Data mining for lifetime prediction of metallic components

The ability to accurately predict the lifetime of building components is crucial to optimizing building design, material selection and scheduling of required maintenance. This paper discusses a number of possible data mining methods that can be applied to do the lifetime prediction of metallic components and how different sources of service life information could be integrated to form the basis of the lifetime prediction model

Prediction of fluidization behaviour and a quasi-stationary approach to drying kinetics of irregular particulate food materials

Changes in fluidization behaviour behaviour was characterised for parallelepiped particles with three aspect ratios, 1:1, 2:1 and 3:1 and spherical particles. All drying experiments were conducted at 500C and 15 % RH using a heat pump dehumidifier system. Fluidization experiments were undertaken for the bed heights of 100, 80, 60 and 40 mm and at 10 moisture content levels. Due to irregularities in shape minimum fluidisation velocity of parallelepiped particulates (potato) could not fitted to any empirical model. Also a generalized equation was used to predict minimum fluidization velocity. The modified quasi-stationary method (MQSM) has been proposed to describe drying kinetics of parallelepiped particulates at 30o C, 40o C and 50o C that dry mostly in the falling rate period in a batch type fluid bed dryer.

Teacher-child relationship quality for young children with parent reported language concerns

Previous research has demonstrated the importance of the qualities of the teacher-child relationship on children’s development. Close teacher-child relationships are especially important for children at risk. Positive relationships have been shown to have beneficial effects on children’s social and academic development (Birch & Ladd, 1997; Pianta & Stuhlman, 2004). Children with language difficulties are likely to face increased risks with regard to long term social and academic outcomes. The purpose of the current research was to gain greater understanding of the qualities of teacher-child relationships for young children with parent reported language concerns. The research analyses completed for this thesis involved the use of data from the public-access database of Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC). LSAC is a longitudinal study involving a nationally representative sample of 10,000 Australian children. Data are being collected biennially from 2004 (Wave 1 data collection) until 2010 (Wave 4 data collection). LSAC has a cross-sequential research design involving two cohorts, an infant cohort (0-1 year at age of recruitment) and a kindergarten cohort (4-5 years at age of recruitment). Two studies are reported in this thesis using data for the LSAC Kindergarten Cohort which had 4983 child participants at recruitment. Study 1 used Wave 1 data to identify the differences between teacher-child relationship qualities for children with parent reported language concerns and their peers. Children identified by parents for whom concerns were held about their receptive and expressive language, as measured by items from the Parents’ Evaluation of Developmental Status (PEDS) (Glascoe, 2000) were the target (at risk) group in the study (n = 210). A matched case control group of peers (n = 210), matched on the child characteristics of sex, age, cultural and linguistic differences (CALD), and socio-economic positioning (SEP), were the comparison group for this analysis. Teacher-child relationship quality was measured by teacher reports on the Closeness and Conflict scales from the short version of the Student-Teacher Relationship Scale (STRS) (Pianta, 2001). There were statistically significant differences in the levels of closeness and conflict between the two groups. The target group had relationships with their teachers that had lower levels of closeness and higher levels of conflict than the control group. Study 2 reports analyses that examined the stability of the qualities of the teacher-child relationships at Wave 1 (4-5 years) and the qualities of the teacher-child relationships at Wave 2 (6-7 years). This time frame crosses the period of the children’s transition to school. The study examined whether early patterns in the qualities of the teacher-child relationship for children with parent reported language concerns at Wave 1 predicted the qualities of the teacher-child relationship outcomes in the early years of formal school. The sample for this study consisted of the group of children identified with PEDS language concerns at Wave 1 who also had teacher report data at Wave 2 (n = 145). Teacher-child relationship quality at Wave 1 and Wave 2 was again measured by the STRS scales of Closeness and Conflict. Results from multiple regression models indicated that teacher-child relationship quality at Wave 1 significantly contributed to the prediction of the quality of the teacher-child relationship at Wave 2, beyond other predictor variables included in the regression models. Specifically, Wave 1 STRS Closeness scores were the most significant predictor for STRS Closeness scores at Wave 2, while Wave 1 STRS Conflict scores were the only significant predictor for Wave 2 STRS Conflict outcomes. These results indicate that the qualities of the teacher-child relationship experienced prior to school by children with parent reported language concerns remained stable across transitions into formal schooling at which time the child had a different teacher. The results of these studies provide valuable insight into the nature of teacher-child relationship quality for young children with parent reported language concerns. These children experienced teacher-child relationships of a lower quality when compared with peers and, additionally, the qualities of these relationships prior to formal schooling were predictive of the qualities of the relationships in the early years of formal schooling. This raises concerns, given the increased risks of poorer social and academic outcomes already faced by children with language difficulties, that these early teacher-child relationships have an impact on future teacher-child relationships. Results of these studies are discussed with these considerations in mind and also discussed in terms of the implications for educational theory, policy and practice.

