Developing models to predicting the effect of crises on construction projects using MLR technique

Yasser Sahib Nassar, Kadhim Raheim Erzaij


Most construction projects are exposed to multiple external and internal problems and obstacles that lead to a crisis within the construction project, which may lead to the failure of the construction project. It is also a source of concern for owners, stakeholders, and contractors alike due to its difficulty. As a result, a new approach to dealing with crises prior to their occurrence is required. Accurate construction project prediction concerns at the early stages of a construction project are critical factors in the success of a project. This study develops anticipatory models for construction project crises by identifying and categorizing the major different variables that affect construction project objectives and indicate time overrun, cost overrun, and poor quality for construction projects before crises occur. The most influential factors on the failure of construction projects in Iraq were identified in this study; some of these factors affect project implementation time, others affect project cost, and the remaining factors affect construction project quality. The independent variables measurement model is designed to collect accurate raw data from the site. This model is based on 53 data samples collected from various multi-story building projects, which were used to construct and test the model. From MLR multiple linear regression results, three equations were derived from calculating the percentage of overrun (time, cost and quality) because of the construction project being affected by crises. Found that the correlation coefficient of the above models is (99.8%, 98.6%, 96.5%), respectively.


Multi Linear Regression MLR, Construction Projects, Crises, Predicting, Iraq

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Copyright (c) 2022 Yasser Sahib Nassar, Kadhim Raheim Erzaij

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License