Classical pressure profile prediction using a hybrid form of artificial neural network algorithm applied to building drainage systems

Classical pressure profile data for building drainage systems (BDS) represent a temporal snapshot of the pressure regime within the system following an event such as a water discharge from an appliance, and therefore can be an indicator of system performance. This research describes, for the first time, a method of predicting the pressure profile using FF(Feed Forward -PSO(Particle Swarm Optimization) artificial neural network (ANN) algorithm. The ANN model was validated against two sets of data: the first from a dedicated 32-storey building drainage experimental test rig at the National Lift Tower (NLT) test facility in Northampton, UK, and the second set of data from a validated numerical model, AIRNET. Both data sets were used to assess the FF- PSO-ANN Model. Calculation errors were minimized by refining weight vectors with a PSO scheme. The convergence of the PSO algorithm was managed through adjusted inertia weights, population size, damping factor, and acceleration coefficients. A generic prediction model was developed using a database of similar building drainage types and configurations. This algorithm refines and trains the ANN model, enhancing its applicability across various applications. The study confirms that the FF-PSO ANN model effectively predicts BDS pressure profile data and system performance. Practical application The ANN model presented develops a new approach with which to assess performance of a BDS at design stage. The model is based on the philosophy of a natural search algorithm which helps to attain global optimisation by refining the weight vectors. It is envisaged that this model can form a part of the assessment of designs at an early stage and provide useful information on the performance of the system. The in-built learning of the model allows accuracy to be improved as the database of existing pressure profiles increases, thus making the tool more relevant with time.