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Ahmad, A G B (1994) Conservation of British colonial buildings built between 1800 and 1930 in Malaysia, Unpublished PhD Thesis, , University of Sheffield.
Ahmed, A L (2019) Development of conceptual constructs for organisational BIM adoption and their systematic application within the UK architecture sector, Unpublished PhD Thesis, School of Architecture, University of Sheffield.
Al-Sedairy, S (1984) Large scale projects: management, design and execution, Unpublished PhD Thesis, School of Architecture, University of Sheffield.
Al-Wareh, M (1979) Investigation of the design procedures for buildings of quality in modern architecture, with notes on the relevant contemporary situation in Syria, Unpublished PhD Thesis, , University of Sheffield.
Andrade de Alencar Loiola, F (2014) The formulation of Public-Private Partnership projects for infrastructure development in Brazil: an institutional analysis of the Municipality of Fortaleza, Unpublished PhD Thesis, , University of Sheffield.
Belhadj, T A (1989) Computer-aided architectural evaluation and design: a cost modelling experiment, Unpublished PhD Thesis, School of Architecture, University of Sheffield.
Brocklesby, M (1999) The environmental impact of frame materials: an assessment of the embodied impacts for building frames in the UK construction industry, Unpublished PhD Thesis, Department of Civil and Structural Engineering, University of Sheffield.
Buckman, A H (2016) An exploration of the applications of increased information availability in smart buildings, Unpublished PhD Thesis, , University of Sheffield.
Butchers, A M (2004) Learning off the job: engineers and professional education, Unpublished PhD Thesis, , University of Sheffield.
Densley Tingley, D (2013) Design for deconstruction: an appraisal, Unpublished PhD Thesis, , University of Sheffield.
Donohoe, S W (2008) Can surveying and construction management undergraduate sudents' attitudes to construction law be changed by changes in teaching?, Unpublished PhD Thesis, , University of Sheffield.
Eccles, S D (2000) Quantitative evaluation of contract strategies for construction, Unpublished PhD Thesis, Department of Civil and Structural Engineering, University of Sheffield.
Fletcher, S L (2001) Developing disassembly strategies for buildings to reduce the lifetime environmental impacts by applying a systems approach, Unpublished PhD Thesis, School of Architecture, University of Sheffield.
Gillott, C (2022) Potential for the vertical extension of existing buildings, Unpublished PhD Thesis, , University of Sheffield.
Gyoh, L E (1999) Design-management and planning for photovoltaic cladding systems within the UK construction industry: An optimal and systematic approach to procurement and installation of building integrated photovoltaics: An agenda for the 21st century, Unpublished PhD Thesis, , University of Sheffield.
Hamed, O (2022) Developing a BIM-based tool to automate green buildings assessment: the case of Jordan Green Building Guide, Unpublished PhD Thesis, , University of Sheffield.
Hughes, A J (2022) On risk-based decision-making for structural health monitoring, Unpublished PhD Thesis, , University of Sheffield.
