Use of artificial neural networks to evaluate the effectiveness of riverbank filtration

Riverbank filtration (RBF) is a low-cost water treatment technology in which surface water contaminants are removed or degraded as the infiltrating water moves from the river/lake to the pumping wells. The removal or degradation of contaminants is a combination of physicochemical and biological processes. This paper illustrates the development and application of three types of artificial neural networks (ANNs) to estimate the effectiveness of two RBF facilities in the US. The feed-forward back-propagation network (BPN) and radial basis function network (RBFN) model prediction results produced excellent agreement with measured data at a correlation coefficient above 0.99 for filtrate water quality parameters, including temperature as well as turbidity, heterotrophic bacteria, and coliform removal. In comparison, the fuzzy inference system network (FISN) predicted only temperature and bacteria removal with reasonable accuracy. It is shown that the predictive performances of the ANNs depend on the model structure and model inputs.     

Prediction of bed load sediments using different artificial neural network models

Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.     

Water distribution systems flow monitoring and anomalous event detection: A practical approach

Methods to detect outliers in network flow measurements that may be due to pipe bursts or unusual consumptions are fundamental to improve water distribution system on-line operation and management, and to ensure reliable historical data for sustainable planning and design of these systems. To detect and classify anomalous events in flow data from district metering areas a four-step methodology was adopted, implemented and tested: i) data acquisition, ii) data validation and normalization, iii) anomalous observation detection, iv) anomalous event detection and characterization. This approach is based on the renewed concept of outlier regions and depends on a reduced number of configuration parameters: the number of past observations, the true positive rate and the false positive rate. Results indicate that this approach is flexible and applicable to the detection of different types of events (e.g., pipe burst, unusual consumption) and to different flow time series (e.g., instantaneous, minimum night flow).     

Leakage detection in a real distribution network using a SOM

One great challenge for waterworks is effective leakage detection. This paper presents a method based on the self-organising map for leakage detection in a water distribution network. The data used for training and validating the test results consist of vectors of the flow meter readings and knowledge of reported leak locations. The most important factor facilitating the self-organising-map-based modelling of leaks is the developed leak function. The results of the experiments presented show that the model trained on flow data can detect leaks in a defined distribution network area.     

分区计量供水管网水力模型的流量分配

结合天津市供水管网实例,通过分析其计量水量的特点与水力模型的构建要求,按照水量数据来源分别侧重于小区表、户表和在线流量计,提供了分区计量供水管网水力模型的三个流量分配方案;从数据健全度、流量分配准确度、实施难度、流量分配校正依据、漏损考察功能、模型动态模拟、模型维护与应用难度和模型构建平台八个方面对三个流量分配方案进行了多角度评价,可为水力模型项目的实施提供参考。     

An Adaptive Wavelet Frame Neural Network Method for Efficient Reliability Analysis

Artificial neural networks (ANNs) method is widely used in reliability analysis. However, the performance of ANNs cannot be guaranteed due to the fitting problems because there is no efficient constructive method for choosing the structure and the learning parameters of the network. To mitigate these difficulties, this article presents a new adaptive wavelet frame neural network method for reliability analysis of structures. The new method uses the single‐scaling multidimensional wavelet frame as the activation function in the network to deal with the multidimensional problems in reliability analysis. Because the wavelet frame is highly redundant, the time–frequency localization and matching pursuit algorithm are respectively utilized to eliminate the superfluous wavelets, thus the obtained wavelet frame neural network can be implemented efficiently. Five examples are given to demonstrate the application and effectiveness of the proposed method. Comparisons of the new method and the classical radial basis function network method are made.     

