Presented by- Group-B3

Asian School Of Business Management

PGDM/13-15

Sec-B

USING RISK ANALYSIS TO MODEL CONSTRUCTION SCHEDULE DELAYS : A BAYESIAN

BELIEF NETWORKS APPROACH

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.

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.

Bayesian belief networkBayesian 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.

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.

Data collectionA 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.

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.

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.

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