Abstracts – Browse Results
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Adams, J (2019) Dynamic criticality analysis of industrial assets and system, Unpublished PhD Thesis, Institute of Manufacturing, University of Cambridge.
Al Asali, M W (2020) Craft-inclusive construction: design strategies for thin-tile vaulting, Unpublished PhD Thesis, , University of Cambridge.
Anagnostopoulos, I (2018) Generating as-is BIMs of existing buildings: from planar segments to spaces, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.
Ariyachandra, M F (2021) Automating the generation of geometric information models to support digital twinning of existing rail infrastructure, Unpublished PhD Thesis, , University of Cambridge.
Bartlett, H V (2006) Understanding the implementation of sustainability principles in UK educational building projects, Unpublished PhD Thesis, Centre for Sustainable Development, University of Cambridge.
Baumgärtner, C E (2000) Collaboration between engineering consultants and their clients: characteristics of success, Unpublished PhD Thesis, , University of Cambridge.
Busic-Sontic, A (2019) Energy efficiency investments in residential buildings: does personality matter?, Unpublished PhD Thesis, , University of Cambridge.
Jimoh, I (2021) What explains the efficiency of major public project delivery in Nigeria?, Unpublished PhD Thesis, , University of Cambridge.
Jin, Y (2018) Supervised learning for back analysis of excavations in the observational method, Unpublished PhD Thesis, , University of Cambridge.
- Type: Thesis
- Keywords: excavation; monitoring; population; learning; safety
- ISBN/ISSN:
- URL: https://doi.org/10.17863/CAM.22830
- Abstract:
In the past few decades, demand for construction in underground spaces has increased dramatically in urban areas with high population densities. However, the impact of the construction of underground structures on surrounding infrastructure raises concerns since movements caused by deep excavations might damage adjacent buildings. Unfortunately, the prediction of geotechnical behaviour is difficult due to uncertainties and lack of information of on the underground environment. Therefore, to ensure safety, engineers tend to choose very conservative designs that result in requiring unnecessary material and longer construction time. The observational method, which was proposed by Peck in 1969, and formalised in Eurocode 7 in 1987, provides a way to avoid such redundancy by modifying the design based on the knowledge gathered during construction. The review process within the observational method is recognised as back analysis. Supervised learning can aid in this process, providing a systematic procedure to assess soil parameters based on monitoring data and prediction of the ground response. A probabilistic model is developed in this research to account for the uncertainties in the problem. Sequential Bayesian inference is used to update the soil parameters at each excavation stage when observations are available. The accuracy of the prediction for future stages improves at each stage. Meanwhile, the uncertainty contained in the prediction decreases, and therefore the confidence on the corresponding design also increases. Moreover, the Bayesian method integrates subjective engineering experience and objective observations in a rational and quantitative way, which enables the model to update soil parameters even when the amount of data is very limited. It also allows the use of the knowledge learnt from comparable ground conditions, which is particularly useful in the absence of site-specific information on ground conditions. Four probabilistic models are developed in this research. The first two incorporate empirical excavation design methods. These simple models are used to examine the practicality of the approach with several cases. The next two are coupled with a program called FREW, which is able to simulate the excavation process, still in a relatively simplistic way. The baseline model with simple assumptions on model error and another one is a more sophisticated model considering measurement error and spatial relationships among the observations. Their efficiency and accuracy are verified using a synthetic case and tested based on a case history from the London Crossrail project. In the end, the models are compared and their flexibility in different cases is discussed.
Konstantinou, E (2018) Vision-based construction worker task productivity monitoring, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.
Lloyd, C A (2020) Modular manufacture and construction of small nuclear power generation systems, Unpublished PhD Thesis, , University of Cambridge.
Mándoki, R (2022) The social sustainability of standardisation in the Hungarian residential building sector, Unpublished PhD Thesis, , University of Cambridge.
Montali, J (2019) Digitised engineering knowledge for prefabricated fac?ades, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.
O'Brien, S (2022) Critical infrastructure organisation management: an analysis of the transition to the Industry 4.0 era, Unpublished PhD Thesis, , University of Cambridge.
Pelenur, M (2014) Retrofitting the domestic built environment: Investigating household perspectives towards energy efficiency technologies and behaviour, Unpublished PhD Thesis, , University of Cambridge.
Robertson, B (2020) On-site installation flexibility for disruption management in modular off-site construction systems, Unpublished PhD Thesis, , University of Cambridge.
TomasÌŒevicÌ, V (2004) Developing productive relationships in the construction industry, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.
Vick, S (2018) Automated spatial progress monitoring for asphalt road construction projects, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.
Zomer, T (2021) Institutional pressures and decoupling in projects: the case of BIM Level 2 and coercive isomorphism in the UK's construction sector, Unpublished PhD Thesis, , University of Cambridge.