Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at https://www.ucl.ac.uk/finance/research/rs-contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
A probabilistic approach for the moisture risk assessment of internally insulated solid walls
  • Publication Type:
  • Authors:
    Marincioni V
  • Date awarded:
  • Status:
  • Awarding institution:
    University College London
  • Language:
The majority of the traditional building stock in the UK is made of solid brick walls. The energy efficient retrofit of such traditional buildings is one of the key measures to fulfil the Government pledge to reduce greenhouse gas emissions by 100% by 2050. Internal wall insulation is one of the possible measures to preserve the external appearance of a building while improving the energy efficiency of its walls. However, it can lead to moisture-related risks, such as excess moisture accumulation and mould growth. This thesis presents the development of a method of risk assessment for the retrofit of solid walls using internal wall insulation, which is faster but retains its accuracy in depicting the main moisture transfer and storage mechanisms occurring in an internally insulated solid wall in the UK and considers the relevant failure criteria. First, results from in-situ monitoring helped identifying the main moisture transfer and storage mechanisms occurring in internally insulated solid walls in the UK and the associated possible failure modes. Then, failure criteria and input data were identified from literature and their suitability for the assessment of moisture risks at the existing wall-insulation interface was analysed, with a particular focus on climate files and mould prediction models. Using the information gathered, predictive models were developed based on statistical methods. Finally, the developed predictive models were used for the risk assessment of an internally insulated wall. This thesis demonstrates that a fast probabilistic risk assessment using predictive models can provide valuable information to support the specification of appropriate internal wall insulation systems for the traditional building stock in the UK, considering the uncertainty and variability of inputs and suitable failure criteria.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Bartlett School Env, Energy & Resources
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by