A risk assessment methodology for incorporating uncertainties using fuzzy concepts

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Abstract

This paper proposes a new methodology for incorporating uncertainties using fuzzy concepts into conventional risk assessment frameworks. This paper also introduces new forms of fuzzy membership curves, designed to consider the uncertainty range that represents the degree of uncertainties involved in both probabilistic parameter estimates and subjective judgments, since it is often difficult or even impossible to precisely estimate the occurrence rate of an event in terms of one single crisp probability.

It is to be noted that simple linguistic variables such as ‘High/Low’ and ‘Good/Bad’ have the limitations in quantifying the various risks inherent in construction projects, but only represent subjective mental cognition adequately. Therefore, in this paper, the statements that include some quantification with giving specific value or scale, such as ‘Close to any value’ or ‘Higher/Lower than analyzed value’, are used in order to get over the limitations.

It may be stated that the proposed methodology will be very useful for the systematic and rational risk assessment of construction projects.

Introduction

Recently, the demand for establishing a systematic risk assessment for construction projects has increased more than ever before. Moreover, the performance of a risk assessment of large-scale construction projects is required in order to guarantee safety as well as high quality in major construction projects. However, the practical, comprehensive and systematic methodologies for some risk-sensitive construction projects are relatively new and rare because the process of the projects is becoming increasingly complex and dynamic in their nature. Also, for those countries where objective probabilistic data for risk assessment is extremely rare or insufficient, the utilization of subjective judgmental data based on expert's experiences is inevitable. In such situations, fuzzy approaches may be very useful.

Several previous studies on the risk assessment of construction projects and preventing failures using fuzzy approaches were performed. Blockley [3] made a break through in the study of structural safety in structural engineering. He [4] continued his work on this subject and introduced fuzzy set concepts for the analysis of causes of structural accidents. Brown [6] uses a merging process in which the objective probabilities are altered to a fuzzy measure based on the subjective information. Yao [17] used fuzzy sets to assess the seismic damage of structures. Also, Cui and Blockley [7] introduced an interval probability theory (IPT) as a measure of evidential support in knowledge-based systems. Recently, Blockley [14] illustrated the application of the IPT for the reliability assessment of corrosion damaged steel members and he [5] tried to describe theoretical developments for fuzzy approaches, which have been inspired by the experience of applying the IPT in practice.

Hadipriono [13] introduced the Fuzzy Event Tree Analysis (FETA) to identify the events that cause failures of temporary structures and to prevent their failures during construction. Fujino [11] demonstrated the applicability of Fuzzy Fault Tree Analysis (FFTA) to some case studies of construction site accidents in Japan. Recently, Huang [10] provided a formal procedure for applying fuzzy concepts to integrate both human-error-dominated and hardware-failure-dominated events into ETA.

The objective of this paper is to propose a new approach to risk assessment for incorporating uncertainties using fuzzy concepts into conventional risk assessment frameworks. And for this purpose, this paper also introduces new forms of fuzzy membership curves, as modified forms of general ramp-type.

Since it is often difficult to estimate the occurrence rate of an event precisely by using one single probability which is usually used in the conventional ETA, a lot of information is lost in the approach. Therefore, the proposed membership curves are designed to consider the uncertainty range that represents the degree of uncertainties involved in both probabilistic parameter estimates and subjective judgments.

Section snippets

Proposed methodology

In comparison with the previous studies [1], [11], [13] which are generally applicable to the limited cases where the characteristics of site and construction conditions are not considered, Hadipriono incorporated the present characteristics of site and construction conditions into his fuzzy ETA model. However, it was found that the linguistic variables of historical analysis data dominated the results of risk events while those of present conditions had little effect, which apparently

Case study

For understanding the applications using the suggested procedure, a simple case study of risk analysis is described herein as an illustration example, based on two simple risk scenarios 1 and 2 as shown in Fig. 8, which maybe stated as

  • Path 1

    Improper excavation (1)⇒Ground Settlement (2)⇒Delay (3)

  • Path 2

    Improper excavation (1)⇒Ground Settlement (2)⇒Injury/Fatality (4)

The probability and linguistic variables for path 1 and 2 are shown in Table 5.

The fuzzy membership curves for uncertainty modeling of each

Concluding remarks

This paper proposes a new methodology for incorporating uncertainties using fuzzy concepts into conventional risk assessment frameworks. This paper also introduces new forms of fuzzy membership curves, designed to consider the uncertainty range that represents the degree of uncertainties involved in both probabilistic parameter estimates and subjective judgments, since it is often difficult or even impossible to precisely estimate the occurrence rate of an event in terms of one single crisp

Acknowledgements

The work described herein is part of a research project on “Development of the Probabilistic Risk Assessment Techniques for construction projects” (Research Project No. R&D/96-0116) funded by Ministry of Construction and Transportation and Seung-Hwa Engineering and Construction, LTD. Their support is gratefully acknowledged.

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