Accounting for Losses: The Bursts and Background Concept

Losses of treated water occur through leakage and overflows from the pressurized pipes and fittings in water undertakers'distribution systems and customers'private supply pipes. The UK National Leakage Control Initiative was formed in 1991 to update previous published work on leakage control policy and practice in the UK.
Although some published technical relationships exist, there has been no overall methodology which attempts to provide a component-based estimate of annual losses in different parts of the distribution system for any particular combination of local circumstances, i.e. pressure, burst frequency, burst flow rate, number of properties, length of mains, method of leakage control, standards of service, and waste notice service/enforcement policy.
The 'bursts and background estimate'spreadsheet-based methodology is designed to provide such estimates. It links 'night-flow'and 'annual losses'concepts, and can be used for a variety of purposes. These include (a) assessment of the likely incidence of losses for different leakage control and waste notice policies, (b) identification (from night flows) of districts in which there are unreported bursts, and (c) assessment of economic target levels for leakage control. The substantial element of annual losses from service pipes, and the considerable influence of pressure on annual losses, are also discussed.     

Analysis of durability of high performance concrete using artificial neural networks

This study aims to determine the influence of the content of water and cement, water–binder ratio, and the replacement of fly ash and silica fume on the durability of high performance concrete (HPC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of high performance concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. The model is developed, trained and tested by using 86 data sets from experiments as well as previous researches. To verify the model, regression equations are carried out and compared with the trained neural network. The results indicate that the developed model is reliable and accurate. Based on the simulating durability model built using trained neural networks, the optimum cement content for designing HPC in terms of durability is in the range of 450–500 kg/m³. The results also revealed that the durability of concrete expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 20% fly ash to replace cement. Furthermore, it can be concluded that increasing silica fume results in reducing the chloride ions penetrability to a higher degree than fly ash. This study also illustrates how ANNs can be used to beneficially predict durability in terms of chloride ions permeability across a wide range of mix proportion parameters of HPC.     

供水管网漏水量数学模型建立方法的研究

假设所有物理漏失量为压力的函数,而账面漏失量为用水模式的函数,指出利用给水管网的延时模拟数据即可合理地区分物理漏失量和账面漏失量,并得到供水管网的物理漏失量和账面漏失量的数学模型。     

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.     

Disinfection by-product formation following chlorination of drinking water: Artificial neural network models and changes in speciation with treatment

Artificial neural network (ANN) models were developed to predict disinfection by-product (DBP) formation during municipal drinking water treatment using the Information Collection Rule Treatment Studies database complied by the United States Environmental Protection Agency. The formation of trihalomethanes (THMs), haloacetic acids (HAAs), and total organic halide (TOX) upon chlorination of untreated water, and after conventional treatment, granular activated carbon treatment, and nanofiltration were quantified using ANNs. Highly accurate predictions of DBP concentrations were possible using physically meaningful water quality parameters as ANN inputs including dissolved organic carbon (DOC) concentration, ultraviolet absorbance at 254 nm and one cm path length (UV₂₅₄), bromide ion concentration (Br⁻), chlorine dose, chlorination pH, contact time, and reaction temperature. This highlights the ability of ANNs to closely capture the highly complex and non-linear relationships underlying DBP formation. Accurate simulations suggest the potential use of ANNs for process control and optimization, comparison of treatment alternatives for DBP control prior to piloting, and even to reduce the number of experiments to evaluate water quality variations when operating conditions are changed. Changes in THM and HAA speciation and bromine substitution patterns following treatment are also discussed.     

Leakage detection and location in water distribution systems using a fuzzy-based methodology

Loss of water due to leakage is a common phenomenon observed practically in all water distribution systems (WDS). However, the leakage volume can be reduced significantly if the occurrence of leakage is detected within minimal time after its occurrence. This paper proposes a novel methodology to detect and diagnose leakage in WDS. In the proposed methodology, a fuzzy-based algorithm has been employed that incorporates various uncertainties into different WDS parameters such as roughness, nodal demands, and water reservoir levels. Monitored pressure in different nodes and flow in different pipes have been used to estimate the degree of membership of leakage and its severity in terms of index of leakage propensity (ILP). Based on the degrees of leakage memberships and the ILPs, the location of the nearest leaky node or leaky pipe has been identified. To demonstrate the effectiveness of the proposed methodology, a small distribution network was investigated which showed very encouraging results. The proposed methodology has a significant potential to help water utility managers to detect and locate leakage in WDS within a minimal time after its occurrence and can help to prioritise leakage management strategies.     

