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The main challenges and barriers facing the effective implementation of risk management (RM) practices in the construction industry have increased the ceiling of challenges associated with the implementation of construction projects during the COVID-19 era. The aim of this paper is to assess the level of significance of risk management implementation challenges and barriers (RMICB) and the level of efficacy of construction RM practices during COVID-19 by considering the case of Iraq. A multistage research methodology was adopted, including semistructured interviews with construction experts to identify the main RMICB and RM practices indicators; surveys to assess the identified RMICB and RM practices indicators; analytical hierarchy process–based and fuzzy-based models to quantify RMICB level of significance, and RM practices level of efficacy. Based on the adopted methodology, the authors identified a total of 34 RMICB grouped under four categories, namely analytical approach–related, behavior-related, management-related, and team-related RMICB. Moreover, the assessment of the current construction RM practices during the COVID-19 era indicated a low level of RM practices efficacy. This study significantly contributes to the body of knowledge by helping construction practitioners and researchers to better understand the RMICB that are hindering effective implementation of RM strategies for construction projects during the COVID-19 era. Furthermore, it provides valuable information to decision-makers regarding the current construction RM practices efficacy during extreme conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]