Prediction of seismic slope stability through combination of particle swarm optimization and neural network

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R ²) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN. R ² values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique.     

Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems

Neural network usually acts as a “black box” in diverse fields to perform prediction, classification, and regression. Different from the conventional usages, neural network is herein attempted to handle factor sensitivity analysis in a geotechnical engineering system. After systematically investigating instability of employing single neural network in factor sensitivity analysis, a neural network committee (NNC)-based sensitivity analysis strategy is first algorithmically presented based on the particular mathematical ideas of weak law of large numbers in probability and optimization. Significantly, this study especially emphasizes the practical application of the NNC-based sensitivity analysis strategy to highlight the mechanism underlying in strata movement. The principal goal is to reveal the relationships among influential factors on strata movement through estimating the relative contribution of each explicative (input) variable on dependent (output) variables of strata movement. It is demonstrated that the NNC-based sensitivity analysis strategy rationally not only reveals the relative contribution of each explicative variable on dependent variables but also indicates the predictability of each dependent variable. In addition, an improved prediction model is resulted from integrating the sensitivity analysis results into neural network modeling, and it is capable of facilitating the convergence training of neural network model and advancing its prediction precision on strata movement angles. The above outcomes indicate that the NNC-based sensitivity analysis strategy provides a new paradigm of applying neural networks to deal with complex geotechnical engineering problems.     

Public transportation trip flow modeling with generalized regression neural networks

Artificial neural networks (ANNs) are one of the recently explored advanced technologies, which show promise in the area of transportation engineering. The presented study comprised the employment of this seldom used ANN method, generalized regression neural network (GRNN), in comparison to both a frequently applied neural network training algorithm, feed-forward back-propagation (FFBP), and a stochastic model of auto-regressive structure for the purpose of forecasting daily trip flows, which is an essential component in demand analysis. The study is carried out under the motivation of knowing that modeling daily trips for available transportation modes will facilitate the arrangement for effective public infrastructure investments and the cited papers in the literature did not make use of and handle any comparison with GRNN method. The ANN predictions are found to be quite close to the observations as reflected in the selected performance criteria. The selected stochastic model performance is quite poor compared with ANN results. It is seen that the GRNN did not provide negative forecasts in contrast to FFBP applications. Besides, the local minima problem faced by FFBP algorithm is not encountered in GRNNs.     

Adaptive modeling of the drying of baker's yeast in a batch fluidized bed

This study investigates the drying of baker's yeast in a fluidized-bed dryer. Mathematical modeling of the process was performed, incorporating the important process and quality parameters of the system. Artificial neural network (ANN) and adaptive neural network-based fuzzy inference system (ANFIS) structures were used to create process and quality models. Due to uncertainty regarding the process parameters, various different ANN structures were built, and the ANN with the optimum performance results for the proposed models was selected. This study also presents an ANFIS modeling approach with adaptive structure. ANN quality modeling was performed using process output parameters, and the quality loss incurred from drying the product was determined. These proposed models are easy to apply and do not impose any additional burden on the process (or the employees). The database used in this work was gathered from large quantities of industrial data (about 570 batches) obtained under various working conditions at random times over one year.     

Developing GEP tree-based,neuro-swarm,and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns

The type of materials used in designing and constructing structures significantly affects the way the structures behave. The performance of concrete and steel, which are used as a composite in columns, has a considerable effect upon the structure behavior under different loading conditions. In this paper, several advanced methods were applied and developed to predict the bearing capacity of the concrete-filled steel tube (CFST) columns in two phases of prediction and optimization. In the prediction phase, bearing capacity values of CFST columns were estimated through developing gene expression programming (GEP)-based tree equation; then, the results were compared with the results obtained from a hybrid model of artificial neural network (ANN) and particle swarm optimization (PSO). In the modeling process, the outer diameter, concrete compressive strength, tensile yield stress of the steel column, thickness of steel cover, and the length of the samples were considered as the model inputs. After a series of analyses, the best predictive models were selected based on the coefficient of determination (R²) results. R² values of 0.928 and 0.939 for training and testing datasets of the selected GEP-based tree equation, respectively, demonstrated that GEP was able to provide higher performance capacity compared to PSO–ANN model with R² values of 0.910 and 0.904 and ANN with R² values of 0.895 and 0.881. In the optimization phase, whale optimization algorithm (WOA), which has not yet been applied in structural engineering, was selected and developed to maximize the results of the bearing capacity. Based on the obtained results, WOA, by increasing bearing capacity to 23436.63 kN, was able to maximize significantly the bearing capacity of CFST columns.

     

A computational literature review of football performance analysis through probabilistic topic modeling

This research aims to illustrate the potential use of concepts, techniques, and mining process tools to improve the systematic review process. Thus, a review was performed on two online databases (Scopus and ISI Web of Science) from 2012 to 2019. A total of 9649 studies were identified, which were analyzed using probabilistic topic modeling procedures within a machine learning approach. The Latent Dirichlet Allocation method, chosen for modeling, required the following stages: 1) data cleansing, and 2) data modeling into topics for coherence and perplexity analysis. All research was conducted according to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses in a fully computerized way. The computational literature review is an integral part of a broader literature review process. The results presented met three criteria: (1) literature review for a research area, (2) analysis and classification of journals, and (3) analysis and classification of academic and individual research teams. The contribution of the article is to demonstrate how the publication network is formed in this particular field of research, and how the content of abstracts can be automatically analyzed to provide a set of research topics for quick understanding and application in future projects.

     

Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures

An improved neuro-wavelet modeling (NWM) methodology is presented, and it aims at improving prediction precision of time-varying behavior of engineering structures. The proposed methodology distinguishes from the existing NWM methodology by featuring the distinctive capabilities of constructing optimally uncoupled dynamic subsystems in light of the redundant Haar wavelet transform (RHWT) and optimizing neural network. In particular, two techniques of imitating wavelet packet transform of RHWT and reconstructing the major crests of power spectrum of analyzed data are developed with the aim of constructing the optimally uncoupled dynamic subsystems from time-varying data. The resulting uncoupled dynamic subsystems make the underlying dynamic law of time-varying behavior more tractable than the raw scale subwaves arose from the RHWT, and they provide a platform for multiscale modeling via individual modeling at the uncoupled dynamic subsystem level. Furthermore, on each uncoupled dynamic subsystem, the technique of optimal brain surgeon in conjunction with a new dynamic mechanism refreshing is employed to optimize the neural network, and the recombination of the modeling outcomes on every subsystem constitutes the overall modeling of time-varying behavior. The improved NMW methodology offers a feasible framework of multiscale modeling due to its flexibility, adaptability and rationality, and it is particularly useful for prediction applications of time-varying behavior of engineering structures. As an illustrative example, the improved NWM methodology is applied to model and forecast dam deformation, and the results show that the methodology possesses positive advantages over the existing multiscale and single scale modeling techniques. The improved NMW methodology is promising and valuable for the safety monitoring and extreme event warning of engineering structures.     

人工神经网络GTAW建模及控制

给出一种用于钨极气体保护电弧焊（ＧＴＡＷ）建及控制的人工神经网络（ＡＮＮ），重点论述利用ＡＮＮ建立焊接参数模型的方法以及在熔深控制方面的应用，通过实验证明，所提出的智能方法具有良好的系统控制性能。     

在非线性建模中神经网络模型的构建与选择

主要研究了如何将平时单独使用的数学方法和统计学方法根据它们各自的优点综合运用,以提高非线性建模过程中神经网络模型构建和选择的效率。所使用的统计学工具包括矩阵的条件数,假设检验,交叉验证。文中对每个方法进行综合分析,进而判断它们分别应用在神经网络模型构建与选择过程的哪个阶段是最有效的。在此基础上,提出了一个系统的神经网络模型的构建与选择程序,并最终通过仿真试验来说明这个程序的有效性。     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

一种应用神经网络技术的车型识别仪

介绍一种新研制的公路收费车型识别仪, 该设备利用环型线圈磁感应技术检测车辆特征信号, 经预处理后由人工神经网络识别车型, 并从标准２３２ 串口输出相应的费额代码。     

A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation

Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall modeling as well as other fields of hydrology.In the current research, the wavelet analysis was linked to the ANN concept for prediction of Ligvanchai watershed precipitation at Tabriz, Iran. For this purpose, the main time series was decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the precipitation 1 month ahead. The obtained results show the proposed model can predict both short- and long-term precipitation events because of using multi-scale time series as the ANN input layer.     

