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Informacje o czasopiśmie
Format
Czasopismo
eISSN
2784-1391
Pierwsze wydanie
12 Apr 2013
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

Wyszukiwanie

Tom 14 (2018): Zeszyt 3 (September 2018)

Informacje o czasopiśmie
Format
Czasopismo
eISSN
2784-1391
Pierwsze wydanie
12 Apr 2013
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

Wyszukiwanie

2 Artykułów
Otwarty dostęp

Experimental Campaign and Analysis on Double-Shear Wooden Connections

Data publikacji: 02 Mar 2019
Zakres stron: 1 - 10

Abstrakt

Abstract

The presence of water in wooden elements is very important because it represents the key in the determination of the resistances of a wooden sample. The influences of the environmental conditions like temperature, relative humidity, wind, rain are constantly changing wood properties. The paper presents the different types of water that are found in a wooden piece together with experimental results on the displacement of double-shear wooden connections with wooden dowels that have been modified in two pre-established environments. The moisture content has been changed from 12% natural moisture of wood in normal building conditions to 8% and 16% for wood that is found in different climatic conditions. The experimental results are then compared with numerical calculations from the international standard Eurocode 5.

Słowa kluczowe

  • moisture content
  • displacement
  • dowel-type joints
  • wooden dowels
  • monotonic tests
Otwarty dostęp

Prediction of Swelling Parameters of Two Clayey Soils from Algeria Using Artificial Neural Networks

Data publikacji: 02 Mar 2019
Zakres stron: 11 - 26

Abstrakt

Abstract

The phenomenon of swelling is one of the more complicated geotechnical problems that the engineer have to deal with. However, its quantification is essential for the design of structures and various methods can be applied to the identification of this phenomenon. Some, such as mineralogical identification and direct measurements of swelling, are more or less long and require very specific equipment. However, there are other methods that offer the advantage of being relatively fast and lesser expensive: they are based on soil mechanics parameters. Using these parameters, several authors have introduced soil swelling prediction models, mostly in the form of classifications and empirical formulas. This work concerns in the first part the identification and classification of the swelling potential of two clays located in north-western Algeria. Followed by a statistical analysis carried out to test the reliability of the observations for the estimation of the pressure and the swelling amplitude using a multiple linear regression.

A second part is devoted to the development of a prediction method by artificial neural networks allowing the estimation of swelling parameters (pressure and amplitude) by minimizing the difference between the experimental measurements and the numerical results. Modeling by artificial neural networks is of great interest in the field of prediction. The application of two networks makes it possible to obtain good forecasts of the swelling parameters.

Słowa kluczowe

  • swelling
  • pressure-amplitude
  • estimation
  • multiple linear regression
  • neural networks
2 Artykułów
Otwarty dostęp

Experimental Campaign and Analysis on Double-Shear Wooden Connections

Data publikacji: 02 Mar 2019
Zakres stron: 1 - 10

Abstrakt

Abstract

The presence of water in wooden elements is very important because it represents the key in the determination of the resistances of a wooden sample. The influences of the environmental conditions like temperature, relative humidity, wind, rain are constantly changing wood properties. The paper presents the different types of water that are found in a wooden piece together with experimental results on the displacement of double-shear wooden connections with wooden dowels that have been modified in two pre-established environments. The moisture content has been changed from 12% natural moisture of wood in normal building conditions to 8% and 16% for wood that is found in different climatic conditions. The experimental results are then compared with numerical calculations from the international standard Eurocode 5.

Słowa kluczowe

  • moisture content
  • displacement
  • dowel-type joints
  • wooden dowels
  • monotonic tests
Otwarty dostęp

Prediction of Swelling Parameters of Two Clayey Soils from Algeria Using Artificial Neural Networks

Data publikacji: 02 Mar 2019
Zakres stron: 11 - 26

Abstrakt

Abstract

The phenomenon of swelling is one of the more complicated geotechnical problems that the engineer have to deal with. However, its quantification is essential for the design of structures and various methods can be applied to the identification of this phenomenon. Some, such as mineralogical identification and direct measurements of swelling, are more or less long and require very specific equipment. However, there are other methods that offer the advantage of being relatively fast and lesser expensive: they are based on soil mechanics parameters. Using these parameters, several authors have introduced soil swelling prediction models, mostly in the form of classifications and empirical formulas. This work concerns in the first part the identification and classification of the swelling potential of two clays located in north-western Algeria. Followed by a statistical analysis carried out to test the reliability of the observations for the estimation of the pressure and the swelling amplitude using a multiple linear regression.

A second part is devoted to the development of a prediction method by artificial neural networks allowing the estimation of swelling parameters (pressure and amplitude) by minimizing the difference between the experimental measurements and the numerical results. Modeling by artificial neural networks is of great interest in the field of prediction. The application of two networks makes it possible to obtain good forecasts of the swelling parameters.

Słowa kluczowe

  • swelling
  • pressure-amplitude
  • estimation
  • multiple linear regression
  • neural networks

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