Management of bridges with shallow foundations exposed to local scour

Abstract

The Serbian road network includes a large portion of bridges with shallow foundations vulnerable to local scour as tragically demonstrated during the extreme flooding in May 2014. Currently, the bridge management procedures in Serbia and worldwide do not comprehensively account for a risk of bridge failure due to flooding and fail to provide sufficient information for the decision-making. Thus, a novel methodology for quantitative vulnerability assessment is suggested as a tool to identify the most vulnerable bridges in a network. Herein, the essential task is evaluation of the conditional probability of a bridge failure due to local scour in a flooding event of a certain magnitude. To apply this approach on a network level, there is a dire need to establish precise practice-ready guidelines on an optimal set of information to be used and/or collected in situ, which is discussed on an example of the Serbian bridge database. The vulnerability of a bridge to local scour may be used as a comprehensive indicator of a bridge performance in a flooding event. For a network level, the vulnerability maps with respect to flooding of different magnitudes will give road operators crucial information to apply adequate quality control plans to vulnerable bridges.     

Densitometric determination of zinc bacitracin and nystatin in animal feed

BACKGROUND: The European Union has forbidden the use of antibiotics as additives in animal feed. Zn‐bacitracin (Zn‐BC) and nystatin (NYS) were frequently used for their growth‐promoting effects and for feed conversion in poultry, pigs and cattle. An HPTLC method has been developed for separating Zn‐BC and NYS in the mixture, for routine quality control. RESULTS: The separation was obtained using RP‐18 F_254S coated HPTLC plates with acetonitrile/methanol (equal volumes):toluene:KH₂PO₄/KOH (buffer, pH 6.8) = 57:3:40 (v/v/v), adjusted with HCl to pH 8.2, as a mobile phase. The densitograms were monitored at 192, 215 and 305 nm and both antibiotics were assayed at 215 nm. The method was shown to be specific, accurate (recoveries were 98.7 ± 0.5% and 104.8 ± 0.7% for Zn‐BC and NYS, respectively), linear over the tested range (correlation coefficients, 0.9982 and 0.9884), and precise (intermediate precision RSD below 2.2% for both analytes) with efficient separation (R_s = 3.5). CONCLUSION: The method was applied for determining Zn‐BC and NYS as additives in spiked matrices of commercial animal feedstuffs. According to LOD values for each antibiotic, the minimum detectable amount in feed is 4.5 and 5.5 ppm of Zn‐BC and NYS, respectively. Copyright © 2008 Society of Chemical Industry     

Three-dimensional simulation of sacrificial etching

Sacrificial etching is one of the most important process steps in micro-electro-mechanical systems technology, since it enables the generation of free-standing structures. These structures are often the main part of micro-mechanical devices, intended to sense or induce a mechanical movement. The etching process transforms an initial multi-segmented geometry and depends on material properties and several process conditions. One of the crucial issues for etching is the etching selectivity on different materials. The major task for the simulation is to give an answer, how sacrificial layer surfaces regress in time under the influence of process parameters and to which magnitude surrounding material segments are affected by the etching process. For this purpose we have developed a fully three-dimensional topography simulation tool, Etcher-Topo3D, which is capable to deal with realistic process conditions. The main concept is demonstrated in this work. During simulation the topography of the initial multi-segment geometry is changed which is handled by a level-set algorithm. After a simulation is finished, the level-set representation has usually to be converted back to a mesh representation to enable further analysis. To illustrate the main features of our simulation tool several examples of MEMS structures with a sacrificial layer are presented.     

Co-deposition and densification of SiC with MgO for biomedical applications

This article focuses on the electrophoretic co-deposition of SiC and MgO, which has not been previously reported. The EPD of SiC has been widely investigated, whilst the EPD of SiC with sintering additives is usually not taken into account since every compound added to the suspension seriously affects the zeta-potential and the conductivity, the two main parameters that should be optimized to achieve a good deposit. We comprehensively observed the effects of the individual compounds on the colloidal behaviour of a suspension suitable for co-deposition to achieve good homogeneity and the highest possible green density. The obtained green densities reached up to 1.92 g/cm³, which correspond to 60% of theoretical density, whilst after sintering in open air at 1350 °C the densities reached 2.33 g/cm³. SEM and TEM analyses revealed that the microstructure is composed of SiC grains embedded in a SiO₂ matrix, whilst XRD confirmed that even though the sintering caused a partial oxidation and the appearance of an amorphous phase, the prevailing crystalline phase is still β-SiC. In the presence of MgO, SiO₂ also appears in the crystalline form as cristobalite.     

