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Previous research on canal automation has dealt with the control of single, in-line canals, while canal operators typically have to control an entire network of canals. Because the branches in a network are hydraulically coupled with each other, control of a branching canal network based on separate controllers for each branch may not be the most effective control strategy. A methodology by which existing automatic control systems could be modified to control branching canal networks is provided in a companion paper. This paper presents results of hydraulic simulations of the new methodology to estimate the controllability of a large portion of the branching canal network operated by the Salt River Project (SRP). Two types of controllers were used for this study: (1) linear quadratic regulator (LQR) and (2) model predictive control (MPC). Both controllers used the same underlying process model [integrator-delay (ID) model], and both controllers were capable of feedback and feedforward control. Under feedback control alone, both controllers gave similar performance, but were unable to effectively control the overall system because of the long delay times. When feedforward control was added to the feedback controller, both of these control systems were able to effectively control the branching canal network operated by SRP. For the LQR controller, the volume compensation method for routing known demand change was used as the feedforward controller. For the MPC controller, the ID model was used as the feedforward controller. Slight differences were noted between the performance of the two feedforward controllers. [ABSTRACT FROM AUTHOR]

Efficiency of hydraulic solvers for the simulation of flows and pressures in water distribution systems (WDSs) is very important, especially in the context of optimization and risk analysis problems, where the hydraulic simulation has to be repeated many times. Among the methods used for hydraulic solvers, the most prominent nowadays is the global gradient algorithm (GGA), based on a hybrid node-loop formulation. Previously, another method based just on loop flow equations was proposed, which presents the advantage that it leads to a system matrix that is in most cases much smaller than in the GGA method, but has also some disadvantages, mainly a less sparse system matrix and the fact that introducing some types of valves requires the redefinition of the set of network loops initially defined. The contribution of this paper is to present solutions for overcoming the mentioned disadvantages of the method based on loop flow equations. In particular, efficient procedures are shown for selecting the network loops so as to achieve a highly sparse matrix and methods are presented to incorporate check valves and automatic control valves while avoiding the need to redefine the loops initially selected. [ABSTRACT FROM AUTHOR]

In this paper, a proportional-integral sliding mode control (PISMC) method is presented to achieve the attitude and position control of an unmanned helicopter system for autonomous hovering. The PISMC includes the advantages of both conventional sliding mode control (SMC) and integral sliding mode control (ISMC). The proposed PISMC algorithm is capable of stabilized attitude and position control for unmanned helicopter autonomous hovering, and it offers a performance improvement over the conventional SMC and ISMC algorithms as well. The system performance is quantified and an experimental unmanned helicopter system is developed to access the performance of the PISMC. Both simulations and actual flight tests are conducted to verify the PISMC attitude and position control. The simulation results show that the PISMC tracking error is smaller and that PISMC converges faster than SMC does. The flight test results prove that the proposed PISMC is effective in the attitude and position control of unmanned helicopter autonomous hovering. [ABSTRACT FROM AUTHOR]

Resource scheduling, or the allocation of resources over time, is a challenging problem in large-scale or multiple-project environments. Traditional network scheduling techniques are ineffective in modeling the dynamic nature and resource interactions of large or multiunit projects. This paper presents a simulation-based auction protocol (SBAP) to solve resource scheduling problems in large-scale construction projects. SBAP is a hybrid framework that integrates multi agent resource allocation (MARA) in a simulation environment. SBAP deploys a centralized resource allocation approach, referred to as an auction protocol, whereby agents bid on different combinations of resources at the start of a simulation cycle. Agents attempt to improve their individual welfare by acquiring a combination of resources; an auctioneer looks at the entire system and allocates resources to the agents using a combinatorial algorithm to maximize an overall objective function (e.g., maximizing the system's revenue or minimizing total costs). The auction is repeated on a regular basis. Simulation is also employed in large-scale projects to track the availability of resources, capture and release the resources, and satisfy constraints of the problem. This paper demonstrates the architecture of the SBAP framework and discusses implementation of SBAP in a real case study of crane allocation in an industrial project. [ABSTRACT FROM AUTHOR]

This paper presents a method of approaching the attitude control of an autonomously hovering unmanned helicopter system. The proportional-integral sliding mode controller (PISMC) features a combination of the conventional sliding mode control (SMC) and the integral sliding mode control (ISMC). By applying the PISMC algorithm, the controller is capable of providing stable roll and pitch control of the main rotor simultaneously. To show how PISMC may improve system performance, this paper develops an experimental unmanned helicopter system to assess the performance of the proposed controller. The results of the simulation show that the tracking error of the PISMC is not only smaller but also converges more quickly than that of the conventional SMC. Resulting roll and pitch controllers are successfully verified in computer simulations and actual flight tests. The flight test results presented in the paper are found to be consistent with the simulation results. [ABSTRACT FROM AUTHOR]

Recent years have witnessed an interest in increasing the resolution of transportation models to allow them to better address emerging transportation issues, such as concerns about emissions, air quality, and emergency preparedness. Microscopic simulation models, especially those that combine routing logic with accurate modeling of traffic flow dynamics, have vast untapped potential for modeling large-scale transportation networks and for achieving the resolution level required for addressing such emerging issues. Building and calibrating such models, however, is quite challenging and is currently not widely understood. This paper describes the process of developing and calibrating a large-scale microscopic model of Chittenden County, Vt., an area of about 1,400 km2 (540 mi2). The model was developed primarily using readily available data to which most metropolitan planning organizations are expected to have access. Following a brief description of how the network was coded and the traffic demand specified, the paper discusses the process of error checking and model calibration. Preliminary calibration results are encouraging, given the complexity of the model. The paper includes a summary of the main lessons learned and modeling pitfalls that should be avoided. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal contraflow lane configuration problem arising in the contraflow lane control strategy that has been implemented successfully in practice. The problem is formulated as a bilevel programming model in which the upper level problem is a binary integer programming formulation that aims to minimize the total travel time of a study area, while the lower level problem is a microscopic traffic simulation model that can simulate the dynamic reaction of the drivers resulting from a contraflow lane configuration scheme. A microscopic traffic simulation model is adopted in this study because it is easily handled by traffic engineers. Such an adoption results in inexistence of analytical expression of the objective function in the upper level problem. Accordingly, some conventional analytical solution methods for solving integer programming problems are no longer available for the proposed model. Therefore, this paper develops a variation of genetic algorithm that embeds with the microscopic traffic simulation model as well as a chromosome repairing procedure to find an optimal contraflow lane configuration solution. A case study in Singapore is carried out to evaluate the proposed methodology, in which PARAMICS as the microscopic traffic simulation model is applied. [ABSTRACT FROM AUTHOR]