Evolutionary multi-objective optimization of artificial neural network for classification of autism spectrum disorder screening

Evolutionary computing algorithms are computational intelligent systems that are used in a wide range of research applications, primarily for optimization. In this paper, an artificial neural network (ANN), a machine learning technique, is used to classify the data. The weights associated with each neuron and the architecture of the neural network always bias the output of the network model. With prior knowledge or trial and error techniques, different metrics or objectives can be used to optimise these weights. The optimization of weights using multiple objectives refers to a "multi-objective optimization problem." In this paper, an evolutionary cultural algorithm is used to optimise weights in ANN, and the results are reported with improved accuracy. Three benchmark datasets for autism screening data are used, trained, and tested for model accuracy in the classification: toddlers (1054,19), children (292,21), and adults (704,21).With the support of the domain expert, real-time data were collected from parents and caregivers and totalled over 1000 records, with a moderate difference in attributes based on CARS-2 (Childhood Autism Rating Scale, 2nd Edition) for ASD screening. In this paper, the proposed model is compared using a curve-fitting mathematical technique. The proposed model is trained and tested, and the results showed that it outperformed other algorithms in terms of precision, accuracy, sensitivity, and specificity.

     

人工神经网络遥感分类方法研究现状及发展趋势探析

从人工神经网络技术本身出发,概括了其在遥感分类中的研究现状,分析了人工神经网络遥感分类方法与其它分类方法相比具有的优势,介绍了人工神经网络遥感分类的一些主要应用,并进一步对人工神经网络遥感分类方法的发展趋势进行了展望。     

Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems (AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network (ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach.     

When does an easy task become hard? A systematic review of human task-evoked pupillary dynamics versus cognitive efforts

The amount of working memory recourses available (or required) to process a cognitive task (easy or hard) represents human cognitive effort. Working memory resources (visual or auditory) and cognitive efforts are interconnected with visual or auditory pathways. In this review, various facets of pupillary dynamics literature are compared in order to determine an optimal method of cognitive effort assessment. Some key categorical areas of interest are identified including the presented stimulus, observed response, comparisons and different methods of analysis. In details review, a set of predetermined evaluation criteria were used and a decision matrix is developed to outline the best practice in papillary dynamics. Based on the summery table in the form of the decision matrix, a quantitative model with artificial neural network (ANN) is selected for a best practice of cognitive effort estimation. The mental multiplication task is found an effective stimulus (cognitive task) to evoke the pupillary response for various level of task difficulty. In most cases, aural and visual are considered as two presentation modes, two sensory inputs, and two mental resources and greatly imparts in cognitive workload. Through this review, it is also explored that, linking together by transfer and error functions, a combination of an ANN and a multinomial processing tree can used in cognitive effort analysis. This research direction can further explored to estimate the relationship between cognitive task and cognitive effort, to facilitate in technology development for neurological disorders, such as narcolepsy, on the neural pathways involved in cognitive processing.

     

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.     

Machine Learning Techniques for Software Maintainability Prediction: Accuracy Analysis

Maintaining software once implemented on the end-user side is laborious and, over its lifetime, is most often considerably more expensive than the initial software development. The prediction of software maintainability has emerged as an important research topic to address industry expectations for reducing costs, in particular, maintenance costs. Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning (ML) for better prediction of software maintainability. This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction (SPMP) using ML techniques. This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria: maintainability prediction techniques, validation methods, accuracy criteria, overall accuracy of ML techniques, and the techniques offering the best performance. The review process followed the well-known systematic review process. The results show that ML techniques are frequently used in predicting maintainability. In particular, artificial neural network (ANN), support vector machine/regression (SVM/R), regression &; decision trees (DT), and fuzzy &; neuro fuzzy (FNF) techniques are more accurate in terms of PRED and MMRE. The N-fold and leave-one-out cross-validation methods, and the MMRE and PRED accuracy criteria are frequently used in empirical studies. In general, ML techniques outperformed non-machine learning techniques, e.g., regression analysis (RA) techniques, while FNF outperformed SVM/R, DT, and ANN in most experiments. However, while many techniques were reported superior, no specific one can be identified as the best.