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Regular use of artificial neural networks (ANN) analysis for predicting the vertical swelling percentage of expansive clays may lead to inappropriate results in terms of their geophysical behavior. This paper presents two new ANN Models derived from a two-stage procedure. The models were estimated using the same data set from the previous paper, and their statistical fit was clearly found to be superior in comparison to the previous models. Furthermore, one of these two models exhibited the expected geophysical behavior. As this new ANN Model yields higher predicted swelling-percentage values, it can definitely be regarded as a preferable one in the sense of enlarging the safety margin in heave calculations. [ABSTRACT FROM AUTHOR]

In this paper, a new type of multilayer feedforward neural network has been proposed based on inspiration from the Preisach model, which has been called the Preisach neural network (Preisach-NN). It is comprised of input, output, and two hidden layers. The input and output layers contain linear neurons, whereas the first hidden layer incorporates neurons called stop neurons, whose activation function represents a stop operator. The second hidden layer includes sigmoidal neurons. The subgradient optimization method with space dilatation has been adopted for training of the Preisach-NN as a nonsmooth problem. Although the proposed Preisach-NN could be mathematically identical to the Preisach model, tuning of the Preisach-NN is easier and also more general than that of the model. To assess their capability, Preisach-NNs are used to model two different types of hysteretic behaviors of Masing and non-Masing problems. The results presented and discussed in this paper show that the neural networks have been capable of learning the material behaviors successfully and with high precision. [ABSTRACT FROM AUTHOR]

The performance of a fuzzy expert system (FES) is significantly affected by the accuracy of its knowledge base parameters (membership functions and rule bases). The main contribution of this paper is in presenting a methodology to integrate an FES with adaptation/optimization techniques and applying the data-based adaptive learning concept to increase the accuracy of an FES developed for contractor default prediction for surety bonding. In addition, this paper investigates two optimization approaches (genetic algorithms and neural network back-propagation) for adaptation of fuzzy membership function (MBF) and rules' degree of support (DoS) to determine the most suitable technique to adapt the FES. The optimized FES, called SuretyQualification, was validated using 30 hypothetical contractor default prediction cases, and the highest accuracy of the system (adapted using neural networks) was found to be 91.83%. Another contribution of this paper is the development of a software tool called SuretyQualification that provides a comprehensive and systematic evaluation process to evaluate a contractor and their risk of default on a project. The presented optimization approaches address FES context adaptation using any changing information conveyed by the input-output data and provide a methodology for continuous adaptation of the FES parameters, using practical cases to adjust the FES according to any contexts changes. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting, especially the short-term case, is an important topic in intelligent transportation systems (ITS). This paper researches network-scale modeling and forecasting of short-term traffic flows. First, the concepts of single-link and multilink models of traffic flow forecasting are proposed. Secondly, four prediction models are constructed by combining the two models with single-task learning (STL) and multitask learning (MTL). The combination of the multilink model and multitask learning not only improves the experimental efficiency but also improves the prediction accuracy. Moreover, a new multilink, single-task approach that combines graphical lasso (GL) with neural network (NN) is proposed. GL provides a general methodology for solving problems involving lots of variables. Using L1 regularization, GL builds a sparse graphical model, making use of the sparse inverse covariance matrix. Gaussian process regression (GPR) is a classic regression algorithm in Bayesian machine learning. Although there is wide research on GPR, there are few applications of GPR in traffic flow forecasting. In this paper, GPR is applied to traffic flow forecasting, and its potential is shown. Through sufficient experiments, all of the proposed approaches are compared, and an overall assessment is made. [ABSTRACT FROM AUTHOR]

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure. [ABSTRACT FROM AUTHOR]

Over the years of the study (1999–2004) presented in this paper, the City of Edmonton, Canada’s Drainage and Maintenance Department has experienced an annual increase of about 12% in the installation of water and sewer services for residential facilities. According to the current estimating procedure, a discrepancy of up to 60% exists between the estimated and actual costs of these projects. A detailed analysis of all activities involved in the installation of the water and sewer services has been carried out and is presented in this paper. The proposed methodology, which is based upon the analysis of past data obtained from the City of Edmonton’s drainage division for the period of 1999–2004, is also presented. The methodology has been incorporated into a computer module, which integrates the concept of artificial neural network (ANN) with the current estimating system used by the City of Edmonton. The following research includes a description of the algorithm used in ANN, as well as an assessment of past data obtained from the city record for over 800 jobs (cases) performed over the period of the study. [ABSTRACT FROM AUTHOR]

This paper describes a novel hybrid intelligent system approach to inversion of nondestructive pavement deflection data and back-calculation of nonlinear stress-dependent pavement layer moduli. Particle swarm optimization (PSO), a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling, is fast emerging as an innovative and powerful computational metaphor for solving complex problems in design, optimization, control, management, business, and finance. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. PSO is especially considered a robust and efficient approach for global optimization of multimodal functions. The hybrid back-calculation system described in this paper integrates finite element modeling, neural networks, and PSO in an efficient manner to mitigate the limitations and take advantages of the strengths to produce a system that is more effective and powerful than those which could be built with single technique. This is the first time the PSO approach is applied to real-time nondestructive evaluation of pavement systems. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced design methodology for optimal mixture proportion of concrete composition with respect to accuracy in the case of using prediction models based on a limited database. In proposed methodology, the search space is constrained as the domain defined by a limited database instead of constructing the database covering the region represented by the possible ranges of all variables in the input space. A model for defining the search space which is expressed by the effective region in this paper and evaluating whether a mix proportion is effective is added to the optimization process, yielding highly reliable results. To demonstrate the proposed methodology, a genetic algorithm, an artificial neural network, and a convex hull were adopted as an optimum technique, a prediction model for material properties, and an evaluation model for the effective region, respectively. And then, it was applied to an optimization problem wherein the minimum cost should be obtained under a given strength requirement. Experimental test results show that the mix proportion obtained from the proposed methodology considering the regional characteristics of the database is found to be more accurate and feasible than that obtained from a general optimum technique that does not consider this aspect. [ABSTRACT FROM AUTHOR]

