@ARTICLE{Straż_Grzegorz_Evaluation_2021,
 author={Straż, Grzegorz and Borowiec, Artur},
 volume={vol. 67},
 number={No 3},
 pages={259-281},
 journal={Archives of Civil Engineering},
 howpublished={online},
 year={2021},
 publisher={WARSAW UNIVERSITY OF TECHNOLOGY FACULTY OF CIVIL ENGINEERING and COMMITTEE FOR CIVIL ENGINEERING POLISH ACADEMY OF SCIENCES},
 abstract={This paper discusses the use of mechanical cone penetration test CPTM for estimating the soil unit weight of selected organic soils in Rzeszow site, Poland. A search was made for direct relationships between the empirically determined the soil unit weight value and cone penetration test leading parameters (cone resistance qc, sleeve friction fs. The selected, existing models were also analysed in terms of suitability for estimating the soil unit weight and tests were performed to predict the value soil unit weight of local, different organic soils. Based on own the regression analysis, the relationships between empirically determined values of soil unit weight and leading parameters cone penetration test were determined. The results of research and analysis have shown that both existing models and new, determined regression analysis methods are poorly matched to the unit weight values determined in laboratory, the main reason may be the fact that organic soils are characterized by an extremely complicated, diverse and heterogeneous structure. This often results in a large divergence and lack of repeatability of results in a satisfactorily range. Therefore, in addition, to improve the predictive performances of the relationships, analysis using the artificial neural networks (ANN) was carried out.},
 type={Article},
 title={Evaluation of the unit weight of organic soils from a CPTM using an Artificial Neural Networks},
 URL={http://journals.pan.pl/Content/120642/16_ACE-00220_A4.pdf},
 doi={10.24425/ace.2021.138055},
 keywords={soil unit weight, artificial neural networks, organic soils, organic content, cone penetration test},
}