Active Pulse Structural Control Using Artificial Neural Networks

Increasing interest has focused on applying active control systems to civil engineering structures subjected to dynamic loading. This study presents an active pulse control algorithm, termed the adaptive neural structural active pulse (ANSAP) controller, to control civil engineering structures under dynamic loading. The ANSAP controller minimizes structural cumulative responses during earthquakes by applying active pulse control forces. The effect of pulses is assumed to be delayed until just before the next sampling time so that the control force can be calculated in time and applied; the newly developed control strategy circumvents the effect of time delay due to the computation time. The ANSAP controller also circumvents the difficulty of obtaining system parameters of a real structure for the algorithm for active structural control. Illustrative examples reveal significant reductions in cumulative structural responses, which demonstrates the feasibility of using the adaptive artificial network for controlling civil engineering structures under dynamic loading.     

Exploring Concrete Slump Model Using Artificial Neural Networks

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.     

Uncertainty of Weekly Nitrate-Nitrogen Forecasts Using Artificial Neural Networks

Nonpoint source pollution affects the quality of numerous watersheds in the Midwestern United States. The Illinois State Water Survey conducted this study to (1) assess the potential of artificial neural networks (ANNs) in forecasting weekly nitrate-nitrogen (nitrate-N) concentration; and (2) evaluate the uncertainty associated with those forecasts. Three ANN models were applied to predict weekly nitrate-N concentrations in the Sangamon River near Decatur, Illinois, based on past weekly precipitation, air temperature, discharge, and past nitrate-N concentrations. Those ANN models were more accurate than the linear regression models having the same inputs and output. Uncertainty of the ANN models was further expressed through the entropy principle, as defined in the information theory. Using several inputs in an ANN-based forecasting model reduced the uncertainty expressed through the marginal entropy of weekly nitrate-N concentrations. The uncertainty of predictions was expressed as conditional entropy of future nitrate concentrations for given past precipitation, temperature, discharge, and nitrate-N concentration. In general, the uncertainty of predictions decreased with model complexity. Including additional input variables produced more accurate predictions. However, using the previous weekly data (week t?1) did not reduce the uncertainty in the predictions of future nitrate concentrations (week t+1) based on current weekly data (week t).     

Integrated Emitter Local Loss Prediction Using Artificial Neural Networks

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.     

Parameter Estimation in Groundwater Hydrology Using Artificial Neural Networks

The capability of artificial neural networks to act as universal function approximators has been traditionally used to model problems in which the relation between dependent and independent variables is poorly understood. In this paper, the capability of an artificial neural network to provide a data-driven approximation of the explicit relation between transmissivity and hydraulic head as described by the groundwater flow equation is demonstrated. Techniques are applied to determine the optimal number of nodes and training patterns needed for a neural network to approximate groundwater parameters for a simulated groundwater modeling case study. Furthermore, the paper explains how such an approximation can be used for the purpose of parameter estimation in groundwater hydrology.     

Using Complex Permittivity and Artificial Neural Networks for Contaminant Prediction

The use of the measured complex permittivity of electrolyte solutions for predicting ionic species and concentration is investigated. Four artificial neural networks (ANNs) are created using a database containing permittivities (at 1.0, 1.5, and 2.0 GHz) and loss factors (at 0.3, 1.5, and 3.0 GHz) of 12 aqueous salts at various concentrations. The first ANN correctly identifies cationic species in 83% of the samples and distinguishes between pure water and electrolyte solutions with 100% accuracy. The second ANN predicts cationic concentrations with a RMS error of 190 mg/L for the range of concentrations examined (0–3,910 mg/L) and explains 90% of the variability in these data. The third ANN correctly identifies 98% of the anionic species in samples and accurately distinguishes between pure water and anion-containing solutions. The last ANN predicts anionic concentrations with a RMS error of 164 mg/L for the range of concentrations examined (0–5,654 mg/L) with an r2 of nearly 98%.     

Quick Seismic Response Estimation of Prestressed Concrete Bridges Using Artificial Neural Networks

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.     

Estimating Drilling Parameters for Diamond Bit Drilling Operations Using Artificial Neural Networks

Diamond bit drilling is one of the most widely used and preferable drilling techniques because of its higher rate of penetration and core recovery in the hardest rocks, the ability to drill in any direction with less deviation, and the ability to drill with greater precision in coring and prospecting drilling. Conventional bit analysis techniques include mathematical methods such as specific energy and formation drillability. In this study, artificial neural network (ANN) analysis as opposed to conventional mathematical techniques is used to estimate major drilling parameters for diamond bit drilling, i.e., weight on bit, rotational speed, and bit type. The use of the proposed methodology is demonstrated using an ANN trained with information obtained from 45,000?m of diamond bit drilling operations conducted on several formations and locations in Turkey. The studied formations include shallow carbonates as well as sandstones in the Zonguldak hard coal basin. The neural network results are compared to those obtained from conventional methods such as specific energy analysis. It was observed that the proposed methodology provided satisfactory results both in relatively less documented and drilled formations as well as in well-known formations.     

Modeling and Predicting Biological Performance of Contact Stabilization Process Using Artificial Neural Networks

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.     

人工神经网络方法在Osprey过程中的应用

以BP反传理论为基础,建立了对Osprey过程的前向多层神经网络,并对其进行测试.利用这一方法研究了Osprey过程中部分参数对孔隙度的影响.结果证明该网络较好地实现了学习和预测.     

Comparison of Case-Based Reasoning and Artificial Neural Networks

The outcome of construction litigation depends on a large number of factors. To predict the outcome of such litigation is difficult because of the complex interrelationships between these many factors. Two attempts are reported in the literature that use, respectively, case-based reasoning (CBR) and artificial neural networks (ANN) to overcome this difficulty. These studies were conducted by using the same 102 Illinois circuit court cases; an additional 12 cases were used for testing. Prediction rates of 83% in the CBR study and 67% in the ANN study were obtained. In this paper, CBR and ANN are compared, and their advantages and disadvantages are discussed in light of these two studies. It appears that CBR is more flexible when the system is updated with new cases, has better explanation facilities, and handles missing data and a large number of features better than ANN in this domain. If the use of CBR and ANN is understood better and if, as a result, the outcome of construction litigation can be predicted with reasonable accuracy and reliability, all parties involved in the construction process could save considerable money and time.     

Forecasting of Reference Evapotranspiration by Artificial Neural Networks

In recent years, artificial neural networks (ANNs) have been applied to forecasting in many areas of engineering. In this note, a sequentially adaptive radial basis function network is applied to the forecasting of reference evapotranspiration (ETo). The sequential adaptation of parameters and structure is achieved using an extended Kalman filter. The criterion for network growing is obtained from the Kalman filter’s consistency test, while the criteria for neuron/connection pruning are based on the statistical parameter significance test. The weather parameter data (air temperature, relative humidity, wind speed, and sunshine) were available at Nis, Serbia and Montenegro, from January 1977 to December 1996. The monthly reference evapotranspiration data were obtained by the Penman-Monteith method, which is proposed as the sole standard method for the computation of reference evapotranspiration. The network learned to forecast ETo,t+1 based on ETo,t?11 and ETo,t?23. The results show that ANNs can be used for forecasting reference evapotranspiration with high reliability.     

Techniques for Predicting Cracking Pattern of Masonry Wallet Using Artificial Neural Networks and Cellular Automata

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.     

Optimal Structural Control Using Neural Networks

An optimal control algorithm using neural networks is proposed. The controller neural network is trained by a training rule developed to minimize cost function. Both the linear structure and the nonlinear structure can be controlled by the proposed neurocontroller. A bilinear hysteretic model is used to simulate nonlinear structural behavior. Three main advantages of the neurocontroller can be summarized as follows. First, it can control a structure with unknown dynamics. Second, it can easily be applied to nonlinear structural control. Third, external disturbances can be considered in the optimal control. Examples show that structural vibration can be controlled successfully.     

