CN-121980124-A - Self-compensating loading method and equipment for concrete compressive strength

CN121980124ACN 121980124 ACN121980124 ACN 121980124ACN-121980124-A

Abstract

The invention provides a self-compensating loading method and equipment for concrete compressive strength, wherein the z-axis morphology data of a pressure bearing surface of a test block are obtained; obtaining a stress concentration coefficient K t by using a deep learning model, controlling the loading rate in a segmented mode according to the value of K t , and calculating the compensation intensity. Compared with the conventional results, the invention reduces the detection intensity deviation or dispersion caused by surface defects such as burrs of the bearing surface of the test block, and can be embedded into the existing electrohydraulic servo press, and the hardware transformation cost is less than 1 ten thousand yuan per table. According to the invention, under the premise of not changing the original state of the test piece, the AI is utilized to predict the stress concentration coefficient K t of the pressure-bearing surface, and the loading curve and the strength calculation formula are adjusted in real time, so that the detection strength error caused by the defect of the pressure-bearing surface can be reduced.

Inventors

HAN ZHENHUA
YIN TINGTING
XU PENG
SU GUANNAN

Assignees

上海建工集团股份有限公司

Dates

Publication Date: 20260505
Application Date: 20251225

Claims (10)

1. The self-compensating loading method for the compressive strength of the concrete is characterized by comprising the following steps of: scanning the bearing surface of the concrete test block to obtain data of the height Cheng Dianyun of the bearing surface; Inputting the data of the bearing surface height Cheng Dianyun into a CNN convolutional neural network after training to perform morphology deduction, and obtaining a stress concentration coefficient K t ; Based on the stress concentration coefficient K t , combining with a preset threshold value, judging and matching a corresponding loading strategy, applying pressure to the bearing surface of the concrete test block according to the matched loading strategy until the test block is damaged, and recording and outputting the maximum damage load F max of the concrete test block; And obtaining the compensated compressive strength F c of the concrete test block based on the maximum breaking load F max of the concrete test block.
2. The method of self-compensating loading of compressive strength of concrete of claim 1, wherein scanning the bearing surface of the concrete test block to obtain data of a height Cheng Dianyun of the bearing surface comprises: the bearing surface of the test block is scanned by using laser inclined at 30 degrees through a visual portal, the shape of the bearing surface is digitized, and the data of the height Cheng Dianyun of the bearing surface of the test block is rapidly obtained, wherein the data comprise coordinates P (x, y, Z) of each point of the bearing surface and the maximum value max (Z) in a Z coordinate matrix.
3. The method for self-compensating loading of compressive strength of concrete according to claim 2, wherein inputting the data of the bearing surface height Cheng Dianyun into the trained CNN convolutional neural network for morphology deduction to obtain a stress concentration coefficient K t comprises: reading an elevation map file generated by laser scanning through cv2.imread, and outputting a z coordinate matrix; Calculating a fixed nominal area A 0 based on the standard size of the concrete test block; calculating basic parameters based on the z-coordinate matrix; and obtaining a calculated stress concentration coefficient K t based on the basic parameters.
4. A method of self-compensating loading of compressive strength of concrete according to claim 3, wherein calculating the base parameters based on the z-coordinate matrix comprises: calculating an arithmetic average value of all Z coordinate values in the Z coordinate matrix, and outputting Z mean ; Judging whether the coordinate value of each Z i in the Z coordinate matrix meets the value of |Z i -Z mean | which is less than or equal to 0.05mm one by one, marking the coordinate value as an effective point if the coordinate value meets the value, otherwise, marking the coordinate value as an ineffective point, and outputting a mask matrix marked with the effective point and the ineffective point, wherein i is a serial number in the Z coordinate matrix; Based on the mask matrix, firstly counting the total number of True effective points in the mask matrix, and multiplying the number of the effective points by the actual area corresponding to a single pixel to obtain an actual compression area A real ; subtracting Z mean from the maximum value max (Z) in the Z coordinate matrix to obtain a maximum burr height h max ; Firstly calculating gradients of a z coordinate matrix in an x direction and a y direction, respectively solving standard deviations of the gradients in the two directions, and finally calculating according to a formula rho min ＝0.25×(D x +D y ) to output a minimum root circle radius rho min , wherein D x 、D y is the standard deviation of the gradients in the x direction (horizontal) and the y direction (vertical) based on the z coordinate matrix.
5. The method of self-compensating loading of compressive strength of concrete according to claim 4, wherein obtaining a calculated stress concentration factor K t based on the base parameters comprises: Substituting the maximum burr height h max and the minimum root circle radius rho min into a formula Obtaining a geometric stress concentration coefficient K tgeo ; the geometric stress concentration coefficient K tgeo , the actual pressure bearing area A real and the fixed nominal area A 0 are substituted into a formula And obtaining the stress concentration coefficient K t of the bearing surface of the comprehensive concrete test block.
6. The method of self-compensating loading of compressive strength of concrete according to claim 5, wherein determining and matching the corresponding loading strategy based on the stress concentration coefficient K t in combination with a preset threshold value comprises: And when the stress concentration coefficient K t exceeds the preset threshold value, adopting a loading strategy of low-speed pre-pressing and adding a preset concrete strength test standard, and when the stress concentration coefficient K t does not exceed the preset threshold value, directly adopting the loading strategy of the preset concrete strength test standard.
7. The method of self-compensating loading of compressive strength of concrete of claim 6, wherein obtaining the compensated compressive strength F c of the concrete test block based on the maximum breaking load F max of the concrete test block comprises: Based on the maximum breaking load F max , the fixed nominal area A 0 and the stress concentration coefficient K t , the compensated compressive strength F c of the concrete test block is obtained;
8. the method for self-compensating loading of compressive strength of concrete according to claim 1, wherein after obtaining the compensated compressive strength F c of the concrete block based on the maximum breaking load F max of the concrete block, further comprising: And on-line retraining the CNN convolutional neural network after training based on the compensated compressive strength f c of the concrete test block, the stress concentration coefficient K t of the pressure bearing surface, loading data and real morphology data.
9. A computer-readable storage medium having stored thereon computer-executable instructions, wherein execution of the computer-executable instructions by a processor causes the processor to perform the method of any one of claims 1 to 8.
10. A calculator device, comprising: processor, and A memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method of any one of claims 1 to 8.