This paper presents a coupled approach using an artificial neural network (ANN) and the finite difference method (FDM) that has been developed to predict the distribution of axial load along fully grouted standard cable bolts in the field using laboratory pullout test data. A back-propagation training algorithm was used in ANN to determine axial loads in the cables tested in the laboratory. The ANN component of the computational model was trained using two different types of data sets. At first, the ANN was trained to predict the axial loads in a series of short cables grouted with Portland cement at a specific water-to-cement ratio and subjected to different radial confining stiffness values. Next, the ANN model was trained for an expanded case to include the influence of lateral confining stress on the distribution of axial load in the cable reinforcement. Finally, the ANN model was implemented into a widely used, FDM-based geotechnical software (FLAC). The accuracy of the ANN–FDM model is demonstrated in this paper against measured data from laboratory and field tests. The analysis approach introduced in this study is a valuable computational tool that can be used to determine the axial load distribution in long standard cable bolts, which are commonly installed to stabilize rock masses in various geotechnical, transportation, and mining applications. [ABSTRACT FROM AUTHOR]

This paper describes the use of inductive models developed using two artificial intelligence (AI)-based techniques for fecal coliform prediction and classification in surface waters. The two AI techniques used include artificial neural networks (ANNs) and a fixed functional set genetic algorithm (FFSGA) approach for function approximation. While ANNs have previously been used successfully for modeling water quality constituents, FFSGA is a relatively new technique of inductive model development. This paper will evaluate the efficacy of this technique for modeling indicator organism concentrations. In scenarios where process-based models cannot be developed and/or are not feasible, efficient and effective inductive models may be more suitable to provide quick and reasonably accurate predictions of indicator organism concentrations and associated water quality violations. The relative performance of AI-based inductive models is compared with conventional regression models. When raw data are used in the development of the inductive models described in this paper, the AI models slightly outperform the traditional regression models. However, when log transformed data are used, all inductive models show comparable performance. While the work validates the strength of simple regression models, it also validated FFSGA to be an effective technique that competes well with other state-of-the-art and complex techniques such as ANNs. FFSGA comes with the added advantage of resulting in a simple, easy to use, and compact functional form of the model sought. This work adds to the limited amount of research on the use of data-driven modeling methods for indicator organisms. [ABSTRACT FROM AUTHOR]

The height difference between the ellipsoidal height h and the orthometric height H is called undulation N. The key issue in transforming the global positioning system (GPS)-derived ellipsoidal height to the orthometric height is to determine the undulation value accurately. If the undulation N for a point whose position is determined by a GPS receiver can be estimated in the field, then the GPS-derived three-dimensional geocentric coordinate in WGS-84 can be transformed into a local coordinate system and the orthometric height in real-time. In this paper, algorithms of applying a back-propagation artificial neural network (BP ANN) to develop a regional grid-based geoid model using GPS data (e.g., ellipsoidal height) and geodetic leveling data (e.g., orthometric height) are proposed. In brief, the proposed algorithms include the following steps: (1) establish the functional relationship between the point’s plane coordinates and its undulation using the BP ANN according to the measured GPS data and leveling data; (2) develop a regional grid-based geoid model using the imaginary grid plane coordinates with a fixed grid interval and the trained BP ANN; (3) develop an undulation interpolation algorithm to estimate a specific point’s undulation using the generated grid-based geoid model; and (4) estimate the point’s undulation in the field and transform the GPS ellipsoidal height into the orthometric height in real-time. Three data sets from the Taiwan region are used to test the proposed algorithms. The test results show that the undulation interpolation estimation accuracy using the generated grid-based geoid is in the order of 2–4 cm. The proposed algorithms and the detailed test results are presented in this paper. [ABSTRACT FROM AUTHOR]

Over the years, many methods have been developed to predict the concrete strength. In recent years, artificial neural networks (ANNs) have been applied to many civil engineering problems with some degree of success. In the present paper, ANN is used as an attempt to obtain more accurate concrete strength prediction based on parameters like concrete mix design, size and shape of specimen, curing technique and period, environmental conditions, etc. A total of 864 concrete specimens were cast for compressive strength measurement and verification through the ANN model. The back propagation-learning algorithm is employed to train the network for extracting knowledge from training examples. The predicted strengths found by employing ANN are compared with the actual values. The results indicate that ANN is a useful technique for predicting the concrete strength. Further, an effort to build an expert system for the problem is described in this paper. To overcome the bottleneck of intricate knowledge acquisition, an expert system is used as a mechanism to transfer engineering experience into usable knowledge through rule-based knowledge representation techniques. [ABSTRACT FROM AUTHOR]

This paper reports on the development of a new approach for simulating the thermal behavior of buildings that overcome the limitations of conventional heat-transfer simulation methods such as the finite difference method and the finite element method. The proposed technique uses a coarse-grain approach to model development whereby each element represents a complete building component such as a wall, internal space, or floor. The thermal behavior of each coarse-grain element is captured using empirical modeling techniques such as artificial neural networks (ANNs). The main advantages of the approach compared to conventional simulation methods are (1) simplified model construction for the end-user; (2) simplified model reconfiguration; (3) significantly faster simulation runs (orders of magnitude faster for two- and three-dimensional models); and (4) potentially more accurate results. The paper demonstrates the viability of the approach through a number of experiments with a model of a composite wall. The approach is shown to be able to sustain highly accurate long-term simulation runs, if the coarse-grain modeling elements are implemented as ANNs. In contrast, an implementation of the coarse-grain elements using a linear model is shown to function inaccurately and erratically. The paper concludes with an identification of on-going work and future areas for development of the technique. [ABSTRACT FROM AUTHOR]

