The impact of green roofs' composition on its overall life cycle.

Green roof systems have been developed to improve the environmental, economic, and social aspects of sustainability. Selecting the appropriate version of the green roof composition plays an important role in the life cycle assessment of a green roof. In this study, 10 compositions of an intensive green roof for moderate zone and 4 green roof compositions for different climatic conditions were designed and comprehensively assessed in terms of their environmental and economic impacts within the "Cradle-to-Cradle" system boundary. The assessment was carried out over a 50-year period for a moderate climate zone. The results showed that asphalt strips and concrete slab produced the highest total emissions. It was found that most greenhouse gases emissions were released in the operational energy consumption phase and in the production phase. The energy consumption phase (48.78%) for automatic irrigation and maintenance caused the highest Global Warming Potential (GWP) value (758.39 kg CO2e) in the worst variant, which also caused the highest life cycle cost (878.47€). On the contrary, in the best variant, planting more vegetation and lower maintenance and irrigation requirements led to a reduction in GWP (445.0 kg CO2e), but in terms of cost (506.6€) this composition didn't represent the best variant. The Global Warming Potential Biogenic (GWP-bio) compared to the Global Warming Potential Total (GWP-total) represents a proportion ranging from 0.8% to 78% depending on the proposed vegetation. Overall higher biogenic carbon values (up to 1525 kg CO2e) were observed for the proposed tall vegetation of Magnolia, Red Mulberry, Hawthorne, Cherry, and Crab-apple Tree. Based on the results of the multicriteria analysis, which included core environmental & economic parameters, biogenic carbon emission levels, the outcome of this paper proposed optimal green roof composition. Optimal intensive green roof composition was subjected to a sensitivity analysis to determine the impact of changing climatic conditions on CO2 emissions and life cycle costs. The results of the sensitivity analysis show that the optimal variant of the green roof can be implemented in the cold and subtropical zone with regard to CO2 emissions, but not with regard to life cycle costs.

Collaborative Robotic Wire + Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation.

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair.