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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 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]

This paper introduces a new fault identification method that uses pseudomodal energies to train probabilistic neural networks (PNNs). The proposed procedure is tested on a population of 20 cylindrical shells and its performance is compared to the procedure which uses modal properties to train probabilistic neural networks. The PNNs trained using pseudomodal energies provide better classification of faults than the PNNs trained using the conventional modal properties. [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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

Based on the basic formulation developed in a companion paper, the writers now present the application of an artificial neural network approach to designing streamlined network models to simulate and identify the nonlinear dynamic response of single-degree-of-freedom oscillators using the restoring force-state mapping interpretation. The neural networks which use sigmoidal activation functions are shown to be highly robust in modeling a wide variety of commonly observed nonlinear structural dynamic response behaviors. By streamlining the networks, individual network model parameters take on physically or geometrically interpretable meaning, and hence, the network initialization can be achieved through an engineered approach rather than through less physically meaningful numerical initialization schemes. Although not proven in general, examples show that by starting with a more meaningful initial design, identification convergence is improved, and the final identified model parameters are seen to have a more physical meaning. A set of model architecture prototypes is developed to capture commonly observed nonlinear response behaviors. [ABSTRACT FROM AUTHOR]

This paper deals with an application of neural networks for computation of fundamental natural periods of buildings with load-bearing walls. The analysis is based on long-term tests performed on actual structures. The identification problem is formulated as the relation between structural and soil basement parameters, and the fundamental period of building. The principal component analysis for compression of input data is also used. Backpropagation neural networks are applied in the analysis. Results of neural network identification of natural periods are compared with data from experiments. The application of the proposed neural networks enables us to identify the natural periods of the buildings with quite satisfactory accuracy for engineering practice. The compression of the input data to principal components by principal component analysis makes it possible to design much smaller neural networks than those without data compression with no greater increase of the neural approximation errors. It appears that this technique would also be very useful in damage detection and health monitoring of structures. [ABSTRACT FROM AUTHOR]

Fly ash and slag concrete (FSC) is a highly complex material whose behavior is difficult to model. This paper describes a method of modeling slump of FSC using artificial neural networks. The slump is a function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. The model built was examined with response trace plots to explore the slump behavior of FSC. This study led to the conclusion that response trace plots can be used to explore the complex nonlinear relationship between concrete components and concrete slump. [ABSTRACT FROM AUTHOR]

The Food and Agriculture Organization of the United Nations (FAO) has proposed using the Penman–Monteith (FAO-56 PM) method as the standard method for estimating reference evapotranspiration (ET0), and for evaluating other methods. The basic obstacle to widely using this method is the numerous required data that are not available at many weather stations. The maximum and minimum air temperatures constitute a set of minimum data necessary for the estimation of ET0. The basic goal of the paper is to examine whether it is possible to attain the reliable estimation of ET0 only on the basis of the temperature data. This goal was reached by the evaluation of the reliability of four temperature-based approaches [radial basis function (RBF) network, Thornthwaite, Hargreaves, and reduced set Penman–Monteith methods] as compared to the FAO-56 PM method. The seven weather stations selected for this study are located in Serbia (Southeast Europe). The Thornthwaite, Hargreaves, and reduced set Penman–Monteith methods mostly underestimated or overestimated ET0 obtained by the FAO-56 PM method. In this study, methods were calibrated using the standard FAO-56 PM method. However, the RBF network better predicted FAO-56 PM ET0 than calibrated temperature-based methods at most locations. It gives reliable results in all locations and it has proven to be the most adjustable to the local climatic conditions. These results are of significant practical use because the adaptive temperature-based RBF network can be used when relative humidity, radiation, and wind speed data are not available. [ABSTRACT FROM AUTHOR]

Engineering and design professionals constitute a major driving force for a successful project undertaking. Although the industry has been active in addressing the performance of construction labor and methods to estimate or predict such performance, relatively fewer efforts have been conducted for the engineering profession. In an attempt to fill out this gap, the paper presents a study to utilize neurofuzzy intelligent systems for predicting the engineering performance in a construction project. First, neurofuzzy systems are introduced as integrated schemes of artificial neural networks and fuzzy control systems. The use of these neurofuzzy intelligent systems, particularly fuzzy neural networks, in predicting engineering performance is then demonstrated in the industrial construction sector. The development of the system is based on actual project data that was collected through questionnaire surveys. Statistical variable reduction techniques are further employed to develop linear regression models of the same engineering performance prediction scheme, and results are being compared between both techniques. [ABSTRACT FROM AUTHOR]

