Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks

Lechowicz, Zbigniew; Fukue, Masaharu; Rabarijoely, Simon; Sulewska, Maria Jolanta

doi:10.3390/app8081395

Cited by 17 publications

(17 citation statements)

References 12 publications

(15 reference statements)

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“…Additionally, the next four regression models of the two variables were used: the three-parameter surface model (10), five-parameter surface model (11,12) and eight-parameter surface model (13).…”

Section: Standard Regreesionmentioning

confidence: 99%

Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks

Straż

Borowiec

2020

Applied Sciences

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The estimation of the unit weight of soil is carried out using laboratory methods; however, it requires high-quality research material in the form of samples with undisturbed structures, the acquisition of which, especially in the case of organic soils, is extremely difficult, time-consuming and expensive. This paper presents a proposal to use artificial neural networks to estimate the unit weight of local organic soils as leading parameters in the process of checking the load capacity of subsoil, under a direct foundation in drained conditions, in accordance with current standards guidelines. The initial recognition of the subsoil, and the locating of organic soils at the Theological and Pastoral Institute in Rzeszow, was carried out using a mechanical cone penetration test (CPTM), using various interpretation criteria, and then, material for laboratory tests was obtained. The analysis of the usefulness of the artificial intelligence method, in this case, was based on data from laboratory tests. Standard multi-layer backpropagation networks were used to predict the soil unit weight based on two leading variables: the organic content LOIT and the natural water content w. The applied neural model provided reliable prediction results, comparable to the standard regression methods.

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Section: Standard Regreesionmentioning

confidence: 99%

Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks

Straż

Borowiec

2020

Applied Sciences

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“…The physical properties of the soil at the Antoniny site were determined during previous WULS-SGGW tests. The peat and gyttja layers have a thickness of 7.5 m; the ground is preconsolidated with the overconsolidated ratio OCR at 3-5 for peat, and 1.5-2.5 for gyttja [29][30][31]. The embankment was located in the Noteć river valley on organic sediments, which contain two layers: peat with a thickness of 4.1 m and gyttja with a thickness of 3.7 m. Generally, the organic subsoil is composed of amorphous peat with varying carbonate gyttja, and variable content of organic matter and calcium carbonate.…”

Section: Geotechnical Conditions Of Test Sitesmentioning

confidence: 99%

“…The physical properties of the soil at the Nielisz site were determined during previous WULS-SGGW tests. The layer of soft subsoil has a thickness of 3 m to 5 m; the ground is slightly preconsolidated [29][30][31]. Two layers of organic subsoil were distinguished at the Nielisz site.…”

Section: Geotechnical Conditions Of Test Sitesmentioning

confidence: 99%

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A New Approach to the Determination of Mineral and Organic Soil Types Based on Dilatometer Tests (DMT)

Rabarijoely

2018

Applied Sciences

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In order to identify the soil type in the ground, Marchetti’s nomogram chart is commonly used on the basis of dilatometer tests (DMT). In this chart, the material index values (ID) and the dilatometer modulus (ED) are used to determine the state and type of soils predominant in mineral soils. Unfortunately, this classification is not accurate enough for the identification of organic soils. This article proposes a new classification based on a nomogram chart for both mineral soils and organic soils using (p0), (p1) readings and pore water pressure (uo).

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“…The MLP neural network, with one hidden layer, was developed to predict the normalized undrained shear strength of organic soils. The MLP neural network, with an architecture of 5-4-1, provided a more reliable prediction in comparison to the empirical methods [53]. The ANNs with MLP topology to forecast winter rapeseed yields and winter wheat yield were developed.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Francik

Kurpaska

2020

Sensors

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It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

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Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks

Cited by 17 publications

References 12 publications

Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks

Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks

A New Approach to the Determination of Mineral and Organic Soil Types Based on Dilatometer Tests (DMT)

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

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