scholarly journals Developing a Model to Estimate the Productivity of Ready Mixed Concrete Batch Plant

2020 ◽  
Vol 26 (10) ◽  
pp. 80-93
Author(s):  
Hussein T. Almusawi ◽  
Abbas M. Burhan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Comprehensive Evaluation ◽  
Construction Materials ◽  
Plant Production ◽  
Training Dataset ◽  
Ann Model ◽  
Batch Plant ◽  
Ready Mixed Concrete ◽  
Mixed Concrete

Productivity estimating of ready mixed concrete batch plant is an essential tool for the successful completion of the construction process. It is defined as the output of the system per unit of time. Usually, the actual productivity values of construction equipment in the site are not consistent with the nominal ones. Therefore, it is necessary to make a comprehensive evaluation of the nominal productivity of equipment concerning the effected factors and then re-evaluate them according to the actual values. In this paper, the forecasting system was employed is an Artificial Intelligence technique (AI). It is represented by Artificial Neural Network (ANN) to establish the predicted model to estimate wet ready mixed concrete (WRMC) plant production and dry ready mixed concrete (DRMC) plant production, in addition to determining the factors affecting productivity. The results showed that the artificial intelligence neural network is an effective technique to estimate the productivity of the dry and wet ready mixed concrete batch plant. The ANN model showed satisfying results of validation for both training and external datasets with the range of training dataset and poor results with the data that exceeds the range of training. At the same time, the skills of the operators, frequent failure of concrete, and lack of construction materials were the most important factor that affected productivity.

scholarly journals Feasibility of Eco-Friendly Binary and Ternary Blended Binders Made of Fly-Ash and Oil-Refinery Spent Catalyst in Ready-Mixed Concrete Production

2018 ◽  
Vol 10 (9) ◽  
pp. 3136 ◽  
Author(s):  
Carla Costa ◽  
José Marques
Keyword(s):  
Large Scale ◽  
Construction Materials ◽  
International Standards ◽  
Oil Refinery ◽  
Industrial Wastes ◽  
Spent Catalyst ◽  
Concrete Production ◽  
Ready Mixed Concrete ◽  
Binary Blended Concrete ◽  
Mixed Concrete

Large-scale recycling of new industrial wastes or by-products in concrete has become a crucial issue for construction materials sustainability, with impact in the three pillars (environmental, social and economic), while still maintaining satisfactory, or improved, concrete performance. The main goal of the paper is to evaluate the technological feasibility of the partial, or total, replacement of fly-ashes (FA), widely used in ready-mixed concrete production, with spent equilibrium catalyst (ECat) from the oil-refinery industry. Three different concrete mixtures with binary binder blends of FA (33.3% by mass, used as reference) and of ECat (16.7% and 33.3%), as well as a concrete mixture with a ternary binder blend with FA and ECat (16.7%, of each) were tested regarding their mechanical properties and durability. Generically, in comparison with commercial concrete (i) 16.7% ECat binary blended concrete revealed improved mechanical strength and durability; (ii): ternary FA-ECat blended binder concrete presented similar properties; and (iii) 33% ECat binary blended concrete has a lower performance. The engineering performance of all ECat concretes meet both the international standards and the reference durability indicators available in the scientific literature. Thus, ECat can be a constant supply for ready-mixed eco-concretes production, promoting synergetic waste recycling across industries.

scholarly journals Experimental Validation for Inverse Kinematics of a Five Axis Robotic Manipulator using Deep Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 3943-3946
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Kinematics ◽  
Human Life ◽  
Robotic Manipulator ◽  
Training Dataset ◽  
Ann Model ◽  
Line Spacing ◽  
Artificial Neural ◽  
Five Axis

The use of robotics is to improvise and simplify human life. Robotic manipulators have been around for a while now and are being used in many different sectors such as industries, households, warehouses, medicine etc. Solving of inverse kinematics is one of the most complex issues faced while designing the robotic manipulator. In this research a Deep Artificial neural network (D-ANN) model is proposed to solve inverse kinematics of a 5-axis robotic manipulator with rotary joints. The D-ANN model is trained in MATLAB. Training dataset was generated using forward kinematics equations obtained easily from transformation matrix of the robotic manipulator. To validate predictions made by this model an experimental robotic arm manipulator Is fabricated. A smart camera setup has been linked to MATLAB for real time image processing and calculating the deviation of the end needle in reaching the desired target coordinate. The trained model yielded satisfactory results with ±0.03 radians error and this was also validated experimentally. This research will help the robotic manipulator reach the desired target coordinates even when one does not have enough input data.Paper Setup must be in A4 size with Margin: Top 0.7”, Bottom 0.7”, Left 0.65”, 0.65”, Gutter 0”, and Gutter Position Top. Paper must be in two Columns after Authors Name with Width 8.27”, height 11.69” Spacing 0.2”. Whole paper must be with: Font Name Times New Roman, Font Size 10, Line Spacing 1.05 EXCEPT Abstract, Keywords (Index Term), Paper Tile, References, Author Profile (in the last page of the paper, maximum 400 words), All Headings, and Manuscript Details (First Page, Bottom, left side).