In this paper, functional networks (FN) proposed by Castillo as an alternative to neural networks are discussed. Unlike neural networks, the functions are learned instead of weights. In general, topology is selected based on data, domain knowledge (properties of the function such as associativity, commutativity, and invariance), or a combination of the two. The object of this paper is to show the application of some functional network architectures to model and predict the behavior of structural systems which are otherwise modeled in terms of differential or difference equations or in terms of neural networks. In this paper, four examples in structural engineering and one example in mathematics are discussed. The results obtained by functional networks are compared with those obtained by neural networks for the first four examples, and it is shown that functional networks are more efficient and powerful and take much less computer time as compared to predictions by conventional neural networks such as the back-propagation network. [ABSTRACT FROM AUTHOR]

Because the complexity of sulfur capture and release during solid-fuel combustion in a circulating fluidized bed combustors (CFBC), especially in the oxygen-enriched combustion, has not been sufficiently recognized, the development of a simple model, which can correctly predict the SO2 emissions from such units over a wide range of operating conditions is of practical significance. The artificial neural network (ANN) approach is proposed in this paper, which may overcome the shortcomings of the experimental procedures and the programmed computing approach. The Ca:S molar ratio, oxygen concentration in inlet gas, excess oxygen, average riser temperature, mean diameter of the coal particles, average gas velocity in the riser, flue gas recycle ratio, and inlet gas pressure are taken into account by the model as the input parameters. The [8-3-7-1] ANN model with hyperbolic tangent sigmoid activation function was successfully applied to calculate the SO2 emissions from coal combustion in several CFB boilers operating under both air-fired and oxygen-enriched conditions. [ABSTRACT FROM AUTHOR]

Many of the empirical formulas used for the prediction of the expected scour depth at piers are excessively conservative, providing substantial overestimations. On the other hand, the recently proposed neural networks methods generally issue accurate predictions but also high percentages of underpredictions, due to the use of a symmetric error function for their parameterization. A novel error function is proposed in this paper for optimizing neural networks, giving more weight to underestimation than to overestimation discrepancies, in order to obtain safer design predictions. The performances of the proposed model on independent field records are compared with those of a conventionally trained neural network and with those of a set of widely used formulas. The asymmetric error function (that might be applied to parameterize any other model or equation, as a proficient alternative to least-square errors or envelope curves) allows researchers to obtain predictions closer to the measurements than those issued by traditional formulas, substantially reducing the extent of unnecessary overdesign and at the same time the percentage of severe underestimations is comparable with those of the safest formulas. [ABSTRACT FROM AUTHOR]

The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other, and the design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load-settlement response of piles to be well predicted. However, it is well known that the actual load-settlement response of pile foundations can be obtained only by load tests carried out in situ, which are expensive and time-consuming. In this paper, recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the full load-settlement response of drilled shafts (bored piles) subjected to axial loading. The developed RNN model was calibrated and validated using several in situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the developed RNN model has the ability to reliably predict the load-settlement response of axially loaded drilled shafts and can thus be used by geotechnical engineers for routine design practice. [ABSTRACT FROM AUTHOR]

In recognition of the importance of historical knowledge in decision making, case based reasoning (CBR) is utilized as a form of an expert system to tackle construction management issues such as quantity takeoff in the proposal development phase of a project. It builds on a proposition that past projects similar to the new one would suggest a reasonable range of craft quantities. This paper finds that when measuring the similarity between the new project and historical projects, traditional similarity measure methods fail to consider the nonlinearity and muticollinearity embedded in the problem, as well as differences across crafts. An innovative similarity measurement algorithm was therefore proposed to tackle the above issues with a carefully designed orthogonalization process and Sobol's total sensitivity analysis. The application of the proposed algorithm to the craft quantity takeoff of a power plant project was introduced, demonstrating a better result compared with traditional methods. It is likely that the proposed algorithm will advance current CBR practices in construction management. [ABSTRACT FROM AUTHOR]

This paper proposes the use of online retrained metamodels for the optimization of water distribution system (WDS) design. In these metamodels, artificial neural networks (ANNs) are used to replace the full hydraulic and water quality simulation models and differential evolution (DE) is utilized to carry out the optimization. The ANNs in the proposed online DE-ANN model are retrained periodically during the optimization in order to improve their approximation to the appropriate portion of the search space. In addition, a local search strategy is used to further polish the final solution obtained by the online DE-ANN model. Three case studies are used to verify the effectiveness of the proposed online retrained DE-ANN model for which both hydraulic and water quality constraints are considered. In order to enable a performance comparison, a model in which a DE is combined with a full hydraulic and water quality simulation model (DE-EPANET2.0) and an offline DE-ANN model (ANNs are trained only once at the beginning of optimization) are established and applied to each case study. The results obtained show that the proposed online retrained DE-ANN model consistently outperforms the offline DE-ANN model for each case study in terms of efficiency and solution quality. Compared with the DE-EPANET2.0 model, the proposed online DE-ANN model exhibits a substantial improvement in computational efficiency, while still producing reasonably good quality solutions. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility of developing and using artificial neural networks (ANNs) for modeling the monotonic behaviors of various angular and rounded rockfill materials is investigated. The database used for development of the ANN models is comprised of a series of 82 large-scale, drained triaxial tests. The deviator stress-volumetric strain versus axial strain behaviors were first simulated by using ANNs. A feedback model using multilayer perceptrons for predicting drained behavior of rockfill materials was developed in the MATLAB environment, and the optimal ANN architecture was obtained by a trial-and-error approach in accordance with error indexes and real data. Reasonable agreement between the simulated behaviors and the test results was observed, indicating that the ANNs are capable of capturing the behavior of rockfill materials. The ability of ANNs to predict the constitutive hardening-soil model parameters, residual deviator stresses, and corresponding volumetric strain was also investigated. Moreover, the generalization capability of ANNs was also used to check the effects of items not tested, such as dry density, grain-size distributions, and Los Angeles abrasion. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network controller is proposed for a nonaffine-nonlinear aircraft cabin pressure system with unknown parameters. A multilayer neural network is used to represent the controller structure. The ultimate boundedness of the closed-loop system is guaranteed through a Lyapunov stability analysis by introducing a suitably driven adaptive rule. The effectiveness of the proposed adaptive controller is illustrated by considering an aircraft cabin pressure system, and the simulation results verify the merits of the proposed controller. [ABSTRACT FROM AUTHOR]

