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3D printing, is a newly adopted technique in the construction sector with the aim to improve the economics and alleviate environmental impacts. This study assesses the eco-efficiency of 3D printing compared to conventional construction methods in large-scale structural fabrication. A single-storey 3D-printed house was selected in the United Arab Emirates to conduct the comparative assessment against traditional concrete construction. The life cycle assessment (LCA) framework is utilized to quantify the environmental loads of raw materials extraction and manufacturing, as well as energy consumption during construction and operation phases. The economics of the selected structural systems were investigated through life cycle costing analysis (LCCA), that included mainly the construction costs and energy savings. An eco-efficiency analysis was employed to aggregate the results of the LCA and LCCA into a single framework to aid in decision making by selecting the optimum and most eco-efficient alternative. The findings revealed that houses built using additive manufacturing and 3D printed materials were more environmentally favourable. The conventional construction method had higher impacts when compared to the 3D printing method with global warming potential of 1154.20 and 608.55 kg CO2 eq, non-carcinogenic toxicity 675.10 and 11.9 kg 1,4-DCB, and water consumption 233.35 and 183.95 m3, respectively. The 3D printed house was also found to be an economically viable option, with 78% reduction in the overall capital costs when compared to conventional construction methods. The combined environmental and economic results revealed that the overall process of the 3D-printed house had higher eco efficiency compared to concrete-based construction. The main results of the sensitivity analysis revealed that up to 90% of the environmental impacts in 3D printing mortars can be mitigated with decreasing cement ratios.

Unconventional wastewater management systems have been developed to cope with the ever-increasing demand on the existing infrastructure. Grey water separation and treatment emerged to address the limitations of conventional sanitation systems. Towards achieving key sustainable development goals, this study aims to assess the performance of grey and black water separation alternatives with on-site treatment (SOT) or dual collection (SDC), compared to conventional collection and treatment (CCT). The on-site treated grey water in the SOT scenario was used for toilet flushing, leading to 25% reduction in domestic water demand. A novel manhole configuration was designed to suit the dual sewer collection network in the SDC scenario, where the sewer-collected grey water was conveyed directly to the tertiary treatment stage due to its low solids and organic load. A comprehensive, cost-integrated life cycle analysis was carried out to quantify the environmental impacts and assess the financial feasibility of the examined wastewater management systems. An eco-efficiency analysis was conducted to aggregate the results of life cycle costing (LCC) and life cycle assessment (LCA). The proposed collection and treatment alternatives were applied on a residential development in Dubai based on local design guidelines. Results of the environmental assessment indicated that SOT had the highest impacts in all midpoint impact categories with a single score of 1.27, followed by CCT (0.94) and SDC (0.89) systems. Over 50-year assessment period, the LCC findings revealed close net present values of CCT and SDC systems ($30 and 25 million, respectively), compared to SOT system ($8 million). The environmental impacts and financial burdens of the SOT system were reduced by 54 and 59%, respectively, when the treated grey water was reused instead of potable water for toilet flushing. The eco-efficiency analysis indicated that the SDC is the most sustainable system. