Traditional building energy consumption calculation methods are characterised by rough approaches providing approximate figures with high and unknown levels of uncertainty. Lack of reliable energy resources and increasing concerns about climate change call for improved predictive tools.A new approach for the prediction of building energy consumption is presented. The approach quantifies the uncertainty of building energy consumption by means of stochastic differential equations. The approach is applied to a general heat balance for an arbitrary number of loads and zones in a building to determine the dynamic thermal response under random conditions. Two test cases are presented.The approach is found to work well, although computation time may be rather high. The results indicate that the impact of a stochastic description compared with a deterministic description may be modest for the dynamic thermal behaviour of buildings. However, for air flow and energy consumption it is found to be much more significant due to less "damping".Probabilistic methods establish a new approach to the prediction of building energy consumption, enabling designers to include stochastic parameters like inhabitant behaviour, operation, and maintenance to predict the performance of the systems and the level of certainty for fulfilling design requirements under random conditions.
On flexible structures such as footbridges and long-span floors, walking loads may generate excessive structural vibrations and serviceability problems. The problem is increasing because of the growing tendency to employ long spans in structural design. In many design codes, the vibration serviceability limit state is assessed using a walking load model in which the walking parameters are modelled deterministically. However, the walking parameters are stochastic (for instance the weight of the pedestrian is not likely to be the same for every footbridge crossing), and a natural way forward is to employ a stochastic load model accounting for mean values and standard deviations for the walking load parameters, and to use this as a basis for estimation of structural response. This, however, requires decisions to be made in terms of statistical distributions and their parameters, and the paper investigates whether statistical distributions of bridge response are sensitive to some of the decisions made by the engineer doing the analyses. For the paper a selected part of potential influences are examined and footbridge responses are extracted using Monte-Carlo simulations and focus is on estimating vertical structural response to single person loading.
Pedestrians may cause vibrations in footbridges and these vibrations may potentially be annoying. This calls for predictions of footbridge vibration levels and the paper considers a stochastic approach to modeling the action of pedestrians assuming walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. to be random variables. By this approach a probability distribution function of bridge response is calculated. The paper explores how sensitive estimates of probability distribution functions of bridge response are to some of the decisions to be made when modelling the footbridge and when describing the action of the pedestrians (such as for instance the number of load harmonics). Focus is on estimating vertical structural response to single person loading.
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