The assessment of the out-of-plane response of unreinforced masonry (URM) buildings with cavity walls has been a popular topic in regions such as Central and Northern Europe, Australia, New Zealand, China and several other countries.Cavity walls are particularly vulnerable as the out-of-plane capacity of each individual leaf is significantly smaller than the one of a solid wall. In the Netherlands, cavity walls are characterized by an inner load-bearing leaf of calcium silicate bricks, and by an outer veneer of clay bricks that has only aesthetic and insulation functions. The two leaves are typically connected by means of metallic ties. This paper utilizes the results of an experimental campaign conducted by the authors to calibrate a hysteretic model that represents the axial cyclic response of cavity wall tie connections. The proposednumerical model uses zero-length elements implemented in OpenSees with the Pinching4 constitutive model to account for the compression-tension cyclic behaviour of the ties. The numerical model is able to capture important aspects of the tie response such as the strength degradation, the unloading stiffness degradation and the pinching behaviour. The numerical modelling approach in this paper can be easily adopted by practitioner engineers who aim to model the wall ties more accurately when assessing the structures against earthquakes.
LINK
Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
MULTIFILE
