Coherent Investigation on a Smart Kinetic Wooden Façade Based on Material Passport Concepts and Environmental Profile Inquiry

Modulus of Elasticity and Flexural Behavior of Glulam Beams Reinforced with Steel Mesh in Different Mesh Openings

In this study, the modulus of elasticity and flexural strength properties of laminated wood elements reinforced with steel mesh with different mesh openings were determined. In accordance with the purpose of the study, three- and five-layer laminated elements were produced from scotch pine (Pinus sylvestris L.) wood material, which is widely used in the wood construction industry in Türkiye. The 50, 70, and 90 mesh steel used as the support layer was placed between each lamella and pressed with polyvinylacetate (PVAc-D₄) and polyurethane (PUR-D₄) adhesives. Afterward, the prepared test samples were kept for 3 weeks at 20 °C temperature and 65 ± 5% relative humidity for 3 weeks. The flexural strength and modulus of elasticity in flexural of the prepared test samples were determined according to the TS EN 408: 2010+A1 standard by the Zwick universal tester. Multiple analysis of variance (MANOVA) was carried out using MSTAT-C 1.2 software to determine the effect of the modulus of elasticity and flexural strength on the obtained flexural properties, the mesh opening of the support layer, and the adhesive type. When the differences within or between groups were significant with a margin of error of 0.05, achievement rankings were made using the Duncan test on the basis of the least significant difference. According to the results of the research, the highest bending strength (120.3 N/mm²) was obtained in three-layer samples reinforced with 50 mesh steel wire and bonded with Pol-D4 glue, and the highest modulus of elasticity (8969.3 N/mm²) was obtained in three-layer samples reinforced with 50 mesh steel wire and bonded with Pol-D4 glue. As a result, the reinforcement of the laminated wood material with steel wire had an increasing effect on the strength. Accordingly, the use of 50 mesh steel wire can be recommended to increase mechanical properties.

Energy in Construction and Building Materials

Energy efficiency in buildings has become a major challenge in both science and industry [...].

Decision-Making Using Big Data Relevant to Sustainable Development Goals (SDGs)

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset of big data is visualized using several two-dimensional plots. These plots are then used to decide a course of action for achieving the relevant SDGs, and the whole process remains rather informal. Consequently, the question of how to make a formal decision using big data-generated two-dimensional plots is a critical one. This article fills this gap by presenting a novel decision-making approach (method and tool). The approach formally makes decisions where the decision-relevant information is two-dimensional plots rather than numerical data. The efficacy of the proposed approach is demonstrated by conducting two case studies relevant to SDG 12 (responsible consumption and production). The first case study confirms whether or not the proposed decision-making approach produces reliable results. In this case study, datasets of wooden and polymeric materials regarding two eco-indicators (CO2 footprint and water usage) are represented using two two-dimensional plots. The plots show that wooden and polymeric materials are indifferent in water usage, whereas wooden materials are better than polymeric materials in terms of CO2 footprint. The proposed decision-making approach correctly captures this fact and correctly ranks the materials. For the other case study, three materials (mild steel, aluminum alloys, and magnesium alloys) are ranked using six criteria (strength, modulus of elasticity, cost, density, CO2 footprint, and water usage) and their relative weights. The datasets relevant to the six criteria are made available using three two-dimensional plots. The plots show the relative positions of mild steel, aluminum alloys, and magnesium alloys. The proposed decision-making approach correctly captures the decision-relevant information of these three plots and correctly ranks the materials. Thus, the outcomes of this article can help those who wish to develop pragmatic decision support systems leveraging the capacity of big data in fulfilling SDGs.