The prediction of damage condition in regards to damage factor influence of light structures on expansive soils in Victoria, Australia

This paper proposes a neural network model using genetic algorithm for a model for the prediction of the damage condition of existing light structures founded in expansive soils in Victoria, Australia. It also accounts for both individual effects and interactive effects of the damage factors influencing the deterioration of light structures. A Neural Network Model was chosen because it can deal with 'noisy' data while a Genetic Algorithm was chosen because it does not get `trapped' in local optimum like other gradient descent methods. The results obtained were promising and indicate that a Neural Network Model trained using a Genetic Algorithm has the ability to develop an interactive relationship and a Predicted Damage Conditions Model.

The ranking of factors influencing the behaviour of light structures on expansive soils in Victoria, Australia

In this paper a Neural Network Model was used to develop a ranking of the potential damage influences for light structures on expansive soils in Victoria. These influences include geology, Thornthwaite moisture index, vegetation covers, construction foundation type, construction wall type, geographical region and age of building when first inspected. Approximately 400 cases of damage to light structures in Victoria, Australia were considered in this study. Feedforward Backpropagation was adopted to train the data. The ranking of importance was estimated using connection weight approach and then compared to results calculated from sensitivity analysis. From the analysis, the ranking of importance for potential damage factor was noted.

An artificial intelligence examination of the influence of geological conditions and changes in climate on damage to light structures in Victoria

Damage to light structures in the state of Victoria can be caused by movements of expansive soils. The presentation will present the results of an examination of reports of increasing complaints of house damage in Victoria and particularly in the Melbourne area. The examination analyses the influence of geology and change in climate using Neural Network and Genetic Algorithm approaches and assesses their relative importance in contributing to the cause of the damage.

Data mining techniques for the assessment of factors contributing to the damage of residential houses in Australia

This paper reports on the preparation and management processes of inconsistent data on damage on residential houses in Victoria, Australia. There are no existing specific and fully relevant databases readily available except for the incomplete paper-based and electronic-based reports. Therefore, the extracting of information from the reports is complicated and time consuming in order to extract and include all the necessary information needed for analysis of damage on residential houses founded on expansive soils. Data mining is adopted to develop a database. Statistical methods and Artificial Intelligence methods are used to quantify the quality of data. The paper concludes that the development of such database could enable BHC to evaluate the usefulness of the reports prepared on the reported damage properties for further analysis.

Predicting house damage class using artificial intelligence method

The unsatisfactory performance of light structures founded on expansive soils subject to seasonal movements is frequently reported since the early 1950's in Australia. Excessive movements have caused damage to numerous structures that have not been adequately designed to accommodate soil volume changes. However, the sole presence of expansive soil is not necessarily the main cause of damage. Other factors such as vegetation, climate factors, types of construction materials and geology type may also contribute. This paper presents a model which predicts the damage class by analyzing combinations of the contributing factors using artificial intelligence methods. This model can help to identify if any serious and urgent repairs are necessary and immediate actions could be initiated without delay.

Modelling solute transport in structured soils: performance evaluation of the ADR and TRM models

The movement of chemicals through the soil to the groundwater or discharged to surface waters represents a degradation of these resources. In many cases, serious human and stock health implications are associated with this form of pollution. The chemicals of interest include nutrients, pesticides, salts, and industrial wastes. Recent studies have shown that current models and methods do not adequately describe the leaching of nutrients through soil, often underestimating the risk of groundwater contamination by surface-applied chemicals and overestimating the concentration of resident solutes. This inaccuracy results primarily from ignoring soil structure and nonequilibrium between soil constituents, water, and solutes. A multiple sample percolation system (MSPS), consisting of 25 individual collection wells, was constructed to study the effects of localized soil heterogeneities on the transport of nutrients (NO−3, Cl−, PO3−4) in the vadose zone of an agricultural soil predominantly dominated by clay. Very significant variations in drainage patterns across a small spatial scale were observed (one-way ANOVA, p < 0.001 indicating considerable heterogeneity in water flow patterns and nutrient leaching. Using data collected from the multiple sample percolation experiments, this paper compares the performance of two mathematical models for predicting solute transport, the advective-dispersion model with a reaction term (ADR), and a two-region preferential flow model (TRM) suitable for modelling nonequilibrium transport. These results have implications for modelling solute transport and predicting nutrient loading on a larger scale.

