Prediction of dust storms in construction projects using intelligent artificial neural network technology

Sandstorms (dust storms) are considered the most events which cause destructive and costly damages in lots of desert regions. These sandstorms may be a reason of huge disasters (damages) on Environmental as well as Health aspects. The aim of this paper is to develop a mathematical model for predicting the Dust Storm in Republic of Iraq using Artificial Neural Network (ANN) technique. As a case study, four construction projects in Iraqi cities were selected (Baghdad, Basrah, Samawa, and Nasiriya) in order to identifying and prediction of the sandstorms, which significantly help to reduce the effects of damages. Only one ANN model was built to predict a dust storm. The datas of this model cited from Iraqi Meteorological Organization and Seismology. Four factors were adapted to develop the model (Max. Temperature, Min. Temperature, Rain and Wind), It was found that ANN has the ability to predict the dust storm with a high accuracys off the correlation coefficient (R) which is 90.00%, with a percentage of average accuracy is 89%

Strategic Evaluation Plan and Improvement of Cement Plants (Iraqi Kurdistan Region - as a Case Study)

The cement plants in the Kurdistan Regional Government of Iraq (KRG) have made a considerable contribution to the economy of the region and almost dominated the cement market in the entire region and middle and south of Iraq. The cement plants as a part of KRG’s industrial sector needs to consider strategic planning, through this research the condition and quality of the cement plants in KRG have been evaluated, using Strengths, Weaknesses, Opportunities, and Threats analysis (SWOT Analysis) technique as a strategic planning tool to demonstrate weakness points and strong points in the cement production process and to review opportunities to turn weaknesses into strengths while identifying the challenges facing this process and suggesting strategies to develop the cement plants. The main aim of the current study is to strategically evaluate, plan, and improve cement plants in KRG through the application (SWOT Analysis Technique). To achieve the study objectives, the methodology consists of a literature survey and field survey; including Interviews, field visits, data collection, presentation, analysis, and discussion of the results. In this study the reality of cement production in Iraq and Kurdistan region of Iraq has been demonstrated, the research sample was Gasin Cement Company (GCC) located in Sulaimaniya Governorate, SWOT analysis has been conducted to study the reality in the plant, some strong points, weakness points, opportunities, and threats have been founded. As a strong point; the quality of cement in GCC is high then as a weakness point Absence supervision on contractor’s work, after that as an opportunity Iraqi government has prevented the import of cement since 2016, finally as a threat the fluctuation of oil price does have dangerous effects on the cement market

Hybrid approach for cost estimation of sustainable building projects using artificial neural networks

Inaccurate estimation in sustainable construction projects is a significant challenge for appraisers, particularly when data and knowledge about the projects are lacking. As a result, there is a need to use cutting-edge technology to solve the issue of estimation inaccuracy. Iraq’s productivity estimates are now made using outdated, ineffective methodologies and procedures. In addition, it is essential to implement cutting-edge, quick, precise, and adaptable technology for productivity estimation. This study’s major goal is to calculate the overall costs of sustainable buildings using the cutting-edge technique known as artificial neural networks (ANNs). For Iraq’s construction industry to handle projects successfully, ANNs must be used as a new technology, a methodology developed to estimate the overall costs of sustainable construction projects. In this study, the process of cost estimation was modeled using ANNs. Investigations of a number of examples involving the creation of ANNs have also been made, including network design and internal elements and how much they impact the effectiveness of models built using ANNs. Equations were developed to determine structural productivity. These networks were shown to have extremely strong predictive power for both accounting coefficients (R) (93.33%) and the overall costs of sustainable construction projects, with a prediction accuracy of 87.00 and 93.33%, respectively

Diagnosing the Causes of Failure in the Construction Sector Using Root Cause Analysis Technique

The aim of this study is identifying and diagnosing the causes of construction project failure by using different project management process groups. These groups were initiation process group, planning process group, design process group, contract process group, executing and monitoring process group, and close process group. Also, the relative importance of the causes of construction project failure was investigated. Three techniques were used in this study: Ishikawa diagrams, Pareto diagrams, and 5-why techniques. The results were generally identified and diagnosed thirty-five causes of the construction project failure; however, only twenty-three of the causes were the most important. The majority of causes (thirteen causes) were obtained by using executing and monitoring project management process group. Seven causes were obtained by using contract project management process group. In addition, fewer causes (only three causes) were obtained by using initiation project management process group

Predicting index to complete schedule performance indicator in highway projects using artificial neural network model

Inaccurate estimation in highway projects represents a major problem facing planners and estimators, especially when data and information about the projects are not available, and therefore the need to use modern technologies that addresses the problem of inaccuracy of estimation arises. The current methods and techniques used to estimate earned value indexes in Iraq are weak and inefficient. In addition, there is a need to adopt new and advanced technologies to estimate earned value indexes that are fast, accurate and flexible to use. The main objective of this research is to use an advanced method known as artificial neural networks to estimate the TSPI of highway buildings. The application of artificial neural networks as a new digital technology in the construction industrial in Republic of Iraq is absolutely necessary to ensure successful project management. One model built to predict the TCSPI of highway projects. In this current study, artificial neural network model were used to model the process of estimating earned value indexes, and several cases related to the construction of artificial neural networks have been studied, including network architecture and internal factors and the extent of their impact on the performance of artificial neural network models. Easy equation was developed to calculate that TSPI. It was found that these networks have the ability to predict the TSPI of highway projects with a very outstanding saucepan of reliability (97.00%), and the accounting coefficients (R) (95.43%)

Validity of artificial neural modeling to estimate time-dependent deflection of reinforced concrete beams

The architecture and weights of an artificial neural network model that predicts time-dependent deflection have been developed and optimized. To satisfy the serviceability limit states, a concrete structure must be serviceable and perform its intended function throughout its working life. Excessive deflection should not impair the function of the structure or be aesthetically unacceptable. Cracks should not be unsightly or wide enough to lead to durability problems. Design for the serviceability limit states involves making reliable predictions of the instantaneous and time-dependent deflection of reinforced concrete beams. This is complicated by the nonlinear behavior of concrete caused mainly by cracking, tension stiffening, creep, and shrinkage. This paper provides a statistical approach for predicting the time-dependent deflection of reinforced concrete beams at service loads and outlines a validity of the proposed method in comparison with the American Concrete Institute (ACI) method