacement change of concrete samples from initial setting to final setting, and the early autogenous shrinkage deformation coefficient of concrete is calculated to detect the performance of concrete. However, since the initial setting moment of concrete is affected by various factors, it is difficult to determine the displacement data at the initial setting. In addition, the sensor is easily affected by environmental vibrations during the detection of the displacement data of concrete setting, resulting in low detection accuracy of the displacement data, which in turn leads to low calculation accuracy of the early autogenous shrinkage deformation coefficient of concrete by existing methods, reducing the accuracy of concrete performance detection. Summary of the invention

[0004] In order to solve the technical problem that the existing method has low calculation accuracy of the early autogenous shrinkage deformation coefficient of concrete and reduces the accuracy of concrete performance detection, the purpose of the present invention is to provide a performance detection method and system for ultra-high performance concrete. The technical scheme adopted is as follows:

[0005] The present invention provides a performance testing method for ultra-high performance concrete, the method comprising:

[0006] Obtain the displacement data of the concrete samples of the test group at each moment in the preset time period, and obtain the penetration resistance data of the concrete samples of the control group at each time point in the preset time period, and simultaneously obtain the first initial setting time period of the control group and the initial setting time point in the first initial setting time period;

[0007] The preset time period is evenly segmented to obtain different time sub-segments, and any time sub-segment is used as a target time sub-segment. According to the change of displacement data at each moment in the target time sub-segment, abnormal displacement data is screened out from the target time sub-segment; according to the number of abnormal displacement data in the target time sub-segment and the number of displacement data between adjacent abnormal displacement data, the vibration influence degree of the target time sub-segment is obtained; based on the vibration influence degree, the displacement data in the time sub-segment is corrected to obtain the optimized displacement data of the test group in the preset time period;

[0008] According to the difference between the penetration resistance data on both sides of the initial setting time point and the optimized displacement data on both sides of the target time sub-segment, the initial setting possibility of the test group in the target time sub-segment is obtained; based on the initial setting possibility, the second initial setting time period of the test group is screened out from all time sub-segments; according to the position of the initial setting time point in the first initial setting time period, the initial setting time of the test group is selected from each moment of the second initial setting time period;

[0009] According to the difference in the optimized displacement data of the test group between the end time of the preset time period and the initial setting time, the early age autogenous shrinkage deformation coefficient of the concrete sample is obtained.

[0010] Furthermore, the step of filtering out abnormal displacement data from the target time subsegment includes:

[0011] The absolute value of the difference between each moment in the target time subsegment and the next adjacent moment in the target time subsegment is used as the displacement data variation at each moment in the target time subsegment;

[0012] Analyzing the overall level of the displacement data variation at all moments in the target time subsegment to obtain the overall variation of the target time subsegment;

[0013] The absolute value of the difference between the displacement data variation and the overall variation at each moment in the target time subsegment is used as the abnormality judgment value at each moment in the target time subsegment;

[0014] In the target time sub-segment, the displacement data corresponding to the moment when the abnormality judgment value is greater than the preset abnormality threshold is used as the abnormal displacement data of the target time sub-segment.

[0015] Further, obtaining the vibration influence degree of the target time sub-segment includes:

[0016] The number of abnormal displacement data in the target time subsegment is used as a numerator, the number of displacement data in the target time subsegment is used as a denominator, and the ratio is used as the first vibration parameter of the target time subsegment;

[0017] According to the number of displacement data between each abnormal displacement data and the adjacent previous abnormal displacement data in the target time subsegment, a vibration influence characteristic sequence is obtained; and the maximum value of the length of all runs in the vibration influence characteristic sequence is used as the second vibration parameter of the target time subsegment;

[0018] The first vibration parameter and the second vibration parameter of the target time subsegment are integrated and normalized to obtain the vibration influence degree of the target time subsegment.

[0019] Further, obtaining the vibration impact characteristic sequence includes:

[0020] The number of displacement data between each abnormal displacement data and the adjacent previous abnormal displacement data in the target time subsegment is used as the vibration period value of each abnormal displacement data;

[0021] The vibration period values ​​of all abnormal displacement data in the target time sub-segment are sorted according to the time corresponding to the abnormal displacement data to form a sequence as the vibration influence feature sequence.

[0022] Furthermore, obtaining the optimized displacement data of the test group within a preset time period includes:

[0023] The product value of the vibration influence degree of the target time sub-segment and the overall change amount is used as the data adjustment amount of the target time sub-segment;

[0024] If the difference between the displacement data change at each moment in the target time subsegment and the overall change of the target time subsegment is greater than the preset abnormal threshold, the difference between the displacement data at each moment in the target time subsegment and the data adjustment amount is used as the optimized displacement data at each moment in the target time subsegment;

[0025] If the difference between the displacement data change at each moment in the target time subsegment and the overall change of the target time subsegment is less than the inverse of the preset abnormal threshold, the sum of the displacement data at each moment in the target time subsegment and the data adjustment amount is used as the optimized displacement data at each moment in the target time subsegment;

[0026] If the difference between the displacement data variation at each moment in the target time subsegment and the overall variation of the target time subsegment is greater than or equal to the inverse of the preset abnormal threshold and less than or equal to the preset abnormal threshold, the displacement data at each moment in the target time subsegment is used as the optimized displacement data at each moment in the target time subsegment;

[0027] The displacement data at each moment in each time sub-segment of the preset time period is replaced by the optimized displacement data to obtain the optimized displacement data of the test group within the preset time period.