In past research, several versions of hybrid genetic algorithm–simulation methodology have been proposed for scheduling of multiple lane closures that aims to minimize a network’s total traffic delay. The genetic algorithm is used as a search engine for generation of lane closure schedule, while a microscopic traffic simulation model is employed to calculate the total network travel time under each lane closure scenario. A difficulty in implementing this methodology practically is the long computing time required, due to the many simulation runs needed to evaluate the average total network travel time of each feasible schedule. This paper applies the precondition technique, standard error criterion, and termination criterion to reduce the number of necessary simulation runs. As a further improvement, traffic simulations are distributed in different processors of a multiprocessor machine. To further reduce the computing time, a two-stage hybrid genetic algorithm methodology has been proposed in this paper. This two-stage methodology consists of a hybrid genetic algorithm–traffic assignment methodology as the first stage, followed by a hybrid genetic algorithm–distributed simulation methodology as the second stage. The traffic assignment model is used to replace the traffic simulation model in the estimation of total network travel time in stage 1. The applications of the improvement techniques have been demonstrated through a hypothetical problem involving 20 lane closure requests in a network consisting of 986 links, 397 nodes, and 22 origin-destination zones. Together, these improvement techniques contributed to up to 87% reduction in waiting time for a solution of the example problem. [ABSTRACT FROM AUTHOR]

Climate change adaptation in the infrastructure sector has received increased attention in recent years, but local governments and asset managers frequently find it difficult to identify the most suitable and efficient adaptation options. This paper proposes a framework for assessing the costs and benefits of infrastructure adaptation at the local level. The framework consists of three steps: (1) selecting target infrastructure and adaptation options, (2) identifying climate factors, and (3) performing an economic assessment algorithm simulation. The suggested economic assessment algorithm considers both gradual and extreme impacts of climate change and provides an integrated economic assessment over the lifecycle of the infrastructure. Three representative concentration pathways climate scenarios are used to estimate the cost of future damage and the conditions of the assessed infrastructure. A case study is undertaken examining adaptation of an erosion control system in a mountain watershed with an 85-year analysis period that yields a comprehensive understanding of the application of the suggested framework. This research is expected to guide decision making in prioritizing the most cost-effective adaptation strategies for infrastructure. [ABSTRACT FROM AUTHOR]

This paper presents and discusses a simple method to deal with zero flows in Newton solvers for water distribution systems, in particular in a previously published global gradient algorithm. The method consists in replacing the Hazen-Williams head loss-flow relationship for flows below a certain threshold with a linear relationship, which coincides with Hazen-Williams' at zero, but has a nonzero derivative at that point, thus avoiding computational troubles associated with zero flows. Manual and automatic random testing of example networks shows that the proposed method compares well with both the algorithm adopted in water distribution modeling software and the recently proposed regularization method in convergence rate while outperforming them in computational veracity and applicability range. [ABSTRACT FROM AUTHOR]

The increased interest in the development and application of large-scale or regional microsimulation transportation models has brought to the forefront the challenges associated with estimating the dynamic demand information needed to run such models. This paper develops a computational-based approach for estimating or adjusting dynamic origin-destination matrices for regional microsimulation models on the basis of hourly traffic counts. The proposed approach, while based on genetic algorithms (GA), includes a special module, called Plan Analyzer, to guide the search process in an intelligent way. This results in a customized algorithm for the problem that can be regarded as an example of a guided genetic algorithm (GGA). To cut down on execution time, a distributed implementation of the algorithm is adopted, and several software design procedures are developed to deal with the demanding memory requirements of the problem. To demonstrate the effectiveness of the algorithm, the Transportation Analysis and Simulation System (TRANSIMS) model, a microsimulation platform designed for regional simulations, is used to model two test networks, a synthetic grid network and a realistic regional model of Chittenden County, Vermont. The GGA is then utilized to estimate the dynamic demand for those two models on the basis of hourly traffic count information. The results clearly demonstrate the effectiveness of the GGA in dramatically reducing the average absolute error (AAE) between the simulated and field counts, and in closely estimating the 'true' demand, which was known in this research by virtue of how the case studies were designed. The results also show that the developed GGA significantly outperforms standard GAs. [ABSTRACT FROM AUTHOR]

This paper constructs a train routing model combined with a train scheduling problem, which is a 0-1 mixed-integer nonlinear programming problem. Except for train route choice, the model considers a system of complicated constraints on headway, trip time, meeting-crossing and overtaking between trains, capacity of siding, and so on. Based on the delay information of each train, a route adjustment algorithm is designed to obtain satisfactory route schemes of trains. Moreover, a tabu search procedure is designed to further improve the route schemes. The simulation results show that, relative to the optimal solution, the solutions obtained by the current method exhibit small relative error. The tabu search algorithm exhibits unstable performance because of dependence on the initial solution. Combined with the route adjust algorithm, the tabu search technique can improve the quality and stability of solutions. In addition, the departure order of heterogeneous trains exerts important influences on train route choice. [ABSTRACT FROM AUTHOR]

In this paper, the response of two-dimensional framed structures made of rectangular cells to the sudden removal of columns is studied. A simulation algorithm based on the discrete-element method is employed, where the structural elements are represented by elasto-plastic Euler-Bernoulli beams with elongation-rotation failure threshold. The effect of structural cell slenderness and of topological hierarchy on the dynamic residual strength after damage is investigated. Topologically hierarchical frames have a primary structure made of a few massive elements, while homogeneous frames are made of many thin elements. It is also shown how depends on the activated collapse mechanisms, which are determined by the mechanical hierarchy between beams and columns, i.e., by their relative strength and stiffness. Finally, principles of robustness-oriented capacity design that seem to be in contrast to the conventional antiseismic capacity design are addressed. [ABSTRACT FROM AUTHOR]