A new method of estimating flutter derivatives using artificial neural networks is proposed. Unlike other computational fluid dynamics based numerical analyses, the proposed method estimates flutter derivatives utilizing previously measured experimental data. One of the advantages of the neural networks approach is that they can approximate a function of many dimensions. An efficient method has been developed to quantify the geometry of deck sections for neural network input. The output of the neural network is flutter derivatives. The flutter derivatives estimation network, which has been trained by the proposed methodology, is tested both for training sets and novel testing sets. The network shows reasonable performance for the novel sets, as well as outstanding performance for the training sets. Two variations of the proposed network are also presented, along with their estimation capability. The paper shows the potential of applying neural networks to wind force approximations. [ABSTRACT FROM AUTHOR]

Seismic early warning has been very important and has become feasible in Taiwan. Perhaps because of the lack of quick and reliable estimations of the induced structural response, however, the triggering criteria of almost all of the existing earthquake protection or early warning systems in the world are merely based on the collected or estimated data of the ground motion, without any information regarding the structural response. This paper presents a methodology of generating quick seismic response estimations of a prestressed concrete (PC) bridge using artificial neural networks (ANNs), which may be incorporated in a seismic early warning system for the bridge. In the methodology ANNs were applied to model the critical structural response of a PC bridge subjected to earthquake excitation of various magnitudes along various directions. The objective was to implement a well-trained network that is capable of providing a quick prediction for the critical response of the target bridge. The well-known multilayer perception (MLP) networks with back propagation algorithm were employed. A simple augmented form of MLP that can be quantitatively determined was proposed. These networks were trained and tested based on the analytical data obtained from the nonlinear dynamic finite fiber element analyses of the target PC bridge. The augmented MLPs were found to be much more efficient than the MLPs in modeling the critical bending moments of the piers and girder of the PC bridge. [ABSTRACT FROM AUTHOR]

In recent years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. In the majority of these applications, data division is carried out on an arbitrary basis. However, the way the data are divided can have a significant effect on model performance. In this paper, the issue of data division and its impact on ANN model performance is investigated for a case study of predicting the settlement of shallow foundations on granular soils. Four data division methods are investigated: (1) random data division; (2) data division to ensure statistical consistency of the subsets needed for ANN model development; (3) data division using self-organizing maps (SOMs); and (4) a new data division method using fuzzy clustering. The results indicate that the statistical properties of the data in the training, testing, and validation sets need to be taken into account to ensure that optimal model performance is achieved. It is also apparent from the results that the SOM and fuzzy clustering methods are suitable approaches for data division. [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]

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]

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 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 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]

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 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 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 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]

The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ET0) is investigated in this paper. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, Calif., are used as inputs to the neurofuzzy model to estimate ET0 obtained using the FAO-56 Penman–Monteith equation. In the first part of the study, a comparison is made between the estimates provided by the neurofuzzy model and those of the following empirical models: The California Irrigation Management System, Penman, Hargreaves, and Ritchie. In this part of the study, the empirical models are calibrated using the standard FAO-56 PM ET0 values. The estimates of the neurofuzzy technique are also compared with those of the calibrated empirical models and artificial neural network (ANN) technique. Mean-squared errors, mean-absolute errors, and determination coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neurofuzzy models could be employed successfully in modeling the ET0 process. In the second part of the study, the potential of the neurofuzzy technique, ANN and the empirical methods in estimation ET0 using nearby station data are investigated. [ABSTRACT FROM AUTHOR]

An efficient training and pruning methodology based on the H∞ filtering algorithm is proposed for artificial neural networks (ANNs). ANNs are first trained by the H∞ filtering algorithm and then some unimportant weights are removed based on the training. The results presented in the paper show that the proposed method provides better pruning results of the network without losing its generalization capacity. It also provides a robust training algorithm for given arbitrary network structures. The usefulness and effectiveness of the proposed methodology are demonstrated in developing an ANN model of a hysteretic structural system. [ABSTRACT FROM AUTHOR]

This paper addresses the modeling problem of nonlinear and hysteretic dynamic behaviors through a constructive modeling approach which exploits existing mathematical concepts in artificial neural network modeling. In contrast with many neural network applications, which often result in large and complex “black-box” models, here, the writers strive to produce phenomenologically accurate model behavior starting with network architecture of manageable/small sizes. This affords the potential of creating relationships between model parameter values and observed phenomenological behaviors. Here a linear sum of basis functions is used in modeling nonlinear hysteretic restoring forces. In particular, nonlinear sigmoidal activation functions are chosen as the core building block for their robustness in approximating arbitrary functions. The appropriateness and effectiveness of this set of basis function in modeling a wide variety of nonlinear dynamic behaviors observed in structural mechanics are depicted from an algebraic and geometric perspective. [ABSTRACT FROM AUTHOR]

