Effective risk management involves

Effective risk management involves a four-phase process:
1.Risks identification: The process of determining which risks may affect the project and documenting their characteristics.
2.Risk assessment: The process of prioritizing risks for further analysis by assessing and combining, generally, their probability of occurrence and impact.
3.Risk response: The process of developing options and actions to enhance opportunities and to reduce threats to the project objectives.
4.Risk monitoring and reviewing: The process of implementing a risk response plan, tracking identified risks, monitoring residual risks, identifying new risks, and evaluating the risk process effectiveness throughout the project.
Risk project management is beneficial if it is implemented in a systematic manner from planning stage through the project completion. The unsystematic and arbitrary risk management can endanger the success of the project since most of the risks are very dynamic throughout the project lifetime.

2. Fuzzy risk assessment procedure
The nature of construction project has imposed, in the risk analysis process, substantial uncertainties and subjectivities, which have hampered the applicability of many risk assessment methods, that are used widely in construction projects and require high quality data, such as Fault Tree Analysis (FTA), Event Tree Analysis (ETA), Probability and impact grids, Sensitivity Analysis, Estimation of System Reliability, Failure Mode and Effect Analysis (Ahmed et al., 2007). Recently, many risk assessment approaches have been based on using linguistic assessments instead of numerical values. Using Fuzzy Sets Theory (Zadeh, 1965), data may be defined on vague, linguistic terms such as low probability, serious impact, or high risk. These terms cannot be defined meaningfully with a precise single value, but Fuzzy Sets Theory provides the means by which these terms may be formally defined in mathematical logic. Several research studies on the risk assessment of construction projects using fuzzy approaches have been performed. Some fuzzy proposals have been inspired in the classical risk assessment methods, such as, ETA and FTA:Fujino (1994) demonstrates the applicability of the proposed fuzzy FTA methodology to some cases of construction site accidents in Japan;Huang et al. (2001) proposes a fuzzy formal procedure in order to integrate both human-error and hardware failure events into a ETA methodology;Cho et al. (2002) proposes a fuzzy ETA methodology characterized by the use of new forms of fuzzy membership curves. However, the research studies have not only been focused on using fuzzy concepts into conventional risk assessment frameworks, but rather new methods have been proposed. Carr and Tah (2001) define a formal model based on a hierarchical risk breakdown structure. The risks descriptions and their consequences are defined using linguistic variables and the relationship between the likelihood of occurrence (L), the severity (V) and the effect of a risk factor (E) is represented by rules such as “If L and V then E”. Zeng et al. (2007) propose a risk assessment model based on fuzzy reasoning and AHP approach. A modified analytical hierarchy process is used to structure and prioritize risks considering three fundamental risk parameters: risk likelihood (RL), risk severity (RS) and factor index (FI), defined all of them in terms of linguistic variables which are transformed into trapezoidal fuzzy numbers. The relations between input parameters FI, RL, RS and output named Risk magnitude (RM) are presented in form of “if...then”rules.Dikmen et al. (2007)propose a methodology for risk rating of international construction projects. Once the risks have been identified and modelled using Influence Diagrams, they are assessed by linguistic terms. The relationships between risks and influencing factors are captured from the knowledge of experts by using ‘‘aggregation rules’’, where the risk knowledge is explained in form of “if...then” rules. The aggregation of fuzzy rules into a fuzzy cost overrun risk rating is carried out by fuzzy operations.Wang and Elhag (2007)proposes a risk assessment methodology which allows experts to evaluate risk factors, in terms of likelihood and consequences, using linguistic terms. Also it is provided two alternative algorithms to aggregate the assessments of multiple risk
factors, one of which offers a rapid assessment and the other one leads to an exact assessment.Zhang and Zou (2007) propose a methodology based on a hierarchical
structure of risks associated with a construction project. Based on expert judgment, the weight coefficients of risk groups and risk factors are acquired with the aid of the
AHP techniques and the fuzzy evaluation matrixes of risk factors. Then the aggregation of weight coefficients and fuzzy evaluation matrices produces the appraisal vector of risky conditions of the construction project. It can be affirmed that all the proposed fuzzy risk ass

Results (Indonesian) 1:

Manajemen resiko yang efektif melibatkan proses empat-fase:1. risiko identifikasi: proses menentukan risiko yang dapat mempengaruhi proyek dan mendokumentasikan karakteristik mereka.2. risiko penilaian: proses memprioritaskan risiko untuk analisa lebih lanjut oleh menilai dan menggabungkan, pada umumnya, mereka kemungkinan terjadinya dan dampak.3. risiko respon: proses pengembangan pilihan dan tindakan untuk meningkatkan peluang dan mengurangi ancaman terhadap proyek tujuan.4. risiko pemantauan dan meninjau: proses pelaksanaan rencana respon risiko, pelacakan mengidentifikasi risiko, pemantauan risiko yang sisa, mengidentifikasi risiko baru dan mengevaluasi efektivitas proses risiko sepanjang proyek.Manajemen risiko proyek ini bermanfaat jika dilaksanakan dalam cara yang sistematis dari tahap perencanaan melalui penyelesaian proyek. Manajemen risiko yang sistematis dan sewenang-wenang dapat membahayakan keberhasilan proyek karena kebanyakan risiko sangat dinamis sepanjang hidup proyek.2. prosedur penilaian risiko fuzzyThe nature of construction project has imposed, in the risk analysis process, substantial uncertainties and subjectivities, which have hampered the applicability of many risk assessment methods, that are used widely in construction projects and require high quality data, such as Fault Tree Analysis (FTA), Event Tree Analysis (ETA), Probability and impact grids, Sensitivity Analysis, Estimation of System Reliability, Failure Mode and Effect Analysis (Ahmed et al., 2007). Recently, many risk assessment approaches have been based on using linguistic assessments instead of numerical values. Using Fuzzy Sets Theory (Zadeh, 1965), data may be defined on vague, linguistic terms such as low probability, serious impact, or high risk. These terms cannot be defined meaningfully with a precise single value, but Fuzzy Sets Theory provides the means by which these terms may be formally defined in mathematical logic. Several research studies on the risk assessment of construction projects using fuzzy approaches have been performed. Some fuzzy proposals have been inspired in the classical risk assessment methods, such as, ETA and FTA:Fujino (1994) demonstrates the applicability of the proposed fuzzy FTA methodology to some cases of construction site accidents in Japan;Huang et al. (2001) proposes a fuzzy formal procedure in order to integrate both human-error and hardware failure events into a ETA methodology;Cho et al. (2002) proposes a fuzzy ETA methodology characterized by the use of new forms of fuzzy membership curves. However, the research studies have not only been focused on using fuzzy concepts into conventional risk assessment frameworks, but rather new methods have been proposed. Carr and Tah (2001) define a formal model based on a hierarchical risk breakdown structure. The risks descriptions and their consequences are defined using linguistic variables and the relationship between the likelihood of occurrence (L), the severity (V) and the effect of a risk factor (E) is represented by rules such as “If L and V then E”. Zeng et al. (2007) propose a risk assessment model based on fuzzy reasoning and AHP approach. A modified analytical hierarchy process is used to structure and prioritize risks considering three fundamental risk parameters: risk likelihood (RL), risk severity (RS) and factor index (FI), defined all of them in terms of linguistic variables which are transformed into trapezoidal fuzzy numbers. The relations between input parameters FI, RL, RS and output named Risk magnitude (RM) are presented in form of “if...then”rules.Dikmen et al. (2007)propose a methodology for risk rating of international construction projects. Once the risks have been identified and modelled using Influence Diagrams, they are assessed by linguistic terms. The relationships between risks and influencing factors are captured from the knowledge of experts by using ‘‘aggregation rules’’, where the risk knowledge is explained in form of “if...then” rules. The aggregation of fuzzy rules into a fuzzy cost overrun risk rating is carried out by fuzzy operations.Wang and Elhag (2007)proposes a risk assessment methodology which allows experts to evaluate risk factors, in terms of likelihood and consequences, using linguistic terms. Also it is provided two alternative algorithms to aggregate the assessments of multiple riskfaktor-faktor, salah satu yang menawarkan sebuah kajian cepat dan satu lagi mengarah pada penilaian yang tepat. Zhang dan Zou (2007) mengusulkan sebuah metodologi yang berdasarkan hierarkistruktur dari risiko yang terkait dengan proyek konstruksi. Berdasarkan penilaian ahli, Koefisien berat kelompok risiko dan faktor risiko diperoleh dengan bantuanAHP teknik dan matriks kabur evaluasi faktor risiko. Kemudian agregasi koefisien berat dan kabur evaluasi matriks menghasilkan vektor penilaian kondisi berisiko proyek konstruksi. Itu dapat menegaskan bahwa semua risiko kabur diusulkan pantat