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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]

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 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 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]

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui Watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate gradient, gradient descent, and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from the LM-NN, and these results were then compared with those from PSO-based ANNs, including the conventional PSO neural network (CPSONN) and the improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. The results show that the PSO-based ANNs performed better than the LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing data set for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multiparameter (rainfall and water level) inputs, the RMSE of the testing data set for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN. [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 an approach to optimize the short-term traffic prediction performance on freeways using multiple artificial neural network topologies under different network and traffic condition settings. The approach encourages multimodel techniques that are capable of improving the prediction system performance over single-model approaches. Using a mix of neural network topologies, the short-term speed prediction performance was extensively evaluated under different input settings and various prediction horizons (from 5 to 20 min). To enable the networks to learn from historical information, a long-term memory component was introduced to the input patterns to allow the networks to build internal representation of recurrent conditions, in addition to the short-term memory that is encoded in the most recent information. Optimal settings were determined by maximizing the performance under different traffic conditions observed at the target location, as well as upstream and downstream locations. Comparative statistical analysis with naive and heuristic approaches showed that the optimized neural network approach resulted in better prediction performance. The study shows that the optimal settings were consistently more dependent on the long-term memory component as prediction horizon increases. [ABSTRACT FROM AUTHOR]

In an extension of a previous paper, prediction accuracy is improved for neural networks to be used as part of an adaptive structural control system. This improvement will enable reliable predictions of performance variables such as displacements and control forces further into the future. This allows more lead time for controller adjustment should a performance variable be predicted to violate a prescribed constraint. The improved prediction accuracy is due to the use of the Levenberg–Marquardt algorithm in training the neural network and the use of a single neural network for more than one performance variable simultaneously. With these improvements, far fewer iterations (and more importantly less computer processor time) are used in the neural network training, and most importantly the prediction accuracy is greatly improved. These improved neural network predictions are then compared to other prediction methods: a polynomial fit of past data and the use of the state transition matrix. [ABSTRACT FROM AUTHOR]

Surge vessels provide efficient protection against low and high transient pressures in water distribution systems. However, they can be quite expensive, and any reduction in surge vessel size can significantly reduce surge protection costs. Graphical and other heuristic methods reported in literature are limited to sizing surge vessels for simple rising mains. Attempts to use more structured optimization techniques have been largely unsuccessful because of their impractical computational requirements. This article proposes a robust framework for developing surge protection design tools and demonstrates the usefulness of the framework through an example surge vessel sizing tool. The essence of the proposed framework is in the identification of key transient response parameters that influence surge vessel characteristics from seemingly unmanageable transient response data. This parameterization helps the sizing tool to exploit the similarity between transient responses of small pipe networks and subsections of large pipe networks. The framework employs a knowledge-base of transient pressures and flows derived from several small network models and corresponding optimal surge vessel sizes obtained from genetic algorithm (GA) optimizers. Key transient response parameters were identified from this knowledge-base and used as input variables for a neural network model along with the associated surge vessel characteristics as output variables. The trained neural network model was successfully applied for complex pipe networks to obtain optimal surge vessel sizes for transient protection. Neural network model predictions were compared with optimal surge vessel characteristics, and their performance was evaluated by transient simulation models to assess the efficiency of the proposed framework and sizing tool. Application of this framework has the advantage of developing surge protection sizing tools independent of network system schematics and boundary conditions. [ABSTRACT FROM AUTHOR]

The operating energy efficiency of the Ulu Pandan membrane bioreactor plant is optimized by artificial neural network (ANN) and bioprocess models. The ANN model mines historical plant data to uncover optimal operating settings. Historical plant data indicate that adjusting the membrane scouring aeration cycle will lead to direct energy savings. The ANN model concurs and shows the same correlation. Changes to plant operations carry substantial risks to the stability of the plant and place limitations on the range of operational variations. A plant risk assessment is conducted to ascertain the risk proposition for the adjustment of operating parameters. The bioprocess model investigates the underlying biological treatment mechanisms to identify the impact of the solids retention time on the volatile suspended solids, soluble microbial products, and endogenous decay coefficient. Results of the modeling show qualitatively good agreement with measured operating data. The concentrations of volatile suspended solids, soluble microbial products, and endogenous decay coefficient are shown to be sensitive to the system solids retention time. [ABSTRACT FROM AUTHOR]

A hybrid rainfall-runoff model that integrates artificial neural networks (ANNs) with Xinanjiang (XAJ) model was proposed in this study. The writers extracted the digital drainage network and subcatchments from digital elevation model (DEM) data considering the spatial distribution of rain-gauge stations. Then the semidistributed XAJ model was established based on DEM. Considering the runoff routing cannot be calculated by the linear superposition of the route runoff from all subcatchments, artificial neural networks as effective tools in nonlinear mapping are employed to explore nonlinear transformations of the runoff generated from the individual subcatchments into the total runoff at the entire watershed outlet. The integrated approach has been demonstrated as feasible and was applied successfully in the Yanduhe watershed, the upper tributary of Yangtze River Basin. The results indicated that the approach of integrating back-propagation ANN with semidistributed XAJ model may achieve the promising results with acceptable accuracy for flood events simulation and forecast. [ABSTRACT FROM AUTHOR]

Optimization of large and hydraulically complex water distribution systems (WDSs) is computationally expensive as simulation models are required to evaluate the performance of solutions to the problem at hand. Metamodels can act as a surrogate or substitute for these simulation models and provide significant speed-ups in the optimization process. The application of metamodels in the field of WDS optimization has been limited to date, and little guidance has been given in terms of constructing metamodels for hydraulically complex systems. While it is relatively straightforward to obtain satisfactory metamodel approximations to simulation models of simple WDSs, this is not necessarily the case for more complex networks. In order to reduce the complexity of the relationship that is to be approximated by the metamodels, a number of factors have to be considered, including the complexity of the hydraulic simulation model, the complexity of the decision space, and the locations at which outputs are required from the hydraulic simulation model. This research presents a systematic methodology for dealing with these factors and demonstrates the effectiveness of the approach by applying it to an actual WDS. A system in Wallan, Victoria, Australia, is selected for demonstration purposes. Four different metamodelling scenarios are presented here. The results show that, for this case study, some skeletonization of the model is required to achieve suitably accurate metamodels. The optimization results show a reduction in the average daily pumping costs from $457 to $363; a saving of 21%. The net present value (NPV) over 25 years is used as the objective function, which includes both pumping and chlorine costs. The current operating regime corresponds to an NPV of $1.56 million, while the optimized solution has an NPV of $1.34 million; a saving of 14%. In addition to these economic benefits, the optimized solution achieves adequate disinfection throughout the system, whereas the current operating regime results in deficits in chlorine residuals at several locations in the system. [ABSTRACT FROM AUTHOR]

A backpropagation artificial neural network (ANN) model has been developed to predict the liquefaction cyclic resistance ratio (CRR) of sands using data from several laboratory studies involving undrained cyclic triaxial and cyclic simple shear testing. The model was verified using data that was not used for training as well as a set of independent data available from laboratory cyclic shear tests on another soil. The observed agreement between the predictions and the measured CRR values indicate that the model is capable of effectively capturing the liquefaction resistance of a number of sands under varying initial conditions. The predicted CRR values are mostly sensitive to the variations in relative density thus confirming the ability of the model to mimic the dominant dependence of liquefaction susceptibility on soil density already known from field and experimental observations. Although it is common to use mechanics-based approaches to understand fundamental soil response, the results clearly demonstrate that non-mechanistic ANN modeling also has a strong potential in the prediction of complex phenomena such as liquefaction resistance. [ABSTRACT FROM AUTHOR]