A fully automated data-driven methodology for the detection of pipe bursts and other events that induce similar abnormal pressure/flow variations (e.g., unauthorized consumptions) at the district metered area (DMA) level has been recently developed by the authors. This methodology works by simultaneously analyzing the data coming on-line from all the pressure and/or flow sensors deployed in a DMA. It makes synergistic use of several self-learning artificial intelligence (AI) and statistical techniques. These include (1) wavelets for the de-noising of the recorded pressure/flow signals; (2) artificial neural networks (ANNs) for the short-term forecasting of pressure/flow signal values; (3) statistical process control (SPC) techniques for the short-term and long-term analysis of the burst/other event-induced pressure/flow variations; and (4) Bayesian inference systems (BISs) for inferring the probability that a pipe burst/other event has occurred in the DMA being studied, raising the corresponding detection alarms, and provide information useful for performing event diagnosis. This paper focuses on the (re)calibration of the above detection methodology with the aim of improving the forecasting performances of the ANN models and the classification performances of the BIS used to raise the detection alarms (i.e., DMA-level BIS). This is achieved by using (1) an Evolutionary Algorithm optimization strategy for selecting the best ANN input structures and related parameter values to be used for training the ANN models, and (2) an Expectation Maximization strategy for (re)calibrating the values in the conditional probability tables (CPTs) of the DMA-level BIS. The (re)calibration procedure is tested on a case study involving several DMAs in the U.K. with real-life pipe bursts/other events, engineered pipe burst events (i.e., simulated by opening fire hydrants), and synthetic pipe burst events (i.e., simulated by arbitrarily adding 'burst flows' to an actual flow signal). The results obtained illustrate that the new (re)calibration procedure improves the performance of the event detection methodology in terms of increased detection speed and reliability. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for the automated near-real-time detection of pipe bursts and other events that induce similar abnormal pressure/flow variations (e.g., unauthorized 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 denoising 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 (U.K.) 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. [ABSTRACT FROM AUTHOR]

In this paper, a neuro-proportional derivative (PD) method is presented to stabilize a satellite with respect to the gravity gradient. Three reaction wheels are employed to produce the necessary torque in the axes of the satellite. Quaternion and angular velocity vectors are used in the PD controller, and a neural network is applied to tune the gains of the PD controller. The error of the Euler angles is employed as an input of the neural network. The closed-loop system with different characteristics is simulated and compared with the variable-structure controller. The results show the superior performance of the proposed controller. [ABSTRACT FROM AUTHOR]

Carbonate soils have some distinctive features such as compressibility and skeletal particle crushability that make them distinguishable from other types of soils. Many experimental models have been developed to describe the complex behavior of carbonate soils, but despite these numerous works, there is no unified approach that can model the behavior of various types of these soils. In this paper, a new approach based on artificial neural networks is presented to predict the mechanical behavior of different carbonate soils. The network had five input neurons, namely, relative density, axial strain, maximum void ratio, calcium carbonate content, and confining pressure; ten neurons in the hidden layer; and two neurons in the output layer, namely, deviatoric stress and volumetric strain at the end of each increment. The network was trained and tested using a database that included results from a comprehensive set of triaxial tests on three carbonate soils. Comparison of the model prediction and experimental results revealed that the proposed approach was accurate and trustworthy in representing the mechanical behavior of various carbonate soils. [ABSTRACT FROM AUTHOR]

The general swelling model has recently been updated in Israel by applying the Excel-solver command (ESC) analysis to new local test results from 897 undisturbed specimens. In this analysis, the goodness-of-fit statistics obtained classify the category of their associated regression only as fair. Thus, it seems necessary to explore the possibility of enhancing the outputs of this regression analysis by applying the artificial neural networks (ANN) methodology to the same 897 undisturbed specimens. However, it is shown that the use of the ANN outputs should be accompanied by an additional check to ensure that they follow the expected physical swelling behavior, as characterized by the index properties of the soil. The ANN methodology applied in this paper is similar to previous studies in geotechnical engineering. Different models were tested using the same database (i.e., the same 897 undisturbed specimens). The statistical fit of the ANN models were clearly found to be superior to the ESC models. However, in the sense of the required physical behavior, as characterized by the index properties of the soil, the ANN models did not predict swelling values as well as ESC models did, in particular values ranging near (or outside) the data set boundaries. Thus, the former ESC models still remain preferable. [ABSTRACT FROM AUTHOR]