Spatial and temporal distribution of solute leaching in heterogeneous soils: analysis and application to multisampler lysimeter data

Accurate assessment of the fate of salts, nutrients, and pollutants in natural, heterogeneous soils requires a proper quantification of both spatial and temporal solute spreading during solute movement. The number of experiments with multisampler devices that measure solute leaching as a function of space and time is increasing. The breakthrough curve (BTC) can characterize the temporal aspect of solute leaching, and recently the spatial solute distribution curve (SSDC) was introduced to describe the spatial solute distribution. We combined and extended both concepts to develop a tool for the comprehensive analysis of the full spatio-temporal behavior of solute leaching. The sampling locations are ranked in order of descending amount of total leaching (defined as the cumulative leaching from an individual compartment at the end of the experiment), thus collapsing both spatial axes of the sampling plane into one. The leaching process can then be described by a curved surface that is a function of the single spatial coordinate and time. This leaching surface is scaled to integrate to unity, and termed S can efficiently represent data from multisampler solute transport experiments or simulation results from multidimensional solute transport models. The mathematical relationships between the scaled leaching surface S, the BTC, and the SSDC are established. Any desired characteristic of the leaching process can be derived from S. The analysis was applied to a chloride leaching experiment on a lysimeter with 300 drainage compartments of 25 cm2 each. The sandy soil monolith in the lysimeter exhibited fingered flow in the water-repellent top layer. The observed S demonstrated the absence of a sharp separation between fingers and dry areas, owing to diverging flow in the wettable soil below the fingers. Times-to-peak, maximum solute fluxes, and total leaching varied more in high-leaching than in low-leaching compartments. This suggests a stochastic–convective transport process in the high-flow streamtubes, while convection–dispersion is predominant in the low-flow areas. S can be viewed as a bivariate probability density function. Its marginal distributions are the BTC of all sampling locations combined, and the SSDC of cumulative solute leaching at the end of the experiment. The observed S cannot be represented by assuming complete independence between its marginal distributions, indicating that S contains information about the leaching process that cannot be derived from the combination of the BTC and the SSDC.

Removal of arsenic from contaminated soils using different salt extractants

This study presents an environmental-friendly and cost effective method for the extraction of arsenic from contaminated soils.
Laboratory experiments using inorganic salts, potassium phosphate (KH2PO4), potassium chloride (KCl), potassium nitrate (KNO3), potassium sulfate (K2SO4), and sodium perchlorate (NaClO4) were evaluated as arsenic extractants. An Andosol soil was artificially contaminated with arsenite [As(III)] and arsenate [As(V)]. The soil was washed in a batch process with different salt solutions in the pH range 3–11 for 24 hours at 20◦C. Among the various potassium and sodium salts tested, KH2PO4 was found to be highly effective in extracting arsenic from As(III)-soil attaining more than 80% and 40% from As(V)-soil in neutral pH range. Other salts were particularly ineffective in extraction of arsenic from both soils. More arsenic was extracted more from the As(III)-soil than the As(V)-soil.

Genotoxicity assessment of soils from wastewater irrigation areas and bioremediation sites using the Vicia faba root tip micronucleus assay

Genotoxicity potential of soils taken from wastewater irrigation areas and bioremediation sites was assessed using the Vicia faba root tip micronucleus assay. Twenty five soils were tested, of which 8 were uncontaminated soils and taken as the control to examine the influence of soil properties; 6 soils were obtained from paddy rice fields with a history of long-term wastewater irrigation; 6 soils were obtained from bioremediation sites to examine effects of bioremediation; and 5 PAH-contaminated soils were used to examine methodological effects between direct soil exposure and exposure to aqueous soil extracts on micronuclei (MN) frequency () in the V. faba root tips. Results indicate that soil properties had no significant influences on MN frequencies (p > 0.05) when soil pH varied between 3.4 to 7.6 and organic carbon between 0.4% and 18.6%. The MN frequency measured in these control soils ranged from 1.6‰ to 5.8‰. MN frequencies in soils from wastewater irrigation areas showed 2- to 48-fold increase as compared with the control. Soils from bioremediation sites showed a mixed picture: MN frequencies in some soils decreased after bioremediation, possibly due to detoxification; whereas in other cases remediated soils induced higher MN frequencies, suggesting that genotoxic substances might be produced during bioremediation. Exposure to aqueous soil extracts gave a higher MN frequency than direct exposure in 3 soils. However, the opposite was observed in the other two soils, suggesting that both exposure routes should be tested in case of negative results from one route. Data obtained from this study indicate that the MN assay is a sensitive assay suitable for evaluating genotoxicity of soils.