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.

Stochasticity, periodicity and localized light structures in partially mode-locked fibre lasers

Physical systems with co-existence and interplay of processes featuring distinct spatio-temporal scales are found in various research areas ranging from studies of brain activity to astrophysics. The complexity of such systems makes their theoretical and experimental analysis technically and conceptually challenging. Here, we discovered that while radiation of partially mode-locked fibre lasers is stochastic and intermittent on a short time scale, it exhibits non-trivial periodicity and long-scale correlations over slow evolution from one round-trip to another. A new technique for evolution mapping of intensity autocorrelation function has enabled us to reveal a variety of localized spatio-temporal structures and to experimentally study their symbiotic co-existence with stochastic radiation. Real-time characterization of dynamical spatio-temporal regimes of laser operation is set to bring new insights into rich underlying nonlinear physics of practical active- and passive-cavity photonic systems.

Slow light optofluidics:a proposal

The resonant slow light structures created along a thin-walled optical capillary by nanoscale deformation of its surface can perform comprehensive simultaneous detection and manipulation of microfluidic components. This concept is illustrated with a model of a 0.5 mm long, 5 nm high, triangular bottle resonator created at a 50 μm radius silica capillary containing floating microparticles. The developed theory shows that the microparticle positions can be determined from the bottle resonator spectrum. In addition, the microparticles can be driven and simultaneously positioned at predetermined locations by the localized electromagnetic field created by the optimized superposition of eigenstates of this resonator, thus exhibiting a multicomponent, near-field optical tweezer.

Optimisation of viscoelastic treatments using genetic algorithms

Viscoelastic treatments are one of the most efficient treatments, as far as passive damping is concerned, particularly in the case of thin and light structures. In this type of treatment, part of the strain energy generated in the viscoelastic material is dissipated to the surroundings, in the form of heat. A layer of viscoelastic material is applied to a structure in an unconstrained or constrained configuration, the latter proving to be the most efficient arrangement. This is due to the fact that the relative movement of both the host and constraining layers cause the viscoelastic material to be subjected to a relatively high strain energy. There are studies, however, that claim that the partial application of the viscoelastic material is just as efficient, in terms of economic costs or any other form of treatment application costs. The application of patches of material in specific and selected areas of the structure, thus minimising the extension of damping material, results in an equally efficient treatment. Since the damping mechanism of a viscoelastic material is based on the dissipation of part of the strain energy, the efficiency of the partial treatment can be correlated to the modal strain energy of the structure. Even though the results obtained with this approach in various studies are considered very satisfactory, an optimisation procedure is deemed necessary. In order to obtain optimum solutions, however, time consuming numerical simulations are required. The optimisation process to use the minimum amount of viscoelastic material is based on an evolutionary geometry re-design and calculation of the modal damping, making this procedure computationally costly. To avert this disadvantage, this study uses adaptive layerwise finite elements and applies Genetic Algorithms in the optimisation process.