Artificial neural network,ANN-PSO and ANN-ICA for modelling the Stirling engine

Nowadays, shortage of fossil fuels resources, especially oil, and also global attention to environmental hazards produced by the internal combustion process have caused extensive researches on the development of renewable energy engine technology. Among all kinds of renewable resources, solar energy Stirling engines have their own special situation for energy generation with lower pollutants and sustainable sources. The Stirling engine is an external combustion engine that uses any external heat source to generate mechanical power. Various parameters affect the performance of the Stirling engine. In this study, artificial neural network (ANN) was applied to estimate the power and torque values obtained from a Stirling heat engine (Philips M102C engine). It employs the Levenberg–Marquardt algorithm for training ANN with back propagation network for estimating the power and torque of the Stirling heat engine. The performances of the imperialist competitive algorithm (ICA)-ANN and ANN-particle swarm optimisation (PSO) are compared with the performance of the ANN based on mean square error (MSE) and correlation coefficient. PSO and ICAs are applied to determine the initial weights of the neural network. The obtained results indicate that ANN-PSO has a better performance than ICA-ANN and ANN alone; also the MSE for the ANN-PSO is lower as well. Considering the results obtained from this study, there is very good agreement between the output of the testing phase of the ANN-PSO model with experimental data and they are very close to each other.     

Pipe pile setup: Database and prediction model using artificial neural network

Over the last few years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, the ability to accurately predict pile setup may lead to more economical pile design, resulting in a reduction in pile length, pile section, and size of driving equipment. In this paper, an ANN model was developed for predicting pipe pile setup using 104 data points, obtained from the published literature and the author's own files. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum ANN model.Finally, the paper compares the predictions obtained by the ANN with those given by a number of empirical formulas. It is demonstrated that the ANN model satisfactorily predicts the measured pipe pile setup and significantly outperforms the examined empirical formulas.     

Assessing artificial neural network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.     

A simplified method for free vibration analysis of cable-stayed bridges

A simplified method for the accurate prediction of the natural frequencies of cable-stayed bridges is proposed in this paper. In the proposed method artificial neural network (ANN) is applied to derive a simple formula to predict the natural frequencies of cable-stayed bridges based on existing natural frequency data. Unlike in the existing empirical methods, no functional relationship among the variables is assumed before we can develop an ANN model. ANN automatically constructs the relationships and adapts based on the training data presented to them. Also, the proposed method takes into account the wide range of parameters which may have a significant effect on the natural frequencies of cable-stayed bridges. The proposed method is particularly useful for the preliminary design stage of cable-stayed bridges where free vibration analysis needs to be carried out. The proposed method is compared with two existing empirical methods. It is found that the simplified method proposed in this study can produce a more accurate prediction of the natural frequencies of cable-stayed bridges than the existing empirical methods.     

应用RBF神经网络的预应力混凝土碳化深度预测研究

在现有混凝土碳化研究成果基础上,建立了预应力混凝土碳化预测模型。随后,运用径向基函数神经网络的基本原理,通过对影响预应力混凝土碳化深度因素的分析,建立了预测碳化深度的RBF和GRNN网络模型。通过实例进行了分析计算和预测,预测结果具有较高的精度。可以说,人工神经网络预测方法是一种可同时考虑各种影响因素组合、行之有效的混凝土碳化预测分析方法。     

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R~2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R~2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.     

Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines

A fuzzy artificial neural network (ANN)–based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation-based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation-based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems.     

基于神经网络的碳纤维布与混凝土极限粘结强度分析

碳纤维布与混凝土的极限粘结强度问题属于高度非线性问题,难以建立精确的数学表达式进行分析。对基于拉出试验的极限粘结强度数据进行分析,建立了人工神经网络,对极限粘结强度进行仿真预测。神经网络的建立考虑了碳纤维布的厚度、宽度、粘结长度、弹性模量、抗拉强度和混凝土试块抗压强度、抗拉强度、宽度这8个参数,运用了118组试验数据对网络进行训练,对15组数据进行了预测分析。将神经网络计算结果同4种经验公式计算结果进行比较,其精度明显高于其他4种模型。结果表明,运用人工神经网络对碳纤维布与混凝土的极限粘结强度进行预测是可行的。     

Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks — elastic investigation

Bridge girders exposed to aggressive environmental conditions are subject to time-variant changes in resistance. There is therefore a need for evaluation procedures that produce accurate predictions of the load-carrying capacity and reliability of bridge structures to allow rational decisions to be made about repair, rehabilitation and expected life-cycle costs. This study deals with the stability of damaged steel I-beams with web opening subjected to bending loads. A three-dimensional (3D) finite element (FE) model using ABAQUS for the elastic flexural torsional analysis of I-beams has been used to assess the effect of web opening on the lateral buckling moment capacity. Artificial neural network (ANN) approach has been also employed to derive empirical formulae for predicting the lateral-torsional buckling moment capacity of deteriorated steel I-beams with different sizes of rectangular web opening using obtained FE results. It is found out that the proposed formulae can accurately predict residual lateral buckling capacities of doubly-symmetric steel I-beams with rectangular web opening. Hence, the results of this study can be used for better prediction of buckling life of web opening of steel beams by practice engineers.     

Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods

In order to determine the appropriate model for predicting the maximum surface settlement caused by EPB shield tunneling, three artificial neural network (ANN) methods, back-propagation (BP) neural network, the radial basis function (RBF) neural network, and the general regression neural network (GRNN), were employed and the results were compared. The nonlinear relationship between maximum ground surface settlements and geometry, geological conditions, and shield operation parameters were considered in the ANN models. A total number of 200 data sets obtained from the Changsha metro line 4 project were used to train and validate the ANN models. A modified index that defines the physical significance of the input parameters was proposed to quantify the geological parameters, which improves the prediction accuracy of ANN models. Based on the analysis, the GRNN model was found to outperform the BP and RBF neural networks in terms of accuracy and computational time. Analysis results also indicated that strong correlations were established between the predicted and measured settlements in GRNN model with MAE = 1.10, and RMSE = 1.35, respectively. Error analysis revealed that it is necessary to update datasets during EPB shield tunneling, though the database is huge.     

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.     

Prediction of elastic modulus of normal and high strength concrete by artificial neural networks

In the present paper, application of artificial neural networks (ANNs) to predict elastic modulus of both normal and high strength concrete is investigated. The paper aims to show a possible applicability of ANN to predict the elastic modulus of both high and normal strength concrete. An ANN model is built, trained and tested using the available test data gathered from the literature. The ANN model is found to predict elastic modulus of concrete well within the ranges of the input parameters considered. The average value of the experimental elastic modulus to the predicted elastic modulus ratio is found to be 1.00. The elastic modulus results predicted by ANN are also compared to those obtained using empirical results of the buildings codes and various models. These comparisons show that ANNs have strong potential as a feasible tool for predicting elastic modulus of both normal and high strength within the range of input parameters considered.     

Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers

In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values.     

Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling

Numerous empirical and analytical relations exist between shield tunnel characteristics and surface and subsurface deformation. Also, 2-D and 3-D numerical analyses have been applied to such tunneling problems. Similar but substantially fewer approaches have been developed for earth pressure balance (EPB) tunneling. In the Bangkok MRTA project, data on ground deformation and shield operation were collected. The tunnel sizes are practically identical and the subsurface conditions over long distances are comparable, which allow one to establish relationships between ground characteristics and EPB – operation on the one hand, and surface deformations on the other hand. After using the information to identify which ground- and EPB-characteristic have the greatest influence on ground movements, an approach based on artificial neural networks (ANN) was used to develop predictive relations. Since the method has the ability to map input to output patterns, ANN enable one to map all influencing parameters to surface settlements. Combining the extensive computerized database and the knowledge of what influences the surface settlements, ANN can become a useful predictive method. This paper attempts to evaluate the potential as well as the limitations of ANN for predicting surface settlements caused by EPB shield tunneling and to develop optimal neural network models for this objective.     

Prediction of blast-induced ground vibration using artificial neural networks

Blasting is still being considered to be one the most important applicable alternatives for conventional tunneling. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby habitants and dwellings and should be prevented. In this paper, an attempt has been made to predict blast-induced ground vibration using artificial neural network (ANN) in the Siahbisheh project, Iran. To construct the model maximum charge per delay, distance from blasting face to the monitoring point, stemming and hole depth are taken as input parameters, whereas, peak particle velocity (PPV) is considered as an output parameter. A database consisting of 182 datasets was collected at different strategic and vulnerable locations in and around the project. From the prepared database, 162 datasets were used for the training and testing of the network, whereas 20 randomly selected datasets were used for the validation of the ANN model. A four layer feed-forward back-propagation neural network with topology 4-10-5-1 was found to be optimum. To compare performance of the ANN model with empirical predictors as well as regression analysis, the same database was applied. Superiority of the proposed ANN model over empirical predictors and statistical model was examined by calculating coefficient of determination for predicted and measured PPV. Sensitivity analysis was also performed to get the influence of each parameter on PPV. It was found that distance from blasting face is the most effective and stemming is the least effective parameter on the PPV.     

Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network

One of the major requirements of advanced traveler information systems (ATISs) is a mechanism to estimate link travel times. This article examines the use of an artificial neural network (ANN) for predicting freeway link travel times for one through five time periods into the future. Actual freeway link travel times from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system were used as a test bed. It was found that when predicting one or two time periods into the future, the ANN model that only considered previous travel times from the target link gave the best results. However, when predicting three to five time periods into the future, the ANN model that employed travel times from upstream and downstream links in addition to the target link gave superior results. The ANN model also gave the best overall results compared with existing link travel time forecasting techniques.     

Backfilling missing microbial concentrations in a riverine database using artificial neural networks

Predicting peak pathogen loadings can provide a basis for watershed and water treatment plant management decisions that can minimize microbial risk to the public from contact or ingestion. Artificial neural network models (ANN) have been successfully applied to the complex problem of predicting peak pathogen loadings in surface waters. However, these data-driven models require substantial, multiparameter databases upon which to train, and missing input values for pathogen indicators must often be estimated. In this study, ANN models were evaluated for backfilling values for individual observations of indicator bacterial concentrations in a river from 44 other related physical, chemical, and bacteriological data contained in a multi-year database. The ANN modeling approach provided slightly superior predictions of actual microbial concentrations when compared to conventional imputation and multiple linear regression models. The ANN model provided excellent classification of 300 randomly selected, individual data observations into two defined ranges for fecal coliform concentrations with 97% overall accuracy. The application of the relative strength effect (RSE) concept for selection of input variables for ANN modeling and an approach for identifying anomalous data observations utilizing cross validation with ANN model are also presented.     

Multiple linear regression,artificial neural network,and fuzzy logic prediction of 28 days compressive strength of concrete

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.     

基于神经网络的供热计量系统热负荷短期预测

针对实行热计量后热负荷变化的特点，采用神经网络中应用最广泛的BP网络对热负荷进行预测，利用MATLAB仿真程序对所建立的人工神经网络负荷预测模型进行验证，仿真误差为6．93％，满足工程需要。     

Hybrid random aggregation model and Bayesian optimization-based convolutional neural network for estimating the concrete compressive strength

Numerous experimental studies have shown the type and gradation of coarse aggregates effect on the mechanical properties of concrete. The type and gradation of coarse aggregates have not been taken into account in the available machine learning prediction models. In this study, a two-dimensional concrete microscopic image was generated by using a random aggregate model (RAM), and the coarse aggregate and other concrete ingredients were represented innovatively using polygons and trichromatic chromaticity values in the RAM images. The RAM image set was created by applying this method to represent 1110 sets of different concrete mixes. Then based on the Bayesian optimization algorithm and the image set, a compressive strength prediction model considering the effect of coarse aggregate types and gradations was developed utilizing a convolutional neural network (CNN) model. Meanwhile, an artificial neural network (ANN) compressive strength prediction model was developed using 1110 sets of mix ratio data. The results show that the proposed RAM image generation method has the capability to represent different concrete mix ratios collected in this study. The prediction performance of the CNN compressive strength model considering aggregate types and gradations is better than that of the ANN model. The method can provide a new perspective for predicting other concrete mechanical properties and technically support performance-based intelligent concrete mix design.