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.
Contracting companies play a prominent role today in economic activity, due to their contribution to the implementation of major construction projects which together constitute the infrastructure of society. Most construction projects also suffer from exceeding the time and cost specified and planned for the completion of the project, and this comes for several reasons, including the work environment, country conditions, The method of managing project costs and the techniques used in its implementation Accordingly, the concepts of lean construction came, which help in addressing the causes of waste, both in time and cost, in addition to the fact that project management needs techniques that are useful in controlling the control and manag
... Show MoreMany water supplies are now contaminated by anthropogenic sources such as domestic and agricultural waste, as well as manufacturing activities, the public's concern about the environmental effects of wastewater contamination has grown. Several traditional wastewater treatment methods, such as chemical coagulation, adsorption, and activated sludge, have been used to eliminate pollution; however, there are several drawbacks, most notably high operating costs, because of its low operating and repair costs, the usage of aerobic waste water treatment as a reductive medium is gaining popularity. Furthermore, it is simple to produce and has a high efficacy and potential to degrade pollu
... Show MorePolyacrylonitrile nanofiber (PANFS), a well-known polymers, has been extensively employed in the manufacturing of carbon nanofibers (CNFS), which have recently gained substantial attention due to their excellent features, such as spinnability, environmental friendliness, and commercial feasibility. Because of their high carbon yield and versatility in tailoring the final CNFS structure, In addition to the simple formation of ladder structures through nitrile polymerization to yield stable products, CNFS and PAN have been the focus of extensive research as potential production precursors. For instance, the development of biomedical and high-performance composites has now become achievable. PAN homopolymer or PAN-based precursor copolymer can
... Show MoreDairy wastewater generally contains fats, lactose, whey proteins, and nutrients. Casein precipitation causes the effluent to decompose into a dark, strong-smelling sludge. Fluid waste contains soluble organic matter, suspended solids, and gaseous organic matter, which cause undesirable taste and smell, grant tone and turbidity, and advance eutrophication, which plays an essential role in increasing biological oxygen demand (BOD) in water. It also contains detergents and disinfecting agents from the rinses and washing processes, which increase the need for chemical oxygen (COD). One of the characteristics of dairy effluents is their relatively high temperature, high organic contents, and wide pH range, so the discharge of wastewater into
... Show MoreThis article aims to establish and evaluate standards for critical equipment and materials in highway projects in Iraq. Delphi technique has been used to analyze, explore, and discover the main criteria and sub-criteria that affect equipment and materials in highway construction projects in Iraq. To determine the correct response to the criteria presented in this study, a program (IBM, SPSS/V25) was used to assess the main criteria and sub-criteria using the mean score (MS) and standard deviation (SD) technique, as well as to check reliability using Cronbach's alpha factor (α). The experts' qualifications and the extent to which the person is ready to commit are both important factors in panel selection. The design of a
... Show MoreThe purpose of this study is to avoid delays and cost changes that occur in emergency reconstruction projects especially in post disaster circumstances. This study is aimed to identify the factors that affect the real construction period and the real cost of a project against the estimated period of construction and the estimated cost of the project. The case study is related to the construction projects in Iraq. Thirty projects in different areas of construction in Iraq were selected as a sample for this study. Project participants from the projects authorities provided data about the projects through a data collection distributed survey made by the authors. Mathematical data analysis was used to construct a model to predict change
... Show MoreThis paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n
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