Asset management provides a managerial decision-making framework for public agencies to monitor, evaluate, and make informed decisions about how to best maintain vital civil infrastructure assets. Among many steps required for implementing asset management, developing an accurate deterioration model is one of the key components because it helps infrastructure agencies predict remaining asset life. The accuracy of deterioration models highly depends on the quality of input data and the computational technique used in data analysis. Among many options of computational techniques, a decision tree offers the combination of visual representation and sound statistical background. The visual representation enables the decision maker to identify the relationship and interdependencies of each decision and formulate an appropriate prediction. This study developed a decision tree-based deterioration model for sewer pipes. The performance of the new model is then compared with conventional regression- and neural networks-based models that are also developed using the same data sets. The result shows that the decision tree outperformed other techniques in terms of accuracy (error rate). The paper also discusses different deterioration patterns of different categories of pipes. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive control approach, named adaptive Chebyshev retrofit control (ACRC), retrofitting an existing baseline controller with an adaptive Chebyshev function approximator is presented. The approximator is composed of a linear combination of a parameter and a basis function. Instead of using neural networks as a function approximator, the new approach utilizes a Chebyshev polynomial as a basis function for function approximation, and a parameter update law is derived via a Lyapunov-like analysis method. The benefits of the proposed method are twofold. First, the computational time is approximately 1.7 times faster than that of the method using the neural network. Second, the implementation is very efficient, because the structure of the approximator is significantly simpler in comparison with those of neural network approaches. Because the complexity of the software is the major contributing factor to software reliability, the high complexity of the implementation of a control algorithm that adopts neural networks could lead to a reduction in software reliability. Therefore, the new adaptive control method is valuable in terms of the improvement in software reliability. In particular, it is important in the field of aerospace control, which requires exceptional reliability for flight control software. Moreover, the short computational time in comparison with neural network approaches is very crucial for small unmanned aerial vehicles that have restricted on-board hardware performance. From simulation results, it is found that the performance of the proposed method in several responses is on par with that of the neural network method in the presence of varying flight conditions. Considering the computation time and simplicity of the proposed method, the authors conclude that the proposed approach is very effective, particularly relative to the neural network method. [ABSTRACT FROM AUTHOR]

Complex analysis and design of structures, especially landmark structures such as long-span bridges, have been conducted by many engineers and researchers. Currently, it is possible to collect more and precise monitoring data as well as to develop complex three-dimensional (3D) FEM models. These models, which can be calibrated using structural health-monitoring (SHM) data, can be used for the estimation of component and system reliability of bridges. However, the uncertainties related to the data, analysis, and nonstationary nature of the structural behavior need to be better incorporated by using a set of models that are continuously updated with monitoring data. This set of models constitutes a family as a result of the approach by which the models are obtained and the relationships among them. The objective of this paper is to explore the impact of uncertainty in predicting the system reliability obtained by a one-time, initially calibrated FEM model as well as by a family of FEM models continuously calibrated with monitoring data. To explore the uncertainty effects, a laboratory structure that has a combined system configuration with main and secondary elements is monitored. The monitoring data are employed for the FEM model calibration by using artificial neural networks (ANNs) to obtain parent (calibrated) FEM models from which a set of offspring FEM models is generated to incorporate the uncertainties. It is shown that the use of parent-offspring FEM models becomes important especially when critical parameters that have an impact on the model responses cannot be precisely defined. Finally, it is shown in a comparative fashion that the prediction of reliability using a family of FEM models and a single model can be quite different because the family of models provides a more realistic estimate of the structural responses and probability of failure. [ABSTRACT FROM AUTHOR]

In this paper, models for short-term prediction of influent flow rate in a wastewater-treatment plant are discussed. The prediction horizon of the model is up to 180 min. The influent flow rate, rainfall rate, and radar reflectivity data are used to build the prediction model by different data-mining algorithms. The multilayer perceptron neural network algorithm has been selected to build the prediction models for different time horizons. The computational results show that the prediction model performs well for horizons up to 150 min. Both the peak values and the trends are accurately predicted by the model. There is a small lag between the predicted and observed influent flow rate for horizons exceeding 30 min. The lag becomes larger with the increase of the prediction horizon. [ABSTRACT FROM AUTHOR]

Scheduling of the rehabilitation activities of water main networks depends mainly on available budget and planning time. Other factors such as network reliability, criticality, location, contract size, and rehabilitation method(s) affect the optimization of the scheduling process. This paper presents a method for optimized scheduling of rehabilitation work for water distribution networks. The method utilizes unsupervised neural networks (UNNs) and mixed-integer nonlinear programming (MINLP) and performs the scheduling in two stages. In the first stage, UNNs are used to cluster water mains into groups according to locations and rehabilitation methods of water mains. In the second stage, MINLP is used to determine the number of rehabilitation contract packages and the generation of optimized scheduling of these packages considering network reliability, criticality, contract size, and planning time. In order to demonstrate the essential features of the developed method, a case study was analyzed and the results obtained are discussed; highlighting their utilization in practice. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of the principle of demographic gravitation to estimate link-level annual average daily traffic (AADT) based on land-use characteristics. According to the principle, the effect of a variable on AADT of a link decreases with an increase in distance from the link. The spatial variations in land-use characteristics were captured and integrated for each study link using the principle of demographic gravitation. The captured land-use characteristics and on-network characteristics were used as independent variables. Traffic count data available from the permanent count stations in the city of Charlotte, North Carolina, were used as the dependent variable to develop statistical and neural network models. Negative binomial count statistical models (with log-link) were developed as data were observed to be over-dispersed while neural network models were developed based on a multilayered, feed-forward, back-propagation design for supervised learning. The results obtained indicate that statistical and neural network models ensured significantly lower errors when compared to outputs from traditional four-step method used by regional modelers. Overall, the neural network model yielded better results in estimating AADT than any other approach considered in this research. The neural network approach can be particularly suitable for their better predictive capability, whereas the statistical models could be used for mathematical formulation or understanding the role of explanatory variables in estimating AADT. [ABSTRACT FROM AUTHOR]