Results of a numerical exercise, substituting a numerical operator by an artificial neural network (ANN) are presented in this paper. The numerical operator used is the explicit form of the finite difference (FD) scheme. The FD scheme was used to discretize the one-dimensional transport equation, which included both the advection and dispersion terms. Inputs to the ANN are the FD representation of the transport equation, and the concentration was designated as the output. Concentration values used for training the ANN were obtained from analytical solutions. The numerical operator was reconstructed from a back calculation of the weights of the ANN. Linear transfer functions were used for this purpose. The ANN was able to accurately recover the velocity used in the training data, but not the dispersion coefficient. This capability was improved when numerical dispersion was taken into account; however, it is limited to the condition: C/P<0.5, where C is the Courant number and P, the Peclet number (i.e., the restriction imposed by the Neumann stability condition). [ABSTRACT FROM AUTHOR]

The paper presents a methodology for arriving at optimum bid markups in static competitive bidding environments by use of neurofuzzy systems and integrated multidimensional risk analysis algorithms. The neurofuzzy framework enables integration of the objective and subjective factors found in the underlying decision-making process, and serves as the stepping stone for the generation of the multidimensional probability distribution function that governs competitive bidding. Subsequent bid optimization is achieved by employing a multidimensional risk analysis algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the microfauna distribution data of a contact stabilization process were used in a neural network system to model and predict the biological activity of the effluent. Five uncorrelated components of the microfauna were used as the artificial neural network model input to predict the dehydrogenase activity of the effluent (DAE) using back-propagation and general regression algorithms. The models’ optimum architectures were determined for the back-propagation neural network (BPNN) model by varying the number of hidden layers, hidden transfer functions, test set size percentages, and initial weights. Comparison of the two model prediction results showed that the genetic general regression neural network model demonstrated the ability to calibrate the multicomponent microfauna, and yielded reliable DAE close to that resulting from direct experimentation, and thus was judged superior to BPNN models. [ABSTRACT FROM AUTHOR]

This paper presents an approach for handling uncertainties arising mainly from ignored or misrepresented processes in physically based models. The approach is based on the application of a parallel artificial neural network (ANN) model that uses state variables, input and output data, and previous model errors at specific time steps to predict the errors of a physically based model. Concepts from information theory are used to discover the relationships between the variables and the model errors, which also serves as a mechanism to detect the predictability of the errors. The resulting information is used to select the best related input data for the error prediction model. The error prediction model is then trained and applied to improve the forecasts made by the physically based model. This approach was applied to a routing model of a 70 km reach of the River Wye, United Kingdom. The results demonstrate that errors from the physically based model show a consistent trend governed by some dynamics of their own, which can be modeled with learning algorithms. Errors were forecasted at different lead times. In all cases the forecasts made by the combined application of both models were more accurate than those made by the physically based model alone. From this it was concluded that, along with proper information analysis techniques, the use of ANN models to predict the forecast errors of physically based models can help to improve significantly the prediction and therefore to reduce the associated uncertainty. [ABSTRACT FROM AUTHOR]

This paper discusses flying objects' adaptive control with direct application to the flight of helicopters. Two new automatic adaptive control systems are suggested: the former is used for pitch angle control, while the latter is used for control of helicopter pitch angle and velocity; this second system is an extension of the first one. The adaptive control is based on the dynamic inversion principle and the use of neural networks. The two adaptive control systems have reference models, linear dynamic compensators, linear observers, and neural networks. The adaptive components of the automatic control laws compensate for the approximation errors of the dynamic model's nonlinear functions. The used actuators are linear or nonlinear. To eliminate the neural networks' adapting difficulties, a pseudo-control hedging (PCH) block is inserted in the adaptive system; it limits the adaptive pseudo-control by means of a component that represents the estimation error of the actuator dynamics. Thus, the PCH block moves back the reference model-i.e., it introduces a reference model response correction with respect to the actuator position estimation; the signal provided by the PCH block represents a reference model's additional input. For the two new automatic adaptive control systems, a technical computing environment is used to obtain time characteristics of the adaptive systems with linear and nonlinear actuators. Phase trajectories of the two adaptive control systems with nonlinear actuators express the convergence of the nonlinear systems to stable limit cycles. [ABSTRACT FROM AUTHOR]