Gafel Kareem Aswed

Sustainability principles are considered one of the vital challenges facing developing countries (including Iraq), which are trying vigorously to implement sustainable development in most of their development projects. The projects of constructing educational buildings, such as schools and universities, are among the most important development projects to be established in Iraq. This study aims to develop standards to help improve the understanding of the sustainability requirements of educational buildings in Iraq. Through a series of studies, this study has developed a system that can be used to measure the sustainability of these buildings. First, information about sustainability assessment systems was gathered from the literature and references. Second, personal interviews with specialists, field questionnaires, and the software Analytical Hierarchal Process (AHP) to identify the weights of the main categories and factors for sustainable educational buildings. The results showed that the criteria of Energy Efficiency received the greatest weight (29.6%). On the other hand, less weight was obtained from the Precious Water and Waste (3.6%). Finally, the study developed a rating system model, "Educational Building Sustainability Rate (EBSR)," under Iraqi environmental conditions.

The owner of construction projects often faces difficulty choosing the appropriate project delivery system. The construction project’s owner must choose a suitable project delivery system throughout the project's early decision-making stages. This choice greatly impacts the success of the project. This paper aims to construct a method for project delivery decisions. This method can provide a reference for the owner to select the proper project delivery system. This research described the factors affecting the decision about the project delivery method through a literature review. These factors were categorized into eight groups: scope, time, quality, cost, risk and relationships, owner organization, project characteristics, and external environmental factors. Then, a combined methodology of Stepwise Weight Assessment Ratio Analysis (SWARA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are proposed for project delivery system selection. The SWARA is used to determine the factors’ weights. The TOPSIS technique determines the rank of the alternatives proposed for the delivery system in the various categories. The results showed the most significant factors related to the cost with a weight of 0.335, and the design-build is the closest to the ideal solution and ranked first among the other suggested project delivery systems with a relative closeness of 0.829. This paper stipulates the basis for such a process of decision-making.

Factors Affecting Safety in Construction Projects

Subbase strength characteristics is one of the main inputs of pavement design, and such strength characteristics are normally represented by indices such as resilient modulus, dynamic modulus, and California Bearing Ratio (CBR), with the latter being a widely used index among pavement and geotechnical engineers. This paper examines the capability of Artificial Neural Networks (ANN) to develop a correlation between subbase CBR and primary soil data, which could help with estimating CBR for prediction purposes and with identifying the significance of each index with regard to subbase strength. Data were sampled from different areas in Karbala, Iraq, and a total of 358 subbase samples were used for model training and validation. The results showed that the proposed ANN model could successfully predict the CBR value using soil index data. Additionally, a sensitivity analysis was conducted to determine the importance of each contributing factor, and within the boundaries of the local subbase characteristics, the test results indicated that soluble salts were the most effective factor among soil parameters with an importance percentage of 39.46%, while the Plasticity Index (PI) was the least important factor, with a percentage of 2.06%. Based on the validity and quality of subbase soil tests, using ANN to predict CBR value may offer a suitable replacement for lengthy and expensive laboratory testing based on validated data for materials supplied from Karbala quarries.

Investigating factors affecting feasibility study of construction projects in iraq

This study aims to investigate the relationship between the water quality index (WQI) for irrigation purposes and four independent climate variables. Our case study was conducted on the Euphrates River within Karbala city, Iraq over the period between 2008 to 2016. The Bhar-gava WQI was calculated using nine physicochemical parameters, the electrical conductivity (EC), total dissolved solids, turbidity, pH, and calcium, magnesium, sodium, chloride and sulfate levels. The Bhragava WQI classified the Euphrates river as generally "good". Artificial neural network (ANN) and non-linear regression models were developed and used to forecast the relationship between the WQI and four independent climate variables (temperature, relative humidity, and rainfall depth and sunshine duration). The non-linear regression model was adopted to predicate the WQI because the coefficient of determination and minimum error value were better than those obtained with the ANN model. The non-linear model matched the calculated Bhargava WQI values and recorded meteorological data with a coefficient of determination (R2) = 78.2 and standard error = 2.1.

The actual final cost of public school building projects, like other construction projects, is unknown to the owner till the final account statement is prepared. An attempt to predict the final cost of such projects before work starts, using backward elimination regression analysis technique is carried out. The study covers two story (12 classes) school projects awarded by the lowest bid system. Records of (65) school projects completed during (2007-2012) are employed to develop and verify the regression model. Based on experts ‘convictions, nine factors are considered to have the most significant impact on the final cost. Hence they are used as model input parameters. These factors are; awarded bid price, average bid price, estimated cost, contractor rank, resident engineer experience, project location, number of bidders, year of contracting, and contractual project duration. It was found that the developed regression model have the ability to predict the final cost (FC) for school projects, as an output, with a very good accuracy having a correlation coefficient (R) of (93%), determination coefficient (R2)of(86.5%)and average accuracy percentage of(92.02%).