Prediction of shear strength of concrete produced by using pozzolanic materials and partly replacing NFA by MS using ANN

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kiran Mansingrao Mane ◽  
D.K. Kulkarni ◽  
K.B. Prakash
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Fine Aggregate ◽  
Ann Model ◽  
Content Type ◽  
Manufactured Sand ◽  
Software Model ◽  
Data Record ◽  
Pozzolanic Materials ◽  
Artificial Neural Network Ann

Purpose The use of huge quantity of natural fine aggregate (NFA) and cement in civil construction work which have given rise to various ecological problems. The industrial waste like blast furnace slag (GGBFS), fly ash, metakaolin and silica fume can be partly used as a replacement for cement and manufactured sand obtained from crusher and partly used as fine aggregate. The purpose of this paper is to predict the shear strength of concrete using artificial neural network (ANN) for concrete made by using different pozzolans and partly replacing NFA by manufactured sand (MS) which can reduce the time and experimental cost. Design/methodology/approach In this work, MATLAB software model is developed using neural network toolbox to predict the shear strength of concrete made by using pozzolanic materials and partly replacing NFA by manufactured sand (MS). Shear strength was experimentally calculated, and results obtained from experiment were used to develop the ANN model. A total of 131 results values were used to modeling formation, and from that 30% data record was used for testing purpose and 70% data record was used for training purpose. In total, 25 input materials properties were used to find the 28 days shear strength of concrete obtained from partly replacing cement with pozzolans and partly replacing NFA by manufactured sand (MS). Findings The results obtained from ANN model provide very strong accuracy to predict shear strength of concrete obtained from partly replacing cement with pozzolans and NFA by manufactured sand. Originality/value This research study is on determining shear strength of concrete using ANN. The use of this study is to predict the shear strength of concrete using ANN for concrete made by using different pozzolans and partly replacing NFA by manufactured sand (MS) which can reduce the time and experimental cost.

scholarly journals Predicting Slump Values of Concrete Made by Pozzolans and Manufactured Sand using ANN

2022 ◽  
Vol 10 (5) ◽  
pp. 66-72
Author(s):  
Kiran M.Mane ◽  
◽  
S.P. Chavan ◽  
S.A. Salokhe ◽  
P.A. Nadgouda ◽  
...  
Keyword(s):  
Fine Aggregate ◽  
Ann Model ◽  
Natural Sand ◽  
Manufactured Sand ◽  
Environmental Consequences ◽  
Pozzolanic Material ◽  
Software Model ◽  
Alternative Materials ◽  
Model Training ◽  
Artificial Neural Network Ann

Large amounts of natural fine aggregate (NFA) and cement are used in building, which has major environmental consequences. This view of industrial waste can be used in part as an alternative to cement and part of the sand produced by the crusher as fine aggregate, similar to slag sand (GGBFS), fly ash, metacaolin, and silica fume. Many times, there are issues with the fresh characteristics of concrete when using alternative materials. The ANN tool is used in this paper to develop a Matlab software model that collapses concrete made with pozzolanic material and partially replaces natural fine aggregate (NFA) with manufactured sand (MS). Predict. The slump test was carried out in reference with I.S11991959, and the findings were used to create the artificial neural network (ANN) model. To mimic the formation, a total of 131 outcome values are employed, with 20% being used for model testing and 80% being used for model training. 25 enter the material properties to determine the concrete slump achieved by partially substituting pozzolan for cement and artificial sand (MS) for natural fine aggregate (NFA). According to studies, the workability of concrete is critically harmed as the amount of artificial sand replacing natural sand grows. The ANN model's results are extremely accurate, and they can forecast the slump of concrete prepared by partly substituting natural fine aggregate (NFA) and artificial sand (MS) with pozzolan.

Predicting of proactive environmental management for unhairing wastewater treatment in Tunisia using neural network learning algorithms

2020 ◽  
Vol 31 (4) ◽  
pp. 931-944
Author(s):  
Mohamed Turki ◽  
Hamden Zahrani ◽  
Meriem Ayadi ◽  
Monem Kallel ◽  
Jalel Bouzid
Keyword(s):  
Neural Network ◽  
Industrial Process ◽  
Ann Model ◽  
Content Type ◽  
Neural Network Learning ◽  
Treatment Techniques ◽  
Leather Processing ◽  
Before And After ◽  
Artificial Neural Network Ann ◽  
Flocculation Process

PurposeThe purpose of this study is to focus on Tunisian tannery sector that causes a considerable damage to the environment and consequently leads to serious health problems due to the untreated effluents generated from the various leather processing stages.Design/methodology/approachThis paper discusses a voluntary initiative taken by the top managers of tannery enterprise to prevent pollution and disseminate the concept of eco-industrial activities between employees and stakeholders. In addition, this research assesses the performance of such treatment that characterizes the chemical parameters of generated pollutants. It also aims at optimizing the industrial process for cleaner production. Coagulation–flocculation process is investigated in this study. Moreover, oxidation phase by ozone is taking into account before and after coagulation–flocculation process to measure the effectiveness of the combined method for reducing the main pollutant concentrations.FindingsThe unhairing and chrome (Cr) tanning steps are considered the most polluting steps. Therefore, the application of various treatment techniques, including chemical and physicochemical processes, is realized to reduce the toxicity of the effluents. The correlation between experimental and modeling results, using artificial neural network (ANN) method, was investigated in this research. The results of the constructed ANN model are measured by the correlation of experimental and model results during coagulation–flocculation and oxidation stages. The validation of the elaborated model through the error calculation (MSE) and the correlation coefficient (R) confirm the reliability of ANN method.Originality/valueEventually, the establishment of ANN model for performance prediction of wastewater parameters is investigated due to different measurements of physical effluent outputs, such as: pH, turbidity, TSS, DS, COD, fat, TSS, S2- and Cr. This study uses predictive modeling, a machine learning technique to tackle the problem of accurately predicting the behavior of unseen configuration.