Preliminary report on data mining techniques for life prediction

This project is an extension of a previous CRC project (220-059-B) which developed a program for life prediction of gutters in Queensland schools. A number of sources of information on service life of metallic building components were formed into databases linked to a Case-Based Reasoning Engine which extracted relevant cases from each source. In the initial software, no attempt was made to choose between the results offered or construct a case for retention in the casebase. In this phase of the project, alternative data mining techniques will be explored and evaluated. A process for selecting a unique service life prediction for each query will also be investigated. This report summarises the initial evaluation of several data mining techniques.

Final Report : learning system for life prediction of infrastructure

The project has further developed two programs for the industry partners related to service life prediction and salt deposition. The program for Queensland Department of Main Roads which predicts salt deposition on different bridge structures at any point in Queensland has been further refined by looking at more variables. It was found that the height of the bridge significantly affects the salt deposition levels only when very close to the coast. However the effect of natural cleaning of salt by rainfall was incorporated into the program. The user interface allows selection of a location in Queensland, followed by a bridge component. The program then predicts the annual salt deposition rate and rates the likely severity of the environment. The service life prediction program for the Queensland Department of Public Works has been expanded to include 10 common building components, in a variety of environments. Data mining procedures have been used to develop the program and increase the usefulness of the application. A Query Based Learning System (QBLS) has been developed which is based on a data-centric model with extensions to provide support for user interaction. The program is based on number of sources of information about the service life of building components. These include the Delphi survey, the CSIRO Holistic model and a school survey. During the project, the Holistic model was modified for each building component and databases generated for the locations of all Queensland schools. Experiments were carried out to verify and provide parameters for the modelling. These included instrumentation of a downpipe, measurements on pH and chloride levels in leaf litter, EIS measurements and chromate leaching from Colorbond materials and dose tests to measure corrosion rates of new materials. A further database was also generated for inclusion in the program through a large school survey. Over 30 schools in a range of environments from tropical coastal to temperate inland were visited and the condition of the building components rated on a scale of 0-5. The data was analysed and used to calculate an average service life for each component/material combination in the environments, where sufficient examples were available.

Report : stochastic analysis of road asset data for maintenance cost prediction

Reliable budget/cost estimates for road maintenance and rehabilitation are subjected to uncertainties and variability in road asset condition and characteristics of road users. The CRC CI research project 2003-029-C ‘Maintenance Cost Prediction for Road’ developed a method for assessing variation and reliability in budget/cost estimates for road maintenance and rehabilitation. The method is based on probability-based reliable theory and statistical method. The next stage of the current project is to apply the developed method to predict maintenance/rehabilitation budgets/costs of large networks for strategic investment. The first task is to assess the variability of road data. This report presents initial results of the analysis in assessing the variability of road data. A case study of the analysis for dry non reactive soil is presented to demonstrate the concept in analysing the variability of road data for large road networks. In assessing the variability of road data, large road networks were categorised into categories with common characteristics according to soil and climatic conditions, pavement conditions, pavement types, surface types and annual average daily traffic. The probability distributions, statistical means, and standard deviation values of asset conditions and annual average daily traffic for each type were quantified. The probability distributions and the statistical information obtained in this analysis will be used to asset the variation and reliability in budget/cost estimates in later stage. Generally, we usually used mean values of asset data of each category as input values for investment analysis. The variability of asset data in each category is not taken into account. This analysis method demonstrated that it can be used for practical application taking into account the variability of road data in analysing large road networks for maintenance/rehabilitation investment analysis.

The user query based learning system for lifetime prediction of metallic components

Real-World Data Mining Applications generally do not end up with the creation of the models. The use of the model is the final purpose especially in prediction tasks. The problem arises when the model is built based on much more information than that the user can provide in using the model. As a result, the performance of model reduces drastically due to many missing attributes values. This paper develops a new learning system framework, called as User Query Based Learning System (UQBLS), for building data mining models best suitable for users use. We demonstrate its deployment in a real-world application of the lifetime prediction of metallic components in buildings

Bayesian model averaging for harmful algal bloom prediction

Harmful Algal Blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian Model Averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with Temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate average monthly minimum temperature showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilised the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.

Prediction of highway truck conveyance configuration under over-limit prohibition and conveyance equilibrium

Aiming at the shortage of prevailing prediction methods about highway truck conveyance configuration in over-limit freight research that transferring the goods attributed to over-limit portion to another fully loaded truck of the same configuration and developing the truck traffic volume synchronously, a new way to get accumulated probability function of truck power tonnage in basal year by highway truck classified by wheel and axle type load mass spectrum investigation was presented. Logit models were used to forecast overall highway freight diversion and single cargo tonnage diversion when the weight rules and strict of enforcement intensity of overload were changed in scheme year. Assumption that the probability distribution of single truck loadage should be consistent with the probability distribution of single goods freighted, the model describes the truck conveyance configuration in the future under strict over-limit prohibition. The model was used and tested in Highway Over-limit Research Project in Anhui by World Bank.