This study is intended to facilitate damage detection in concrete bridge girders without the need for visual inspection while minimizing field measurements. Beams with different material and cracking parameters were modeled using ABAQUS finite-element analysis software to obtain stiffness values at specified nodes. The resulting database was then used to train an artificial neural network (ANN) model to inversely predict the most probable cracking pattern. The aim is to use the ANN approach to solve an inverse problem where a unique analytical solution is not attainable. Accordingly, simple span beams with three, five, seven, and nine stiffness nodes and a single crack were modeled in this work. To confirm that the ANN approach can characterize the logic within the databases, networks with geometric, material, and cracking parameters as inputs and stiffness values as outputs were created. These networks provided excellent prediction accuracy measures (). For the inverse problem, the noted trend shows that better prediction accuracy measures are achieved when more stiffness nodes are used in the ANN modeling process. It was also observed that providing some outputs to the ANN as inputs, thus decreasing the number of required outputs, immensely improves the quality of predictions provided by the ANN. An experimental verification program will be conducted to qualify the effectiveness of the method proposed. This test program is described in details in the present paper. [ABSTRACT FROM AUTHOR]

Modernization of today's irrigation systems attempts to improve system efficiency and management effectiveness of every component of the system (reservoirs, canals, and gates) using automation technologies, along with hydraulic simulation models. The canal flow control scheme resulting from the coupling of the system automation and the simulation models has proven to be an excellent irrigation water management instrument around the world. Nevertheless, the harsh environment of irrigation systems can induce uncertainties or errors in the components of canal flow control that can worsen over time, misleading or confusing both human and computer controllers. These errors can be attributed to parameter measurement and conceptual sources, with the complexity of locating their individual origin. In this paper, a framework is presented to minimize the collective or aggregate error within an irrigation canal flow control scheme that uses a learning machine algorithm (multilayer perceptron and relevance vector machine) embedded in a hydraulic simulation model fed by a canal automation system. This framework is evaluated using actual data from an irrigation conveyance canal located at the Lower Sevier River Basin in Utah. The results obtained prove the adequacy of the proposed framework in minimizing the aggregate error, which affects the simulation results of the automation system (up to 91% in bias and 83% in maximum absolute error) when compared with the original values obtained in the verification period. The temporal correlation of the aggregate error was also significantly reduced, thus resulting in reduced local biases and structures in the model prediction error. [ABSTRACT FROM AUTHOR]

This paper studied the removal of the herbicide Glyphosate in aqueous solution using ions capable of forming an insoluble complex Glyphosate salt (ferric chloride). A maximum Glyphosate removal of 67.4% was achieved using of ferric chloride. The precipitation of the insoluble Glyphosate salt was affected by rapid mixing velocity, rapid mixing time, dosage of flocculant, and types of impellers. An artificial neural networks (ANN) model was used to predict the removal of Glyphosate. The results showed good agreement over the range of experimental and predicted data. Increasing the velocity gradient increased the Glyphosate removal. The Glyphosate removal decreased with further increase in shear stress. At higher flocculant dosage, the effect of impeller shear is less as the flocs are stronger. The results showed that the effect of impeller type was highly dependent on the rapid mixing velocity and rapid mixing time. [ABSTRACT FROM AUTHOR]

This paper presents an automatic landing control design using adaptive, integrated guidance and control (IGC) logic. The proposed IGC design uses a combination of an adaptive output feedback inversion and backstepping techniques. The problem is formulated as an adaptive output feedback control problem for a line-of-sight-based chasing flight configuration. The design objective is to regulate the relative distance between the aircraft and the moving reference point on a landing pattern and two bearing angles maintaining turn coordination. Adaptive neural networks are trained online with available measurements to compensate for inversion error as a result of unmodeled dynamics and modeling errors of the aircraft in the design process. In addition, a reference command trajectory for the automatic landing control is designed in a way that the aircraft follows the landing pattern regardless of its initial position. The automatic landing system using IGC logic is evaluated using a sophisticated six-degrees-of-freedom nonlinear simulation program with the approach and landing scenario. [ABSTRACT FROM AUTHOR]

This paper describes the application of artificial neural network (ANN) and support vector machine (SVM) methods for prediction of field hydraulic conductivity of clay liners based on in situ test results such as compaction characteristics, lift thickness, number of lift, and soil classification tests like Atterberg's limits and grain size. Statistical performances criteria, root mean square error, correlation coefficient, coefficient of determination, and overfitting ratio are used to compare different ANN and SVM models. Different algorithms are discussed for identification of important soil parameters affecting the hydraulic conductivity of clay liners. A model equation based on the parameters obtained using SVM is also discussed. [ABSTRACT FROM AUTHOR]

In this paper, the problem of site characterization is treated as a task of function approximation of the large existing data from standard penetration tests (SPTs) in three-dimensional subsurface of Bangalore, India. More than 2,700 field SPT values (N) has been collected from 766 boreholes spread over an area of 220-km2 area in Bangalore, India. To get N corrected value (Nc), N values have been corrected for different parameters such as overburden stress, size of borehole, type of sampler, length of connected rod. In three-dimensional analysis, the function Nc=Nc(X,Y,Z), where X, Y, and Z are the coordinates of a point corresponds to Nc value, is to be approximated with which Nc value at any half-space point in Bangalore, India can be determined. An attempt has been made to develop artificial neural network (ANN) model using multilayer perceptrons that are trained with Levenberg-Marquardt back-propagation algorithm. Also, a geostatistical model based on ordinary kriging technique has been adopted. The knowledge of the semivariogram of the Nc values is used in the ordinary kriging method to predict the Nc values at any point in the subsurface of Bangalore, India where field measurements are not available. The results obtained show that ANN model is fairly accurate in predicting Nc values. In case of ordinary kriging, a new type of cross-validation analysis shows that it is a robust model for prediction of Nc values. A comparison between the ANN and geostatistical model demonstrates that the ANN model is superior to Geostatistical model in predicting Nc values in the subsurface of Bangalore, India. [ABSTRACT FROM AUTHOR]