[0028] Further, obtaining the initial setting possibility of the test group in the target time subsegment includes:

[0029] The sequence of penetration resistance data at each time point less than the initial setting time point in the control group is taken as the first penetration resistance sequence of the control group, and the sequence of penetration resistance data at each time point greater than the initial setting time point is taken as the second penetration resistance sequence of the control group;

[0030] The sequence composed of the optimized displacement data at each moment less than the initial moment of the target time sub-segment in the test group is used as the first optimized displacement sequence of the test group, and the sequence composed of the optimized displacement data at each moment greater than the terminal moment of the target time sub-segment is used as the second optimized displacement sequence of the test group;

[0031] Using a dynamic time warping algorithm, the first penetration resistance sequence and the first optimized displacement sequence are processed to obtain a first possibility parameter of the test group in a target time subsegment, and the second penetration resistance sequence and the second optimized displacement sequence are processed to obtain a second possibility parameter of the test group in the target time subsegment;

[0032] The first possibility parameter and the second possibility parameter are integrated and normalized to obtain the initial setting possibility of the test group in the target time sub-segment.

[0033] Further, the second initial setting time period of the test group is screened out from all time sub-segments based on the initial setting possibility, including:

[0034] The time sub-segment corresponding to the maximum value of the initial setting possibility is used as the second initial setting time period of the test group.

[0035] Further, the selecting the initial setting time of the test group from each time in the second initial setting time period includes:

[0036] The difference between the initial setting time point and the initial time point of the first initial setting time period is used as the numerator, the difference between the end time point and the initial time point of the first initial setting time period is used as the denominator, and the ratio is used as the position ratio parameter;

[0037] The product of the position ratio parameter and the number of moments in the second initial setting time period of the test group is rounded up to obtain a position coefficient of the second initial setting time period, whose value is K;

[0038] The Kth moment in the second initial setting time period of the test group is taken as the initial setting moment of the test group.

[0039] Furthermore, the early-age autogenous shrinkage coefficient of the concrete sample is obtained by:

[0040] The difference of the optimized displacement data between the end time of the preset time period and the initial setting time of the test group is used as the numerator, the preset measurement gauge length is used as the denominator, and the ratio is used as the early age autogenous shrinkage deformation coefficient of the concrete sample.

[0041] The present invention also proposes a performance detection system for ultra-high performance concrete, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing any one of the steps of a performance detection method for ultra-high performance concrete when executing the computer program.

[0042] The present invention has the following beneficial effects:

[0043] The present invention takes into account that the existing methods have low calculation accuracy for the early autogenous shrinkage deformation coefficient of concrete, which reduces the accuracy of concrete performance detection. Therefore, the present invention sets up two groups of experiments, one is an experimental group for detecting the displacement data of concrete samples during the setting process, and the other is a control group for detecting the penetration resistance data of concrete during the setting process, and obtains the first initial setting time period of the control group and the initial setting time point in the first initial setting time period. Considering that the displacement data in the experimental group will be disturbed by environmental vibration, the present invention first screens out abnormal displacement data disturbed by vibration from the target time sub-segment, and corrects the displacement data in the time sub-segment according to the degree of vibration influence, thereby eliminating The interference of environmental vibration on displacement data improves the accuracy of subsequent concrete performance analysis. Since the displacement data of the test group and the penetration resistance data of the control group can both reflect the degree of concrete setting, the more similar the change characteristics of the displacement data on both sides of a certain time sub-segment of the test group and the penetration resistance data on both sides of the initial setting time point of the control group are, the higher the possibility that the initial setting time of the test group is in this time sub-segment. Therefore, the second initial setting time period of the test group can be selected according to the initial setting possibility, and then the initial setting time of the test group can be accurately selected from the second initial setting time period, and the early age autogenous shrinkage deformation coefficient of the concrete sample can be accurately calculated to improve the accuracy of concrete performance detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A flow chart of a method for detecting performance of ultra-high performance concrete provided by one embodiment of the present invention;

[0046] Figure 2 A schematic diagram of the first initial setting time period and the initial setting time points in the first initial setting time period of a control group provided by an embodiment of the present invention;

[0047] Figure 3A flow chart of a method for obtaining the vibration influence degree of a target time subsegment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the performance detection method and system of ultra-high performance concrete proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0049] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0050] The specific scheme of the performance detection method and system of ultra-high performance concrete provided by the present invention is described in detail below with reference to the accompanying drawings.

[0051] See also Figure 1 , which shows a flow chart of a performance detection method for ultra-high performance concrete provided by an embodiment of the present invention, the method comprising:

[0052] Step S1: Obtain the displacement data of the concrete samples of the test group at each moment within the preset time period, and obtain the penetration resistance data of the concrete samples of the control group at each time point within the preset time period, and at the same time obtain the first initial setting time period of the control group and the initial setting time point in the first initial setting time period.

