Abstract
Thermal conductivity is a critical parameter playing an important role in the heat transfer process in thermal engineering and enormous other engineering fields. Thus, the accurate acquisition of thermal conductivity has significant meaning for thermal engineering. However, compared to density test, moisture content test, and other physical property tests, the thermal conductivity is hard and expensive to acquire. Apparently, it has great meaning to accurately predict conductivity around a site through easily accessible parameters. In this paper, 40 samples are taken from 37 experimental points in Changchun, China, and the BPNN optimized by genetic algorithm (GA-BPNN) is used to evaluate the thermal conductivity by moisture content, porosity, and natural density of undisturbed soil. The result is compared by two widely used empirical methods and BPNN method and shows that the GA-BPNN has better prediction ability for soil thermal conductivity. The impact weight is obtained through mean impact value (MIV), where the natural density, moisture content, and porosity are 30.98%, 55.57%, and 13.45%, respectively. Due to high complexity of different parameter on thermal conductivity, some remolded soil specimens are taken to study the influence of individual factors on thermal conductivity. The correlations between moisture content and porosity with thermal conductivity are studied through control variable method. The result demonstrates that the impact weight of moisture content and porosity can be explained by remolded soil experiment to some extent.
Highlights
Research ArticleAssessment of Soil Thermal Conductivity Based on BP Neural Network (BPNN) Optimized by Genetic Algorithm
With the development of human activity, a great consumption of traditional fuels has produced huge amounts of greenhouse gases that are bad to the climate and a corresponding further increased shortage of the traditional fuels, such as oil, natural gas, and coal [1,2,3,4,5,6,7,8,9]
The Impact Weight through Remolded Soil Experiments e abovementioned factors have a significant effect on the method; it is hard to study the relationship between single factor and thermal conductivity. us, control variable method is adopted to conduct the correlation between moisture content, porosity, and thermal conductivity
Summary
Assessment of Soil Thermal Conductivity Based on BPNN Optimized by Genetic Algorithm. It has great meaning to accurately predict conductivity around a site through accessible parameters. 40 samples are taken from 37 experimental points in Changchun, China, and the BPNN optimized by genetic algorithm (GA-BPNN) is used to evaluate the thermal conductivity by moisture content, porosity, and natural density of undisturbed soil. E result is compared by two widely used empirical methods and BPNN method and shows that the GA-BPNN has better prediction ability for soil thermal conductivity. E impact weight is obtained through mean impact value (MIV), where the natural density, moisture content, and porosity are 30.98%, 55.57%, and 13.45%, respectively. E result demonstrates that the impact weight of moisture content and porosity can be explained by remolded soil experiment to some extent Due to high complexity of different parameter on thermal conductivity, some remolded soil specimens are taken to study the influence of individual factors on thermal conductivity. e correlations between moisture content and porosity with thermal conductivity are studied through control variable method. e result demonstrates that the impact weight of moisture content and porosity can be explained by remolded soil experiment to some extent
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