Modeling the Completion Time of Public School Building Projects Using Neural Networks

The Ministry of Education in Iraq is confronting a colossal deficiency in school buildings while stakeholders of government funded school buildings projects are experiencing the ill effects of extreme delays caused by many reasons. Those stakeholders are particularly worried to know ahead of time (at contract assignment) the expected completion time of any new school building project. As indicated by a previous research conducted by the authors, taking into account the opinions of Iraqi experts involved with government funded school building projects, nine major causes of delay in school building projects were affirmed through a questionnaire survey specifically are; the contractor's financial status, delay in interim payments, change orders, the contractor rank, work stoppages, the contract value, experience of the supervising engineers, the contract duration and delay penalty. In this research, two prediction models (A and B) were produced to help the concerned decision makers to foresee the expected completion time of typically designed school building projects having (12) and (18) classes separately. The ANN multi-layer feed forward with back-propagation algorithm was utilized to build up the mathematical equations. The created prediction equations demonstrated a high degree of average accuracy of (96.43%) and (96.79%) for schools having (12) and (18) classes, with (R2) for both ANN models of (79.60%) and (85.30%) respectively. It was found that the most influential parameters of both models were the ratio of the sum of work stoppages to the contract duration, the ratio of contractor's financial status to the contract value, the ratio of delay penalty to the total value of contract and the ratio of mean interim payments duration to the contract duration

Estimate final cost of roads using support vector machine

The cost overrun in road construction projects in Iraq is one of the major problems that face the construction of new roads. To enable the concerned government agencies to predict the final cost of roads, the objective this paper suggested is to develop an early cost estimating model for road projects using a support vector machine based on (43) sets of bills of quantity collected in Baghdad city in Iraq. As cost estimates are required at the early stages of a project, consideration was given to the fact that the input data for the support vector machine model could be easily extracted from sketches or the project's scope definition. The data were collected from contracts awarded by the Mayoralty of Baghdad for completed projects between 2010-2013. Mathematical equations were constructed using the Support Vector Machine Algorithm (SMO) technique. An average of accuracy (AA) (99.65%) and coefficient of determination (R2) (97.63%) for the model was achieved by the created prediction equations

Determination of Difference Amount in Reference Evapotranspiration between Urban and Suburban Quarters in Karbala City

Evapotranspiration represents one of the main parameters in the hydrological cycle. It is usually expressed by the term reference evapotranspiration (ETo) that is affected by certain meteorological parameters. This study aimed to find the difference amount in ETo between urban and suburban quarters in Karbala city. The study methodology involved selecting once urban area and four suburban quarters. Two methods of determining the reference evapo- transpiration were applied: first, a direct method which measured ETo at selected fields by using a hand-held device, and second, an indirect method using the Penman-Monteith equation. The findings showed that the magnitudes of ETo by the Penman-Monteith equation are higher than the values measured by the direct method for urban and suburban quarters. Moreover, it was found that the absolute percentage of difference in the average amount of ETo between urban and suburban quarters is 13% by using the direct method and 61% by using Penman-Monteith equation. The study conclusion is that suburban area has higher magnitude of ETo than urban quarter by using any of direct method and indirect method (Penman-Monteith equation)