Computer algorithms used to identify recurrent freeway bottlenecks have been studied since the deployment of loop detecting systems. Such algorithms automatically analyze the archived loop detector data and identify potential recurrent bottlenecks and their characteristics, such as location, time of day, and activation rate, for further investigation. In a highway congestion mitigation project, such algorithms can save time and resources for the initial screening of bottlenecks over a large freeway network. These algorithms include rule-based, contour-map-based, and simulation-based methods. However, existing methods require loop detector data with high accuracy and consistency, which is difficult to achieve in prevailing loop detecting systems. This paper proposes a new bottleneck identification algorithm with strong error and noise tolerance. Several simple denoising methods to improve the error resistance of existing algorithms are also proposed. Using statistical error analysis methods, the proposed algorithm and the denoising methods were calibrated and evaluated using field data collected from two distinct freeway corridors (US 12/14 and I-894) in the U.S. state of Wisconsin. Ground truth data for this study come from the manual inspection of 287,055 traffic video snapshots in the course of a month. In the evaluation tests, the proposed algorithm can produce quality congestion identification results with fewer false alarms than the existing algorithms, especially when identifying severe bottleneck congestion. [ABSTRACT FROM AUTHOR]

When selecting construction design-build teams, subjective multi-evaluator decision making can be controversial and can lead to project delay, loss of public trust, and increased legal fees. This paper introduces a mathematical algorithm that supports multi-evaluator selection decisions by detecting and reducing the effect of possible uncertainty in the scores given by the evaluators. The algorithm was coded and tested using several scenarios. The study results show that the model is robust and capable of extracting the maximum knowledge from the scores given by evaluators with varying degrees of expertise. Simulation results show that the proposed model performed better than the simple averaging method 89% of the time. The outcome of this research provides the decision maker with a justified basis to proceed with the selection decision. [ABSTRACT FROM AUTHOR]

The growth of viscoelastic cracks in hot-mix asphalt (HMA) concrete mixtures was simulated by using a disk-shaped compaction test. Modeling techniques, including the interconversion and continuous spectrum methods, the harmony search algorithm, and the state-variable approach, substantially enhanced the computational efficiency of the generalized J-integral. The generalized J-integral was used for determining the viscoelastic crack growth parameter on the basis of the fracture mechanics under cyclic loading conditions. This paper shows that the parameter can be used for simulating the viscoelastic crack growth in HMA concrete mixtures even under conditions of different loading magnitudes and frequencies. [ABSTRACT FROM AUTHOR]

In this paper, an optimization model for force finding of tensegrity systems with considerations of the geometrical stability condition, the unilateral property of members, and the evenness requirement of internal forces was developed. The simulated annealing algorithm was used to solve this optimization problem. Force finding of several typical tensegrity systems was carried out with the proposed method. It was found that the optimized solutions were very close to the analytical solutions and able to reflect the geometrical symmetry of the structures. It also verified that the proposed method is applicable to tensegrity systems under consideration including simple two-dimensional structures and complex three-dimensional multimodular structures. [ABSTRACT FROM AUTHOR]

Real-time hybrid simulation combines experimental testing and numerical simulation by dividing a structural system into experimental and analytical substructures. Servohydraulic actuators are typically used in a real-time hybrid simulation to apply command displacements to the experimental substructure(s). Servohydraulic actuators may develop a time delay due to inherent actuator dynamics that results in a desynchronization between the measured restoring force(s) and the integration algorithm in a real-time hybrid simulation. Inaccuracy or even instability will occur in a hybrid simulation if actuator delay is not compensated properly. This paper presents an adaptive compensation method for actuator delay. An adaptive control law is developed using an error tracking indicator to adapt a compensation parameter used in the proposed compensation method. Laboratory tests involving large-scale real-time hybrid simulations of a single degree of freedom moment resisting frame with an elastomeric damper are conducted to experimentally demonstrate the effectiveness of the proposed adaptive compensation method. The actuator tracking capability is shown to be greatly improved and exceptional experimental results are still achieved when a good estimate of actuator delay is not available. [ABSTRACT FROM AUTHOR]

Adaptive ramp metering has undergone significant theoretical developments in recent years. However, the applicability and potential effectiveness of such algorithms depend on a number of complex factors that are best investigated during a planning phase prior to any decision on their implementation. The use of traffic simulation models can provide a quick and cost-effective way to evaluate the performance of such algorithms prior to implementation on the target freeway network. In this paper, a capability-enhanced PARAMICS simulation model has been used in an evaluation study of three well-known adaptive ramp-metering algorithms: ALINEA, BOTTLENECK, and ZONE. ALINEA is a local feedback-control algorithm, and the other two are areawide coordinated algorithms. The evaluation has been conducted in a simulation environment over a stretch of the I-405 freeway in California, under both recurrent congestion and incident scenarios. Simulation results show that adaptive ramp-metering algorithms can reduce freeway congestion effectively compared to the fixed-time control. ALINEA shows good performance under both recurrent and nonrecurrent congestion scenarios. BOTTLENECK and ZONE can be improved by replacing their native local occupancy control algorithms with ALINEA. Compared to ALINEA, the revised BOTTLENECK and ZONE algorithms using ALINEA as the local control algorithm are found to be more efficient in reducing traffic congestion than ALINEA alone. The revised BOTTLENECK algorithm performs robustly under all scenarios. The results also indicate that ramp metering becomes less effective when traffic experiences severe congestion under incident scenarios. [ABSTRACT FROM AUTHOR]

