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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]

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]

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]

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

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]

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]

Recently, the writers developed a new mesoscopic-wavelet model for simulating freeway traffic flow patterns and extracting congestion characteristics. As an extension of that research, in this paper, a new neural network-wavelet microsimulation model is presented to track the travel time of each individual vehicle for traffic delay and queue length estimation at work zones. The model incorporates the dynamics of a single vehicle in changing traffic flow conditions. The extracted congestion characteristics obtained from the mesoscopic-wavelet model are used in a Levenberg–Marquardt backpropagation (BP) neural network for classifying the traffic flow as free flow, transitional flow, and congested flow with stationary queue. The neural network model is trained using simulated data and tested using both simulated and real data. The computational model presented is applied to five examples of freeways with two and three lanes and one lane closure with varying entry flow or demand patterns. The new microsimulation model is more accurate than macroscopic models and substantially more efficient than microscopic models. [ABSTRACT FROM AUTHOR]

This paper investigates the use of two well-known artificial neural network (ANN) paradigms: the multilayer perceptron (MLP) and fuzzy adaptive resonance theory (ART) neural networks in analyzing driver injury severity. The objective of this study is to investigate the viability and potential benefits of using the ANN in predicting driver injury severity conditioned on the premise that a crash has occurred. The performance of the ANN was compared to a calibrated ordered probit model. Modeling results showed that the testing classification accuracy was 73.5% for the MLP, 70.6% for the fuzzy ARTMAP, and 61.7% for the ordered probit model. This result indicates a more accurate prediction capability of injury severity for ANN (particularly the MLP) over other traditional methods. The results of the models showed that gender, vehicle speed, seat belt use, type of vehicle, point of impact, and area type (rural versus urban) affect the likelihood of injury severity levels. [ABSTRACT FROM AUTHOR]

Droughts are destructive climatic extreme events that may cause significant damage both in natural environments and in human lives. Drought forecasting plays an important role in the control and management of water resources systems. In this study, a conjunction model is presented to forecast droughts. The proposed conjunction model is based on dyadic wavelet transforms and neural networks. Neural networks have shown great ability in modeling and forecasting nonlinear and nonstationary time series in a water resources engineering, and wavelet transforms provide useful decompositions of an original time series. The wavelet-transformed data aid in improving the model performance by capturing helpful information on various resolution levels. Neural networks are used to forecast decomposed subsignals in various resolution levels and reconstruct forecasted subsignals. The model was applied to forecast droughts in the Conchos River Basin in Mexico, which is the most important tributary of the Lower Rio Grande/Bravo. The performance of the conjunction model was measured using various forecast skill criteria. The results indicate that the conjunction model significantly improves the ability of neural networks to forecast the indexed regional drought. [ABSTRACT FROM AUTHOR]