While the transfer equations for moisture and heat in building components are currently undergoing standardisation, atmospheric boundary conditions, conservative modelling and numerical efficiency are not addressed. In a first part, this paper adds a comprehensive description of those boundary conditions, emphasising wind-driven rain and vapour exchange, the main moisture supply and removal mechanism, respectively. In the second part the numerical implementation is tackled, with specific attention to the monotony of the spatial discretisation, and to the mass and energy conservation of the temporal discretisation. Both issues are illustrated with exemplary hygrothermal simulations. Numerical efficiency is treated in two follow-up papers.

Monte Carlo analysis has become nearly ubiquitous since its introduction, now over 65 years ago. It is an important tool in many assessments of the reliability and robustness of systems, structures or solutions. As the deterministic core simulation can be lengthy, the computational costs of Monte Carlo can be a limiting factor. To reduce that computational expense as much as possible, sampling efficiency and convergence for Monte Carlo are investigated in this paper. The first section shows that non-collapsing spacefilling sampling strategies, illustrated here with the maximin and uniform Latin hypercube designs, highly enhance the sampling efficiency, and render a desired level of accuracy of the outcomes attainable with far lesser runs. In the second section it is demonstrated that standard sampling statistics are inapplicable for Latin hypercube strategies. A sample-splitting approach is put forward, which in combination with a replicated Latin hypercube sampling allows assessing the accuracy of Monte Carlo outcomes. The assessment in turn permits halting the Monte Carlo simulation when the desired levels of accuracy are reached. Both measures form fairly noncomplex upgrades of the current state-of-the-art in Monte-Carlo based uncertainty analysis but give a substantial further progress with respect to its applicability. KEYWORDSMonte Carlo, uncertainty analysis, space-filling Latin hypercube, sampling efficiency, sampling convergence, sample-splitting HIGHLIGHTS  Monte Carlo is virtually universal, but computational expense is important barrier  A good sampling strategy and convergence assessment will improve applicability  Space-filling Latin hypercube designs are most efficient, should be generally used  Sample-splitting on replicated Latin hypercube designs allows assessing accuracy  Both measures are undemanding upgrades, but substantially boost the applicability Postprint: Janssen H. 2013. Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence, Reliability Engineering & System Safety,

Interior insulation is often the only possible post-insulation technique to improve the thermal performance of single leaf masonry walls. As a result of potential damage patterns such as frost damage, interstitial condensation and mould growth however, there is often some reluctance to adopt this technique. To fully exploit the capacity for energy savings offered by interior insulation while avoiding hygrothermal failure, a reliable risk assessment is extremely important. This requires a probabilistic Postprint: Vereecken E, Van Gelder L, Janssen H, Roels S, 2015. Interior insulation for wall retrofitting -A probabilistic analysis of energy savings and hygrothermal risks. Energy and Buildings 89: 231-244. doi.org/10.1016/j.enbuild.2014.12.031 approach, since the uncertainty of all influencing parameters might result in widely varying results.As, so far, no real methodology is available to select the interior insulation system and thickness resulting in the best balance between energy savings and hygrothermal risks, this paper presents a decision tool based on a Monte Carlo analysis. Additionally, the influence of the rain load and some masonry characteristics is discussed. In the study, both vapour tight interior insulation systems and a capillary active insulation system are considered. Overall, vapour tight systems tend to be preferable for structures that are resistant to frost damage. For buildings sensitive to frost damage or when wooden beam ends are present, however, capillary active systems are shifted forward.

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This paper presents an experimental and quantitative analysis of capillary transport across the interface brick-mortar joint in masonry. Moisture profiles are measured with X-ray projection. The influence of curing conditions is analyzed by considering three types of mortars: cured in a mould, between capillary wet and dry bricks. A decrease in moisture inflow for the mortars cured between bricks is measured. The pore structure and the moisture transport properties of mortar change significantly due to water extraction from the initially wet mortar to the bricks during curing. Numerical simulations reveal the existence of a hydraulic interface resistance between brick and wet/dry cured mortar.

a b s t r a c tComputer simulation of real system behaviour is increasingly used in research and development. As simulation models become more reliable, they also often become more complex to capture the progressive complexity of the real system. Calculation time can be a limiting factor for using simulation models in optimisation studies, for example, which generally require multiple simulations. Instead of using these time-consuming simulation models, the use of metamodels can be considered. A metamodel approximates the original simulation model with high confidence via a simplified mathematical model. A series of simulations then only takes a fraction of the original simulation time, hence allowing significant computational savings.In this paper, a strategy that is both reliable and time-efficient is provided in order to guide users in their metamodelling problems. Furthermore, polynomial regression (PR), multivariate adaptive regression splines (MARS), kriging (KR), radial basis function networks (RBF), and neural networks (NN) are compared on a building energy simulation problem. We find that for the outputs of this example and based on Root Mean Squared Error (RMSE), coefficient of determination (R 2 ), and Maximal Absolute Error (MAE), KR and NN are the overall best techniques. Although MARS perform slightly worse than KR and NN, it is preferred because of its simplicity. For different applications, other techniques might be optimal.

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