Predicting body fat percentage based on gender,age and BMI by using artificial neural networks

In the human body, the relation between fat and fat-free mass (muscles, bones etc.) is necessary for the diagnosis of obesity and prediction of its comorbidities. Numerous formulas, such as Deurenberg et al., Gallagher et al., Jackson and Pollock, Jackson et al. etc., are available to predict body fat percentage (BF%) from gender (GEN), age (AGE) and body mass index (BMI). These formulas are all fairly similar and widely applicable, since they provide an easy, low-cost and non-invasive prediction of BF%. This paper presents a program solution for predicting BF% based on artificial neural network (ANN). ANN training, validation and testing are done by randomly divided dataset that includes 2755 subjects: 1332 women (GEN = 0) and 1423 men (GEN = 1), with AGE from 18 to 88 y and BMI from 16.60 to 64.60 kg/m². BF% was estimated by using Tanita bioelectrical impedance measurements (Tanita Corporation, Tokyo, Japan). ANN inputs are: GEN, AGE and BMI, and output is BF%. The predictive accuracy of our solution is 80.43%. The main goal of this paper is to promote a new approach to predicting BF% that has same complexity and costs but higher predictive accuracy than above-mentioned formulas.     

Mathematical Modelling of Cutting Force as the Most Reliable Information Bearer on Cutting Tools Wearing Phenomenon

Being one of their prominent exploitative characteristics, cutting tools durability depends on the character, intensity and the speed of wearing. Identification of tool wearing is of great significance for the purpose of avoiding sooner or later replacement of tools. The parameters of tool wearing can be measured by out-process and in-process-measuring systems. Given the extremely limiting role of the former in modern production lines, development of the latter （the indirect measuring systems） has gained prominence, The basis of indirect measuring systems comprises a set of various signals originating from the units of the system under treatment which stand in certain correlations with the wearing parameters. The paper presents mathematical models of axial force designed on the basis of experimental research in drilling tempered steel by twist drills made of high-speed steel manufactured by powder metallurgy.     

Exact collision detection for a virtual manufacturing simulator

Collision detection is a critical aspect of Virtual Reality (VR)-based simulation of manufacturing processes. Despite the significant progress that has been made in developing efficient, exact collision detection algorithms for convex objects, limited and slow progress has been reported in developing collision detection algorithms for general, nonconvex objects. To narrow this gap we introduce the concept of virtual objects which extends the applicability of exact collision detection algorithms to nonconvex objects. The outcome of our methodology is the fast creation of convex objects for a class of frequently encountered nonconvex manufacturing objects that can be used in any existing collision detection approach. The collision detection technique described in this paper is best suited for interactive simulation and animation applications where a high accuracy of object contact modeling is required. Examples include virtual assembly; mobile robot simulation; robotic painting; robotic welding; and coordinate measuring processes.     

Fabrication of CNT-SiC/SiC composites by electrophoretic deposition

Due to the outstanding mechanical and thermal properties of carbon nanotubes (CNTs), they are considered suitable reinforcement for structural materials. In this study, for the first time, electrophoretic deposition (EPD) was used to deposit (multi-walled) CNTs onto SiC fibres (SiC_f) to form an effective CNT interphase layer for SiC_f/SiC composites. This deposition was followed by electrophoretic infiltration of the CNT-coated SiC fibre mats with SiC powder to fabricate a new CNT-SiC-fibre-reinforced SiC-matrix (SiC_f/SiC) composite for fusion applications. In these EPD experiments, a commercial aqueous suspension of negatively charged CNTs and an optimized aqueous suspension of negatively charged SiC particles were used. The CNT-coatings on the SiC fibres were firm and homogenous, and uniformly distributed nanotubes were observed on the fibre surfaces. In a following step of EPD, a thick SiC layer was formed on the fibre mat when the CNT-coated SiC fibres were in contact with the positive electrode of the EPD cell; however, spaces between the fibres were not fully filled with SiC. Conversely, when CNT-coated SiC fibres were isolated from the electrode, the SiC particles were able to gradually fill the fibre mat resulting in relatively high infiltration, which leads to dense composites.