Automated Detection of Pipe Bursts and other Events in Water Distribution Systems

Copyright 2012 by the American Society of Civil EngineersThis paper presents a new methodology for the automated near real-time detection of pipe bursts and other events which induce similar abnormal pressure/flow variations (e.g., unauthorised consumptions) at the District Metered Area (DMA) level. The new methodology makes synergistic use of several self-learning Artificial Intelligence (AI) techniques and statistical data analysis tools including wavelets for de-noising of the recorded pressure/flow signals, Artificial Neural Networks (ANNs) for the short-term forecasting of pressure/flow signal values, Statistical Process Control (SPC) techniques for short and long term analysis of the pipe burst/other event-induced pressure/flow variations, and Bayesian Inference Systems (BISs) for inferring the probability of a pipe burst/other event occurrence and raising corresponding detection alarms. The methodology presented here is tested and verified on a case study involving several DMAs in the United Kingdom (UK) with both real-life pipe burst/other events and engineered (i.e., simulated by opening fire hydrants) pipe burst events. The results obtained illustrate that it can successfully identify these events in a fast and reliable manner with a low false alarm rate

The Selection of Pattern Features for Structural Damage Detection Using an Extended Bayesian ANN Algorithm

Pattern recognition is a promising approach for the detection of structural damage using measured dynamic data. Much research of pattern recognition has employed artificial neural networks (ANNs) as a systematic way of matching pattern features. When such methods are used, the ANN design becomes the most fundamental factor affecting performance and effectiveness of the pattern recognition process. The Bayesian ANN design algorithm is proposed in Lam et al. [Lam HF, Yuen KV, Beck JL. Structural health monitoring via measured Ritz vectors utilizing artificial neural networks. Computer-Aided Civil and Infrastructure Engineering 2006;21:232-41] provides a mathematically rigorous way of determining the number of hidden neurons for a single-hidden-layer feedforward ANN. The first objective of this paper is to extend this Bayesian ANN design algorithm to cover the selection of activation (transfer) functions for neurons in the hidden layer. The proposed algorithm is found to be computationally efficient and is suitable for real-time design of an ANN. As most existing ANN design techniques require the ANN model class to be known before the training process, a technique that can automatically select an "optimal" ANN model class is essential. As modal parameters and Ritz vectors are commonly used pattern features in the literature, the second objective of this paper is to compare the performance of these two pattern features in structural damage detection using pattern recognition. To make a fair judgment between the features, the IASC-ASCE benchmark structure is employed in a case study. The results show that the performance of ANNs trained by modal parameters is slightly better than that of ANNs trained by Ritz vectors. © 2008 Elsevier Ltd. All rights reserved.Heung Fai Lam, Ching Tai N

Multiple-input-multiple-output general regression neural networks model for the simultaneous estimation of traffic-related air pollutant emissions

Traffic-related air pollutant emissions have become a global environmental problem, especially in urban areas. The estimation of pollutant emissions is based on complex models that require the use of detailed travel-activity data, which is often unavailable and in particular, in developing countries. In order to overcome this issue, an alternative multiple-input-multiple-output general regression neural network model, based on basic socioeconomic and transport related indicators, is proposed for the simultaneous prediction of sulphur oxides (SOx), nitrogen oxides (NOx), ammonia (NH3 ), non-methane volatile organic compounds (NMVOC) and particulate matter emissions at the national level. The best model, created using only six inputs, has MAPE (mean absolute percentage error) values on testing in the range of 12-15% for all studied pollutants, except NMVOC (MAPE = 21%). The obtained predictions for SOx, NH3 and PM10 emissions were in good agreement with the reported emissions (R-2 gt = 0.93), while the predictions for NOx and NMVOC are somewhat less accurate (R-2 approximate to 0.85). It can be concluded that the presented ANN approach can offer a simple and relatively accurate alternative method for the estimation of traffic-related air pollutant emissions

Color Computer Vision and Artificial Neural Networks for the Detection of Defects in Poultry Eggs

A blood spot detection neural network was trained, tested, and evaluated entirely on eggs with blood spots and grade A eggs. The neural network could accurately distinguish between grade A eggs and blood spot eggs. However, when eggs with other defects were included in the sample, the accuracy of the neural network was reduced. The accuracy was also reduced when evaluating eggs from other poultry houses. To minimize these sensitivities, eggs with cracks and dirt stains were included in the training data as examples of eggs without blood spots. The training data also combined eggs from different sources. Similar inaccuracies were observed in neural networks for crack detection and dirt stain detection. New neural networks were developed for these defects using the method applied for the blood spot neural network development

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R-2 = 0.82), but it was not robust in extrapolation (R-2 = 0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD