Next Article in Journal
Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves
Previous Article in Journal
Applications of Electrical Resistivity Surveys in Solving Selected Geotechnical and Environmental Problems
Open AccessArticle

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

Faculty of Civil and Environmental Engineering and Architecture, Rzeszow University of Technology, Powstancow Warszawy 12 Av., 35-959 Rzeszow, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(7), 2261; https://doi.org/10.3390/app10072261
Received: 24 February 2020 / Revised: 21 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
(This article belongs to the Section Civil Engineering)
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. View Full-Text
Keywords: artificial neural networks; organic soils; soil unit weight; organic content; cone penetration test artificial neural networks; organic soils; soil unit weight; organic content; cone penetration test
Show Figures

Figure 1

MDPI and ACS Style

Straż, G.; Borowiec, A. Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks. Appl. Sci. 2020, 10, 2261.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop