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Prediction of compressive and flexural strengths of jarosite mixed cement concrete pavements using artificial neural networks

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ROAD MATERIALS AND PAVEMENT DESIGN 2021年第7期22卷 1521-1542页

作者： Gupta, Tanvi Sachdeva, S. N. Natl Inst Technol Dept Civil Engn Kurukshetra Haryana India

In this paper, an attempt has been made to apply and compare the prediction capability of two variants of Artificial Neural Networks: feed-forward neural network (FFNN) and the radial basis function network (RBFN) for modelling the flexural and compressive strengths of jarosite mixed cement concrete for pavements. The compressive strength and the flexural strength are dependent upon a total of eight inputs. Their values are experimentally determined from the specimens containing 0%, 10%, 15%, 20% and 25% of the jarosite as a partial replacement to cement in M40 concrete mix. These specimens had undergone a curing for 3, 7, 28, 90, 180 and 365 days providing the inputs-outputs experimental data for the 90 observations. The simulation experiments showed that the performance of FFNN is found to be better than that of the RBFN model as the former model provided lesser values of the performance indicators such as Mean Average Error and Mean Squared Error. Further, the FFNN model required a lesser number of parameters to be tuned during the training as compared to the RBFN model.

关键词： jarosite radial basis function network back-propagation algorithm compressive and flexural strengths feed-forward neural network

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Studies on the applicability of artificial neural network (ANN) in emulsion liquid membranes

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JOURNAL OF MEMBRANE SCIENCE 2003年第1-2期220卷 155-164页

作者： Chakraborty, M Bhattacharya, C Dutta, S SV Reg Coll Engn & Technol Dept Chem Engn Surat 395007 India Jadavpur Univ Dept Chem Engn Kolkata 700032 W Bengal India

An artificial neural network (ANN) model of emulsion liquid membrane (ELM) process is proposed in the present study which is able to predict solute concentration in feed during extraction operation and ultimate % extraction at different initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time. Because of the complexity in generalization of the phenomenon of ELM process by any mathematical model, the neural network proves to be a very promising method for the purpose of process simulation. The network uses the back-propagation algorithm (BPA) for evaluating the connection strengths representing the correlations between inputs (initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time) and outputs (solute concentration in feed during extraction operation and % extraction). The network employed in the present study uses five input nodes corresponding to the operating variables and two output nodes corresponding to the measurement of the performance of the network (solute concentration in feed during extraction and % extraction). Batch experiments are performed for separation of nickel(II) from aqueous sulphate solution of initial concentration in the 200-100 mg/l ranges. The network employed in the present study uses two hidden layers of optimum number of nodes being thirty and twenty. A leaning rate of 0.3 and momentum factor of 0.4 is used. The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be well within +/-10%. (C) 2003 Elsevier B.V. All rights reserved.

关键词： emulsion liquid membrane artificial neural network back-propagation algorithm simulation solute concentration in feed phase

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Treatment of leather plant effluent using NF followed by RO and permeate flux prediction using artificial neural network

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CHEMICAL ENGINEERING JOURNAL 2009年第1-3期151卷 275-285页

作者： Purkait, M. K. Kumar, V. Dinesh Maity, Damodar Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 India Indian Inst Technol Guwahati Dept Civil Engn Gauhati 781039 India Indian Inst Technol Dept Civil Engn Kharagpur 721302 W Bengal India

This paper presents experimental data and modeling for membrane-based treatment of leather plant effluent. The effluent coming out from the various upstream steps of leather plant are combined and pressure driven membrane processes like nanofiltration (NF) and reverse osmosis (RO) are undertaken after a pretreatment consisting of gravity settling and coagulation followed by cloth filtration. Performances of two NF membranes (200 and 400 molecular weight cut offs (MWCO)) are evaluated. Experiments are conducted using an unstirred batch cell. It is observed that a combined operation of NF using 400 MWCO membrane followed by RO operation is better option compared to a single operation of NF with 200 MWCO (membrane). After selection of proper NF membrane from batch experimental data, the entire membrane separation scheme is validated by conducting experiments using a cross flow cell. A detailed parametric study for cross flow experiment is investigated and the suitable operating trans-membrane pressure and the cross flow rates are found out (experimentally) in both NF and RO. The experimental flux data are correlated and analyzed using artificial neural network (ANN). A multi-layered feed-forward network with back-propagation algorithm is used for training of ANN models. Two artificial neural network models with input, output and hidden layer(s) are used to predict the flux data for both the batch and cross flow run. A good agreement has been observed using the ANN model with the experimental flux data with a deviation not more than 1% for most of the cases considered. The BOD and COD values of the finally treated effluent are well within the permissible limits. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Permeate flux Leather effluent Artificial neural networks back-propagation algorithm Membrane filtration