[0053] In the embodiment of the present invention, the ultra-high performance concrete sample is first cast in the concrete test mold, the temperature in the laboratory is controlled at 18-22 degrees Celsius, the relative humidity is maintained between 55% and 65%, and the laser displacement sensor fixed on the side end of the concrete test mold is used to detect the displacement data of the concrete sample at each moment in a preset time period. The unit of the displacement data is millimeter, wherein the preset time period is set to 72 hours, and the data acquisition frequency of the laser displacement sensor is set to 50Hz. The specific values ​​of the preset time period and the data acquisition frequency of the laser displacement sensor can also be set by the implementer according to the specific implementation scenario, which is not limited here. In order to distinguish another group of subsequent experiments, the embodiment of the present invention uses the collected displacement data as the data of the test group. It should be noted that in the process of coagulation and hardening of the concrete sample, the volume of the concrete gradually shrinks inward, and the laser displacement sensor is fixed on the side end of the test mold, so the displacement data collected by the sensor gradually increases.

[0054] Since the initial setting moment of concrete is affected by various factors, it is difficult to determine the displacement data at the initial setting time. For this reason, the embodiment of the present invention sets up a control group, and the laboratory conditions of the control group are the same as those of the test group, and then uses a penetration resistance meter to measure the penetration resistance of the concrete sample in the test mold, and collects the penetration resistance data of the concrete sample at each time point within a preset time period. The unit of the penetration resistance data is MPa, wherein the collection time interval of the penetration resistance data is set to 1 minute, and the specific value of the collection time interval of the penetration resistance data can also be set by the implementer according to the specific implementation scenario, which is not limited here. Penetration resistance measurement is a technical means well known to those skilled in the art and will not be elaborated here. It should be noted that in the process of coagulation and hardening of the concrete sample, the penetration resistance data of the concrete sample is also getting larger and larger.

[0055] In the process of measuring the penetration resistance of concrete samples, when the penetration resistance data of the concrete samples reaches the standard penetration resistance data, it means that the concrete samples have reached the initial setting condition. Initial setting marks the beginning of the transition of the concrete samples from liquid to solid. The standard penetration resistance data of concrete is usually 3.5MPa. Since the time interval between the penetration resistance data of the control group is short, in each time interval, the penetration resistance of the concrete sample can be considered to change linearly. Therefore, the time period between the time point corresponding to the maximum value of all penetration resistance data less than 3.5MPa in the control group and the time point corresponding to the minimum value of all penetration resistance data greater than 3.5MPa in the control group can be taken as the first initial setting time period of the control group, and then the initial setting time point in the first initial setting time period can be obtained through the proportional relationship. Please refer to Figure 2 , which shows a schematic diagram of the first initial setting time period of a control group and the initial setting time point in the first initial setting time period provided by an embodiment of the present invention, wherein the vertical axis of the coordinate graph represents the penetration resistance in MPa, and the horizontal axis represents the time in minutes, that is, the penetration resistance data is measured every 1 minute, the time period a~c is the first initial setting time period of the control group, and point b is the initial setting time point in the first initial setting time period.

[0056] As an example, in one embodiment of the present invention, the expression of the initial setting time point in the first initial setting time period may be specifically, for example, as follows:

[0057]

[0058] in, represents the initial setting time point in the first initial setting time period of the control group; It represents the initial time point of the first initial setting period, that is, the time point corresponding to the maximum value of all penetration resistance data less than 3.5MPa; It represents the standard penetration resistance data, and the value is 3.5MPa; The penetration resistance data at the end time point of the first initial setting period, that is, the minimum value of all penetration resistance data greater than 3.5 MPa; It indicates the length of the first initial setting period, that is, the time interval for collecting penetration resistance data, and its value is 1 minute.

[0059] Step S2: Evenly segment the preset time period to obtain different time sub-segments, take any time sub-segment as the target time sub-segment, and screen out abnormal displacement data from the target time sub-segment according to the change of displacement data at each moment in the target time sub-segment; obtain the vibration influence degree of the target time sub-segment according to the number of abnormal displacement data in the target time sub-segment and the number of displacement data between adjacent abnormal displacement data; based on the vibration influence degree, correct the displacement data in the time sub-segment to obtain the optimized displacement data of the test group in the preset time period.

[0060] Since the data collection frequency of the control group and the test group is different, in order to determine the accurate initial setting time of concrete in the test group later, the preset time period is firstly evenly segmented to obtain different time sub-segments, and the displacement data in the time sub-segments can be analyzed later. In particular, since the collection time interval of the control group data is 1 minute, in order to ensure the accuracy of the subsequent calculation and analysis, the embodiment of the present invention segments the preset time period every 1 minute to obtain multiple time sub-segments, and analyzes any time sub-segment, and takes any time sub-segment as the target time sub-segment. Considering that the displacement data in the test group will be disturbed by the environmental vibration, so that there are abnormal displacement data, and when the environmental vibration interferes with the displacement data, it will increase or decrease the degree of change of the displacement data at a certain moment relative to the overall data of the target time sub-segment, therefore, the change of the displacement data at each moment in the target time sub-segment can be analyzed, and the abnormal displacement data can be screened out from the target time sub-segment. Subsequently, based on the number of abnormal displacement data in the target time sub-segment and the periodic characteristics of the abnormal displacement data, the degree of influence of the displacement data of the target time sub-segment by the environmental vibration can be analyzed, thereby achieving adjustment and optimization of the displacement data in the target time sub-segment.