This paper presents the development of tests based on one artificial intelligence technique, the Kohonen neural network, for the detection of shifts in hydrometric data. Two new Kohonen-based detection tests are developed, the classification and mapping tests, and their performance is compared with that of well-known conventional detection tests. The efficacy of the tests is demonstrated with synthetic data, for which all the statistical properties and induced shifts are known. These synthetic data are designed to replicate hydrometric data such as annual mean and maximum streamflow. The results show that all tests, conventional and Kohonen based, may be considered equally reliable. However, no one test should be used alone because all generate false diagnostics under different circumstances. Within a decision support environment, a pool of tests may be used to confirm or complement one another depending on their known strengths and weaknesses. The Kohonen-based detection tests also perform well when applied to multivariate cases (i.e., testing more than one data sequence at a time), and their performance for multivariate cases is better than that for the univariate cases. [ABSTRACT FROM AUTHOR]

Water lost through leakage from water distribution networks is often appreciable. As pressure increases on water resources, there is a growing emphasis for water service providers to minimize this loss. The objective of the work presented in this paper was to assess the online application and resulting benefits of an artificial intelligence system for detection of leaks/bursts at district meter area (DMA) level. An artificial neural network model, a mixture density network, was trained using a continually updated historic database that constructed a probability density model of the future flow profile. A fuzzy inference system was used for classification; it compared latest observed flow values with predicted flows over time windows such that in the event of abnormal flow conditions alerts are generated. From the probability density functions of predicted flows, the fuzzy inference system provides confidence intervals associated with each detection, these confidence values provide useful information for filtering and ranking alerts. Additionally an accurate estimate of abnormal flow magnitude is produced to further aid in ranking of alerts. A water supply system in the U.K. was used for a case study with near real-time flow data provided by general packet radio service. The online burst alert system was constructed to operate alongside an existing flat-line alarm system, and continuously analyze a set of 144 DMAs every hour. The new system identified a number of events and alerts were raised prior to their detection in the control room; either through flat-line alarms or customer contacts. Examples are given of alert correlation with burst reports and subsequent mains repairs for a 2-month trial period. Forty four percent of alerts were found to correspond to bursts confirmed by repair data or customer contacts, 32% of alerts were confirmed as unusual short-term demand from manual analysis, 9% were related to known industrial events, and only 15% were ghosts. The results indicate that the system is an effective and viable tool for online burst detection in water distribution systems with the potential to save water and improve customer service. [ABSTRACT FROM AUTHOR]

The need of immediate supportive measures for sustainability of municipal infrastructures calls for better understanding of the behavior of various infrastructure network systems and their components. This paper presents a study which uses artificial neural networks to investigate the importance and influence of certain characteristics of sewer pipes upon their structural performance, expressed in terms of condition rating. In this study, back propagation and probabilistic neural network (NN) models were developed and validated. The data used in the development of these models were provided by the municipality of Pierrefonds, Quebec. It comprised of parameters related to sewer pipelines, pipe diameter, buried depth/cover, bedding material, pipe material, pipeline length, age, and closed circuit television (CCTV) based structural condition rating. The first six parameters are the independent variables of the models whereas CCTV based condition rating for these pipes is the dependent variable (i.e., the output of the models). The developed NN models were used to rank the parameters, in order of their importance/influence on pipe condition. It was found that, among the studied parameters, material attributes have highest influence on pipe structural condition, respectively, followed by the geometric and physical attribute group. Sensitivity analysis was then performed to simulate the structural condition of a pipe at a range of values of each input parameters. Results of sensitivity analysis describe the nature and degree of the influence of each parameter on pipe structural condition. The developed models are expected to benefit academics and practitioners (municipal engineers, consultants, and contractors) to prioritize inspection and rehabilitation plans for existing sewer mains. [ABSTRACT FROM AUTHOR]

This paper introduces innovative artificial intelligent techniques for directly predicting the cracking patterns of masonry wallets, subjected to vertical loading. The von Neumann neighborhood model and the Moore neighborhood model of cellular automata (CA) are used to establish the CA numerical model for masonry wallets. Two new methods—(1) the modified initial value method and (2) the virtual wall panel method—that assist the CA model are introduced to describe the property of masonry wallets. For practical purposes, techniques for the analysis of wallets whose bed courses have different angles with the horizontal bottom edges are also introduced. In this study, two criteria are used to match zone similarity between a “base wallet” and any new “unseen” wallets. This zone similarity information is used to predict the cracks in unseen wallets. This study also uses a back-propagation neural network for predicting the cracking pattern of a wallet based on the proposed CA model of the wallet and some data of recorded cracking at zones. These techniques, once validated on a number of unseen wallets, can provide practical innovative tool for analyzing structural behavior and also help to reduce the number of expensive laboratory test samples. [ABSTRACT FROM AUTHOR]

This paper describes the application of artificial neural networks (ANNs) for estimating reference evapotranspiration (ETo) as a function of local maximum and minimum air temperatures as well as exogenous relative humidity and reference evapotranspiration in different continental contexts of the autonomous Valencia region, on the Spanish Mediterranean coast. The development of new and more precise models for ETo prediction from minimum climatic data is required, since the application of existing methods that provide acceptable results is limited to those places where large amounts of reliable climatic data are available. The Penman-Monteith model for ETo prediction, proposed by the FAO as the sole standard method for ETo estimation, was used to provide the ANN targets for the training and testing processes. Concerning models which demand scant climatic inputs, the proposed model provides performances with lower associated errors than the currently existing temperature-based models, which only consider local data. [ABSTRACT FROM AUTHOR]

This paper describes an application of artificial neural networks (ANNs) to the prediction of local losses from integrated emitters. First, the optimum input-output combination was determined. Then, the mapping capability of ANNs and regression models was compared. Afterwards, a five-input ANN model, which considers pipe and emitter internal diameter, emitter length, emitter spacing, and pipe discharge, was used to develop a local losses predicting tool which was obtained from different training strategies while taking into account a completely independent test set. Finally, a performance index was evaluated for the test emitter models studied. Emitter data with low reliability were removed from the process. Performance indexes over 80% were obtained for the remaining test emitters. [ABSTRACT FROM AUTHOR]

