Rheological Optimization of Self Compacting Concrete with Sodium Lignosulfate Based Accelerant Using Hybrid Neural Network-Genetic Algorithm

2016 ◽  
Vol 866 ◽  
pp. 9-13 ◽  
Author(s):  
Nolan C. Concha
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rapid Change ◽  
Strong Stability ◽  
Self Compacting Concrete ◽  
Concrete Technology ◽  
Optimum Proportion ◽  
Material Homogeneity ◽  
Relationship Of ◽  
The Relationship

One of the most useful innovations in concrete technology is Self Compacting Concrete that has the ability to flow efficiently and maintain material homogeneity. The rapid change in the behavior of concrete due to accelerating admixtures can significantly affect the workability properties of the mixture and reduce its ability to flow efficiently. To describe the influence of superplasticizers blended with accelerant on the rheological properties of SCC, several mixtures were tested for Slump Flow, L-Box, and Screen Stability tests. Artificial neural network was used to obtain a model describing the constitutive relationships between the material components and workability parameters of SCC and was optimized using Genetic Algorithm. Results showed that ANN was able to establish the relationship of rheology to the concrete material components and GA derived the optimum proportion for best rheological performance. Most of the design samples of SCC with blended superplasticizer and sodium lignosulfate accelerant were not able to perform well in the flowing ability due to inefficiency of the fresh SCC to flow. The increasing dosage of accelerant however rendered strong stability between the concrete particles allowing the SCC samples to resist segregation and maintain material homogeneity.

An improvement to the ANN-enhanced flow rating method

2021 ◽  
Author(s):  
Bernhard Schmid
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Water Resources ◽  
Flow Rate ◽  
Major Ions ◽  
Rate Of Change ◽  
Data Set ◽  
Input Variables ◽  
Relationship Of ◽  
The Relationship

<p>The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain ‘better’, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author’s experience.</p><p>The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).</p><p>The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the Mödling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the Mödling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only.    </p><p> </p><p>References</p><p>Moatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.</p><p>Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.</p><p>Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.</p><p>Tunqui Neira, J.M., Andréassian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.</p><p>Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.</p>

scholarly journals Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

2020 ◽  
Vol 12 (7) ◽  
pp. 1096
Author(s):  
Zeqiang Chen ◽  
Xin Lin ◽  
Chang Xiong ◽  
Nengcheng Chen
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Machine Learning Algorithms ◽  
Jinsha River ◽  
Precipitation Water ◽  
Relationship Of ◽  
The Relationship

Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

scholarly journals Hand–Eye Calibration Algorithm Based on an Optimized Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 85
Author(s):  
Jiang Hua ◽  
Liangcai Zeng
Keyword(s):  
Neural Network ◽  
Coordinate System ◽  
Calibration Method ◽  
Calibration Model ◽  
The Neural Network ◽  
Calibration Algorithm ◽  
Robot Grasping ◽  
Relationship Of ◽  
The Relationship ◽  
Camera Coordinate System

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B=fA, which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system.

scholarly journals Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
André Cyr ◽  
Frédéric Thériault
Keyword(s):  
Neural Network ◽  
Operant Conditioning ◽  
Concept Learning ◽  
Specific Pattern ◽  
Spiking Neural Network ◽  
Conditioning Procedure ◽  
Spatial Concept ◽  
Rule Changes ◽  
Relationship Of ◽  
The Relationship

This paper proposes an artificial spiking neural network (SNN) sustaining the cognitive abstract process of spatial concept learning, embedded in virtual and real robots. Based on an operant conditioning procedure, the robots learn the relationship of horizontal/vertical and left/right visual stimuli, regardless of their specific pattern composition or their location on the images. Tests with novel patterns and locations were successfully completed after the acquisition learning phase. Results show that the SNN can adapt its behavior in real time when the rewarding rule changes.

Application of Self-Organizing Neural Network Method in Logging Sedimentary Microfacies Identification

2012 ◽  
Vol 616-618 ◽  
pp. 38-42 ◽  
Author(s):  
Wen Bo Li ◽  
Yun Liang Yu ◽  
Jian Qiang Wang ◽  
Ye Bai ◽  
Xin Wang
Keyword(s):  
Neural Network ◽  
Recognition Rate ◽  
Identification System ◽  
Characteristic Parameters ◽  
Network Method ◽  
Image Characteristic ◽  
Sedimentary Microfacies ◽  
Relationship Of ◽  
The Relationship ◽  
Self Organizing

Studing the identification methods of sedimentary microfacies by the implement of self-organizing neural network model. Picking up the geometrical characteristic parameters and image characteristic parameters of the logging curves. Establishing the relationship of sedimentary microfacies patterns and well logging curves shapes by characteristic parameters. Developing the Sedimentary microfacies patterns identification system and applying it to 1000 wells of the southern area of Daqing Changyuan, the recognition rate can reach 90%, it can prove the validity of the method.

scholarly journals Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 40
Author(s):  
Siti Zulaikha Mohd Jamaludin ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Ahmad Izani Md Ismail ◽  
Mohd. Asyraf Mansor ◽  
Md Faisal Md Basir
Keyword(s):  
Neural Network ◽  
Data Extraction ◽  
Hopfield Neural Network ◽  
Data Set ◽  
Reverse Analysis ◽  
Insurance Agency ◽  
Logical Representation ◽  
Relationship Of ◽  
Extraction Model ◽  
The Relationship

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.

Sales Forecast for Garment Companies with Cluster Analysis and Modified Neural Networks

2011 ◽  
Vol 127 ◽  
pp. 490-495
Author(s):  
Li Yu ◽  
Yun Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Sales Forecast ◽  
The Neural Network ◽  
Linkage Cluster ◽  
Relationship Of ◽  
Marketing Resource Allocation ◽  
The Relationship

For the companies of the garment industry, managers often dedicate their efforts to forecast the sales accurately while making decisions for marketing resource allocation and scheduling. Based on the historical database, this paper constructs a method to investigate the relationship of the relating factors and sales values. The proposed method combines the cluster analysis and modified neural networks to fulfill the sales forecast task. Firstly, the average linkage cluster algorithm is applied to cluster similar sales values. Secondly, a modified neural network is used to investigate the mapping relationship between those influencing factors and the sales clusters. The method employs a self-adjust mechanism to determine the structure of the neural network. The effectiveness of the proposed method is illustrated with a case study of a garment company in Shanghai.

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