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Due to the rising prices of houses and rent, apartment buildings have become the preferred and most utilized dwellings in Australia. However, there have been cases of serious defects in multi-unit dwellings (apartment buildings), posing various risks to and reducing potential residents' confidence in such dwellings. To address this issue, the current study investigated the types, causes, risk severity, and impacts of defects on residents in mid- to high-rise multi-unit dwellings in Australia. The study utilized a mixed approach involving quantitative (online questionnaire) and qualitative (interviews) methods. Data were collected from 104 apartment building residents in Brisbane, Queensland, Australia, with support from interviews with six experienced local construction professionals. The results revealed that there are 11 key defects present in local apartment buildings, with waterproofing, internal finishes, and structural issues being the most frequently reported. Residents typically report these defects to building owners, agents, and managers, who take more than three months, on average, to rectify them, adding to the residents' psychological issues and their inability to use the property as intended. The reasons for the delayed rectification of defects include non-responsive owners, agents, and building management teams, scheduling conflicts, litigation, and high costs. The interviewed professionals acknowledged the presence of defects and identified the reliance on Design and Construct (D&C) and fixed price contracts, lack of public awareness, and absence of a system to capture builders' reputation, as well as a manipulative quality check system, as key reasons for defects. This study addresses the defect concerns in multi-unit Australian dwellings (apartments) and expects to spark a much-needed debate around reforms in the construction sector to address these issues and minimize their risks and impact on residents. [ABSTRACT FROM AUTHOR]

Rapid and widespread monitoring of inland and coastal water quality occurs through the use of remote sensing and near-surface water quality sensors. A new addition is the development of smartphone applications (Apps) to measure and record surface reflectance, water color and water quality parameters. In this paper, we present a field study of the HydroColor (HC, measures RGB reflectance and suspended particulate matter (SPM)) and EyeOnWater (EoW, determines the Forel–Ule scale—an indication to the visual appearance of the water surface) smartphone Apps to evaluate water quality for inland waters in Eastern Australia. The Brisbane river, multiple lakes and reservoirs and lagoons in Queensland and New South Wales were visited; hyperspectral reflection spectra were collected and water samples were analysed in the laboratory as reference. Based on detailed measurements at 32 sites, covering inland waters with a large range in sediment and algal concentrations, we find that both water quality Apps are close, but not quite on par with scientific spectrometers. EoW is a robust application that manages to capture the color of water with accuracy and precision. HC has great potential, but is influenced by errors in the observational procedure and errors in the processing of images in the iPhone. The results show that repeated observations help to reduce the effects of outliers, while implementation of camera response functions and processing should help to reduce systematic errors. For both Apps, no universal conversion to water quality composition is established, and we conclude that: (1) replicated measurements are useful; (2) color is a reliable monitoring parameter in its own right but it should not be used for other water quality variables, and; (3) tailored algorithms to convert reflectance and color to composition could be developed for lakes individually. [ABSTRACT FROM AUTHOR]

Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, sediment transport index (STI), and slope played the most important roles, whereas stream power index (SPI) did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods. [ABSTRACT FROM AUTHOR]