Groundwater contaminant transport modeling is basically performed to predict contaminant concentration and to understand the biochemical and physical processes that happen in the subsurface of porous media. Modelers have been faced with the challenge of accurately modeling the behavior and fate of contaminants in groundwater with models and techniques that incorporate the appropriate noise statistics and estimates the hydrogeologic parameters effectively. Unaccounted noise and uncertainties in the modeling greatly affect the accuracy of these predictions. In this paper, two Monte Carlo-based techniques, particle filter (PF) and Ensemble Kalman filter (EnKF), were applied to a three-dimensional (3D) groundwater contaminant transport model to accurately estimate the first-order decay rate and contaminant concentration at each time step. The PF and EnKF are embedded with Sampling Importance Resampling (SIR) and Singular Value Decomposition (SVD) concepts to avoid degeneracy and matrix singularity, respectively. The simulation is performed with a specified domain space and with ensembles and particles size of 50 for parameter estimation and concentration prediction. A set of sparse observation points selected at specific locations were used to update the predictions from the filter at each time step. An analytical solution is generated as true solution to test the accuracy of the predicted values. Algorithms to generate the simulation results were run. The first-order decay rate estimated using PF and EnKF all converged at 0.041/day, which matches the actual decay rate. The performance and accuracy of the numerical method, PF, and EnKF were tested using Mean Absolute Error (MAE) and Maximum Absolute Error (Emax) equations. The results show that the EnKF performs better than both the PF and the numerical method. Also, the EnKF is capable of reducing the error in the numerical solution by approximately 85%. The R-square adjusted values for the numerical solution, PF, and EnKF were 55, 91.2, and 97.3%, respectively. This statistical value further confirms that EnKF approach performs better than PF. [ABSTRACT FROM AUTHOR]

The classic spectral representation method (SRM)-based nonstationary process simulation algorithm is used extensively in the engineering community. However, it is less efficient owing to the unavailability of fast Fourier transform (FFT). In this paper, an efficient, almost accurate, and straightforward algorithm is developed for the simulation of the multivariate nonstationary process. In this method, an evolutionary spectral matrix is decomposed via Cholesky method, and then proper orthogonal decomposition (POD) is used to factorize decomposed spectra as the summation of the products of time and frequency functions. Because original time-dependent decomposed spectra are decoupled via factorization, FFT can be used to significantly expedite the simulation efficiency. This POD-based factorization is totally data-driven and optimal, and fewer items are required in matching decomposed spectra. Therefore, the accuracy and efficiency of the factorization can be guaranteed at the same time. Another attractive feature of this factorization is straightforwardness, because only regular eigenvector decomposition is involved. Numerical examples of nonstationary processes are used to demonstrate the effectiveness and accuracy of the proposed approach. Results show that the factorization and simulation agree with the targets very well. In addition, the speed at which sample functions are generated is significantly improved over classic SRM, in which the full summation of sine/cosine terms is needed. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient method for the conditional simulation of nonstationary fluctuating wind speeds for the buffeting analysis of long-span bridges. The fluctuating wind speeds, with part of their time histories measured at some points, are regarded as a zero-mean nonstationary Gaussian vector process characterized by an evolutionary power spectral density (EPSD) matrix. The Kriging method is then applied to the random vector of the Fourier coefficients of the evolutionary vector process for conditional simulation. A mathematical proof of this conditional simulation is provided. A fast algorithm of the conditional simulation is further derived, by converting the Cholesky decomposition of a large size covariance matrix of the Fourier coefficients into the Cholesky decomposition of a series of small-size coherence matrices with much less computational time. The procedure for implementing the fast algorithm-based conditional simulation method is detailed. Finally, this method is applied for the conditional simulation of typhoon-induced nonstationary wind speed time histories for the buffeting analysis of Stonecutters Bridge in Hong Kong. The time domain buffeting responses of the bridge obtained using the conditionally simulated wind speed time histories indicate that the proposed simulation method is feasible and applicable. [ABSTRACT FROM AUTHOR]

The microplane material model for concrete, formulated mathematically in the companion paper, is calibrated by material test data from all the typical laboratory tests taken from the literature. Then, the model is verified by finite-element simulations of data for some characteristic tests with highly nonuniform strain fields. The scaling properties of model M7 are determined. With the volumetric stress effect taken from the previous load step, the M7 numerical algorithm is explicit, delivering in each load step the stress tensor from the strain tensor with no iterative loop. This makes the model robust and suitable for large-scale finite-element computations. There are five free, easily adjustable material parameters, which make it possible to match the given compressive strength, the corresponding strain, the given hydrostatic compression curve, and certain triaxial aspects. In addition, there are many fixed, hard-to-adjust parameters, which can be taken to be the same for all concretes. The optimum values of material parameters are determined by fitting a particularly broad range of test results, including the important tests of compression-tension load cycles, mixed-mode fracture, tension-shear failure of double-edge-notched specimens, and vertex effect when axial compression is followed by torsion. Because of the lack of information on the material characteristic length or fracture energy, which can be obtained only by size effect tests on the same concrete, and on the precise boundary conditions and precise gauge locations, the finite-element fitting of the present test data can hardly be expected to give better results than single-point simulations of specimens with approximately homogeneous strain states within the gauge length. Nevertheless, tensile test data with severe localization are delocalized on the basis of assumed material length. Model M7 is shown to fit a considerably broader range of test data than the preceding models M1-M6. [ABSTRACT FROM AUTHOR]

Recently, increasing terrorist threats on bridges have illustrated the vulnerabilities of this type of transportation infrastructure. Because of cost and safety constraints, it is impractical to obtain the complete structural response by testing a real bridge, so in recent years, a fully coupled fluid-structure interaction model, which still has several assumptions, was developed to simulate the blast wave propagation process and its interaction with the structure; this has become an advanced method to better analyze blast loading effects. However, because of the complicated computational algorithm and desktop computer resource limitations involved in using this model, it is not practical to simulate large structural responses, especially for long-span bridges. The purpose of this paper is to develop a multi-Euler domain method to solve the problems listed earlier. Both the verification of the method and its application for a RC composite slab-on-girder bridge proved its accuracy and efficiency. The blast-resistant capacity of three different detonation scenarios was investigated, including one above-deck detonation and two under-deck detonations, with different trinitrotoluene (TNT)-equivalent charge weights. This study established the dynamic performance and the damage mechanisms of the whole bridge and identified the critical blast event for this typical slab-on-girder bridge. The results and observations of this study provide a global understanding of protection strategies for highway bridges. [ABSTRACT FROM AUTHOR]

