Risk assessment and model for community-based construction projects in Zambia

2010 ◽  
Vol 18 (1) ◽  
Author(s):  
I Mañelele ◽  
M Muya
2018 ◽  
Vol 170 ◽  
pp. 01091
Author(s):  
Yuliya Anoshina ◽  
Valery Gusev ◽  
Svetlana Suchkova ◽  
Roman Gorshkov ◽  
Elena Smorodina

The purpose of the paper is to identify factor space influencing on the value of discount rate in the assessment of effectiveness of investment and construction projects. As a result of investigation, a general classification of investment and construction risks was drawn up, depending on influence of external and internal environment of the enterprise. The factor space, which is used for initial data of separate investment and construction projects, was identified. A general algorithm of risk assessment of investment and construction project is developed, taking into account the reasoned justification of the method used for discount rate calculation, with the possible application of the variable discount rate at different stages of project’s life cycle.


2019 ◽  
Vol 13 (3) ◽  
pp. 106-109
Author(s):  
Bria J. McAllister ◽  
Marva I. Patterson ◽  
Benedict T. Sherwood ◽  
Jodie A. Howarth ◽  
Kerry J. Malone

2019 ◽  
Vol 11 (19) ◽  
pp. 5388 ◽  
Author(s):  
Agnieszka Leśniak ◽  
Filip Janowiec

The implementation of railway infrastructure construction projects including sustainable development goals is a complex process characterized by a significant extension of individual investment stages. The need for additional works has a big impact on construction railway projects, representing a risk which is the result of many different factors. During the execution of works, both the design assumptions and the conditions of the project’s implementation can be changed. An attempt to eliminate potential risks is a key element of construction projects. The article proposes a proprietary management method for the risk of additional works in railway projects. A methodology for creating risk management strategies using a standard algorithm that includes risk identification, risk analysis, and risk assessment is presented. The original elements of the work include risk identification followed by analysis using Bayesian networks. Using the example of a scenario of events, it is shown that a well-programmed network can be used to implement risk mitigation methods. Using the network, it is possible to compare different ways to reduce risk, check the effect of reducing the risk factors, and determine a satisfactory level of effects, e.g., increased financial resources as a result of additional works.


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