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Structural health monitoring has been increasingly used due to the advances in sensing technology and data analysis, facilitating the shift from time-based to condition-based maintenance. This work is part of the efforts which have applied structural health monitoring to the Sydney Harbour Bridge -one of Australia's iconic structures. It combines dimensionality reduction and pattern recognition techniques to accurately and efficiently distinguish faulty components from wellfunctioning ones. Specifically, random projection is used for dimensionality reduction on the vibration feature data. Then, healthy and damaged patterns of bridge components are learned in the lower dimensional projected space using supervised and unsupervised machine learning methods, namely, support vector machine and one-class support vector machine. The experimental results using data from a laboratory-based building structure and the Sydney Harbour Bridge showed high feasibility of applying machine learning techniques to dimensionality reduction and damage detection in structural health monitoring. Random projection combined with support vector machine significantly reduces the computational time while maintaining the detection accuracy. The proposed method also outperformed popular dimensionality reduction techniques. The computational time of the method using random projection can be more than 200 times faster than that without using dimensionality reduction while still achieving similar detection accuracy.

Prediction of water pipe condition through statistical modelling is an important element for the risk management strategy of water distribution systems. In this work a hierarchical nonparametric model has been used to enhance the performance of pipe condition assessment. The main aims of this work are threefold: (1) For sparse incident data, develop an efficient approximate inference algorithm based on hierarchical beta process. (2) Apply the hierarchical beta process based method to water pipe condition assessment. (3) Interpret the outcomes in financial terms usable by the water utilities. The experimental results show superior performance of the proposed method compared to current best practice methods, leading to substantial savings on reactive repairs and maintenance, as well as improved prioritization for capital expenditure.

Plant viruses and entomopathogenic fungi (EPF) can both elicit immune responses in insects. This study was designed to clarify whether plant viruses could affect the efficacy of EPF and explore the immune responses of brown planthopper (BPH), Nilaparvata lugens, in response to different pathogen infections. In this study, a strain of Metarhizium anisopliae YTTR with high pathogenicity against BPH was selected and explored whether rice ragged stunt virus (RRSV) could affect its lethality against BPH. RNA-seq was used to detect the inner responses of BPH in response to RRSV and M. anisopliae YTTR infection. Results showed that M. anisopliae YTTR has strong lethality against BPH (RRSV-carrying and RRSV-free). RRSV invasion did not affect the susceptibility of BPH against M. anisopliae YTTR at all concentrations. At 1 × 108 spores/mL, M. anisopliae YTTR caused a cumulative mortality of 80% to BPH at 7 days post-treatment. The largest numbers of differentially expressed genes (DEGs) was obtained in BPH treated with the two pathogens than in other single pathogen treatment. In addition, KEGG enrichment analysis showed that the DEGs were mostly enriched in immune and physiological mechanisms-related pathways. Both RRSV and M. anisopliae YTTR could induce the expression changes of immune-related genes. However, most of the immune genes had varying expression patterns in different treatment. Our findings demonstrated that RRSV invasion did not have any significant effect on the pathogenicity of M. anisopliae YTTR, while the co-infection of M. anisopliae YTTR and RRSV induced more immune and physiological mechanisms -related genes’ responses. In addition, the presence of RRSV could render the interplay between BPH and M. anisopliae YTTR more intricate. These findings laid a basis for further elucidating the immune response mechanisms of RRSV-mediated BPH to M. anisopliae infection.

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