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Ultimate shear response of ultra-high-performance steel fibre-reinforced concrete elements

Tibea C., Bompa Dan V.

Archives of Civil and Mechanical Engineering

2020

Vol. 20, no. 2

280--295

This paper examines the experimental performance of ultra-high-performance steel fibre-reinforced concrete (UHPSFRC) beams subjected to loads at relatively low shear span-to-depth ratios. The results and observations from six tests provide a detailed insight into the ultimate response including shear strength and failure mode of structural elements incorporating various fibre contents. The test results showed that a higher fibre content results in an increase in ultimate capacity and some enhancement in terms of ductility. Detailed nonlinear numerical validations and sensitivity studies were also undertaken in order to obtain further insights into the response of UHPSFRC beams, with particular focus on the influence of the shear span-to-depth ratio, fibre content and flexural reinforcement ratio. The parametric investigations showed that a reduction in shear span-to-depth ratio results in an increase in the member capacity, whilst a reduction in the flexural reinforcement ratio produces a lower ultimate capacity and a relatively more flexible response. The test results combined with those from numerical simulations enabled the development of a series of design expressions to estimate the shear strength of such members. Validations were performed against the results in this paper, as well as against a collated database from previous experimental studies.

Zastosowanie sieci neuronowych w geologii

Palmąka Michał

Geologos

2007

Vol. 11

541--548

Szybki rozwój technik badawczych w geologii oraz możliwości gromadzenia dużej liczby danych dotyczących środowiska gruntowo-wodnego wymagają korzystania z nowych metod ich analizy. W artykule przedstawiono podstawowe informacje dotyczące zasad funkcjonowania wybranych rodzajów sieci neuronowych oraz ich cechy, które umożliwią efektywne przetwarzanie licznych zbiorów danych. Wskazano na możliwość zastosowania modeli neuronowych do rozwiązywania problemów regresyjnych i klasyfikacyjnych oraz połączenie tego typu analiz z systemem informacji przestrzennej (GIS). Prezentowane metody poparto wybranymi przykładami literaturowymi dotyczącymi zastosowania sieci neuronowych w geologii.

The rapid development of research techniques in geology and storage of a huge amount of digital data concerning a non-biotic environment require new methods to analyze and to draw correct conclusions from available information. This paper presents characteristics and basic principles of running and operating artificial neural networks (ANN), which enable to effectively process numerous data sets. Different geological applications of neural systems are discussed in the context of regression models and classification issues. Additionally some efficient neural network models with a Geographical Information System (GIS) combination are described and proved as a complex spatial methodology.