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Setup of axial bearing capacity of open ended tubular steel piles driven in sand

.K., 1994. Setup and relaxation in glacial sand. J. Geotech. Eng. 120, 1498–1513. 10.1061/(ASCE)0733-9410(1994)120:9(1498) York D.L. Brusey W.. Clemente F.M. Law S.K. 1994 Setup and relaxation in glacial sand J. Geotech. Eng 120 1498 1513 [32] Reddy, S.C., Stuedlein, A.W. (2014) Reddy and Stuedlein (2014) Time dependant capacity Increase of piles driven in the puget sound lowlands. Geo-congress from soil behavior fundamentals to innovations in Geotechnical Engineering. Reddy S.C. Stuedlein A

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Application of artificial neural networks to predict the deflections of reinforced concrete beams


Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies.

Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

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Endurance of the wooden bridge reinforced by the dowel plates

. Advances in Science, Technology & Innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Cham. Dahoua L. Yakovitch S.V. Hadji R. Farid Z. 2018 Landslide susceptibility mapping using analytic hierarchy process method in BBA-Bouira Region, case study of East-West Highway, NE Algeria Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions Edited by A. Kallel, M. Ksibi, H. Ben Dhia, N. Khélifi. EMCEI 2017. Advances in Science, Technology & Innovation (IEREK Interdisciplinary Series for

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Treatment of a collapsible soil using a bentonite–cement mixture

–cement mixture, the sodium (Na + ) and calcium (Ca +2 ) ions captured by the clay particles to form cementitious products lead to a decrease in porosity, which results in a rigid and stable structure. References [1] Abbeche, K., Ayadat, T., Lahmadi, A. (2009). Treatment of a soil with sudden collapse by lime. Seminar International Innovation and Valorization in Civil Engineering 164-168. Abbeche K. Ayadat T. Lahmadi A. 2009 Treatment of a soil with sudden collapse by lime Seminar International Innovation and Valorization in Civil Engineering

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Complex analysis of uniaxial compressive tests of the Mórágy granitic rock formation (Hungary)

-equilibrium continuum thermodynamics along the lines of Asszonyi et al. [ 23 , 25 ] and beyond. These relationships can be used for determining the mechanical parameters of the rock mass, as well [ 24 , 26 ]. Acknowledgements This paper has been published with the permission of Public Limited Company for Radioactive Waste Management (PURAM). The project presented in this article is supported by National Research, Development and Innovation Office – NKFIH 124366 and NKFIH 124508 and the Hungarian-French Scientific Research Grant (No. 2018-2.1.13-TÉT-FR-2018

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