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.

The influence of climate change on Thorthwaite moisture index on the design of light structures

Significant long term changes in the earth’s climate have occurred in the past but recently there has been more severe climate fluctuation than have occurred in the past few centuries. The effect of this climate change on the foundation conditions of roads and low-rise buildings is costing several hundred billion dollars world-wide. A method which tracks this climate change will be of great value for companies and governments. C.W. Thornthwaite (1948) defined the Thornthwaite Moisture Index (TMI) as the first base for his climate classification system and mapping in the United States. There are 3 important factors to predict ground movement: (a) the degree of moisture index change (b) the depth at which this change occurs and (c) the foundation soil type. The water budget model was used by Thornthwaite (1948) to calculate the moisture index. This paper also discusses two typical examples of the use of this model. Originally TMI’s were mainly used to map soil moisture conditions for agriculture but soon became a method to predict environmental and pavement foundation changes.

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.

Assembly of 3-D network structures containing oxydiacetate and CeIII or CeIV

Reaction of CeCl₃·7H₂O with Na₂(oda) (oda = O(CH₂CO₂)₂²— oxydiacetate) in a 2:3 ratio gives the neutral cerium(III) complex [Ce₂(oda)₃(H₂O)₃]·9H₂O (1). Treatment of a 1:3 mixture of CeCl₃·7H₂O and H₂oda in water with 4 molar equivalents of NaOH also gives 1 but, with a larger excess of NaOH, the tri-sodium salt Na₃[Ce(oda)₃]·9H₂O (2) is isolated. Formation of a tri-ammonium analogue of 2 can be achieved by neutralisation of an aqueous solution of CeCl₃·7H₂O and H₂(oda) in a 1:3 ratio by NH₄OH, giving (NH₄)₃[Ce(oda)₃]·7H₂O (3). Use of the cerium(IV) reagent (NH₄)₂[Ce(NO₃)₆] with Na₂(oda) results in reduction to cerium(III) under ambient conditions and isolation of 1. However, in the absence of light this reaction yields crystals of the novel cerium(IV) heterobimetallic [Ce(oda)₃Na₄(NO₃)₂] (4). Each of these complexes exhibit a 3-D network structure having a common nine-coordinate [Ce(oda)₃]ⁿ— (n = 2 or 3) subunit, irrespective of the oxidation state of cerium. In 1, six [Ce(oda)₃]³— anions are connected, through bridging bidentate carboxylates, to a second Ce³⁺ site further coordinated by three water molecules. In contrast, the ammonium salt 2, displays isolated [Ce(oda)₃]³— anions, devoid of further carboxylate bonding, but enmeshed within a network of hydrogen-bonded NH₄⁺ cations and water molecules. The remarkable structure of 4 consists of infinite 2-D sheets of [Na₂(NO₃)]+ pillared by [Ce(oda)₃]^2— units, the connectivity arising by multidentate nitrate and carboxylate bridging.

Cooperative and competitive structures of trust relations in teams

This article argues that it is not just trust-generating but also trust-inhibiting mechanisms that operate in teams, and that these cooperative and competitive structures of interpersonal relations of trust within teams may affect team performance. Specifically, we propose that the presence of trust-generating structures (e.g., reciprocity, trusting in the referrals of others we trust, trusting in high performers and more experienced people) and the absence of trust-inhibiting structures (e.g., not trusting in the referrals of others we trust) are more likely to be associated with successful teams. Using exponential random graph models, a particular class of statistical model for social networks, we examine three professional sporting teams from the Australian Football League for the presence and absence of these mechanisms of interpersonal relations of trust. Quantitative network results indicate a differential presence of these postulated structures of trust relations in line with our hypotheses. Qualitative comparisons of these quantitative findings with team performance measures suggest a link between trust-generating and trust-inhibiting mechanisms of trust and team performance. Further theorization on other trust-inhibiting structures of trust relations and related empirical work is likely to shed further light on these connections.