Analytical macroscopic models are presented to quantify delay at signalized intersections for extended control periods (multiple cycles) attributable to downstream time-variable congestion. Each of the models consists of an upstream demand estimation algorithm, a downstream queue build-up prediction algorithm, a shockwave propagation and dissipation tracking algorithm, and an algorithm that carries traffic aftereffect of a cycle's control into the following cycle. Model inputs are basic traffic flow properties, signal control parameters, and link geometry. The models are modular and can be incorporated into any size system for one or multiple cycles. The models were tested on a hypothetical system of closely spaced intersections and under different traffic flow, control, and geometric conditions. Results show that delay induced by downstream traffic operations on an upstream intersection can be significant and that it may change with time and reach steadiness once traffic flow, downstream queues, and signal control measures stabilize and start replicating over time. Green ratios of neighboring intersections exert a significant influence on delay. For certain green ratios and geometric combinations, offsets can exert appreciable influence as well. The delay values and their temporal variation compared well with those from a microscopic simulation model. [ABSTRACT FROM AUTHOR]

Since original publication in 1997, the CS2 numerical model has been widely used for consolidation analysis and has formed the basis for several more advanced consolidation models. In this paper, an enhanced version of the CS2 model is presented along with simulations to illustrate its new capabilities. The algorithm includes features of the original model with the addition of time-dependent loading, an external hydraulic gradient, and unload/reload effects. Verification checks show excellent agreement with analytical solutions for time-dependent loading under small strain conditions. A variety of numerical examples indicates that the new capabilities describe important considerations for consolidation analysis. The simulations also indicate that failure to correctly account for compressibility and hydraulic conductivity constitutive relationships and self-weight of the soil in particular can lead to significant errors. [ABSTRACT FROM AUTHOR]

This work presents a modification to steady-state water distribution network (WDN) simulation models to account for directional devices such as check valves (CHVs) and flow-control valves (FCVs). These devices, allowing water flow control in a definite direction, are important to manage the hydraulic system functioning over time by considering the variation of some boundary conditions (e.g., required demands and tank levels). However, the simulation models are built on the assumption that water can flow in both directions of each pipe in the hydraulic system, and the information on directionality of some devices is not automatically allowed. Thus, in WDN models, those devices are currently modeled using a heuristic approach intermixed with solving the problem of steady-state WDN analysis. For this reason, a different approach using content and cocontent theory was recently proposed to define the conditions that guarantee the existence and uniqueness of the solution. The alternative proposed in this paper presents an adjustment of the energy balance equations to account for flow-control valves. Check valves are treated as a special case of flow-control valves, whereas the directionality of pumps, which are equipped with a check valve to avoid reverse flow, is modeled by means of their implicit check valve. Once the status of such directional devices is identified, a topological analysis of the network is performed. The methodology is applied to the demand-driven and pressure-driven analysis of a WDN solved by means of the global gradient algorithm, although it could be easily extended to other algorithms. [ABSTRACT FROM AUTHOR]

The main goal of the work described in this paper was to set up a procedure for modeling a thermal protection system for a hypersonic reentry vehicle by extending earlier work done by the authors. A multiphysics framework has been setup for the simulation of hypersonic reentry vehicles using commercial codes FLUENT and LS-DYNA with user-defined programming. The computational fluid dynamics code (FLUENT) and the material thermal and structural response code (LS-DYNA) are loosely coupled to achieve the solution. The surface recession resulting from ablation in the reentry vehicle is simulated by implementing a mesh movement algorithm in LS-DYNA. The vehicle considered in the calculation is an axisymmetric vehicle flying at zero degree angle of attack. [ABSTRACT FROM AUTHOR]

This paper describes a simulation-optimization model aimed at helping water utilities determine the best way to operate large-scale multisource water-supply systems. The operation of water systems is optimized taking into account the principal planning objectives defined for interventions that include reducing operating costs, satisfying demand, delivering water of appropriate quality, and not prompting the use of emergency sources. The model allows that these planning objectives may not be completely fulfilled in critical circumstances (e.g., droughts) when goals have to be prioritized by the water utilities. The model is a highly nonlinear programming problem and is solved with the general algebraic modeling system (GAMS), using the MINOS algorithm (GAMS/MINOS). The application of the model to the multimunicipal urban water-supply system of the Algarve shows its capabilities for optimizing the operation considering economic and noneconomic goals included in the objective function and to cope with future shortages in critical circumstances by improving the conjunctive use of the different water sources with interannual planning time horizons. [ABSTRACT FROM AUTHOR]

The classical assumption of representing total demand along a pipe as two lumped withdrawals at its terminal nodes is hitherto common. It is a simplification of the network topology which is useful in order to drastically reduce the number of nodes during network simulation. Conversely, this simplification does not preserve energy balance equation of pipes and, for this reason, it is an approximation that could generate significant head loss errors. This paper presents a modification of the global gradient algorithm (GGA) which entails an enhancing of GGA (EGGA) permitting the effective introduction of the lumped nodal demands, without forfeiting correctness of energy balance, by means of a pipe hydraulic resistance correction. The robustness and convergence properties of the algorithm are compared with those of the classical GGA. Furthermore, the effectiveness of EGGA is demonstrated by computing the network pressure status under different configurations of the connections along the pipes of a test network. [ABSTRACT FROM AUTHOR]

This paper presents a simple and quick method for estimating furrow infiltration using a single advance point based on the volume balance equation. The furrow infiltration and water front advance along the furrow are assumed to follow the modified Kostiakov infiltration and power advance equations, respectively. The volume balance equation, including these equations, is simplified to a function containing two parameters, i.e., the exponents of power advance and Kostiakov infiltration equation (with a prior-known basic infiltration rate). These parameters are estimated by minimizing the function to zero using a quasi-Newton search algorithm, provided with Excel Solver. The estimated exponents are used to determine the Kostiakov infiltration parameters. The proposed one-point method is tested with seven independent furrow irrigation evaluation data sets and the estimated cumulative infiltration is compared with the observed counterparts. Performance of the proposed method was evaluated using the root-mean-square error and index of agreement (Ia). The results show that the proposed one-point method estimated cumulative infiltration is closer to the observed; the method performed as good as Valiantzas’ method. Shepard’s method did not perform well for the tested data sets. The algorithm and the results of the proposed method reveal that the proposed method can be used as a tool for quick estimation of furrow infiltration using a single advance point. [ABSTRACT FROM AUTHOR]