[0061] Preferably, in one embodiment of the present invention, the method for acquiring abnormal displacement data in a target time subsegment specifically includes:

[0062] The absolute value of the difference between the displacement data at each moment in the target time subsegment and the next adjacent moment is used as the displacement data change at each moment in the target time subsegment. Since there is no next adjacent moment in the last moment in the target time subsegment, the average value of the displacement data change at all moments before the last moment of the target time subsegment can be used as the displacement data change at the last moment. Then, the overall level of the displacement data change at all moments in the target time subsegment is analyzed to obtain the overall change of the target time subsegment. Since the environmental vibration will only affect a small amount of displacement data in the target time subsegment, the absolute value of the difference between the displacement data change at each moment in the target time subsegment and the overall change can be used as the abnormal judgment value at each moment in the target time subsegment. The larger the abnormal judgment value, the greater the degree of change of the displacement data at each moment in the target time subsegment relative to the overall data of the target time subsegment, and further the greater the possibility that the displacement data at this moment is disturbed by vibration. Therefore, the displacement data corresponding to the moment when the abnormal judgment value is greater than the preset abnormal threshold value can be used as the abnormal displacement data of the target time subsegment. The value range of the preset abnormal threshold value is generally In one embodiment of the present invention, the preset abnormal threshold is set to 0.01. The specific value of the preset abnormal threshold can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0063] In an embodiment of the present invention, the average value or median of the displacement data change at all moments in the target time sub-segment can be used as the overall change of the target time sub-segment to achieve an overall level analysis of the displacement data change at all moments in the target time sub-segment, which is not limited here.

[0064] As an example, in one embodiment of the present invention, the expression of the abnormality judgment value at each moment in the target time subsegment may be specifically, for example, as follows:

[0065]

[0066]

[0067] in, Indicates the first The abnormal judgment value at each moment; Indicates the first The displacement data change at each moment; Indicates the overall change in the target time subsegment; Indicates the first Displacement data at each moment; Indicates the first The displacement data at the moment, i.e. the displacement data at the target time subsegment The displacement data of the next moment adjacent to a moment.

[0068] Considering that the interference of vibration on displacement data usually shows a certain degree of periodic characteristics, that is, there are some abnormal displacement data in the target time sub-segment, which will appear once every the same number of displacement data, and the more the number of abnormal displacement data in the target time sub-segment, the greater the degree of vibration interference to the displacement data in the target time sub-segment. Therefore, the number of abnormal displacement data in the target time sub-segment and the number of displacement data between adjacent abnormal displacement data can be analyzed, and the degree of interference of environmental vibration on the displacement data in the target time sub-segment can be reflected by the obtained vibration influence degree. Subsequently, the displacement data in the time sub-segment can be corrected based on the vibration influence degree to eliminate the interference of environmental vibration on the displacement data.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining the vibration influence degree of the target time subsegment specifically includes:

[0070] See also Figure 3 , which shows a flow chart of a method for obtaining the vibration influence degree of a target time sub-segment provided by an embodiment of the present invention.

[0071] Step S201: The number of abnormal displacement data in the target time subsegment is used as the numerator, the number of displacement data in the target time subsegment is used as the denominator, and the ratio is used as the first vibration parameter of the target time subsegment.

[0072] From the above analysis, it can be seen that the more abnormal displacement data there are in the target time subsegment, the greater the degree to which the displacement data of the target time subsegment is affected by vibration. Therefore, the number of abnormal displacement data in the target time subsegment can be used as the numerator, the number of displacement data in the target time subsegment can be used as the denominator, and the ratio can be used as the first vibration parameter of the target time subsegment. Subsequently, the degree of vibration impact of the target time subsegment can be accurately calculated based on the first vibration parameter.

[0073] Step S202: Obtain a vibration influence characteristic sequence according to the number of displacement data between each abnormal displacement data and the adjacent previous abnormal displacement data in the target time sub-segment; and use the maximum value of the length of all runs in the vibration influence characteristic sequence as the second vibration parameter of the target time sub-segment.

[0074] Since the interference of environmental vibration on some displacement data in the target time sub-segment has a periodic characteristic, that is, there are some abnormal displacement data that will appear once every the same number of displacement data, therefore, the vibration influence feature sequence can be first obtained according to the number of displacement data between each abnormal displacement data and the adjacent previous abnormal displacement data in the target time sub-segment, and each element in the vibration influence feature sequence represents the number of displacement data between each abnormal displacement data and the adjacent previous abnormal displacement data. The longer the length of the run in the vibration influence feature sequence, the more obvious the periodic influence characteristics of the environmental vibration on the displacement data in the target time sub-segment, and further the greater the degree to which the displacement data of the target time sub-segment is affected by the vibration, therefore the maximum value of the length of all runs in the vibration influence feature sequence can be used as the second vibration parameter of the target time sub-segment, and the vibration influence degree of the target time sub-segment can be accurately calculated based on the second vibration parameter later, wherein the run refers to a sequence composed of consecutive identical elements in the vibration influence feature sequence, and the length of the run refers to the number of elements contained in the run. For example, if the vibration influence feature sequence is , then the runs are , , , , and The corresponding lengths are 1, 2, 2, 1, 3 and 1 respectively. The run is an existing concept and will not be elaborated here.

