Measured and Predicted Durability and Mechanical Properties of Frozen-Thawed Fine Soils

Vahdani, Mohammad; Ghazavi, Mahmoud; Roustaei, Mahya

doi:10.1007/s12205-020-2178-4

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…The properties of the soil will change significantly after freeze‒thaw cycles, which is one of the leading causes of engineering problems in seasonally frozen regions, and this has been widely recognized by researchers 2 , 3 . For this reason, it is significant to analyse the mechanical properties of seasonally frozen soil 2 , 4 – 6 .…”

Section: Introductionmentioning

confidence: 99%

Principal component analysis–artificial neural network-based model for predicting the static strength of seasonally frozen soils

Sun,

Zhou,

Meng

et al. 2023

Sci Rep

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Seasonally frozen soils are exposed to freeze‒thaw cycles every year, leading to mechanical property deterioration. To reasonably describe the deterioration of soil under different conditions, machine learning (ML) technology is used to establish a prediction model for soil static strength. Six key influencing factors (moisture content, compaction degree, confining pressure, freezing temperature, number of freeze‒thaw cycles and thawing duration) are included in the modelling database. The accuracy of three typical ML algorithms (support vector machine (SVM), random forest (RF) and artificial neural network (ANN)) is compared. The results show that the ANN outperforms the SVM and RF. Principal component analysis (PCA) is combined with the ANN, and the PCA–ANN algorithm is proposed, which further improves the prediction accuracy. The deterioration of soil static strength is systematically researched using the PCA–ANN algorithm. The results show that the soil static strength decreased considerably after the first several freeze‒thaw cycles before the strength plateau occurred, and the strength reduction increased significantly with increasing moisture content and compaction degree. The PCA–ANN model can generate a reasonable prediction for the static strength or other soil properties of seasonally frozen soil, which will provide a scientific reference for practical engineering.

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Section: Introductionmentioning

confidence: 99%

Principal component analysis–artificial neural network-based model for predicting the static strength of seasonally frozen soils

Sun,

Zhou,

Meng

et al. 2023

Sci Rep

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show abstract

“…Liu et al 57 carried out tests on the tailing soil to study the dynamic response under varying times of freeze–thaw cycles. Vahdani et al. 58 designed an experiment to study the mechanical properties of fine soil considering freeze–thaw cycles.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al 57 carried out tests on the tailing soil to study the dynamic response under varying times of freeze-thaw cycles. Vahdani et al 58 designed an experiment to study the mechanical properties of fine soil considering freeze-thaw cycles. However, most of the current research only takes separated components as study subjects, rather than an integration of the pile-soil system.…”

mentioning

confidence: 99%

Effect of freeze–thaw cycles on the performance of cast‐in‐place piles in permafrost regions: Working state and action effect sharing

Shi

Wen

et al. 2022

Permafrost & Periglacial

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Owing to a minor thermal disturbance to the permafrost environment, cast-in-place piles are widely used for building and bridge foundations in permafrost regions. However, because of the dynamic and cyclic variation in frozen ground affected by the atmosphere, the load transfer mechanism is not yet clear, and the current design is economically insufficient. To illustrate the bearing pattern of cast-in-place piles subjected to freeze-thaw cycles, a systematic in situ investigation was carried out.Results show that the load from the superstructure has a marginal action effect, while freeze-thaw cycles have a more significant action effect. Freeze-thaw cycles have a decisive effect on the dynamic variations of the pile's working state and action effect sharing while the mechanisms are quite different, which vary with depths. Action effect sharing of the pile shaft and tip experiences a cyclic variation and is affected by the long-term effect of freeze-thaw cycles. The shaft takes an increasing sharing proportion gradually and has a 19% rise after two freeze-thaw cycles, while the pile tip goes the opposite way. Two years after the building is completed, the bearing capacity is almost entirely provided by shaft resistance and mainly by the upper onethird of the pile. This research clarifies several essential issues about the bearing pattern and provides solid scientific support and novel opinions for the pile design in permafrost regions.

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“…Another problem is the performance function of system. e performance function of loess cave dwellings is unknown, to solve the failure probability, Monte Carlo simulation (MCS) [5], and response surface method (RSM) can be used [6]. For loess cave dwellings, MCS means that millions of finite-element models should be set up, which is not possible.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Loess Cave Dwellings Based on Fuzzy Failure Criterion

Pinggong

2021

Mathematical Problems in Engineering

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The reliability of loess cave dwellings based on fuzzy failure criterion is researched to analyze the influence of fuzzy set, membership function, and different combinations of random variables. Due to the strength reduction method, the basic failure criterion is established, the fuzzy property of failure criterion is characterized by fuzzy set and membership function, and the performance function of loess cave dwellings is expressed by a quadratic polynomial without cross terms. Reliability is analyzed with different random variable combinations. The reliability research of loess cave dwellings in Shan Plateau, Henan province, China, shows that the loess property in this area is suitable for loess cave dwelling construction and the reliability index of loess cave dwellings is high, which will be decreased when considering the fuzzy failure criterion and will be increasing sharply when the small cave leg width is improved to average value.

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Measured and Predicted Durability and Mechanical Properties of Frozen-Thawed Fine Soils

Cited by 9 publications

References 21 publications

Principal component analysis–artificial neural network-based model for predicting the static strength of seasonally frozen soils

Principal component analysis–artificial neural network-based model for predicting the static strength of seasonally frozen soils

Effect of freeze–thaw cycles on the performance of cast‐in‐place piles in permafrost regions: Working state and action effect sharing

Reliability Analysis of Loess Cave Dwellings Based on Fuzzy Failure Criterion

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