Application of artificial neural networks to fracture analysis at the Äspö HRL, Sweden: fracture sets classification

This study investigates the potential of artificial neural networks (ANNs) to recognize, classify and predict patterns of different fracture sets in the top 450 m in crystalline rocks at the Äspö Hard Rock Laboratory (HRL), Southeastern Sweden. ANNs are computer systems composed of a number of processing elements that are interconnected in a particular topology which is problem dependent. ANNs have the ability to learn from examples using different learning algorithms; these involve incremental adjustment of a set of parameters to minimize the error between the desired output and the actual network output. Six fracture-sets with particular ranges of strike and dip have been distinguished. A series of trials were carried out using backpropagation (BP) neural networks for supervised classification, and the BP networks recognized different fracture sets accurately. Self-organizing neural networks have been used for data clustering analysis with supervised learning algorithms; (competitive learning and learning vector quantization), and unsupervised learning algorithms; (self-organizing maps). The self-organizing networks adapted successfully to different fracture clusters (sets). A set of trials has been carried out to investigate the effect of changing the network's topologies on the performance of the BP networks. Using two hidden layers with tan-sigmoid and linear transfer functions was beneficial for the performance of BP classification. ANNs improved fracture sets classification that was based on Kamb contouring method with constraint on areas between fracture clusters.     

城市供水管网漏损时间的预测模型研究

城市供水企业迫切需要加强给水管网的漏损管理，以减少漏损水量和提高经济效益。在对华北某市供水管网漏损数据进行统计和分析的基础上，按照管段实际发生漏损次数分两种情况建立了供水管网漏损时间的预测模型，对漏损次数≤4次的管段采用基于SAS系统的多元线性回归方法，对漏损次数〉4次的管段则采用灰色预测方法。经实例验证，多元线性回归方法预测的平均相对误差为21％，灰色预测方法预测的平均相对误差〈6％，整套模型的精度可满足城市供水管网漏损宏观管理的需要，能够提高管网漏损防治的效率。     

A Comparative Analysis of Two Artificial Neural Networks Using Pavement Performance Prediction

The management of pavements requires the ongoing allocation of substantial manpower and capital resources by the responsible agencies. These agencies ultimately report to the executive and legislative branches of government, which require justification and proof of the efficacy of these expenditures. This and the need for improved engineering technical feedback have encouraged the development of pavement management systems (PMS). One goal of a PMS is to provide decision makers at all levels with optimal resource-allocation strategies. This requires evaluation of alternatives over an analysis period based on predicted values of pavement performance. This necessitates more reliable pavement performance prediction models. Traditional modeling uses multiple regression techniques to predict pavement performance from traffic, time, and pavement distress or various combinations of these factors. Within the last 10 years, new modeling techniques, including artificial neural networks (ANNs), have been applied to transportation problems. The ANNs examined usually have been of a single type called a dot product ANN. This paper examines a different type called the quadratic function ANN and compares the results to the dot product ANN. The quadratic function ANN is a generalized adaptive, feedforward neural network that combines supervised and self-organizing learning. Models were developed to predict roughness using both types of ANN on the same data samples and the results compared. The data samples were drawn from the Kansas Department of Transportation's PMS database. The results indicate a significant improvement in the use of the self-organizing quadratic function ANNs and lead to recommendations for specific areas of additional research.     

