Prediction of swelling pressures of expansive soils using artificial neural networks

Swelling behavior of expansive soil is a complicated phenomenon. In order to cope with the complications in describing the swelling behavior of expansive soil, researchers developed alternative approaches. In this paper, the prediction model of transmitted lateral swelling pressure, and vertical swelling pressures on a retaining structure was developed using artificial neural network (ANN) approach. In the first stage of this study, the lateral and vertical swelling pressures were measured with different thicknesses of expanded polystyrene (EPS) geofoam placed between one of the vertical walls of the steel testing box and the expansive soil. Then, artificial neural network was trained using these pressures for prediction transmitted lateral swelling pressure, and vertical swelling pressures on a retaining structure. Results obtained from this study showed that neural network-based prediction models could satisfactorily be used in obtaining the swelling pressures of the expansive soils.     

体育馆大跨屋盖空间风振分析

采用高效的完全背景响应法与共振分量法,结合有限元法对某体育馆大跨屋盖进行较为准确的空间风振分析. 在屋盖同步风洞试验的基础上,按空间随机风振分析方法考虑多个振型的影响,计算3个风向下屋盖各节点的静力等效风载荷和最大动位移以及杆件最大动内力,并给出位移和应力两个风振因数,为结构的抗风设计提供风载荷数据. 数值计算结果证明完全背景响应方法与共振分量法在工程应用上的可行性,可为同类屋盖的风振分析提供参考.     

Prediction of vehicle reliability performance using artificial neural networks

Product development is an important but also dynamic, lengthy and risky phase in the life of a new product. The optimisation of the product development phase through extensive knowledge of the involved procedures is believed to reduce the risks and improve the final product quality. Artificial intelligence and expert systems have been used successfully in optimising the development phase of some new products as it will be demonstrated by the first sections of this publication. This paper presents the first module of an expert system, a neural network architecture that could predict the reliability performance of a vehicle at later stages of its life by using only information from a first inspection after the vehicle’s prototype production. The paper demonstrates how a tool like neural networks can be designed and optimised for use in reliability performance predictions. Also, this paper presents an optimisation methodology that enabled the neural network to deal with the limited amount of available training data, common during new product development, and to finally achieve acceptable prediction performance with small error. A case example is presented to demonstrate the methodology.     

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.     

Prediction of sulphur dioxide concentration using artificial neural networks

A three-layer neural network model with a hidden recurrent layer is used to predict sulphur dioxide concentration and the predicted values are compared with the measured concentrations at three sites in Delhi. The Levenberg–Marquardt algorithm is used to train the network. The neural network is used to simulate the behaviour of the system. A multivariate regression model is also used for comparison with the results obtained by using the neural network model. The study results indicate that the neural network is able to give better predictions with less residual mean square error than those given by multivariate regression models.     

Prediction of flyrock launch velocity using artificial neural networks

Due to a potential to cause damage to machinery and structures and cause injuries to personnel, flyrock is the most dangerous adverse effect of blasting operations. Because of that, it is of primary importance to predict flyrock events and maximum range of flyrock fragments in order to define safety limits and secure the perimeter. There are various models for flyrock range prediction, and most of them rely on proper calculations of flyrock launch velocity. However, a unique and universally applicable model of launch velocity prediction still does not exist. Work presented in this article is a concept of adaptive system application for the prediction of flyrock launch velocities. It shows the principles of input data selection, acquisition and processing and presents the principles of design, training, validation and verification of applied artificial neural network.     

Prediction of daily sea surface temperature using artificial neural networks

We present an artificial neural network model to predict the sea surface temperature (SST) and delineate SST fronts in the northe-astern Arabian Sea. The predictions are made one day in advance, using current day’s SST for predicting the SST of the next day. The model is used to predict the SST map for every single day during 2013–2015. The results show that more than 75% of the time the model error is ≤ ±0.5ºC. For the years 2014 and 2015, 80% of the predictions had an error ≤±0.5ºC. The model performance is dependent on the availability of data during the previous days. Thus during the summer monsoon months, when the data availability is comparatively less, the errors in the prediction are slightly higher. The model is also able to capture SST fronts.     

Prediction of building energy consumption by using artificial neural networks

In this study, the main objective is to predict buildings energy needs benefitting from orientation, insulation thickness and transparency ratio by using artificial neural networks. A backpropagation neural network has been preferred and the data have been presented to network by being normalized. The numerical applications were carried out with finite difference approach for brick walls with and without insulation of transient state one-dimensional heat conduction. Three different building samples with different form factors (FF) were selected. For each building samples 0–2.5–5–10–15 cm insulations are assumed to be applied. Orientation angles of the samples varied from 0° to 80° and the transparency ratios were chosen as 15–20–25%. A computer program written in FORTRAN was used for the calculations of energy demand and ANN toolbox of MATLAB is used for predictions. As a conclusion; when the calculated values compared with the outputs of the network, it is proven that ANN gives satisfactory results with deviation of 3.43% and successful prediction rate of 94.8–98.5%.     

Prediction of gait speed from plantar pressure using artificial neural networks

The goal of this study was to predict gait speed over the entire cycle in reference to plantar pressure data acquired by means of the insole-type plantar pressure measuring device (Novel Pedar-x system). To predict gait speed, the artificial neural network is adopted to develop the model to predict gait speed in the stance phase (Model I) and the model to predict gait speed in the swing phase (Model II). The predicted gait speeds were validated with actual values measured using a motion capturing system (VICON 460 system) through a five-fold cross-validation method, and the correlation coefficients (R) for the gait speed were 0.963 for normal walking, 0.978 for slow walking, and 0.950 for fast walking. The method proposed in this study is expected to be widely used clinically in understanding the progress and clarifying the cause of such diseases as Parkinsonism, strike, diabetes, etc. It is expected that the method suggested in this study will be the basis for the establishment of a new research method for pathologic gait evaluation.     

Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks

Neural Computing and Applications - Fly ash-based geopolymer has been studied extensively in recent years due to its comparable properties to Portland cement and its environmental benefits....     

Inversion of a velocity model using artificial neural networks

We present a velocity model inversion approach using artificial neural networks (NN). We selected four aftershocks from the 2000 Tottori, Japan, earthquake located around station SMNH01 in order to determine a 1D nearby underground velocity model. An NN was trained independently for each earthquake-station profile. We generated many velocity models and computed their corresponding synthetic waveforms. The waveforms were presented to NN as input. Training consisted in associating each waveform to the corresponding velocity model. Once trained, the actual observed records of the four events were presented to the network to predict their velocity models. In that way, four 1D profiles were obtained individually for each of the events. Each model was tested by computing the synthetic waveforms for other events recorded at SMNH01 and at two other nearby stations: TTR007 and TTR009.     

Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing

In this study, new models are derived to predict the peak time-domain characteristics of strong ground-motions utilizing a novel hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA. The principal ground-motion parameters formulated are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The proposed models relate PGA, PGV and PGD to earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. A database of strong ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER) is used to establish the models. For more validity verification, the ANN/SA models are employed to predict the ground-motion parameters of a part of the database beyond the training data domain. ANN and multiple linear regression analyses are performed to benchmark the proposed models. Contributions of the input parameters to the prediction of PGA, PGV and PGD are evaluated through a sensitivity analysis. The ANN/SA attenuation models give precise estimations of the site ground-motion parameters. The proposed models perform superior than the single ANN, regression and existing attenuation models. The optimal ANN/SA models are subsequently converted into tractable design equations. The derived equations can readily be used by designers as quick checks on solutions developed via more in-depth deterministic analyses.     

Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks

The paper presents a comparative performance of the models developed to predict 28 days compressive strengths using neural network techniques for data taken from literature (ANN-I) and data developed experimentally for SCC containing bottom ash as partial replacement of fine aggregates (ANN-II). The data used in the models are arranged in the format of six and eight input parameters that cover the contents of cement, sand, coarse aggregate, fly ash as partial replacement of cement, bottom ash as partial replacement of sand, water and water/powder ratio, superplasticizer dosage and an output parameter that is 28-days compressive strength and compressive strengths at 7 days, 28 days, 90 days and 365 days, respectively for ANN-I and ANN-II. The importance of different input parameters is also given for predicting the strengths at various ages using neural network. The model developed from literature data could be easily extended to the experimental data, with bottom ash as partial replacement of sand with some modifications.     

Predicting grinding burn using artificial neural networks

This paper introduces a method for predicting grinding burn using artificial neural networks (ANN). First, the way to model grinding burn via ANN is presented. Then, as an example, the prediction of grinding burn of ultra-strength steel 300M via ANN is given. Very promising results were obtained.     

Gradientless shape optimization using artificial neural networks

In this paper a new zero order method of structural shape optimization, in which material shrinks or grows perpendicular to the design boundary, has been proposed in order to satisfy fully stressed design criteria. To avoid mesh distortion that results in undesirable shape, design element concept and for nodal movement and convergence checking, fuzzy set theory have been used. To accelerate the convergence, artificial neural networks are employed. The proposed approach, named as GSN technique, has been incorporated in a FORTRAN software GSOANN. Using this software shape optimization of four structures are carried out. It is demonstrated that proposed technique overcomes most of the shortcomings of mundane zero order methods.     

Microstrip antenna design using artificial neural networks

Neural‐network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. This work describes the fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them to model microstrip patch antenna. This work studies in‐depth different designs and analysis methods of microstrip patch antenna using artificial neural‐network and different network structure are also described from the RF/microwave designer's perspective. This article also illustrates two examples of microstrip antenna design and validating the utility of ANN in the area of microstrip antenna design. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Fault detection on robot manipulators using artificial neural networks

Nowadays, gas welding applications on vehicle’s parts with robot manipulators have increased in automobile industry. Therefore, the speed of end-effectors of robot manipulator is affected on each joint during the welding process with complex trajectory. For that reason, it is necessary to analyze the noise and vibration of robot’s joints for predicting faults. This paper presents an experimental investigation on a robot manipulator, using neural network for analyzing the vibration condition on joints. Firstly, robot manipulator’s joints are tested with prescribed of trajectory end-effectors for the different joints speeds. Furthermore, noise and vibration of each joint are measured. And then, the related parameters are tested with neural network predictor to predict servicing period. In order to find robust and adaptive neural network structure, two types of neural predictors are employed in this investigation. The results of two approaches improved that an RBNN type can be employed to predict the vibrations on industrial robots.     

Modeling layered artificial neural networks using a visual programming paradigm

The understanding of soft computing methodology often requires grasping abstract concepts or imagining complex interactions of large models over long computing cycles. However, this can be difficult for students with a weak background in mathematics, especially in the early stages of soft computing education. This article introduces the idea of applying a visual programming paradigm as a tool for an educational introduction to soft computing methods. IntelligentPad, proposed by Y. Tanaka, was used as the visual programming paradigm. IntelligentPad gives a visual appearance to objects or classes, and allows users to operate and link different objects together using a mouse. This article reports on using IntelligentPad to teach the basic mechanisms of artificial neural networks. The proposed method was applied to 3rd-year college students to verify its validity as a teaching method.