Evaluation of the Compressibility Parameters of Soils Using Soft Computing Methods

Dağdeviren, Uğur; Demir, Alparslan Serhat; Kurnaz, T. Fikret

doi:10.1007/s11204-018-9522-4

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…The objective of these field drilling operations is to gather representative soil samples, enabling the evaluation of variations in different soil parameters as one moves deeper into a soil cross section. The data obtained from laboratory experiments, coupled with the results from field drilling operations, collectively contribute to identifying the fundamental soil parameters essential for accurate calculations and estimations of the anticipated range of settlement values [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach on Prediction of Soil Consolidation Characteristics

Kim,

Senturk,

Tan

et al. 2024

Buildings

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Artificial neural network models, crucial for accurate predictions, should be meticulously designed for specific problems using deep learning-based algorithms. In this study, we compare four distinct deep learning-based artificial neural network architectures to evaluate their performance in predicting soil consolidation characteristics. The consolidation features of fine-grained soil have a significant impact on the stability of structures, particularly in terms of long-term stability. Precise prediction of soil consolidation under planned structures is vital for effective foundation design. The compression index (Cc) is an important parameter used in predicting consolidation settlement in soils. Therefore, this study examines the use of deep learning techniques, which are types of artificial neural network algorithms with deep layers, in predicting compression index (Cc) in geotechnical engineering. Four neural network models with different architectures and hyperparameters were modeled and evaluated using performance metrics such as mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). The dataset contains 916 samples with variables such as natural water content (w), liquid limit (LL), plasticity index (PI), and compression index (Cc). This approach allows the results of soil consolidation tests to be seen more quickly at less cost, although predictively. The findings demonstrate that deep learning models are an effective tool in predicting consolidation of fine-grained soil and offering significant opportunities for applications in geotechnical engineering. This study contributes to a more accurate prediction of soil consolidation, which is critical for the long-term stability of structural designs.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Approach on Prediction of Soil Consolidation Characteristics

Kim,

Senturk,

Tan

et al. 2024

Buildings

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“…Fine-grained soils are commonly encountered in the practice of geotechnical engineering, making it crucial to understand and predict their engineering behaviors, which are strongly dependent on water contents. Previous studies have shown that the water-holding capacity of soil affects its compression characteristics [1,2], and the loosely bound water (LBW), the main form of water in fine-grained soil [3,4], affects the microstructure during the compression process. Soil compression characteristics have consistently been the focus of attention in engineering design and construction, as the postconstruction soil settlement significantly affects the safety and stability of superstructures.…”

Section: Introductionmentioning

confidence: 99%

Influence of Loosely Bound Water on Compressibility of Compacted Fine‐Grained Soils

Zhang

Kumar

et al. 2020

Advances in Civil Engineering

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This study aimed to investigate the influence of loosely bound water (LBW) on the compressibility of compacted fine-grained soils and accurately determine the soil’s compression index. Four fine-grained soils (i.e., heavy clay, heavy silt, lean clay, and lean silt) and a coarse-grained soil were examined. The volumetric flask method was used to measure the LBW contents of the five soils. X-ray diffraction (XRD) analysis was then performed to test the mineral compositions and help explain the reason why the LBW content varied between different soils. A concept of modified void ratio was proposed by assuming that LBW is a part of the solid phase in soil. Subsequently, consolidation tests and permeability tests were conducted on saturated compacted specimens. The results show that the compression indexes or permeability coefficients tend to be the same for the soils with identical initial modified void ratios. Consolidation tests were also carried out on the unsaturated compacted heavy silt of four different dry densities prepared at a water content higher than the optimum. They show that the compression of unsaturated soil occurs if pore air is discharged when the water content is less than the LBW content. This confirms the previous assumption that LBW can be regarded as a part of the soil solid phase. A modified compression index was deduced and implemented to predict the settlement of a road embankment. The result suggests that the modified compression index is capable of calculating the compression of fine-grained soils, whose water contents are higher than their LBW contents.

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Prediction of Compression Index of the Soil of Al-Nasiriya City Using Simple Linear Regression Model

Mandhour

2020

Geotech Geol Eng

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Evaluation of the Compressibility Parameters of Soils Using Soft Computing Methods

Cited by 10 publications

References 23 publications

Deep Learning Approach on Prediction of Soil Consolidation Characteristics

Deep Learning Approach on Prediction of Soil Consolidation Characteristics

Influence of Loosely Bound Water on Compressibility of Compacted Fine‐Grained Soils

Prediction of Compression Index of the Soil of Al-Nasiriya City Using Simple Linear Regression Model

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