Abstract
Nuclear magnetic resonance (NMR) technology provides an innovative method employed in detecting the porous structures in frozen rock and soil masses. On the basis of NMR relaxation theory, fuzzy random characteristics of the NMR T2 spectrum and pore structure are deeply analyzed in accordance with the complex and uncertain distribution characteristics of the underground rock and soil structure. By studying the fuzzy random characteristics of the NMR T2 spectrum, the fuzzy random conversion coefficient and conversion method of the T2 spectrum and pore size distribution are generated. Based on the niche principle, the traditional genetic algorithm is updated by the fuzzy random method, and the improved niche genetic algorithm is proposed. Then, the fuzzy random inversion of the conversion coefficient is undertaken by using the improved algorithm. It in turn makes the conversion curve of the T2 spectrum and pore size distribution align with the mercury injection test curve in diverse pore apertures. Compared with the previous least square fitting method, it provides a more accurate approach in characterizing complicated pore structures in frozen rock and soil masses. In addition, the improved niche genetic algorithm effectively overcomes the shortcomings of the traditional genetic algorithm, such as low effectiveness, slow convergence, and weak controllability, which provides an effective way for parameter inversion in the section of frozen geotechnical engineering. Finally, based on the T2 spectrum test of frozen sandstone, the fuzzy random characterization of frozen sandstone pore distribution is carried out by using this transformation method. The results illustrate that the conversion coefficient obtained through the improved algorithm indirectly considers the different surface relaxation rates of different pore sizes and effectively reduces the diffusion coupling effects, and the pore characteristics achieved are more applicable in engineering practices than previous methods.
Highlights
As a key objective in frozen geotechnical engineering, frozen rock and soil masses are random, porous, and heterogeneous, characterised by noticeable composition complexity, structural diversity, and extreme anisotropy. erefore, accurately obtaining the microscopic structures for frozen rock and soil under different conditions is of great significance when studying its physical and mechanical properties
Assuming the linear relationship between T2 spectrum and pore distributions, the NMR T2 spectrum detected was converted to the NMR capillary force curve and fitted to determine the best conversion coefficient through the mercury intrusion test [3]. e second was underlaid on the principle of similarity
The fuzzy random characteristics of the NMR T2 spectrum and pore structure are deeply analyzed in accordance with the complex and uncertain distribution characteristics of the underground frozen rock and soil structures
Summary
As a key objective in frozen geotechnical engineering, frozen rock and soil masses are random, porous, and heterogeneous, characterised by noticeable composition complexity, structural diversity, and extreme anisotropy. erefore, accurately obtaining the microscopic structures for frozen rock and soil under different conditions is of great significance when studying its physical and mechanical properties. Erefore, the accurate acquisition of the pore distribution law relies on nuclear magnetic resonance testing technology and requires an accurate conversion method of relaxation time and pore size measurement. In view of this problem, previous scholars had carried out a lot of research that generally fell among four types of methods: the first type was based on nuclear magnetic resonance test theory. It can be seen that existing testing and converting technologies are disputable in characterizing the complex structures of the rock and soil masses, and their anisotropic distribution characteristics, because of ignoring the natural phenomena of randomness and fuzziness, lead to misleading results in pore structure observation. Based on fuzzy random theory, an improved niche genetic algorithm was proposed to enhance the intelligence of the existing computational model and applied in the domain of NMR T2 spectrum detecting optimization
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
