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.
The views at the construction field emboss that construction parties are looking for a higher benefit during implementing aspects of any project. The feasibility study is one of the serious and significant matters in construction sectors as well as in other engineering fields as it has a high impact on investment decision-making. To gain rigorous decisions from decision-makers or contractual parties (client, consultant, and contractor), there is a need for valuable feasibility studies in any construction project. This paper aims to study the influence of some identified factors on feasibility studies as well as the extents of priorities of various feasibility studies. In this paper, the critical factors that have an impact on sequences of initial feasibility studies during the lifecycle of a construction project were identified as well as the associated studies (legal, environment, marketing, technical, managerial, schedule, financial and economic). In this study, 12 factors were identified, analyzed, and thoroughly discussed to have recommendations of their impact on initial feasibility in construction projects in Iraq via a questionnaire survey and a series of expert interviews conducted in Iraq. The Analytical Hierarchy Process (AHP) as a multi-criteria decision support system was adopted to examine the weight of each factor. In addition, an analysis by the Relative Importance Index (RII) was carried out to rank eight types of feasibility studies in terms of their perceived importance. The results of AHP indicated that the local shortage of database about the feasibility study was the most critical factor. Indeed, RII's result showed that all kinds of feasibility for construction projects in Iraq are not adopted by the client. In fact, the economic feasibility study was the most popular than others.
It is essential to organize and plan any project at all stages, from start to finish; however, it is also necessary to certify completion by means of correct and effective methods. Scheduling projects using scientific approaches and sophisticated scheduling tools helps guarantee to project completion by the scheduled dates, as well as allowing institutions and municipal departments to make accurate estimations for project execution. The construction of roads includes several activity packages, one of which is the generation of road infrastructure such as that related to the sewer work (such as water and wastewater), the drilling and pouring work, the electrical work, and finally the paving. One of the most difficult jobs facing a manager in terms of such a project is the scheduling of activities over time. In many projects, the scheduling is done compactly, such that each activity that matches one scheduled in previous projects is scheduled but not detailed, with estimates based on previous overall times. In this research, however, each major activity was divided into several sub-activities to allow more accurate calculation of duration based on several criteria that affect the activities’ use of time. Three timings were thus estimated for each activity: a pessimistic time, an optimistic time, and a normal time. The PERT method was improved by using a Fuzzy Delphi technique to estimate the actual time required to implement each activity. The overall scheduling of a project in Karbala city, the rehabilitation of the road connecting the intersection of the Al-Ghadeer district to the intersection of the Al-Mudraa district was done in this manner. The project implementation period was predicted at 301 days, which is very close to the 322-day actual duration in which the work was carried out. The findings confirmed that the improved PERT method using the Fuzzy Delphi technique was the most accurate way to predict execution time.
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 ofsuch projects before work starts, using backward elimination regression analysis technique is carried out.The study covers two story (12 classes) school projectsawardedby the lowest bid system. Records of (65) school projects completed during (2007)(2008)(2009)(2010)(2011)(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 inputparameters. These factorsare;awarded bid price, average bid price, estimated cost, contractor rank, resident engineerexperience,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 avery good accuracy havinga correlation coefficient(R)of(93%),determination coefficient (R 2 )of(86.5%)and average accuracy percentage of(92.02%).
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