The efficient and effective search for the optimum design solution of a water distribution network using genetic algorithms (GAs) is governed by several factors such as representation scheme, population size, hydraulic simulation model, fitness function, penalty method, GA operators, number of generations, and more importantly the size of the search space. This paper proposes a modified GA that uses basic operators along with their derivatives randomly. Further, a methodology based on critical path method is suggested to reduce the search space. A software tool, GA-WAT, based on the proposed methodology is developed and first tested and verified for its efficiency and effectiveness on two previously published single source networks. Later, it is applied to the optimal design of a larger, two-source hypothetical network. The results obtained indicate that the modified GA with reduction in search space proposed herein is more effective, especially for large practical networks. [ABSTRACT FROM AUTHOR]

A common method for assessing the energy potential of hydropower systems in Iran is the sequential streamflow routing with control on the energy-yield reliability. The method results in developing a reliability-based simulation (RBS) model that is used to analyze single-reservoir hydropower systems. In most of the cascade hydropower systems in the country, a single-reservoir RBS model is usually employed in the design and operation of each of the systems reservoirs. This paper presents a multireservoir RBS model considering the integrated operation of the systems. The model employs the general algorithm of the single-reservoir RBS model; however, with a single-period optimization submodel in determining releases from reservoirs during the system simulation. The objective function of the submodels minimizes the sum of reservoir releases and/or maximizes the sum of reservoir storages in each of the time periods, subject to a reliability constraint on the system’s energy yield. The single and multireservoir RBS models are applied in the Khersan hydropower system located in Iran as a case study. The multireservoir RBS, compared to the single-reservoir model, results in 7.9% excess firm-energy yield in reliability of 90%. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a new approach, the simulation–optimization method, to solve a nonlinear contaminant-source and release-history identification problem for a complex water distribution system. This approach is based on optimization analysis, using the EPANET water distribution system model as a simulator. In this approach, EPANET is used to generate concentrations at arbitrarily selected monitoring locations by specifying release histories of potential contaminant sources that are arbitrarily located within the water distribution system. This information is used in a continuous optimal predictor–corrector algorithm to identify the sources and their release histories. Throughout the simulation sequence, the water distribution system being studied is assumed to operate under known hydraulic operational patterns. The optimization model that is used as a corrector to estimate the release histories of the contaminant sources is designed to identify the similarity between the simulation response and measured data at monitoring locations. This information exchange is developed as a closed-loop system resulting in a convergent algorithm. Three scenarios, derived from the publicly documented Dover Township (Toms River), N.J., water-distribution system are tested using the proposed approach. Results show that the approach is effective, efficient, and robust in identifying locations and release histories of contaminant sources. [ABSTRACT FROM AUTHOR]

A single-gimbal control moment gyro (CMG) is considered to be an ideal torque-generating actuator for rapid attitude maneuvering due to its relatively large output torque. A new attitude control method that is based on the modified Rodrigues parameter (MRP) sliding mode control law for a pyramidal configuration using four CMGs is proposed. Robust inverse steering logic is used to avoid issues of the singularity of the CMGs. The proposed approach is expected to provide rapid and stable attitude maneuvering while avoiding the singularity of the CMGs. In addition, this method is demonstrated on a small satellite, named TSUBAME, designed by the Laboratory of Space Systems at the Tokyo Institute of Technology. The proposed control algorithm is verified through a Monte Carlo simulation and a software-in-the-loop attitude simulator for TSUBAME. The Monte Carlo simulation demonstrated that the new sliding mode control approach successfully tracks a time varying attitude for a nanosatellite model with a range of system uncertainties and unmodeled dynamics. The software-in-the-loop simulation results successfully demonstrated the efficacy and feasibility of the proposed control law. [ABSTRACT FROM AUTHOR]

A parameter identification method based on genetic algorithm (GA) is presented to solve the multimodel parameters identification problem for the main steam temperature of ultra-supercritical (USC) units. Linear ranking selection, nonuniform linear crossover, and Gaussian mutation are employed in the algorithm design to enhance the convergence speed and the accuracy of the identification. Besides, the uniform design method is executed to initialize the population and the sigmoid function with adaptation is applied to adjust the probabilities of crossover and mutation. Simulations carried out with the field operation data from Haimen USC units, including two processes-parameters identification and model verification. The simulation results show that the improved genetic algorithm performs well in global parameters searching and the proposed identification methodology offers good results for the multimodel parameters identification. [ABSTRACT FROM AUTHOR]

A practical method is presented for modeling of two-dimensional arbitrary shape aggregates. An efficient algorithm is developed for mass concrete whose aggregate content reaches 60–70%. All the same kind of random triangular fundamental aggregates are generated at one time, then the two-dimensional arbitrary shape aggregates are generated by the method of random extension. The presented algorithm is superior to common methods, in which all aggregates are generated one by one. This algorithm avoids the inefficiency and low aggregate content of other algorithms. Corresponding software 2D-RAS is developed on the basis of this algorithm. And by means of this software, behaviors of the three-grading concrete specimens under uniaxial tension and bending are studied. The effect of shape of aggregates on mechanical behaviors of concrete is preliminarily investigated. The results indicate that critical load capacity of specimens with random circular aggregate is greater than that of the specimen with arbitrary polygonal aggregate. The cracking depth of the specimen with random arbitrary shape aggregate is larger than that of the specimen with circular aggregate. [ABSTRACT FROM AUTHOR]