scholarly journals Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6241
Author(s):  
Israel Campero-Jurado ◽  
Sergio Márquez-Sánchez ◽  
Juan Quintanar-Gómez ◽  
Sara Rodríguez ◽  
Juan M. Corchado
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Health And Safety ◽  
Working Environment ◽  
Training Dataset ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

scholarly journals Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Aydin Azizi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Industrial Robots ◽  
Optimal Parameters ◽  
Ann Model ◽  
Robotic Arms ◽  
Revolute Joints ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Industrial robots have a great impact on increasing the productivity and reducing the time of the manufacturing process. To serve this purpose, in the past decade, many researchers have concentrated to optimize robotic models utilizing artificial intelligence (AI) techniques. Gimbal joints because of their adjustable mechanical advantages have been investigated as a replacement for traditional revolute joints, especially when they are supposed to have tiny motions. In this research, the genetic algorithm (GA), a well-known evolutionary technique, has been adopted to find optimal parameters of the gimbal joints. Since adopting the GA is a time-consuming process, an artificial neural network (ANN) architecture has been proposed to model the behavior of the GA. The result shows that the proposed ANN model can be used instead of the complex and time-consuming GA in the process of finding the optimal parameters of the gimbal joint.

Application of Artificial Neural Networks to Predict Cotton Production: A Case Study in Diyarbakır Province, Turkey

2020 ◽  
Author(s):  
Nazire Mikail ◽  
Mehmet Fırat BARAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Plant Production ◽  
Good Prediction ◽  
Ann Model ◽  
Cotton Production ◽  
Factors Affecting ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Abstract Cultivators are always curious about the factors affecting yield in plant production. Determining these factors can provide information about the yield in the future. The reliability of information is dependent on a good prediction model. According to the operating process, artificial neural networks imitate the neural network in humans. The ability to make predictions for the current situation by combining the information people have gained from different experiences is designed in artificial neural networks. Therefore, in complex problems, it gives better results than artificial neural networks.In this study, we used an artificial neural network method to model the production of cotton. From a comprehensive datum collection spanning 73 farms in Diyarbakır, Turkey, the mean cotton production was 559.19 kg da-1. There are four factors that are selected as pivotal inputs into this model. As a result, the ultimate ANN model is able to forshow cotton production, which is built on elements such as farm states (cotton area and irrigation periodicity), machinery usage and fertilizer consumption.At the end of the study, cotton yield was estimated with 84% accuracy.

scholarly journals An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1810
Author(s):  
María Berenice Fong-Mata  ◽  
Enrique Efrén García-Guerrero  ◽  
David Abdel Mejía-Medina ◽  
Oscar Roberto López-Bonilla  ◽  
Luis Jesús Villarreal-Gómez  ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Vein Thrombosis ◽  
Data Augmentation ◽  
Training Dataset ◽  
Ann Model ◽  
Vein Thrombosis ◽  
Augmentation Algorithm ◽  
Artificial Neural ◽  
Deep Vein

The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.

scholarly journals Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Munenori Uemura ◽  
Morimasa Tomikawa ◽  
Tiejun Miao ◽  
Ryota Souzaki ◽  
Satoshi Ieiri ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Training Dataset ◽  
Artificial Intelligence System ◽  
Correct Judgment ◽  
Skill Levels ◽  
Intelligence System ◽  
The Neural Network ◽  
Expert And Novice ◽  
Novice Surgeons

This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively reflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixty-seven surgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded using a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the neural network. Optimization of the neural network was achieved after seven trials with a training dataset of 38 surgeons, with a correct judgment ratio of 0.99. The neural network that prospectively worked with the remaining 29 surgeons had a correct judgment rate of 79% for distinguishing between expert and novice surgeons. In conclusion, our artificial intelligence system distinguished between expert and novice surgeons among surgeons with unknown skill levels.

scholarly journals Development of an Optimised Dataset for Training a Deep Neural Network

2021 ◽  
Author(s):  
Callum Newman ◽  
Jon Petzing ◽  
Yee Mey Goh ◽  
Laura Justham
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Light Intensity ◽  
Test Performance ◽  
Deep Neural Network ◽  
Training Dataset ◽  
Multiple Factors

Artificial intelligence in computer vision has focused on improving test performance using techniques and architectures related to deep neural networks. However, improvements can also be achieved by carefully selecting the training dataset images. Environmental factors, such as light intensity, affect the image’s appearance and by choosing optimal factor levels the neural network’s performance can improve. However, little research into processes which help identify optimal levels is available. This research presents a case study which uses a process for developing an optimised dataset for training an object detection neural network. Images are gathered under controlled conditions using multiple factors to construct various training datasets. Each dataset is used to train the same neural network and the test performance compared to identify the optimal factors. The opportunity to use synthetic images is introduced, which has many advantages including creating images when real-world images are unavailable, and more easily controlled factors.

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