Optical Classification of Quartz Lascas by Artificial Neural Networks

A gradation method based on quartz lascas (lumps) transparency level is proposed. The samples were irradiated by transmitting light, and the images histograms were processed by artificial neural networks. Additionally, the results were compared to conventional classification methods, including density and visual analysis. The network designed with backpropagation architecture using 4 hidden layers of 10 neurons yielded to a relative error <24% in relation to manual classification, indicating a good agreement to the miners criteria. Furthermore, the implementation of competitive learning with 5 neurons resulted in correct discrimination of samples regarding their optical characteristics with a completely non-subjective approach.     

人工神经网络在设备诊断中的应用研究

简述国内外在设备诊断中应用人工神经网络(Artificial Neural Networks-ANN)的发展状况,阐述人工神经网络在设备振动诊断领域的应用方法,现场使用表明人工神经网络对于设备故障的识别及诊断具有较好的应用前景.     

Wind Power Estimation Using Artificial Neural Network

Wind energy conversion systems appear as an attractive alternative for electricity generation. To maximize the use of wind generated electricity when connected to the electric grid, it is important to estimate and predict power produced by wind farms. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and wind direction. Wind power can be affected by many other factors such as terrain, air density, vertical wind profile, time of a day, and seasons of a year and usually fluctuates rapidly, imposing considerable difficulties on the management of combined electric power systems. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes—lower than expected wind power may be an early indicator of a need for maintenance. A multilayer perceptron (MLP) network can be used to estimate wind turbine power generation. It is usually important to train a neural network with multiple influence factors and big training data set. The extended Kalman filter training algorithm has to be parallelized so that it can provide fast training even for large training data sets. The MLP network can then be trained with the consideration of various possible factors, which can cause influence on turbine power production.     

Classifying Construction Contractors Using Unsupervised-Learning Neural Networks

Contractor prequalification involves the screening of contractors by a project owner to determine their competence to complete the project on time, within budget, and to expected quality standards. The process of prequalification involves a large number of contractors, each being represented by many attributes. A neural network model was applied to aid in the prequalification process by classifying contractors into groups based on similarity in performance using the financial ratios of liquidity, activity, profitability, and leverage. Contractors are represented in this model by patterns in four-dimensional space. Patterns of similar performance tend to form clusters intercepting regions of low pattern density in between. A neuron with weights is used as a classifier to set a decision boundary between clusters. The method basically iterates the neuron weights to move the decision boundary to a place of low pattern density. Then, the statistical hypothesis testing of the mean difference of two independent samples was used to validate the classification of the parent class to the two child classes considering the four ratios separately. The method was used hierarchically to classify a group of 245 contractors into classes of small numbers. Finally, the inferred procedure of classification proves that the neural network model classified the four-dimension pattern representing contractors efficiently.     

Damage Identification Using Committee of Neural Networks

This paper presents a committee of neural networks technique, which employs frequency response functions (FRFs), modal properties (natural frequencies and mode shapes), and wavelet transform (WT) data simultaneously to identify damage in structures. The experimental demonstration of the method is obtained by studying the sensitivities of the FRFs, modal properties, and WT data to four types of faults in a cylindrical shell. The experimental results show that different faults affect data in a different manner. The proposed approach is tested on simulated data from a three-degree-of-freedom mass-spring-damper system. The results from the simulated study show that the performance of the approach is not influenced by the noise in the data. Finally, the method is used to identify damage in a population of ten steel seam-welded cylindrical shells. The proposed method is able to identify damage cases better than the three approaches used individually. The committee approach gives results that generally have a lower mean square error (MSE) than the average MSE of the individual methods. It is found that the effectiveness of the method is enhanced when experimentally measured data are used, which is in contrast to many existing methods. This is because the committee approach assumes that the errors given by the three approaches are uncorrelated, a situation that becomes more apparent when using measured data rather than simulated data.