[0075] Preferably, in one embodiment of the present invention, the method for obtaining the vibration impact feature sequence specifically includes:

[0076] The number of displacement data between each abnormal displacement data and the adjacent previous abnormal displacement data in the target time subsegment is used as the vibration period value of each abnormal displacement data, and the vibration period value reflects the periodic influence characteristics of the environmental vibration on the displacement data.

[0077] It should be noted that for the first abnormal displacement data in the target time subsegment, there is no adjacent previous abnormal displacement data. In this case, the number of displacement data before the first abnormal displacement data in the target time subsegment can be used as the vibration period value of the first abnormal displacement data.

[0078] Then, the vibration period values ​​of all abnormal displacement data in the target time sub-segment are sorted according to the time corresponding to the abnormal displacement data to form a sequence as the vibration influence feature sequence. The sorting method can be from small to large or from large to small, which is not limited here. For example, if the displacement data in the target time period is , where displacement data 2 and 5 are abnormal displacement data. There is only one displacement data before the first abnormal displacement data 2, so the vibration period value of abnormal displacement data 2 is 1. There are two displacement data between abnormal displacement data 5 and 2, so the vibration period value of abnormal displacement data 5 is 2. The vibration influence characteristic sequence is .

[0079] Step S203: The first vibration parameter and the second vibration parameter of the target time sub-segment are integrated and normalized to obtain the vibration influence degree of the target time sub-segment.

[0080] The larger the first vibration parameter and the second vibration parameter are, the greater the degree to which the displacement data of the target time sub-segment is disturbed by the environmental vibration. Therefore, the first vibration parameter and the second vibration parameter of the target time sub-segment can be integrated and normalized to obtain the vibration influence degree of the target time sub-segment.

[0081] In the embodiment of the present invention, the integration of the first vibration parameter and the second vibration parameter of the target time subsegment can be achieved by calculating the sum or product of the first vibration parameter and the second vibration parameter of the target time subsegment, which is not limited here.

[0082] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of numerical values, which will not be described in detail.

[0083] As an example, in one embodiment of the present invention, the expression of the vibration influence degree of the target time sub-segment may be specifically, for example, as follows:

[0084]

[0085] in, Indicates the vibration impact degree of the target time sub-segment; Indicates the number of abnormal displacement data in the target time subsegment; Indicates the number of displacement data in the target time subsegment; A first vibration parameter representing a target time subsegment; Indicates the maximum value of the length of all runs in the vibration impact feature sequence, i.e., the second vibration parameter of the target time subsegment; Represents the normalization function.

[0086] The same method as above can be used to preset the vibration influence degree of each time sub-segment in the time period, and then based on the vibration influence degree, the displacement data in the time sub-segment can be corrected to obtain the optimized displacement data of the test group in the preset time period, thereby eliminating the interference of environmental vibration on the displacement data and improving the accuracy of subsequent concrete performance analysis.

[0087] Preferably, in one embodiment of the present invention, the method for obtaining the optimized displacement data of the test group within a preset time period specifically includes:

[0088] The greater the degree of interference of environmental vibration on displacement data, the greater the degree of adjustment will be. Therefore, the product value of the vibration influence degree and the overall change amount of the target time sub-segment can be firstly used as the data adjustment amount of the target time sub-segment.

[0089] If the difference between the displacement data change at each moment in the target time subsegment and the overall change of the target time subsegment is greater than the preset abnormal threshold, it means that the environmental vibration causes the displacement data change at each moment to exceed the overall change. At this time, the displacement data at each moment needs to be adjusted down. The difference between the displacement data at each moment in the target time subsegment and the data adjustment amount can be used as the optimized displacement data at each moment in the target time subsegment.

[0090] If the difference between the displacement data change at each moment in the target time subsegment and the overall change of the target time subsegment is less than the inverse of the preset abnormal threshold, it means that the environmental vibration causes the displacement data change at each moment to fail to reach the overall change. At this time, the displacement data at each moment needs to be adjusted higher. The sum of the displacement data at each moment in the target time subsegment and the data adjustment amount can be used as the optimized displacement data at each moment in the target time subsegment.

[0091] If the difference between the displacement data change at each moment in the target time subsegment and the overall change of the target time subsegment is greater than or equal to the inverse of the preset abnormal threshold and is less than or equal to the preset abnormal threshold, it means that the displacement data is not abnormal displacement data and is basically not affected by vibration. At this time, there is no need to adjust the displacement data, and the displacement data at each moment in the target time subsegment can be used as the optimized displacement data at each moment in the target time subsegment.

[0092] The optimized displacement data at each moment in each time sub-segment within the preset time period can be obtained by the same method as above, so that the displacement data at each moment in each time sub-segment within the preset time period can be replaced by the optimized displacement data to obtain the optimized displacement data of the test group within the preset time period.

[0093] As an example, in one embodiment of the present invention, the expression of the optimized displacement data at each moment in the target time subsegment may be specifically, for example, as follows:

[0094]

[0095] in, Indicates the first Optimized displacement data at each moment; Indicates the first Displacement data at each moment; Indicates the vibration impact degree of the target time sub-segment; Indicates the overall change in the target time subsegment; Indicates the first The displacement data change at each moment; Indicates the data adjustment amount for the target time sub-segment.

