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Title: Knowledge-based integrated project duration-cost risk simulation model
Author: Poh, Yan Ping.
ISNI:       0000 0001 3493 3085
Awarding Body: London South Bank University
Current Institution: London South Bank University
Date of Award: 2005
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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. Factor-based risk analysis techniques have been developed in the past to promote an in-depth 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 factor-based 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 knowledge-based 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 knowledge-based 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 risk-adjusted duration and cost. In the risk simulation, Monte Carlo simulation is incorporated to generate different scenarios of risk-adjusted 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 real-world 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available