Characteristics and Prediction of Frost Heave of Saline Soil in Western Jilin Province

Sun, Dongyan; Wang, Wenhua; Wang, Qing; Chen, Jianping; Niu, Cencen; Cao, Chen

doi:10.18280/ijht.340422

Cited by 12 publications

(9 citation statements)

References 2 publications

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“…Uncompacted soil has high pore space that frozen ice lens cannot fill, while compacted soil has tightly structured soil particles with low pore space that is more easily filled by frozen ice lenses, leading to obvious frost heaving deformation [38]. This result was consistent with Sun et al [21] and Bao [22], but other studies have concluded that increasing compactness reduces frost heave [13,39,40]. This contrast shows that the relationship between compactness and frost heave is affected by interactions [23] between compactness and other factors such as water content, as demonstrated in our analysis.…”

Section: Results Of Improved Frost Heave Experimentssupporting

confidence: 85%

“…The amount of water content also directly affects frost heaving, while highly saline soils will produce salt expansion at low temperatures [17][18][19]. The compactness, temperature gradient, and depth to groundwater table are additional factors affecting this process [20,21]. Most frost heave experiments have focused on investigating the effects of single factors [19][20][21][22] using the one-factor-at-a-time (OFAT) experimental approach, in which tests are conducted by systematically changing the levels of one single factor while holding the levels of all other factors fixed.…”

Section: Introductionmentioning

confidence: 99%

“…The compactness, temperature gradient, and depth to groundwater table are additional factors affecting this process [20,21]. Most frost heave experiments have focused on investigating the effects of single factors [19][20][21][22] using the one-factor-at-a-time (OFAT) experimental approach, in which tests are conducted by systematically changing the levels of one single factor while holding the levels of all other factors fixed. This approach is not capable of considering possible interactions between multiple factors and is less efficient than other methods based on a statistical approach [23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Seasonal Frost Heave Behavior in Unsaturated Soil in Northeastern China Using Interactive Factor Analysis with Split-Plot Experiments and GRNN

Wan

Bian

et al. 2019

Water

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Few studies of frost heave mechanisms have considered multifactor interactions, particularly in unsaturated saline soils typical of northeastern China. We collected soil samples in western Jijin Province and assessed their potential frost heave behavior with reference to four controllable factors: soluble salt content (CSS), compactness (C), temperature (T), and water content (WC) using a two-level split-plot experiment. The resulting frost heave ratio was between −0.6% and 2.1%. Analysis of variance showed that water content, compactness, and temperature had significant effects on frost heave behavior, with water content having the strongest correlation (factor coefficient of 0.82), while content of soluble salt (CSS) had no significant effect. The interaction factors (products of single factors) CSS × WC and C × WC had significant effects on frost heave behavior. A correlation analysis using these interaction factors with experimental data drawn from previous research showed results consistent with the improved frost heave experiment as the significant effects of single factors on frost heave behavior ranked from WC > C > T and the interaction factors CSS × WC and C × WC gain had significant effects. We then established two generalized regression neural network (GRNN) models based on the single and interaction factors in order to predict frost heave behavior, showing that adding the latter to the input dataset improved the model accuracy. Thus, future research on predicting frost heave behavior in unsaturated saline soils should consider multiple interacting factor for greater accuracy.

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Section: Results Of Improved Frost Heave Experimentssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Seasonal Frost Heave Behavior in Unsaturated Soil in Northeastern China Using Interactive Factor Analysis with Split-Plot Experiments and GRNN

Wan

Bian

et al. 2019

Water

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“…The basic motivation of deep learning is to establish a deep neural network to simulate the leaning and analysis mechanism of the human brain (Fu et al, 2017). FCN, as a research method, was proposed by deepened on the basis of artificial neural network which is formed based on biology principles of brain neural network, and is an effective method for solving the problem of image segmentation (Gonçalves, 2016;Long et al, 2015;Kadri and Mouss, 2017;Li, 2017;Hu et al, 2016;Sun et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

Urban Expansion Pattern Analysis and Planning Implementation Evaluation Based on using Fully Convolution Neural Network to Extract Land Range

Jia

Zhou

2018

Neuroquantology

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In recent years, due to the rapid development of China's urban, it is significant for effective implementation of urban science development and planning that grasp the process of urban development, analyze the potential of subsequent development, and evaluate the matching degree of the development status and the planning. Thereinto, an effective way we exercise today is to evaluate urban expansion pattern analysis and planning implementation. According to research results of the urban land range extraction method based on the support vector machine (SVM) and fully convolution neural network (FCN) of the depth learning method for the night light image data, this paper describes an integration of remote sensing (RS) and geographic information system (GIS) and analyzes the urban expansion pattern of Beijing based on the computed results of landscape pattern indices. The results unveil that from 1990s to 2010s, Beijing took on a circle expansion mode on the ground the spatial agglomeration degree gradually increases and the expansion potential has spatial distinctions, which basically meets the requirements of the overall planning.

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“…Currently, it is mainly used to diagnose concrete structures and predict pile bearing capacity and concrete deformation and strength. By simulating the neural connections and their functions in biological brain circuits, the artificial neural network uses multiple networks and multilayered processing units to construct a nonlinear complex causality system with multiple influencing factors (Gent et al, 2016;Li, 2017;Nygaard and Geiker, 2005;González et al, 1996;Sun et al, 2016;Vu and Stewart, 2007;. This system does not consider the complex changes within the concrete structure, but directly learns the environmental samples to acquire the inherent law, and then establishes a method for prediction, which provides an effective way to solve concrete structure degradation modelling and calculation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Concrete Structure Service Life Based on the Principle of Neural Connections in Brain Circuits

Zhou¹,

Gao²,

Fan³

et al. 2018

Neuroquantology

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Concrete is the most widely used structural material in the world; however, its structural safety is affected by concrete carbonization and reinforcement corrosion. According to the principles and characteristics of concrete carbonization and reinforcement corrosion, this paper establishes artificial neural network prediction and evaluation models for the depth of carbonization and the degree of reinforcement corrosion based on the principle of neural connections in brain circuits. The results show that the artificial neural network prediction models are of high accuracy, and that the RBF neural network prediction model is more accurate and requires far less training time than the BP neural network model.

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Characteristics and Prediction of Frost Heave of Saline Soil in Western Jilin Province

Cited by 12 publications

References 2 publications

Prediction of Seasonal Frost Heave Behavior in Unsaturated Soil in Northeastern China Using Interactive Factor Analysis with Split-Plot Experiments and GRNN

Prediction of Seasonal Frost Heave Behavior in Unsaturated Soil in Northeastern China Using Interactive Factor Analysis with Split-Plot Experiments and GRNN

Urban Expansion Pattern Analysis and Planning Implementation Evaluation Based on using Fully Convolution Neural Network to Extract Land Range

Prediction of Concrete Structure Service Life Based on the Principle of Neural Connections in Brain Circuits

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