[0096] Step S3: According to the difference between the penetration resistance data on both sides of the initial setting time point and the optimized displacement data on both sides of the target time sub-segment, the initial setting possibility of the test group in the target time sub-segment is obtained; based on the initial setting possibility, the second initial setting time period of the test group is screened out from all time sub-segments; according to the position of the initial setting time point in the first initial setting time period, the initial setting moment of the test group is selected from each moment of the second initial setting time period.

[0097] Since the displacement data of the test group and the penetration resistance data of the control group can both reflect the degree of solidification of concrete, for the test group, if the initial setting time of the concrete samples of the test group exists in the target time sub-segment, the change trend of the optimized displacement data on both sides of the target time sub-segment is highly similar to the change trend of the penetration resistance data on both sides of the initial setting time point of the control group. Therefore, the difference in changes between the penetration resistance data on both sides of the initial setting time point and the optimized displacement data on both sides of the target time sub-segment can be analyzed, and the possibility of the initial setting time of the concrete samples of the test group in the target time sub-segment can be reflected by the obtained initial setting possibility. Subsequently, based on the initial setting possibility, the second initial setting time period of the test group and the initial setting time of the second initial setting time period can be accurately selected.

[0098] Preferably, in one embodiment of the present invention, the method for obtaining the initial setting possibility of the test group in the target time sub-segment specifically includes:

[0099] The sequence composed of the penetration resistance data of each time point less than the initial setting time point in the control group is taken as the first penetration resistance sequence of the control group, and the sequence composed of the penetration resistance data of each time point greater than the initial setting time point is taken as the second penetration resistance sequence of the control group.

[0100] The sequence composed of the optimized displacement data of each moment in the test group that is less than the initial moment of the target time sub-segment is taken as the first optimized displacement sequence of the test group, and the sequence composed of the optimized displacement data of each moment that is greater than the terminal moment of the target time sub-segment is taken as the second optimized displacement sequence of the test group.

[0101] The more similar the changes between the penetration resistance data and the optimized displacement data on the same side are, the more likely it is that the initial setting time of the concrete samples in the test group will exist in the target time sub-segment. Since the data collection frequencies of the test group and the control group are different, the lengths of the first penetration resistance sequence and the first optimized displacement sequence, as well as the lengths of the second penetration resistance sequence and the second optimized displacement sequence are different. The dynamic time warping algorithm can analyze the similarity of the changing trends of time series of different lengths. Therefore, the dynamic time warping algorithm can be used to process the first penetration resistance sequence and the first optimized displacement sequence to obtain the first possibility parameter of the test group in the target time sub-segment, and to process the second penetration resistance sequence and the second optimized displacement sequence to obtain the second possibility parameter of the test group in the target time sub-segment. The dynamic time warping algorithm is a technical means well known to those skilled in the art and will not be elaborated here.

[0102] Then, the first possibility parameter and the second possibility parameter are integrated and normalized to obtain the initial setting possibility of the test group in the target time sub-segment.

[0103] In the embodiment of the present invention, the integration of the first possibility parameter and the second possibility parameter can be achieved by calculating the sum or product of the two, which is not limited here.

[0104] As an example, in one embodiment of the present invention, the expression of the initial setting possibility of the test group in the target time sub-segment can be specifically, for example, as follows:

[0105]

[0106] in, Indicates the initial setting possibility of the test group in the target time sub-segment; Represents the first probability parameter of the experimental group in the target time subsegment; represents the second probability parameter of the experimental group in the target time subsegment; Represents the normalization function.

[0107] The same method as above can be used to obtain the initial setting possibility of the test group in each time sub-segment. The greater the initial setting possibility, the greater the possibility of the initial setting time of the concrete samples of the test group in each time sub-segment. Therefore, based on the initial setting possibility, the second initial setting time period of the test group can be screened out from all time sub-segments, and the initial setting time of the test group can be further accurately selected from the second initial setting time period.

[0108] Preferably, in one embodiment of the present invention, the time subsegment corresponding to the maximum value of the initial setting possibility is used as the second initial setting time period of the test group.

[0109] Although the second initial setting time period of the test group and the first initial setting time period of the control group may not be the same time period, the embodiment of the present invention believes that the position of the initial setting time of the test group in the second initial setting time period is roughly the same as the position of the initial setting time point of the control group in the first initial setting time period. Therefore, the initial setting time of the test group can be selected from the various moments in the second initial setting time period according to the position of the initial setting time point in the first initial setting time period, and subsequently the early age autogenous shrinkage deformation coefficient of the concrete sample can be accurately calculated based on the optimized displacement data at the initial setting time.

[0110] Preferably, in one embodiment of the present invention, the method for obtaining the initial setting time of the test group specifically includes:

[0111] The difference between the initial setting time point and the initial time point of the first initial setting time period is taken as the numerator, the difference between the end time point and the initial time point of the first initial setting time period is taken as the denominator, and the ratio is taken as the position proportion parameter; the product value of the position proportion parameter and the number of moments in the second initial setting time period of the test group is rounded up to obtain the position coefficient of the second initial setting time period; the Kth moment in the second initial setting time period of the test group is taken as the initial setting time of the test group.

[0112] As an example, in one embodiment of the present invention, the expression of the position coefficient of the second initial setting time period may be specifically, for example, as follows:

[0113]

[0114] in, The position coefficient representing the second initial setting time period; represents the initial setting time point in the first initial setting time period of the control group; represents the initial time point of the first initial setting period; represents the difference between the end time point and the initial time point of the first initial setting time period, that is, the length of the first initial setting time period; represents the location scale parameter; represents the number of moments in the second initial setting time period of the test group, .

