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Artificial Neural Network Model for Wastewater Projects Maintenance Management Plan
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Wastewater projects are one of the most important infrastructure projects, which require developing strategic plans to manage these projects. Most of the wastewater projects in Iraq don’t have a maintenance plan. This research aims to prepare the maintenance management plan (MMP) for wastewater projects. The objective of the research is to predict the cost and time of maintenance projects by building a model using ANN. The research sample included (15) completed projects in Wasit Governorate, where the researcher was able to obtain the data of these projects through the historical information of the Wasit Sewage Directorate. In this research artificial neural networks (ANN) technique was used to build two models (cost and time) for the maintenance of wastewater projects. The output shows there is a high correlation (R) between real and expected cost with 95.4%, minimized testing error (8.5%), and training error (19%). The mean absolute present error (MAPE) and Average Accuracy Percentage (AA) are (13.9% and 86.1%) respectively. Also, the results showed a strong correlation (R) between actual and predicted time (99.1%), minimized testing error (8%), and an additional MAPE% and AA% with (11.7% and 88.3%) respectively. These models are in agreement with the real values, as well as gives good prediction for future maintenance projects.

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

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Publication Date
Sun Nov 26 2017
Journal Name
Journal Of Engineering
Compression Index and Compression Ratio Prediction by Artificial Neural Networks
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Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

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Publication Date
Sat Dec 31 2011
Journal Name
Al-khwarizmi Engineering Journal
Back stepping-Based-PID-Controller Designed for an Artificial Pancreas model
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Artificial pancreas is simulated to handle Type I diabetic patients under intensive care by automatically controlling the insulin infusion rate. A Backstepping technique is used to apply the effect of PID controller to blood glucose level since there is no direct relation between insulin infusion (the manipulated variable) and glucose level in Bergman’s system model subjected to an oral glucose tolerance test by applying a meal translated into a disturbance. Backstepping technique is usually recommended to stabilize and control the states of Bergman's class of nonlinear systems. The results showed a very satisfactory behavior of glucose deviation to a sudden rise represented by the meal that increase the blood glucose

 

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Publication Date
Wed Jan 13 2016
Journal Name
University Of Baghdad
Employ Mathematical Model and Neural Networks for Determining Rate Environmental Contamination
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Publication Date
Wed Jun 01 2016
Journal Name
Journal Of Engineering
Application of Total Quality Management (TQM) Requirements in Risk Management in Construction Projects in Iraq
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Total quality management  considers one of the modern scientific entrances  which practiced by productivity service organizations alike to provide appropriate quality required outputs according to the needs and desires of customers manage , enable the organization seeking to continue and grow in light of the increasing competition from the satisfy and provide the appropriate total quality management requirements whenever led to face risks that they may have  in a manner in which they can be addressed and find ways to avoid them in the future when repeated.                          &n

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Publication Date
Wed May 01 2019
Journal Name
Iop Conference Series: Materials Science And Engineering
Applications of GIS for Bridges Maintenance Service
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Abstract<p>AThe Bridge Maintenance Management System (BMMS) is an application system that uses existing data from a Bridge Management System database for monitoring and analysis of current bridges performance, as well as for estimating the current and future maintenance and rehabilitation needs of the bridges. In a transportation context, the maintenance management is described as a cost-effective process to operate, construct, and maintain physical money. This needs analytical tools to support the allocation of resources, materials, equipment, including personnel, and supplies. Therefore, Geographic Information System (GIS) can be considered as one tool to develop the road and bridge maintenanc</p> ... Show More
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Publication Date
Mon May 01 2023
Journal Name
Ain Shams Engineering Journal
Neural network modeling of rutting performance for sustainable asphalt mixtures modified by industrial waste alumina
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Publication Date
Mon Aug 01 2016
Journal Name
Journal Of Engineering
Application Artificial Forecasting Techniques in Cost Management (review)
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For the duration of the last few many years many improvement in computer technology, software program programming and application production had been followed with the aid of diverse engineering disciplines. Those trends are on the whole focusing on synthetic intelligence strategies. Therefore, a number of definitions are supplied, which recognition at the concept of artificial intelligence from exclusive viewpoints. This paper shows current applications of artificial intelligence (AI) that facilitate cost management in civil engineering tasks. An evaluation of the artificial intelligence in its precise partial branches is supplied. These branches or strategies contributed to the creation of a sizable group of fashions s

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Publication Date
Thu May 05 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Classification SINGLE-LEAD ECG by using conventional neural network algorithm
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Publication Date
Mon Jan 01 2024
Journal Name
Communications In Computer And Information Science
Automatic Identification of Ear Patterns Based on Convolutional Neural Network
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Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

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