- Type: Thesis
- Keywords: bias; failure; motivation; active learning; decision framework; learning; monitoring; operation and maintenance; safety; probability; population; inspection; risk assessment
- ISBN/ISSN:
- URL: https://etheses.whiterose.ac.uk/32179/
- Abstract:
Structural health monitoring (SHM) technologies seek to detect, localise, and characterise damage present within structures and infrastructure. Arguably, the foremost incentive for developing and implementing SHM systems is to improve the quality of operation and maintenance (O&M) strategies for structures, such that safety can be enhanced, or greater economic benefits can be realised. Given this motivation, SHM systems can be considered primarily as decision-support tools. Although much research has been conducted into damage identification and characterisation approaches, there has been relatively little that has explicitly considered the decision-making applications of SHM systems. In light of this fact, the current thesis seeks to consider decision-making for SHM with respect to risk. Risk, defined as a product of probability and cost, can be interpreted as an expected utility. The keystone of the current thesis is a general framework for conducting risk-based, SHM generated by combining aspects of probabilistic risk assessment (PRA) with the existing statistical pattern recognition paradigm for SHM. The framework, founded on probabilistic graphical models (PGMs), utilises Bayesian network representations of fault-trees to facilitate the flow of information between observations of discriminative features to failure states of structures of interest. Using estimations of failure probabilities in conjunction with utility functions that capture the severity of consequences enables risk assessments -- these risks can be minimised with respect to candidate maintenance actions to determine optimal strategies. Key elements of the decision framework are examined; in particular, a physics-based methodology for initialising a structural degradation model defining health-state transition probabilities is presented. The risk-based framework allows aspects of SHM systems to be developed with explicit consideration for the decision-support applications. In relation to this aim, the current thesis proposes a novel approach to learn statistical classification models within an online SHM system. The approach adopts an active learning framework in which descriptive labels, corresponding to salient health states of a structure, are obtained via structural inspections. To account for the decision processes associated with SHM, structural inspections are mandated according to the expected value of information for data-labels. The resulting risk-based active learning algorithm is shown to yield cost-effective improvements in the performance of decision-making agents, in addition to reducing the number of manual inspections made over the course of a monitoring campaign. Characteristics of the risk-based active learning algorithm are further investigated, with particular focus on the effects of \sampling bias. Sampling bias is known to degrade decision-making performance over time, thus engineers have a vested interest in mitigating its negative effects. On this theme, two approaches are considered for improving risk-based active learning; semi-supervised learning, and discriminative classification models. Semi-supervised learning yielded mixed results, with performance being highly dependent on base distributions being representative of the underlying data. On the other hand, discriminative classifiers performed strongly across the board. It is shown that by mitigating the negative effects of sampling bias via classifier and algorithm design, decision-support systems can be enhanced, resulting in more cost-effective O&M strategies. Finally, the future of risk-based decision-making is considered. Particular attention is given to population-based structural health monitoring (PBSHM), and the management of fleets of assets. The hierarchical representation of structures used to develop the risk-based SHM framework is extended to populations of structures. Initial research into PBSHM shows promising results with respect to the transfer of information between individual structures comprising a population. The significance of these results in the context of decision-making is discussed. To summarise, by framing SHM systems as decision-support tools, risk-informed O&M strategies can be developed for structures and infrastructure such that safety is improved and costs are reduced.
Ibarra, G (2016) The meaning of 'social' in Mexican social housing: A study of housing developments in Mazatlán, Mexico, Unpublished PhD Thesis, , University of Sheffield.
Jiang, H (2019) An institutional analysis of the green housing transition in China: examining developers' capacity to deliver green housing in the Chinese housing market, Unpublished PhD Thesis, Department of Urban Studies and Planning, University of Sheffield.
Li, S (2018) Knowledge domains and skills that facilitate knowledge sharing in project management: a case study in the Chinese construction industry, Unpublished PhD Thesis, Information School, University of Sheffield.
Morland, K V (2020) Multi-level learning of a quality management routine: a UK housebuilder case study, Unpublished PhD Thesis, Management School, University of Sheffield.
Nguyen, B K (2012) Developing a framework for assessing sustainability of tall-building projects, Unpublished PhD Thesis, , University of Sheffield.
Palit, N (2017) Analysis of the project supply chains: coordination and fair allocation, Unpublished PhD Thesis, Management School, University of Sheffield.
Pirooz Far Poorang, A E (2008) Mass-constomisation: The application on design, fabrication and implementation (DFI) processes of building envelopes, Unpublished PhD Thesis, , University of Sheffield.
Price, D A (2003) Community involvement in the design of social housing, Unpublished PhD Thesis, Department of Town and Regional Planning, University of Sheffield.
Sami Kashkooli, A M (2013) A critical building lifecycle assessment framework for building designers and decision makers, Unpublished PhD Thesis, School of Architecture, University of Sheffield.
Sanusi, I E (2019) Optimal and adaptive control frameworks using reinforcement learning for time-varying dynamical systems, Unpublished PhD Thesis, Department of Automatic Control and Systems Engineering, University of Sheffield.