The direct search approach to determine optimal reservoir operating policies is proposed with a real coded genetic algorithm (GA) as the optimization method. The parameters of the policies are optimized using the objective values obtained from system simulations. Different reservoir release rules or forms, such as linear, piecewise linear, fuzzy rule base, and neural network, are applied to a single reservoir system and compared with conventional models such as stochastic dynamic programming and dynamic programming and regression. The results of historical and artificial time series simulations show that the GA models are generally superior in identifying better expected system performance. Parsimony of policy parameters is inferred as a principle for selecting the structure of the policy, and Fourier series can be helpful for reducing the number of parameters by defining the time variations of coefficients. The proposed method has shown to be flexible and robust in optimizing various types of policies, even in models that include nonlinear, nonseparable objective functions and constraints. [ABSTRACT FROM AUTHOR]

Methods for stochastic simulation of sample functions have increasingly addressed the preservation of both spectral and probabilistic contents to offer an accurate description of the dynamic behavior of system input for reliability analysis. This study presents an efficient, flexible and easily applied stochastic non-Gaussian simulation method capable of reliably converging to a target power spectral density function and marginal probability density function, or a close relative thereof. Several existing spectral representation-based non-Gaussian simulation algorithms are first summarized. The new algorithm is then presented and compared with these methods to demonstrate its efficacy. The advantages and limitations of the new method are highlighted and shown to complement those of the existing algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a combined simulation-optimization model for simulating reservoir operations without any detailed operation rules; it features the integration of a nonlinear, multiple-objective function and a heuristic search method, the Shuffled Complex Evolution method, developed at the University of Arizona. The model is applied to optimal water allocations from both Balan Reservoir and Seomjingang Dam in South Korea, an irrigation reservoir and a multipurpose, multioutlet reservoir, respectively, that primarily supply irrigation water. In the case of the optimal operation of Balan Reservoir, the simulation results show that the optimal release patterns are similar to those of historical operations because the demands for irrigation water are accurately predicted and guide optimal reservoir operations. In addition, the model is successfully applied to establishing a long-term reservoir operation plan. As for the optimal operation of Seomjingang Dam, the reservoir operations are affected by the target water stages, and the model provides reasonable results, irrespective of inflow conditions. Moreover, the model is employed to create a new operation rule that is necessary to adapt to changes in the circumstances related to water management. Consequently, it is concluded that the model is useful for assessing reservoirs' irrigation water supply capacities when establishing operation plans and providing feasible alternatives for new operation rules, and it could be applied to real-time reservoir operations. [ABSTRACT FROM AUTHOR]

Inertial/astronomic integration is an effective way to improve the accuracy of attitude determination of hypersonic cruise vehicles (HCVs). Compared with common low-dynamic aircraft, the environmental affection during hypersonic flight leads to the non-Gaussian noise character of astronomic observation. Meanwhile, rapid star geometry changing during HCVs' rapid movement causes redistribution of errors in astronomic measurements and significant variation of its main Gaussian characteristic. A kind of robust inertial/astronomic attitude determination algorithm with adaptive star geometrical error model is proposed. The adaptive star geometrical error distribution model is established for obtaining the main Gaussian model of astronomic measurement misalignment errors in flight. After that, inertial/astronomic integration model-based on misalignment errors is proposed, which avoids Euler angle transformation. On these bases, the improved robust filter algorithm is designed, which utilizes real-time astronomic error distribution as the weighting standard of Huber-based optimal estimation. Simulation results indicate that by taking the changing of main Gaussian distribution into consideration, the accuracy of inertial/ astronomic integration is improved approximately 30% more than traditional algorithms adopting constant main Gaussian model in nonGaussian HCV navigation environments. [ABSTRACT FROM AUTHOR]

Existing nonlinear optimization-based algorithms for estimating Markov transition probability matrix (TPM) in bridge deterioration modeling sometimes fail to find optimum TPM values, and hence lead to invalid future condition prediction. In this study, a Metropolis-Hasting algorithm (MHA)-based Markov chain Monte Carlo (MCMC) simulation technique is proposed to overcome this limitation and calibrate the state-based Markov deterioration models (SBMDM) of railway bridge components. Factors contributing to rail bridge deterioration were identified; inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered. The TPMs corresponding to a typical bridge element were estimated using the proposed MCMC simulation method and two other existing methods, namely, regression-based nonlinear optimization (RNO) and Bayesian maximum likelihood (BML). Network-level condition state prediction results obtained from these three approaches were validated using statistical hypothesis tests with a test data set, and performance was compared. Results show that the MCMC-based deterioration model performs better than the other two methods in terms of network-level condition prediction accuracy and capture of model uncertainties. [ABSTRACT FROM AUTHOR]

The current study was undertaken to analyze the performance of three back-propagation training algorithms of artificial neural network (ANN) along with a multiple linear regression model (MLR) for transient simulation of short/midterm soil temperature (TS). Each ANN used a different type of learning algorithm (gradient descent, conjugate gradient, and Levenberg-Marquardt) as methods for daily and weekly TS prediction. The analysis was performed as a case study using three meteorological parameters [air temperature (TA), net radiation (NR), and relative humidity (RH)] and two hydrological variables [precipitation (P) and runoff (Q)] as input data for a region in Wyoming. Pearson and cross correlation analyses were applied to investigate the relationships between input and target values to determine several input combinations. The correlation analysis indicated that the meteorological parameters of TA and NR were reasonably correlated with TS. Based on the input combinations, 14 models were constructed for each of the daily/weekly MLR and ANNs. The accuracy of the predictions was evaluated by the RMS error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe coefficients between the measured and predicted TS values. The Levenberg-Marquardt ANN method was found to provide a more accurate prediction than the other two types of ANNs and the MLR model. Furthermore, it was found that weekly structure of ANNs performance surpasses that of any other daily structures. Although results illustrated that the soil temperature is a function of meteorological variables, the measured TS is also shown to be positively influenced by hydrological parameters-a fact that has not been pointed out by correlation analyses. [ABSTRACT FROM AUTHOR]