[0115] Step S4: obtaining the early-age autogenous shrinkage deformation coefficient of the concrete sample according to the difference in the optimized displacement data of the test group between the end time of the preset time period and the initial setting time.

[0116] The above steps optimize the displacement data of the test group and determine the accurate initial setting time of the test group. Therefore, the early-age autogenous shrinkage deformation coefficient of the concrete sample can be obtained based on the difference in the optimized displacement data between the end time and the initial setting time of the test group in the preset time period, thereby improving the calculation accuracy of the early-age autogenous shrinkage deformation coefficient.

[0117] Preferably, in one embodiment of the present invention, the method for obtaining the early-age autogenous shrinkage coefficient of a concrete sample specifically includes:

[0118] The absolute value of the difference in the optimized displacement data of the test group between the end time of the preset time period and the initial setting time is used as the numerator, the preset measurement scale length is used as the denominator, and the ratio is used as the early-age autogenous shrinkage deformation coefficient of the concrete sample, wherein the preset measurement scale length is set to 400 mm. The preset measurement scale length is related to the concrete test mold used. The specific value of the preset measurement scale length can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0119] As an example, in one embodiment of the present invention, the expression of the early-age autogenous shrinkage coefficient of the concrete sample can be specifically, for example, as follows:

[0120]

[0121] in, represents the early-age autogenous shrinkage deformation coefficient of concrete specimens; Represents the optimized displacement data of the test group at the end of the preset time period; Represents the optimized displacement data of the test group at the initial setting time in the preset time period; Indicates the preset measurement scale distance.

[0122] In other embodiments of the present invention, two laser displacement sensors are usually fixed at both ends of the concrete test mold, and the two laser displacement sensors are used to collect displacement data of the concrete sample at each moment in a preset time period. Then, the test group has displacement data collected by two sensors. At this time, the displacement data collected by each sensor can be processed by the same method as above, so as to obtain the optimized displacement data of the test group for each sensor at the initial setting moment of the preset time period, and then obtain the early-age autogenous shrinkage deformation coefficient of the concrete sample.

[0123] As an example, in one embodiment of the present invention, the expression of the early-age autogenous shrinkage deformation coefficient of the concrete sample can also be specifically expressed as:

[0124]

[0125] in, represents the early-age autogenous shrinkage deformation coefficient of concrete specimens; represents the optimized displacement data of the test group at the end time of the preset time period obtained by the displacement data collected by the first sensor; represents the optimized displacement data of the test group at the initial setting time of the preset time period obtained by the displacement data collected by the first sensor; represents the optimized displacement data of the test group at the end time of the preset time period obtained by the displacement data collected by the second sensor; represents the optimized displacement data of the test group at the initial setting time of the preset time period obtained by the displacement data collected by the second sensor; Indicates the preset measurement scale distance.

[0126] By obtaining the early-age autogenous shrinkage deformation coefficient of concrete samples, the performance of concrete samples can be accurately evaluated.

[0127] An embodiment of the present invention provides a performance detection system for ultra-high performance concrete, the system comprising a memory, a processor and a computer program, wherein the memory is used to store the corresponding computer program, the processor is used to run the corresponding computer program, and when the computer program runs in the processor, the method described in steps S1 to S4 can be implemented.

[0128] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for detecting the performance of ultra-high performance concrete, characterized in that: The method comprises: Obtain the displacement data of the concrete samples of the test group at each moment in the preset time period, and obtain the penetration resistance data of the concrete samples of the control group at each time point in the preset time period, and simultaneously obtain the first initial setting time period of the control group and the initial setting time point in the first initial setting time period; The preset time period is evenly segmented to obtain different time sub-segments, and any time sub-segment is used as a target time sub-segment. According to the change of displacement data at each moment in the target time sub-segment, abnormal displacement data is screened out from the target time sub-segment; according to the number of abnormal displacement data in the target time sub-segment and the number of displacement data between adjacent abnormal displacement data, the vibration influence degree of the target time sub-segment is obtained; based on the vibration influence degree, the displacement data in the time sub-segment is corrected to obtain the optimized displacement data of the test group in the preset time period; According to the difference between the penetration resistance data on both sides of the initial setting time point and the optimized displacement data on both sides of the target time sub-segment, the initial setting possibility of the test group in the target time sub-segment is obtained; based on the initial setting possibility, the second initial setting time period of the test group is screened out from all time sub-segments; according to the position of the initial setting time point in the first initial setting time period, the initial setting time of the test group is selected from each moment of the second initial setting time period; According to the difference in the optimized displacement data of the test group between the end time of the preset time period and the initial setting time, the early age autogenous shrinkage deformation coefficient of the concrete sample is obtained.

2. The method for detecting the performance of ultra-high performance concrete according to claim 1, characterized in that: The step of screening out abnormal displacement data from the target time sub-segment includes: The absolute value of the difference between each moment in the target time subsegment and the next adjacent moment in the target time subsegment is used as the displacement data variation at each moment in the target time subsegment; Analyzing the overall level of the displacement data variation at all moments in the target time subsegment to obtain the overall variation of the target time subsegment; The absolute value of the difference between the displacement data variation and the overall variation at each moment in the target time subsegment is used as the abnormality judgment value at each moment in the target time subsegment; In the target time sub-segment, the displacement data corresponding to the moment when the abnormality judgment value is greater than the preset abnormality threshold is used as the abnormal displacement data of the target time sub-segment.