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Characterizing Volume Density of Subwavelength Particles at 220-325 GHz Using Deep Neural Network and Nonfeatured Scattering Matrix

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IEEE ANTENNAS AND WIRELESS propagation LETTERS 2019年第11期18卷 2240-2243页

作者： Jung, Sohyeon Hong, Wonbin Pohang Univ Sci & Technol Dept Elect Engn Pohang 37673 South Korea

The feasibility of using machine-learning algorithm on classification and numerical prediction method for characterizing volume density is explored. The deep neural network (DNN) is exploited to describe the relationship of input and output data when the analytical modeling or simulation is unavailable. In this letter, this approach is exemplified for the extraction of relative volume density of subwavelength particles at 220-2013;325-00A0;GHz. The training based on the phase of transmission coefficients ascertains classification accuracies of 99.9-0025;and prediction mean squared error of 0.0186. In addition, the training based on the real and imaginary parts of the scattering matrix can also achieve high classification accuracy (> 94.6-0025;). It concludes that the DNN can autonomously retrieve correlation of electromagnetic properties from the nonfeatured real and imaginary parts of the scattering matrix.

关键词： back-propagation algorithm deep neural network (DNN) electromagnetics (EM) machine learning sensing subwavelength particles volume density wave propagation modeling

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Introduction of orthonormal transform into neural filter for accelerating convergence speed

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2000年第2期E83A卷 367-370页

作者： Nakanishi, I Itoh, Y Fukui, Y Tottori Univ Fac Educ & Reg Sci Tottori 6808551 Japan Tottori Univ Fac Engn Tottori 6808551 Japan

As the nonlinear adaptive filter: the neural filter is utilized to process the nonlinear signal and/or system. However, the neural filter requires large number of iterations for convergence. This letter presents a new structure of the multilayer neural filter where the orthonormal transform is introduced into all inter-layers to accelerate the convergence speed. The proposed structure is called the transform domain neural filter (TDNF) for convenience. The weights are basically updated by the back-propagation (BP) algorithm but it must be modified since the error back-propagates through the orthogonal transform. Moreover, the variable step size which is normalized by the transformed signal power is introduced into the BP algorithm to realize the orthonormal transform. Through the computer simulation, it is confirmed that the introduction of the orthonormal transform is effective for speedup of convergence in the neural filter.

关键词： transform domain neural filter neural filter multilayer neural networks back-propagation algorithm normalized step size

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Fuzzy deep learning based urban traffic incident detection

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COGNITIVE SYSTEMS RESEARCH 2018年 50卷 206-213页

作者： El Hatri, Chaimae Boumhidi, Jaouad Sidi Mohamed Ben AbdEllah Univ Fac Sci Dhar Mahraz Comp Sci Dept LIIAN Lab Fes 30000 Morocco

Traffic incident detection (TID) is an important part of any modern traffic control because it offers an opportunity to maximise road system performance. For the complexity and the nonlinear characteristics of traffic incidents, this paper proposes a novel fuzzy deep learning based TID method which considers the spatial and temporal correlations of traffic flow inherently. Parameters of the deep network are initialized using a Stacked Auto-Encoder (SAE) model following a layer by layer pre-training procedure. To conduct the fine tuning step, the back-propagation algorithm is used to precisely adjust the parameters in the deep network. Fuzzy logic is employed to control the learning parameters where the objective is to reduce the possibility of overshooting during the learning process, increase the convergence speed and minimize the error. To find the best architecture of the deep network, we used a separate validation set to evaluate different architectures generated randomly based on the Mean Squared Error (MSE). Simulation results show that the proposed incident detection method has many advantages such as higher detection rate and lower false alarm rate. (C) 2017 Published by Elsevier B.V.

关键词： Automatic traffic incident detection Fuzzy deep learning Deep artificial neural network Stacked auto-encoder model back-propagation algorithm Fuzzy logic controller

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Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers

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ADVANCES IN ENGINEERING SOFTWARE 2007年第2期38卷 102-111页

作者： Bateni, S. Mohyeddin Jeng, Dong-Sheng Melville, Bruce W. Univ Sydney Sch Civil Engn Sydney NSW 2006 Australia Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 2G7 Canada Univ Auckland Dept Civil & Environm Engn Auckland 1 New Zealand

The physical process of scour around bridge piers is complicated. Despite various models presented to predict the equilibrium scour depth and its time variation from the characteristics of the current and sediment, scope exists to improve the existing models or to provide alternatives to them. In this paper, a neural network technique within a Bayesian framework, is presented for the prediction of equilibrium scour depth around a bridge pier and the time variation of scour depth. The equilibrium scour depth was modeled as a function of five variables;flow depth and mean velocity, critical flow velocity, median grain diameter and pier diameter. The time variation of scour depth was also modeled in terms of equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. The Bayesian network predicted equilibrium and time-dependent scour depth much better when it was trained with the original (dimensional) scour data, rather than using a non-dimensional form of the data. The selection of water, sediment and time variables used in the models was based on conventional scour depth data analysis. The new models estimate equilibrium and time-dependent scour depth more accurately than the existing expressions. A committee model, developed by averaging the predictions of a number of individual neural network models, increased the reliability and accuracy of the predictions. A sensitivity analysis showed that pier diameter has a greater influence on equilibrium scour depth than the other independent parameters. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： Bayesian neural network scour bridge piers back-propagation algorithm

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Frequency estimation in wind farm integrated systems using artificial neural network

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 62卷 72-79页

作者： Jiang, Wang Lu, Jiping Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing 630044 Peoples R China

Owing to the random features of wind generator outputs, power system frequency becomes increasingly variable when considerable wind farms are integrated into a power system. Frequency forecast in a power system is difficult and challenging. This study develops an improved elastic back-propagation neural network method to forecast system frequency. The effectiveness of the proposed method is verified using field data from a real wind farm in Guangdong, China. Simulation results show that even in different types of wind farms, the proposed method is applicable and can accurately identify changes in system frequency. Therefore, the forecast results can be used to design appropriate frequency control strategies and to enhance security and stability for the whole system. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Frequency prediction Wind farms back-propagation algorithm

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Robust speed-controlled induction motor drive based on recurrent neural network

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ELECTRIC POWER SYSTEMS RESEARCH 2006年第12期76卷 1064-1074页

作者： Ren, Tsai-Jiun Chen, Tien-Chi Natl Cheng Kung Univ Dept Engn Sci Tainan 70101 Taiwan

This paper proposes a recurrent neural network speed controller for an induction motor drive. This speed controller consists of a recurrent neural network identifier (RNNI) and recurrent neural network controller (RNNC). The RNNI is used to provide real-time adaptive identification of the unknown motor dynamics. The RNNC is used to produce an adaptive control force so that the motor speed can accurately track the reference command. A back-propagation algorithm was used as the learning algorithm to automatically adjust the weights of the RNNI and RNNC in order to minimize the performance functions. The proposed control scheme can quickly estimate the plant parameters and produce a control force, such that the motor speed can accurately track the reference command. Both computer simulations and experimental results demonstrated that the proposed control scheme was able to obtain robust speed control. (c) 2006 Elsevier B.V. All rights reserved.

关键词： recurrent neural network controller back-propagation algorithm induction motor drive

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Application of a combined response surface methodology (RSM)-artificial neural network (ANN) for multiple target optimization and prediction in a magnetic coagulation process for secondary effluent from municipal wastewater treatment plants

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第24期29卷 36075-36087页

作者： Wang, Kemei Mao, Yuxuan Wang, Chuanhua Ke, Qiang Zhao, Min Wang, Qi Wenzhou Univ Coll Life & Environm Sci Wenzhou 325035 Peoples R China Wenzhou Univ Natl & Local Joint Engn Res Ctr Ecol Treatment Te Wenzhou 325600 Peoples R China

In this study, an enhanced coagulation-flocculant process incorporating magnetic powder was used to further treat the secondary effluent of domestic wastewater from a municipal wastewater treatment plant. The purpose of this work was to improve the discharged water quality to the surface water class IV standard of China. A novel approach using a combination of the response surface methodology and an artificial neural network (RSM-ANN) was used to optimize and predict the total phosphorus (TP) pollutant removal and turbidity. This work was first evaluated by RSM using the concentrations of coagulant, magnetic powder, and flocculant as the controllable operating variables to determine the optimal TP removal and turbidity. Next, an ANN model with a back-propagation algorithm was constructed from the RSM data along with the non-controllable variables, raw TP concentration, and raw water turbidity. Under the optimized experimental conditions (28.42 mg/L coagulant, 623 mg/L magnetic powder, and 0.18 mg/L flocculant), the TP and turbidity removal reached 88.79 +/- 5.45% and 63.48 +/- 9.60%, respectively, compared with 83.28% and 59.80%, predicted by the single RSM model, and 87.71 +/- 5.74% and 64.62 +/- 10.75%, predicted by the RSM-ANN model. The treated water were 0.17 +/- 6.69% mg/L of TP and 2.46 +/- 5.09% NTU of turbidity, respectively, which completely met the surface water class IV standard (TP < 0.3 mg/L;turbidity < 3 NTU). Therefore, this work demonstrated that the discharged water quality was completely improved using the magnetic coagulation process. In addition, the combined RSM-ANN approach could have potential application in municipal wastewater treatment plants.

关键词： Wastewater treatment plant Magnetic coagulation-flocculation Phosphorus removal Secondary effluent Domestic wastewater Turbidity removal Box-Behnken Design back-propagation algorithm

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