Permeable concrete performance prediction method and system based on meso-model reconstruction

Abstract

The invention discloses a permeable concrete performance prediction method and system based on meso-model reconstruction. The method comprises the following steps of: acquiring a coarse aggregate sectional image; Extracting coarse aggregate distribution areas from coarse aggregate sectional images; Calculating the maximum thickness of the cladding layer; By means of morphological manipulation, adding a preset thickness of cement-based cladding layer to the coarse aggregate surface in the coarse aggregate distribution area to obtain the image after adding the cladding layer; using The three-dimensional reconstruction method to reconstruct the image after adding the cladding layer, and obtaining the three-dimensional model of permeable concrete; Extracting a pore distribution region in the image after adding a cladding layer; using the Three-dimensional reconstruction method to reconstruct the pore distribution area, and obtaining the pore three-dimensional model; prediciting The performance parameters of permeable concrete corresponding to the three-dimensional model of permeable concrete. The invention can not only realize the prediction of the pore characteristics, but also can predict the water permeability and the strength, and has high reliability.

Description

technical field [0001] The invention relates to the technical field of performance prediction of permeable concrete, in particular to a performance prediction method and system of permeable concrete based on mesoscopic model reconstruction. Background technique [0002] Permeable concrete is composed of coarse aggregate, coarse aggregate cement-based coating and pores. The performance of pervious concrete depends directly on the properties of coarse aggregate and coarse aggregate cement-based coating. Since there is only a small amount of cement matrix in pervious concrete and it is often formed by pressure, the coarse aggregate in its structure can be considered to be in a tightly packed state. For the selected coarse aggregate, the structure and performance of the tightly packed state are known, so the pore structure can be changed by artificially designing the performance and distribution of the cement base, so as to realize the reconstruction of the pervious concrete mo...

AI Technical Summary

Problems solved by technology

[0003] At present, the traditional method adopts the volume method recommended in the national industry standard "Technical Regulations for Pervious Concrete Pavement" to reconstruct the pervious concrete model and predict its performance. It can predict the total porosity of pervious concrete, but cannot predict pore characteristics, such as pore size distribution, connected porosity, channel tortuosity and other pore characteristics, let alone predict the water permeability and strength of pervious concrete, and the model reconstruction performance and actual Large performance deviation, difficult to control

In addition, the traditional method is only a rough summary of engineering experience, which is difficult to meet the requirements of fine design and prediction of pervious concrete performance

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