A data mining approach to modelling of water supply assets

The economic and social costs associated with pipe bursts and associated leakage problems in modern water supply systems are rapidly rising to unacceptably high levels.Pipe burst risks depend on a number of factors which are extremely difficult to characterise. A part of the problem is that water supply assets are mainly situated underground, and therefore not visible and under influence of various highly unpredictable forces. This paper proposes the use of advanced data mining methods in order to determine the risks of pipe bursts. For example, analysis of the database of already occurred bursts events can be used to establish a risk model as a function of associated characteristics of bursting pipe (its age, diameter, material of which it is built, etc.), soil type in which a pipe is laid, climatological factors (such as temperature), traffic loading, etc.In addition to the immediate aid with the the choice of pipes to be replaced, the outlined approach opens completely new avenues in asset management: the one of asset modeling. The condition of an asset such as a water supply network deteriorates with age. With reliable risk models, addressing the evolution of risk with aging asset, it is now possible to plan optimal rehabilitation strategies in advance, before the burst actually occurs.     

Crack detection in beam-like structures

Sensibility analysis of experimentally measured frequencies as a criterion for crack detection has been extensively used in the last decades due to its simplicity. However the inverse problem of the crack parameters (location and depth) determination is not straightforward. An efficient numerical technique is necessary to obtain significant results. Two approaches are herein presented: The solution of the inverse problem with a power series technique (PST) and the use of artificial neural networks (ANNs). Cracks in a cantilever Bernoulli–Euler (BE) beam and a rotating beam are detected by means of an algorithm that solves the governing vibration problem of the beam with the PST. The ANNs technique does not need a previous model, but a training set of data is required. It is applied to the crack detection in the cantilever beam with a transverse crack. The first methodology is very simple and straightforward, though no optimization is included. It yields relative small errors in both the location and depth detection. When using one network for the detection of the two parameters, the ANNs behave adequately. However better results are found when one ANN is used for each parameter. Finally, a combination between the two techniques is suggested.     

Source tracking of microbial intrusion in water systems using artificial neural networks

A "what-if" scenario where biological agents are accidentally or deliberately introduced into a water system was generated, and artificial neural network (ANN) models were applied to identify the pathogenic release location to isolate the contaminated area and minimize its hazards. The spatiotemporal distribution of Escherichia coli 15597 along the water system was employed to locate pollutants by inversely interpreting transport patterns of E. coli using ANNs. Results showed that dispersion patterns of E. coli were positively correlated to pH, turbidity, and conductivity (R2=0.90-0.96), and the ANN models successfully identified the source location of E. coli introduced into a given system with 75% accuracy based on the pre-programmed relationships between E. coli transport patterns and release locations. The findings in this study will enable us to assess the vulnerability of essential water systems, establish the early warning system and protect humans and the environment.     

远传监控系统在DMA漏损控制工作中的应用

城市供水管网漏失长期以来是供水企业关注的重点,直接关系到企业的经营成本和经济效益。近年来城市发展迅速,供水管网新旧结合,分布复杂,给供水管网漏损控制管理工作带来很大不便,造成城市管网漏损率偏高。针对上述问题,主要介绍了郑州自来水投资控股有限公司借助远传监控系统在DMA漏损控制方面的一些应用,并探讨了DMA漏损控制的有效方法。     

Prediction of effective thermal conductivity of moist porous materials using artificial neural network approach

An artificial neural networks (ANNs) approach is presented for the prediction of effective thermal conductivity of porous systems filled with different liquids. ANN models are based on feedforward backpropagation network with training functions: Levenberg–Marquardt (LM), conjugate gradient with Fletcher–Reeves updates (CGF), one-step secant (OSS), conjugates gradient with Powell–Beale restarts (CGB), Broyden, Fletcher, Goldfrab and Shanno (BFGS) quasi-Newton (BFG), conjugates gradient with Polak–Ribiere updates (CGP). Training algorithm for neurons and hidden layers for different feedforward backpropagation networks at the uniform threshold function TANSIG-PURELIN are used and run for 1000 epochs. The complex structure encountered in moist porous materials, along with the differences in thermal conductivity of the constituents makes it difficult to predict the effective thermal conductivity accurately. For this reason, artificial neural networks (ANNs) have been utilized in this field. The resultant predictions of effective thermal conductivity (ETC) of moist porous materials by the different models of ANN agree well with the available experimental data.