Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

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.

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.

Re-inoculation of autoclaved soil as a non-sterile treatment for xenobiotic sorption and biodegradation studies

Autoclaved soil is commonly used for the study of xenobiotic sorption and as an abiotic control in biodegradation experiments. Autoclaving has been reported to alter soil physico-chemical and xenobiotic sorption characteristics such that comparison of autoclaved with non-autoclaved treatments in soil aging and bioavailability studies may yield misleading results. Experiments could be improved by using autoclaved soil re-inoculated with indigenous microorganisms as an additional or alternative non-sterile treatment for comparison with the sterile, autoclaved control. We examined the effect of autoclaving (3 x 1 h, 121°C, 103.5 KPa) on the physico-chemical properties of a silt loam soil (pH 7.2, 2.3% organic carbon) and the establishment of indigenous microorganisms reintroduced after autoclaving. Sterilisation by autoclaving significantly (p ≤ 0.05) decreased pH (0.6 of a unit) and increased concentrations of water-soluble organic carbon (WSOC; nontreated = 75 mg kg^-1; autoclaved = 1526 mg kg^-1). The initial first-order rate of ¹⁴C-2,4-dichloro-UL-phenol (2,4-DCP) adsorption to non-treated, autoclaved and re-inoculated soil was rapid (K₁ = 16.8-24.4 h^-1) followed by a slower linear phase (K₂). In comparison with autoclaved soil (0.038% day^-1), K₂ values were higher for re-inoculated (0.095% day^-1) and nontreated (0.181% day^-1) soil. This was attributed to a biological process. The Freundlich adsorption coefficient (K(f)) for autoclaved soil was significantly (p ≤ 0.05) higher than for re-inoculated or non-treated soil. Increased adsorption was attributed to autoclaving-induced changes to soil pH and solution composition. Glucose-induced respiration of autoclaved soil after re-inoculation was initially twice that in the non-treated control, but it decreased to control levels by day 4. This reduction corresponded to a depletion of WSOC. 2,4-DCP mineralisation experiments revealed that the inoculum of nonsterile soil (0.5 g) contained 2,4-DCP-degrading microorganisms capable of survival in autoclaved soil. The lag phase before detection of significant 2,4-DCP mineralisation was reduced (from 7 days to ≤3 days) by pre-incubation of re-inoculated soils for 7 and 14 days before 2,4-DCP addition. This was attributed to the preferential utilisation of WSOC prior to the onset of 2,4-DCP mineralisation. Cumulative ¹⁴CO₂ evolved after 21 days was significantly lower (p ≤ 0.05) from non-treated soil (25.3%) than re-inoculated soils (ca 45%). Experiments investigating sorption-biodegradation interactions of xenobiotics in soil require the physico-chemical properties of sterile and non-sterile treatments to be as comparable as possible. For fundamental studies, we suggest using re-inoculated autoclaved soil as an additional or alternative non-sterile treatment.

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.