1. USING RISK ANALYSIS TO MODEL
CONSTRUCTION SCHEDULE DELAYS : A
BAYESIAN BELIEF NETWORKS APPROACH
Presented by- Group-B3
Asian School Of Business Management
PGDM/13-15
Sec-B

2. Objectives
 To find out the Schedule delay from the Delay analysis of
the Ash handling plant construction project.
 To identify the risk factors responsible for the schedule
delay.
 To rank the risk factors and to find a method for
evaluating the probability of construction schedule delay.

3. Key Literature Survey
Sl
Author
Description
1.
Chapman (1990)
This paper outlines an approach to the management of project risk
which was initially developed for offshore North Sea projects and was
subsequently adapted for a range of projects in the USA, Canada and
elsewhere.
2
Ward et. al (1991)
The willingness of contracting parties to take on risks is an important
consideration in the allocation of project risks. A number of factors
contribute to willingness to bear risks, but not all motivate
conscientious, effective project risk management.
3
Shen et. al (2001)
established a risk significance index to show the relative significance
among the risks associated with the joint ventures in the Chinese
construction procurement practice.
4
Luu et. al (2009)
This paper describes how Bayesian belief network (BBN) is applied
to quantify the probability of construction project delays in a
developing country.
5
Jha et. al (2011)
This research presents the international construction risk factors from
the Indian construction professionals’ viewpoint, in a comprehensive
format to enable practitioners to prioritize the efforts to manage the
risk factors.

4. Bayesian belief network
Bayesian belief networks are directed graphical methods
developed
at Stanford University in the 1970s.
BBNs can be very useful for modelling situations where historical
data is utilized and input data is malfunctioned or is partially
unavailable.
The basic nature of BBN consists of node,arc,and variables with
properties.
A node can be parent or child and the cause-effect relationship
between them is by connecting parent node to child nodes.
BBNs use Bayes’ theorem of conditional probability.

5. Model development:
Provides accurate solutions even if input data is incomplete
or malfunctioning.
It is user friendly,i.e;additions or modifications in the
knowledge database are easy.
It is flexible is accepting inputs and providing outputs.
Analytical calculations can be corrected efficiently with latest
updates on data.
Input to the network need not be historical data or may be a
set of expert opinions.

6. Data collection
A web based survey was designed to collect data on experts.
Perceptions of risk factor and their significance in causing
schedule delays for a specific construction project.
The survey was used to collect historical data on each project
and its schedule on each project and its schedule
performance results.
The reliability of the collected data was tested using
CHRONBACH’S ALPHA coefficient between the frequency
of factors and the schedule impact was 0.892 and 0.925
respectively.

7. BNN model for estimating the probability of
schedule Delay
The collected data was converted into quantitative output based on
the numerical scale adapted from Shen(2001).
The probabilty of occurrence of each factor in a certain project was
set to 50% due to the discrete nature of potential risk delays.
For the parent nodes, representing six groups of collected data,the
relative frequency of each individual group was calculated based on
the summation of the frequency index of each child node.

9. Conclusion:
As scheduled delays continue to be one of the biggest
problems in construction.
This paper developed a model that will help project
managers to estimate the likelihood of a project’s schedule
delay resulting from different risk factors.
This model will allow project managers to make sufficient
arrangement to mitigate these causes in order to avoid
schedule growth.
This paper also demonstrates the benefits of using BBN as a
modeling tool for schedule delays.

10. References
Abdelgawas,M.,Hybrid Decision support System for Risk Critically
Assessment
Al-bahar, Risk management in construction project : A systematic
analytical approach for contractors,Ph.D., Universitynof
California,Berkeley,1988.
Assaf S.& Al-Hejji,”causes of delay in large construction
projects”,international journal of projects management,Vol-2,No-4,
Al-Momani”reasons for delays in public projects in Turkey”,construct
management economics,vol-3
Bordoli et.al,”causes of delays in large builiding construction
proojects”,ASCE Journal of Mnagement in Engineering, Vol-11