3. The method for detecting the performance of ultra-high performance concrete according to claim 1, characterized in that: The step of obtaining the vibration influence degree of the target time sub-segment includes: The number of abnormal displacement data in the target time subsegment is used as a numerator, the number of displacement data in the target time subsegment is used as a denominator, and the ratio is used as the first vibration parameter of the target time subsegment; According to the number of displacement data between each abnormal displacement data and the adjacent previous abnormal displacement data in the target time subsegment, a vibration influence characteristic sequence is obtained; and the maximum value of the length of all runs in the vibration influence characteristic sequence is used as the second vibration parameter of the target time subsegment; The first vibration parameter and the second vibration parameter of the target time subsegment are integrated and normalized to obtain the vibration influence degree of the target time subsegment.

4. The method for detecting the performance of ultra-high performance concrete according to claim 3, characterized in that: The obtaining of the vibration influence characteristic sequence comprises: The number of displacement data between each abnormal displacement data and the adjacent previous abnormal displacement data in the target time subsegment is used as the vibration period value of each abnormal displacement data; The vibration period values ​​of all abnormal displacement data in the target time sub-segment are sorted according to the time corresponding to the abnormal displacement data to form a sequence as the vibration influence feature sequence.

5. The method for detecting the performance of ultra-high performance concrete according to claim 2, characterized in that: The obtaining of the optimized displacement data of the test group within a preset time period includes: The product value of the vibration influence degree of the target time sub-segment and the overall change amount is used as the data adjustment amount of the target time sub-segment; If the difference between the displacement data change at each moment in the target time subsegment and the overall change of the target time subsegment is greater than the preset abnormal threshold, the difference between the displacement data at each moment in the target time subsegment and the data adjustment amount is used as the optimized displacement data at each moment in the target time subsegment; If the difference between the displacement data change at each moment in the target time subsegment and the overall change of the target time subsegment is less than the inverse of the preset abnormal threshold, the sum of the displacement data at each moment in the target time subsegment and the data adjustment amount is used as the optimized displacement data at each moment in the target time subsegment; If the difference between the displacement data variation at each moment in the target time subsegment and the overall variation of the target time subsegment is greater than or equal to the inverse of the preset abnormal threshold and less than or equal to the preset abnormal threshold, the displacement data at each moment in the target time subsegment is used as the optimized displacement data at each moment in the target time subsegment; The displacement data at each moment in each time sub-segment of the preset time period is replaced by the optimized displacement data to obtain the optimized displacement data of the test group within the preset time period.

6. The method for detecting the performance of ultra-high performance concrete according to claim 1, characterized in that: The method of obtaining the initial setting possibility of the test group in the target time sub-segment includes: The sequence of penetration resistance data at each time point less than the initial setting time point in the control group is taken as the first penetration resistance sequence of the control group, and the sequence of penetration resistance data at each time point greater than the initial setting time point is taken as the second penetration resistance sequence of the control group; The sequence composed of the optimized displacement data at each moment less than the initial moment of the target time sub-segment in the test group is used as the first optimized displacement sequence of the test group, and the sequence composed of the optimized displacement data at each moment greater than the terminal moment of the target time sub-segment is used as the second optimized displacement sequence of the test group; Using a dynamic time warping algorithm, the first penetration resistance sequence and the first optimized displacement sequence are processed to obtain a first possibility parameter of the test group in a target time subsegment, and the second penetration resistance sequence and the second optimized displacement sequence are processed to obtain a second possibility parameter of the test group in the target time subsegment; The first possibility parameter and the second possibility parameter are integrated and normalized to obtain the initial setting possibility of the test group in the target time sub-segment.

7. The method for detecting the performance of ultra-high performance concrete according to claim 1, characterized in that: The second initial setting time period of the test group is selected from all time sub-segments based on the initial setting possibility, and includes: The time sub-segment corresponding to the maximum value of the initial setting possibility is used as the second initial setting time period of the test group.

8. The method for detecting the performance of ultra-high performance concrete according to claim 1, characterized in that: The step of selecting the initial setting time of the test group from each time point in the second initial setting time period includes: The difference between the initial setting time point and the initial time point of the first initial setting time period is used as the numerator, the difference between the end time point and the initial time point of the first initial setting time period is used as the denominator, and the ratio is used as the position ratio parameter; The product value of the position ratio parameter and the number of moments in the second initial setting time period of the test group is rounded up to obtain a position coefficient of the second initial setting time period, whose value is K; The Kth moment in the second initial setting time period of the test group is taken as the initial setting moment of the test group.

9. The method for detecting the performance of ultra-high performance concrete according to claim 1, characterized in that: The early-age autogenous shrinkage deformation coefficient of the concrete sample obtained includes: The difference of the optimized displacement data between the end time of the preset time period and the initial setting time of the test group is used as the numerator, the preset measurement gauge length is used as the denominator, and the ratio is used as the early age autogenous shrinkage deformation coefficient of the concrete sample.

10. A performance detection system for ultra-high performance concrete, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

Images