Infl uence of the rotation of principal stress directions on undrained shear strength. The paper presents the results of research on natural cohesive soil carried out in the Hollow Cylinder Apparatus (HCA). The main goal of this study was to determine the values of undrained shear strength at different angle of the rotation of principal stress directions. The research were carried out with anisotropic consolidation and shearing in undrained conditions (CAU) on cohesive soil with overconsolidation ratio (OCR) equals 4 and plasticity index (I p ) about 77%. The results of laboratory tests allow to assess the infl uence of the rotation of principal stress directions on undrained shear strength.
This paper presents a method describing the application of artificial neural networks to evaluate the change in undrained shear strength in cohesive soils due to principal stress rotation. For analysis, the results of torsional shear hollow cylinder (TSHC) tests were used. An artificial neural network with an architecture of 7-6-1 was able to predict the real value of normalized undrained shear strength, τ fu /σ' v , based on soil type, over-consolidation ratio (OCR), plasticity index, I P , and the angle of principal stress rotation, α, with an average relative error of around ±3%, and a single maximum value of relative error around 6%.
Exceeding the approved budget is often an integral part of the implementation of construction projects, especially those where unforeseen threats may occur. Therefore, each construction investment should contain elements of risk forecasting, mainly in terms of the cost of its implementation. Only a small number of institutions apply effective cost control methods, taking into account the specifics of a given industry. Especially small construction companies that participate in the structure of the implementation of large construction projects as subcontractors. The article presents a method by which it is possible to determine, with certain probability, the final cost of railway construction investments carried out in Poland. The method was based on a reliable database of risk factors published in sources. In this article, the main presumptions of the original method are presented, which take into account the impact of potential, previously recognized, risks specific to railway investments, and enable project managers to relate them to the conditions where the implementation of a specific object is planned. The authors assumed that such a relatively simple method, supported by a suitable computational program, would encourage teams that plan to implement railway projects to use it and increase the credibility of their schedules.
The paper presents a method of application of an ANN (Artificial Neural Network) to predict the permeability coefficient k in sandy soils: FSa, MSa, CSa. To develop an ANN the results of permeability coefficients from pumping and consolidation tests were applied. The proposed ANN with an architecture 6-8-1 predicts the value of permeability coefficient k based on the following parameters: soil type, relative density ID, void ratio e and effective soil diameter d10. The mean relative error and single maximum value of the relative error for the proposed ANN are following: Mean RE = ±4%, Max RE = 7.59%. The use of the ANN to predict the soil permeability coefficient allows the reduction of the costs and time needed to conduct laboratory or field tests to determine this parameter.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.