Title:

Knowledgebased integrated project durationcost risk simulation model

Quantitative risk analysis is an essential part of a systematic project risk management
process. Although numerous techniques, either conventional or ad hoc, are available,
quantitative risk analysis is not commonly implemented in the construction industry. A
literature review has led to the identification of critical shortcomings in available
quantitative techniques. Usually, project risk information is vague and incomplete, and
is available qualitatively. Many existing techniques adopt statistical and probabilistic
approaches. Though it is possible to convert qualitative risk information to subjective
statistical inputs, practitioners often lack the knowledge to do so. In addition, certain
statistical techniques are developed using the risk data of specific types of works, and
hence, their areas of application are limited. Another limitation of conventional
techniques is duration and cost risks are analysed separately although both measures are
correlated. The exclusion of the correlation in the analysis affects the accuracy of the
results. Factorbased risk analysis techniques have been developed in the past to
promote an indepth understanding of the root causes of project poor performance.
However, they do not provide a means to understand the nature of risks, which can help
handling risk impacts more effectively. Furthermore, many developed factorbased
techniques limit the number of risk factors to be analysed. Since every construction
project is unique, certain excluded risk factors may be crucial in some projects. Risk
management often involves intuitive judgements. Hence, a knowledgebased risk
analysis technique, which can capture users' intuitions about risk impacts, is viewed as
an added advantage for promoting the application of quantitative risk analysis.
This research aims at developing a risk simulation model that addresses the abovementioned
shortcomings of existing techniques. An influence network has been
developed to integrate the parameters that determine the duration and cost of a
construction task. Mathematical equations representing the dependencies among the
parameters have been formulated. The generic structure of the integrated network can
explicitly model risk impacts on any type of construction tasks, and hence, it is adopted
as the core for risk simulation in this research. A novel risk assessment approach that
assesses risk factors against the duration and cost parameters has been developed.
Fuzzy set theory and fuzzy logic has been applied for enabling linguistic risk
assessment and evaluation. This approach can systematically capture the nature of
risks, and reflect it in the simulation. A knowledgebased risk aggregation algorithm
for computing the combined risk effects on the duration and cost parameters has been
developed by extracting and exploiting the algorithms in fuzzy sets theory and fuzzy
logic, and artificial neural networks. The algorithm can be applied to different
combinations of risk factors, and the parameter values in it can be modified for
effectively representing different assumptions of risk impacts. Work has also been
undertaken to develop mathematical equations for propagating the risk effects on the
parameters through the influence network, and for quantifying the riskadjusted
duration and cost. In the risk simulation, Monte Carlo simulation is incorporated to
generate different scenarios of riskadjusted outcomes.
A prototype system has been developed using Microsoft (MS) Excel VBA for
demonstrating the risk simulation model developed. The prototype system has been
tested using a realworld bridge construction project that involves various types of
construction activities and resources. The outputs have shown that the developed risk
simulation model has huge potential in improving the quantitative risk analysis process,
and hence, makes contribution to knowledge in this area.
