Intelligent detection method for material qualification of earth-rock dam based on digital image processing

The qualification testing of earth-rock dam materials is usually realized by judging whether the gradation characteristic parameters obtained from on-site screening test meet the design requirements. However, the method of obtaining the gradation characteristic parameters through the test has some shortcomings, such as low sampling rate, cumbersome operation process and poor intelligent perception, resulting in poor representativeness of the tes-ting results. In order to improve the intelligent detection of dam material gradation parameters, relying on the ima-ges and gradation data at the test location of one pumped storage power station in Liaoning Province, the intuitionis-tic fuzzy C-means clustering algorithm fused with spatial information (SIFCM) is used to segment the image of earth-rock dam materials. Next, the 3D volume reconstruction of earth-rock dam material is achieved by the equivalent ellipsoidal volume method. Then the gradation characteristic curve of dam material under real conditions is obtained through the gradation correction model based on BP neural network. Finally, four evaluation indexes of dam material qualification are obtained: maximum particle size, P5 content, curvature coefficient Cc, and uneven coefficient Cu. The practical engineering application shows that the intelligent identification and correction model of dam material gradation characteristics based on the SIFCM_BP algorithm established in this paper has high identification accuracy. The method in this paper provides an important support for the rapid identification of dam material qualification before the compaction construction and the real-time evaluation of dam material compaction characteristics during construction. © 2022 China Water Power Press. All rights reserved.