Risks affecting the delivery of HVAC systems: Identifying and analysis

Mosaad, Salah Attia; Issa, Usama H.; Hassan, M. S.

doi:10.1016/j.jobe.2017.12.004

Cited by 18 publications

(20 citation statements)

References 10 publications

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“…From previous research conducted by the authors (Mosaad, Issa, & Hassan, 2018), the main activities of the HVAC systems in the construction project and the risk factors that affect each activity have been identified. Three activities associated with the installation of HVAC systems were declared as follows: (A) air distribution duct work; (B) chilled water distribution pipe work; and (C) HVAC equipment installation.…”

Section: Methodsmentioning

confidence: 99%

“…These two indices will be used to assess the risk factors by ranking them and illustrating a linear indication. To evaluate the results of this module, fifty-five risk factors for HVAC system activities that were previously identified (Mosaad et al, 2018) can be ranked due to their severity, which can be calculated as the magnitude of the probability of occurrence multiplied by the impact of the risk factor.…”

Section: Fmqra Applicationmentioning

confidence: 99%

“…The risk factor index for cost (RFIC) and the risk factor index for time (RFIT) were calculated using the following equations (Mosaad et al, 2018):…”

Section: Fmqra Applicationmentioning

confidence: 99%

“…A Gaussian-shaped membership function did not previously apply in the risk assessment models. In this research, four-member-shaped functions will be applied to select the best shaped function after comparison with the previous results from Mosaad et al (2018), by determining the Spearman's rank correlation coefficient from the SPSS program. A fuzzy inference mechanism is a process of mapping a given input to an output using the theory of fuzzy sets.…”

Section: Membership Functionsmentioning

confidence: 99%

“…As shown in Figure 3, the triangle-, trapezoid-, generalized bell-and Gaussian-shaped membership functions are applied for the comparison to select the best membership function shape. The linguistic description assigned to a fuzzy set in this model is similar to the labels used in the field survey (Mosaad et al, 2018). For example, for the input factor, the probability of occurrence for a certain risk factor -the fuzzy label -can be VL, L, M, H, or VH.…”

Section: Membership Functionsmentioning

confidence: 99%

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A Model for Evaluating the Risk Effects on Construction Project Activities

Issa

Mosaad

Hassan

2019

Journal of Civil Engineering and Management

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Cost overruns and time delays are considered to be very important challenges for the majority of construction projects. These challenges are typically attributed to their associated risks. Due to the risky and uncertain nature of construction projects, an increasing amount of attention is given to estimating and overcoming cost overruns and time delays. New techniques are being developed to help project managers to contractually complete projects within cost and time constraints. The objective of this study was to develop a new qualitative and quantitative risk analysis model that can be employed for construction projects. The proposed model, which is based on a fuzzy logic tool, consists of two modules for assessing risk factors that affect the main construction activities and computing the expected cost overruns and time delays that are associated with these risks. Using numerous logical rules, the model applies the probability of occurrences and impacts of the risks on the cost and time of the main activities. The Spearman and Kendall correlation coefficient tests are applied to verify and select a suitable membership function. Using four proposed membership functions, the results of these tests confirmed that the triangle membership function is suitable for the model. The model is verified by application to HVAC system activities in two actual construction projects, which serve as case studies. Two different methods are proposed and applied to quantify the cost overruns and time delays. The first method is based on determining the cost overruns and time delay values for each activity according to their weight in the system. Triple premise rules are proposed and applied in the second method, which is established to relate all activities. The results from the second method are more accurate compared with the first method based on actual data from the case study projects. In addition, the results demonstrated that the proposed model can be used to quantify the expected cost overrun and time delays in construction project activities and can be generalized and implemented in different construction activities.

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Section: Methodsmentioning

confidence: 99%

Section: Fmqra Applicationmentioning

confidence: 99%

“…The risk factor index for cost (RFIC) and the risk factor index for time (RFIT) were calculated using the following equations (Mosaad et al, 2018):…”

Section: Fmqra Applicationmentioning

confidence: 99%

Section: Membership Functionsmentioning

confidence: 99%

Section: Membership Functionsmentioning

confidence: 99%

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