In a decision making study, design alternatives are compared with respect to cost, performance, and reliability, and the best is selected. Often, due to the time constraints imposed by the design schedule and budget restrictions, the number of design alternatives considered is limited. This research focuses on composite flooring systems for multi-story buildings and the application of value techniques to the construction industry. The study uses data that obtained from the RSMeans Assemblies Books for the period 1997 to 2019. The data obtained from RSMeans consists of assemblies cost ($/sf of floor) as the dependent variable; and the structural span (ft.), superimposed load (p.s.f), unit cost of sheet metal ($/LF of sheet), unit cost of concrete ($/CY), unit cost of steel structural ($/ton), and total load (p.s.f) as the independent variables. A simple computer model is designed for recommending the optimal composite flooring system of a multi-story building, during the preliminary design stage. The value engineering (VE) team to achieve the VE goals mentioned above can use the model.
Indonesia is one of the countries where tourism is the major contributors to the GDP. There are province and districts in Indonesia that mainly focused on the tourism business, Pangandaran is one of them. As a new district, Pangandaran is still trying to develop its brand as a tourist destination. This study aims to provide perceptual maps of Pangandaran as a brand compared to other coastal tourism destination. The study used Multidimensional Scaling (MDS), to inquire about the brand image of various coastal destination in Indonesia among domestic tourist. Two dimensions consisted of performance and value are used to measure the destination brand. More profound questions are also asked to inquire about factors that may influence tourist visitation. The result indicates that the Pangandaran brand as a tourism destination is perceived as a domestic oriented destination. A suggestion based on five steps of brand building is proposed to improve Pangandaran.
This paper aims at developing a model to measure and evaluate the performance and productivity of the construction of steel structure projects (SSPs). Practitioners and experts comprising a statistically representative sample were invited to participate in a structured questionnaire survey to achieve the objective. The questionnaire consisted of 17 factors that were classified under the following four primary categories, with terms such as feasibility study stage, planning stage, design, and engineering stage, and construction stage. Artificial neural networks (ANNs) were used for designing a model on MATLAB for measuring and evaluating the projects’ performance of the Construction of SSP based on the 14 factors that affect the steel structure process. The results suggest that the proposed ANN model for SSP can produce measures and evaluate the projects’ performance quickly and accurately when actual data is available for model training. The user can enter the values of main factors that affect their projects’ performance to produce an accurate output of the evaluation for the projects’ performance and productivity. The construction industry can use the findings of this paper as a basis for improving the projects’ performance of the construction for SSP.
With the emergence of the acquired immunodeficiency syndrome, we witnessed a higher incidence of disseminated and extrapulmonary tuberculosis. The infection sites commonly include lymph nodes, pleura, and osteoarticular areas, although any organ can be involved. Given the atypical presentation of the extrapulmonary disease, it poses a significant diagnostic challenge for the physicians; therefore, a high index of suspicion should be maintained, particularly where tuberculosis is endemic. Here we present a case of isolated chest wall tuberculosis in an immunocompetent patient.
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