The spectral analysis of surface waves (SASW) method is a nondestructive testing method of pavements based on the dispersive characteristic of seismic surface waves. The method can provide the thickness and stiffness of pavement layers. One of the more complex aspects of the SASW method is an iterative process to estimate the pavement parameters, called the inversion procedure. In this paper, the feasibility of completely automating the inversion process and substituting it with artificial neural network (ANN) models was explored. A number of different ANN models were developed using various ANN training strategies. To improve the performance of some ANN models, a sequential modeling technique was implemented. In the sequential modeling, some pavement parameters are estimated first from an initial set of ANN models, which is then the input to subsequent ANN models to estimate other parameters of interest. Furthermore, the performance of the ANN models was evaluated using a number of well-characterized pavement sections. The results illustrated that ANN models could estimate the upper layers’ parameters so well that they could replace the inversion process. The ANN models for other layers were capable of generating robust initial estimates for a well-constrained formal inversion that can be readily automated. [ABSTRACT FROM AUTHOR]

The parametric approach to eliminating the temperature-caused modal variability in vibration-based structural damage detection requires a correlation model between the modal properties and environmental temperatures. This paper examines the generalization capability of neural network models, established using long-term monitoring data, for correlation between the modal frequencies and environmental temperatures. A total of 770 h modal frequency and temperature data obtained from an instrumented bridge are available for this study, which are further divided into three sets: training data, validation data, and testing data. A two-layer back-propagation neural network (BPNN) is first trained using the training data by the conventional training algorithm, in which the number of hidden nodes is optimally determined using the validation data. Then two new BPNNs are configured with the same data by applying the early stopping technique and the Bayesian regularization technique, respectively. The reproduction and prediction capabilities of the two new BPNNs are examined in respect of the training data and the unseen testing data, and compared with the performance of the baseline BPNN model. This study indicates that both the early stopping and Bayesian regularization techniques can significantly ameliorate the generalization capability of BPNN-based correlation models, and the BPNN model formulated using the early stopping technique outperforms that using the Bayesian regularization technique in both reproduction and prediction capabilities. [ABSTRACT FROM AUTHOR]

A linked simulation-optimization model using artificial neural networks (ANNs) and genetic algorithms (GAs) is developed for deriving multiple objective management strategies for coastal aquifers. The GA-based optimization approach is especially suitable for externally linking a numerical simulation model within the optimization model. However, the solution of a linked simulation-optimization model is computationally intensive, as a very large number of iterations between the optimization and the simulation models are necessary to arrive at an optimal management strategy. Computational efficiency and feasibility for such linked models can be enhanced by simplifying the simulation process by an approximation. A possible approach for such approximation is the use of an ANN model. In this paper, an ANN model is developed initially as an approximate simulator of the three-dimensional density dependent flow and transport processes in a coastal aquifer. A simulation-optimization model is then developed by linking the ANN model with a GA-based optimization model for solving multiple objective saltwater management problems. The performance of the optimization models is evaluated using an illustrative study area. For comparison of the solution results, a multiple objective management model is also solved using embedded formulation and classical nonlinear optimization technique. The comparison of results shows potential feasibility of the proposed methodology in solving multiple objective management model for coastal aquifers. [ABSTRACT FROM AUTHOR]

This paper presents the development of a dynamic artificial neural network model (DAN2) for comprehensive urban water demand forecasting. Accurate short-, medium-, and long-term demand forecasting provides water distribution companies with information for capacity planning, maintenance activities, system improvements, pumping operations optimization, and the development of purchasing strategies. We examine the effects of including weather information in the forecasting models and show that such inclusion can improve accuracy. However, we demonstrate that by using time series water demand data, DAN2 models can provide excellent fit and forecasts without reliance upon the explicit inclusion of weather factors. All models are validated using data from an actual water distribution system. The monthly, weekly, and daily models produce forecasting accuracies above 99%, and the hourly models above 97%. The excellent model accuracy demonstrates the effectiveness of DAN2 in forecasting urban water demand across all time horizons. Finally, we compare our results with those of an autoregressive integrated moving average model and a traditional artificial neural network model. [ABSTRACT FROM AUTHOR]

Bridge risk assessment often serves as the basis for bridge maintenance priority ranking and optimization and conducted periodically for the purpose of safety. This paper presents an application of artificial neural networks in bridge risk assessment, in which back-propagation neural networks are developed to model bridge risk score and risk categories. The study investigated and utilized 506 bridge maintenance projects to develop the models. It is shown that neural networks have a very strong capability of modeling and classifying bridge risks. The average accuracies for risk score and risk categories are both over 96%. A comparative study is conducted with an alternative methodology using multiple regression techniques. The results revealed that neural networks achieved much better performances than regression analysis models. In addition an integrated forecasting approach was utilized to combine neural networks and regression analysis to generate hybrid models, which produced better accuracies than any of the individually developed models. [ABSTRACT FROM AUTHOR]

The impact of topography on design wind speed is addressed in current building codes and design standards by providing “speed-up” ratios for limited cases of terrain geometries. This paper proposes a combined numerical–neural network (NN) approach to provide speed-up ratios for a wide range of topographic features such as single and multiple hills, escarpments, and valleys. In this approach learning data required by the NN is generated via a detailed numerical approach based on computational fluid dynamics (CFD). Use of the developed model only requires simple geometrical input such as slope, height, and ground roughness while producing results of comparable accuracy to complex numerical evaluations. This combined CFD-NN approach not only produces data for new cases but also conveys the results of complex CFD simulations to the engineering profession (end user). Results compare well with an independent set of experimental data demonstrating the feasibility of the CFD-NN approach to generate data to apply wind design load provisions to buildings with upstream complex terrain. [ABSTRACT FROM AUTHOR]