Special Issue: Real World Application of SHM in Australia

Australian Network of Structural Health Monitoring (ANSHM) was established in 2009 to promote and advance the field of SHM in Australia and the association has grown considerably since then. By November of 2018, ANSHM has the membership made of 45 organisations including 20 universities, 16 private companies, 6 road authorities and 3 research institutions. Every year ANSHM organises an annual workshop and/or conference sessions for members to exchange their research and practical developments in SHM. One edited book and nine journal special issues have been produced since the establishment of ANSHM. One of these special issues was organised in Structural Health Monitoring - an International Journal (SHMIJ) in 2014. On 6–7 December 2017, ANSHM held its 9th annual workshop as part of the prestigious 8th International Conference on Structural Health Monitoring of Intelligent Infrastructures (SHMII-8) in Brisbane, Queensland, Australia. The main focus of both SHMII-8 and the 9th ANSHM workshop was SHM in real-world application. Interestingly, all sessions of SHMII-8 and ANSHM workshop were held within the P block building at Gardens Point Campus of Queensland University of Technology (QUT) that was instrumented with Australia's first ever long-term full-scale SHM system. Inspired by this theme and high-quality presentations at the workshop, a special issue named 'Real World Application of SHM in Australia' was established in SHMIJ and the 9th ANSHM workshop speakers were invited to submit enhanced and extended versions of their papers to this Special Issue. After rigorous pre-screening, peer review and revision processes, fourteen papers were accepted for inclusion in the Special Issue. The contributions include deterioration assessment of the instrumented P block building at QUT using hybrid model updating and long-term vibration monitoring data, reliability-based load-carrying capacity assessment of bridges using SHM and non-linear analysis, and innovative vibration based damage identification methods with applications to cable-stayed, steel-truss or timber bridges as well as to frame, utility-pole or building structures. The Special Issue also includes new research on non-destructive evaluation of (i) incipient pitting corrosion in reinforced concrete structures, (ii) gaps between carbon fibre reinforced polymer composite and concrete surfaces, (iii) fatigue cracks in pipes, (iv) bolted joints, and (v) in-situ stress. Most studies were verified on real civil structures or large-scale laboratory models well reflecting the high applicability of the developed methods to solve real-world problems. As the guest editors of this Special Issue, we thank the authors for their contribution and all the anonymous reviewers who provided constructive review comments to the manuscripts submitted to this Special Issue. We would also like to express our sincere gratitude to the Managing Editor Professor Michael Todd and the journal executive committee for their support and assistance during the submission and review process. Finally, we would like to thank the SAGE Publications team for their diligence in assuring the efficient and timely production of the papers toward the publication of this Special Issue.

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[10]  Yang Yu,et al.  Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory , 2018, Structural Health Monitoring.

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[14]  Bijan Samali,et al.  Characterization of carbon fiber reinforced polymer strengthened concrete and gap detection with a piezoelectric-based sensory technique , 2018, Structural Health Monitoring.

[15]  Yang Yu,et al.  A novel deep learning-based method for damage identification of smart building structures , 2018, Structural Health Monitoring.

[16]  Tommy H.T. Chan,et al.  Deterioration assessment of buildings using an improved hybrid model updating approach and long-term health monitoring data , 2018, Structural Health Monitoring.

[17]  Tommy H.T. Chan,et al.  Damage identification in a complex truss structure using modal characteristics correlation method and sensitivity-weighted search space , 2018, Structural Health Monitoring.