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EN
In this paper, the results of a research on thin-plate single-lap connections are presented. Such type of connections is popular in steel roofs made of trapezoidal plates and other thin-walled elements. In case of a building safety it is necessary to ensure that materials with proper durability and ductility are used. Connections are one of the most important components in such structures, particularly when in-plane strength of a roof is taken into account. So far, in many existing regulations, only general calculations of such connections are conducted. However recently, discrete and computational methods can be used to build new, expanded mathematical design models, such as those presented here. Such models could be useful in an advanced design where a static analysis is combined with the safety assessment of the connections in a structural system. This is difficult when sheeting is utilized as a structural in-plane shear diaphragm. These require to take into consideration the important interactions of structure with covering and covering with another covering elements. The research is an effect of authors works on practical design approaches. Such methods can be effectively used for structural designs of buildings where the stressed skin diaphragm action is involved. Finally, practical input values about connections can be acquired from the presented data.
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EN
The determination of the physico-mechanical characteristics of rocks is very essential for the planning and implementation of engineering structures as well as for the classification of the rock mass. These physico-mechanical properties are often obtained directly in the laboratory by using standard tests on specific core samples and/or cut samples. However, these experiments are difficult to perform, destructive, time-consuming, costly, and are impossible to execute in some cases due to the complex nature of some rocks. Hence, there is a need to develop an indirect approach to estimate these physico-mechanical properties of rocks. The artificial neural network (ANN) technique has been proven to be well suited for developing predictive models for the estimation of the physico-mechanical characteristics of rocks. Therefore, this study presents new ANN models to predict uniaxial compressive strength (UCS), dry unit weight (DUW), Brazilian tensile strength (TS), point load index (Is(50)), porosity (ɸ), and the Schmidt hardness (RN) based on the seismic P-wave velocity (Vp), and to compare the ANN models with conventional empirical models. Three error indexes including determination coefficient (R2 ), average absolute percentage error, and root mean square error were determined to assess the reliability of the newly developed ANN models. The results show that the developed models were able to predict the UCS, DUW, TS, Is(50), ɸ and the RN from the Vp of intact rocks with high accuracy, determination coefficient (R2 ) of more than 0.89 was achieved. The ANN models also showed better performance compared to the conventional empirical models.
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