Description

Self-compensating loading method and equipment for concrete compressive strength Technical Field The invention belongs to the technical field of concrete compressive strength detection, and particularly relates to a self-compensating loading method and equipment for concrete compressive strength. Background The flatness tolerance of the bearing surface of the test block is required to be not more than 0.0005d by the existing standard GB/T50081-2019 'test method Standard for physical and mechanical properties of concrete', d is the side length of the test block, but burrs, blunt edges and unfilled corners are arranged at the common side parts of the test block after molding and demolding, so that stress is concentrated when compressive strength is loaded, the correlation coefficient Kt can reach more than 3, and the test block has the false low-strength phenomenon that the actual strength is damaged. Traditional grinding or sulfur leveling is low in efficiency, toxic and easy to be too thick to implement on line. Therefore, there is a need for a compensation method that automatically eliminates the effect of pressure bearing face errors during loading without manual leveling. Disclosure of Invention The invention aims to provide a self-compensating loading method and equipment for concrete compressive strength. In order to solve the problems, the invention provides a self-compensating loading method for concrete compressive strength, comprising the following steps: scanning the bearing surface of the concrete test block to obtain data of the height Cheng Dianyun of the bearing surface; Inputting the data of the bearing surface height Cheng Dianyun into a CNN convolutional neural network after training to perform morphology deduction, and obtaining a stress concentration coefficient K t; Based on the stress concentration coefficient K t, combining with a preset threshold value, judging and matching a corresponding loading strategy, applying pressure to the bearing surface of the concrete test block according to the matched loading strategy until the test block is damaged, and recording and outputting the maximum damage load F max of the concrete test block; And obtaining the compensated compressive strength F c of the concrete test block based on the maximum breaking load F max of the concrete test block. Further, in the above method, scanning the bearing surface of the concrete test block to obtain data of a height Cheng Dianyun of the bearing surface, including: The pressure bearing surface of the test block is scanned by using laser to incline 30 degrees through the visual portal 1, the shape of the pressure bearing surface is digitalized, and the data of the height Cheng Dianyun of the pressure bearing surface of the test block is rapidly obtained, wherein the data comprise coordinates P (x, y, Z) of each point of the pressure bearing surface and the maximum value max (Z) in a Z coordinate matrix. Further, in the above method, inputting the data of the bearing surface height Cheng Dianyun into the trained CNN convolutional neural network to perform morphology deduction to obtain a stress concentration coefficient K t, including: reading an elevation map file generated by laser scanning through cv2.imread, and outputting a z coordinate matrix; Calculating a fixed nominal area A 0 based on the standard size of the concrete test block; calculating basic parameters based on the z-coordinate matrix; and obtaining a calculated stress concentration coefficient K t based on the basic parameters. Further, in the above method, calculating the base parameter based on the z-coordinate matrix includes: calculating an arithmetic average value of all Z coordinate values in the Z coordinate matrix, and outputting Z mean; Judging whether the absolute value of each Zi coordinate value in the Z coordinate matrix is smaller than or equal to 0.05mm or not according to the Z coordinate matrix and the calculated Zmean, marking the effective point if the absolute value is smaller than or equal to 0.05mm, otherwise, marking the effective point as an invalid point, and outputting a mask matrix marked with the effective point and the invalid point, wherein i is a serial number in the Z coordinate matrix; Based on the mask matrix, firstly counting the total number of True effective points in the mask matrix, and multiplying the number of the effective points by the actual area corresponding to a single pixel to obtain an actual compression area A real; subtracting Z mean from the maximum value max (Z) in the Z coordinate matrix to obtain a maximum burr height h max; The gradient of the z coordinate matrix in the x direction and the y direction is calculated, standard deviation of the gradients in the two directions is calculated respectively, and finally, the minimum root circle radius rho min is calculated according to the formula rho min = 0.25 x (dx+dy), wherein Dx and Dy are the standard deviation of the gradients in the x direc