The controllability of an irrigation canal depends on its physical characteristics, on the control algorithm used, and on the actual condition of the canal in relation to the algorithm tuning conditions. The flow conditions and controllability in an irrigation canal may change if the hydraulic friction changes. Great variations in friction were observed in an irrigation canal in Spain owing to the presence of Rhizoclonium hieroglyphicum, a type of algae that grows in clear water. Such large variations in friction may have a significant effect on the stability and performance of automatic canal controllers. Using the unsteady-state model Simulation of Irrigation Canal (SIC), the influence of roughness on the performance of the study canal under distant downstream control of its four coupled pools was investigated. A set of proportional-integral (PI) controllers was tuned for different values of Manning's n. The controller's performance under real conditions is sensitive to the roughness conditions under which the control gains were tuned, and a method to robustly tune the gains of the PI controllers is proposed. [ABSTRACT FROM AUTHOR]

A collision-avoidance (CA) algorithm is developed and implemented that is applicable to many different unmanned aerial systems (UAS), ranging from a single platform with the ability to perform all collision-avoidance functions independently to multiple vehicles performing functions as a cooperative group with collision-avoidance commands computed at a ground station. The algorithm leverages advances in several theoretical fields, including robotics, homing guidance, and airspace management, and considers several approaches to conflict detection and resolution, including the collision cone approach. The collision-avoidance system is exercised and tested by using operational hardware and platforms. Novel developments by using an aggregated collision cone approach allow each unmanned aircraft to detect and avoid collisions with two or more other aircraft simultaneously. The collision-avoidance system is implemented by using a miniature unmanned aircraft with an onboard autopilot. Various simulation and flight test cases are used to demonstrate the algorithm's robustness to different collision encounters at various engagement angles. The flight test results are compared with ideal, software-in-the-loop, and hardware-in-the-loop tests. The results presented are the first known flight tests of two or more unmanned aircraft systems equipped with the same global, three-dimensional, geometric collision-avoidance system. [ABSTRACT FROM AUTHOR]

This work extends and improves a one-dimensional shallow flow model to two-dimensional (2D) for real-world flood simulations. The model solves a prebalanced formulation of the fully 2D shallow water equations, including friction source terms using a finite volume Godunov-type numerical scheme. A reconstruction method ensuring nonnegative depth is used along with a Harten, Lax, and van Leer approximate Riemann solver with the contact wave restored for calculation of interface fluxes. A local bed modification method is proposed to maintain the well-balanced property of the algorithm for simulations involving wetting and drying. Second-order accurate scheme is achieved by using the slope limited linear reconstruction together with a Runge-Kutta time integration method. The model is applicable to calculate different types of flood wave ranging from slow-varying inundations to extreme and violent floods, propagating over complex domains including natural terrains and dense urban areas. After validating against an analytical case of flow sloshing in a domain with a parabolic bed profile, the model is applied to simulate an inundation event in a 36 km2 floodplain in Thamesmead near London. The numerical predictions are compared with analytical solutions and alternative numerical results. [ABSTRACT FROM AUTHOR]

Generalized models for predicting the storage–yield–reliability functions of surface water reservoirs are developed using multiple linear regression and multilayer perceptron, artificial neural networks (ANNs). Linear regression was used to model the total capacity using the over-year capacity as one of the inputs. However, the ANNs were used to simultaneously model directly the intrinsically nonlinear over-year and total (i.e., within-year+over-year) capacity-yield-reliability functions. The inputs used for the ANNs were basic runoff and systems variables such as the coefficient of variation of annual and monthly runoff, minimum monthly runoff, the demand ratio, and reliability. The results showed that all the models performed well during development and when tested with independent data sets. Both models offer avenues for predicting reservoir capacity at gauged sites without the expense of time-series based simulation alternatives. [ABSTRACT FROM AUTHOR]

An optimal groundwater remediation design problem generally requires determination of the location of extraction and injection wells and their pumping and injection rates once the well locations are selected. According to many researchers, the determination of an optimal well location is even more important than the optimal pumping rate in groundwater remediation problems. The objective of the study is to apply a neural network embedded Monte Carlo approach to determine potential well locations for in situ bioremediation of contaminated groundwater. The methodology developed in this study has three important components: a bioremediation simulation model, an artificial neural network, and an application of Monte Carlo simulations. This method has been further applied to an in situ bioremediation problem, for which data are adopted from the available literature. The results show that the proposed approach can successfully identify potential well locations from a set of preselected well locations, which can be further used in optimization algorithms to identify optimal well locations and the corresponding pumping rates. The advantage of this approach is that it reduces the size of the problem considerably by eliminating redundant well locations and hence the computational burden involved. Furthermore, the computational burden of Monte Carlo simulations is managed within a practical time frame by replacing the bioremediation simulation model with a trained neural network. [ABSTRACT FROM AUTHOR]

Increasingly, water loss via leakage is acknowledged as one of the main challenges facing water distribution system operations. The consideration of water loss over time, as systems age, physical networks grow, and consumption patterns mature, should form an integral part of effective asset management, rendering any simulation model capable of quantifying pressure-driven leakage indispensable. To this end, a novel steady-state network simulation model that fully integrates into a classical hydraulic representation, pressure-driven demand and leakage at the pipe level is developed and presented here. After presenting a brief literature review about leakage modeling, the importance of a more realistic simulation model allowing for leakage analysis is demonstrated. The algorithm is then tested from a numerical standpoint and subjected to a convergence analysis. These analyses are performed on a case study involving two networks derived from real systems. Experimentally observed convergence/error statistics demonstrate the high robustness of the proposed pressure-driven demand and leakage simulation model. [ABSTRACT FROM AUTHOR]

From cost estimation to reliability analysis, Monte Carlo simulation has found its niche in a wide variety of applications in civil engineering. With recognition of correlations among variables, recent efforts have been devoted to model the correlations more accurately and with no restriction on the form of marginal distributions, i.e., being distribution free. Yet, the conventional method introduced by Iman and Conover, although widely accepted, is bound to have errors: The generated correlation matrix may bear no resemblance to the desired correlation matrix. The purposes of this study are to shed light on the underlying premises of the conventional method and to refine the method by reducing the errors to an acceptable level automatically. A particle swarm optimization algorithm is proposed to repair invalid (nonpositive definite) correlation matrices and to bring the generated correlation matrix into conformity with the desired target. The effectiveness of the proposed algorithm has been verified in estimating cost of electrical services based on historical data. [ABSTRACT FROM AUTHOR]