Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

Microbial Ecology and Geo-electrical Responses across a Groundwater Plume

We have used geophysics, microbiology, and geochemistry to link large-scale (30+ m) geophysical self-potential (SP) responses at a groundwater contaminant plume with its chemistry and microbial ecology of groundwater and soil from in and around it. We have found that microbially mediated transformation of ammonia to nitrite, nitrate, and nitrogen gas was likely to have promoted a well-defined electrochemical gradient at the edge of the plume, which dominated the SP response. Phylogenetic analysis demonstrated that the plume fringe or anode of the geobattery was dominated by electrogens and biodegradative microorganisms including Proteobacteria alongside Geobacteraceae, Desulfobulbaceae, and Nitrosomonadaceae. The uncultivated candidate phylum OD1 dominated uncontaminated areas of the site. We defined the redox boundary at the plume edge using the calculated and observed electric SP geophysical measurements. Conductive soils and waste acted as an electronic conductor, which was dominated by abiotic iron cycling processes that sequester electrons generated at the plume fringe. We have suggested that such geoelectric phenomena can act as indicators of natural attenuation processes that control groundwater plumes. Further work is required to monitor electron transfer across the geoelectric dipole to fully define this phenomenon as a geobattery. This approach can be used as a novel way of monitoring microbial activity around the degradation of contaminated groundwater plumes or to monitor in situ bioelectric systems designed to manage groundwater plumes.

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C _n-mim][NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex. © 2012 Copyright the authors.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C _n-mim] [NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Characterizing Aquifer Heterogeneity Using Bacterial and Bacteriophage Tracers

Gravel aquifers act as important potable water sources in central western Europe yet they are subject to numerous contamination pressures. Compositional and textural heterogeneity makes protection zone delineation around groundwater supplies in these units challenging; artificial tracer testing aids characterization. This paper reappraises previous tracer test results in light of new geological and microbiological data. Comparative passive gradient testing, using a fluorescent solute (Uranine), virus (H40/1 bacteriophage), and comparably sized bacterial tracers Escherichia coli and Pseudomonas putida, was used to investigate a calcareous gravel aquifer’s ability to remove microbiological contaminants at a test site near Munich, Germany. Test results revealed E. coli relative recoveries could exceed those of H40/1 at monitoring wells 10 m and 20 m from an injection well by almost four times; P. putida recoveries varied by a factor of up to three between wells. Application of filtration theory suggested greater attenuation of H40/1 relative to similarly charged E. coli occurred due to differences in microorganism size, while estimated collision efficiencies appeared comparable. By contrast, more positively charged P. putida experienced greater attenuation at one monitoring point, while lower attenuation rates at the second location indicated the influence of geochemical heterogeneity. Test findings proved consistent with observations from nearby fresh outcrops that suggested thin open framework gravel beds dominated mass transport in the aquifer, while discrete intervals containing stained clasts reflect localized geochemical heterogeneity. Study results highlight the utility of reconciling outcrop observations with artificial tracer test responses, using microbiological tracers with well-defined properties, to characterize aquifer heterogeneity.