A novel respiration pattern biometric prediction system based on artificial neural network

Sensor Review ◽  
2020 ◽  
Vol 40 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Rafiu King Raji ◽  
Michael Adjeisah ◽  
Xuhong Miao ◽  
Ailan Wan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Learning Rate ◽  
Prediction System ◽  
Ann Model ◽  
Content Type ◽  
Respiration Pattern ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Purpose The purpose of this paper is to introduce a novel respiration pattern-based biometric prediction system (BPS) by using artificial neural network (ANN). Design/methodology/approach Respiration patterns were obtained using a knitted piezoresistive smart chest band. The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data. Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers, ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model’s accuracy as well as to limit the risk of overfitting. Findings Findings establish the feasibility of using respiratory patterns for biometric identification. Experimental results show that, with a learning rate drop factor = 0.5, the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent. Out of 51,338 test set, the model achieved 51,557 correctly classified instances and 169 misclassified instances. Practical implications The findings provide an impetus for possible studies into the application of chest breathing sensors for human machine interfaces in the area of entertainment. Originality/value This is the first time respiratory patterns have been applied in biometric prediction system design.

scholarly journals Artificial Neural Network (ANN) Model for Shear Strength of Soil Prediction

2021 ◽  
Vol 411 ◽  
pp. 157-168
Author(s):  
Jacqueline A. Richard ◽  
Norazzlina M. Sa’don ◽  
Abdul Razak Abdul Karim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Input Parameter ◽  
Random Errors ◽  
Ann Model ◽  
Predictive Tool ◽  
Liquidity Index ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Geotechnical structures, design of embankment, earth and rock fill dam, tunnels, and slope stability require further attention in determining the shear strength of soil and other parameters that govern the result. The shear strength of soil commonly obtained by conducting laboratory testing such as Unconfined Compression Strength (UCS) Test and Unconsolidated Undrained (UU) Test. However, random errors and systematic errors can occur during experimental works and caused the findings imprecise. Besides, the laboratory test also consuming a lot of time and some of them are quite costly. Therefore, soft computational tools are developed to improve the accuracy of the results and time effectively when compared to conventional method. In this study, Artificial Neural Network (ANN) was employed to develop a predictive model to correlate the moisture content (MC), liquid limit (LL), plastic limit (PL), and liquidity index (LI) of cohesive soil with the undrained shear strength of soil. A total of 10 databases was developed by using MATLAB 7.0 - matrix laboratory with 318 of UCS tests and 451 of UU tests which are collected from the verified site investigation (SI) report, respectively. All the SI reports collected were conducted in Sarawak, Malaysia. The datasets were split into ratio of 3:1:1 which is 60:20:20 (training: validation: testing) with one hidden layer and eight hidden neurons. The input parameter of Liquidity index (LI) has shown the highest R-value (regression coefficient) which are 0.926 and 0.904 for UCS and UU model, respectively. In addition, the predictive models were tested and compare with the predicted and observed cohesion obtained from the collected experimental results. In summary, the ANN has the feasibility to be used as a predictive tool in estimating the shear strength of the soil.

Predicting the fiber diameter of spunbonding nonwovens fabrics by means of physical model, statistical method and artifical neural network theory

2015 ◽  
Vol 27 (2) ◽  
pp. 262-271 ◽  
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Statistical Model ◽  
Physical Model ◽  
Process Parameters ◽  
Fiber Diameter ◽  
Good Prediction ◽  
Ann Model ◽  
Content Type ◽  
Modeling Methods ◽  
Artificial Neural Network Ann

Purpose – The purpose of this paper is to establish three modeling methods (physical model, statistical model, and artificial neural network (ANN) model) and use it to predict the fiber diameter of spunbonding nonwovens from the process parameters. Design/methodology/approach – The results show the physical model is based on the inherent physical principles, it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter. Findings – By analyzing the results of the physical model, the effects of process parameters on fiber diameter can be predicted. The ANN model has good approximation capability and fast convergence rate, it can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical model. Originality/value – The effects of process parameters on fiber diameter are also determined by the ANN model. Excellent agreement is obtained between these two modeling methods.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Yanfang Wang ◽  
Saeed Salehi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Field Data ◽  
Ann Model ◽  
Time Data ◽  
Neural Network Approach ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Artificial Neural Network Ann ◽  
Pump Pressure

Real-time drilling optimization improves drilling performance by providing early warnings in operation Mud hydraulics is a key aspect of drilling that can be optimized by access to real-time data. Different from the investigated references, reliable prediction of pump pressure provides an early warning of circulation problems, washout, lost circulation, underground blowout, and kicks. This will help the driller to make necessary corrections to mitigate potential problems. In this study, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab. Following the determination of the optimum model, the sensitivity analysis of input parameters on the created model was investigated by using forward regression method. Next, the remaining data from the selected well samples was applied for simulation to verify the quality of the developed model. The novelty is this paper is validation of computer models with actual field data collected from an operator in LA. The simulation result was promising as compared with collected field data. This model can accurately predict pump pressure versus depth in analogous formations. The result of this work shows the potential of the approach developed in this work based on NN models for predicting real-time drilling hydraulics.

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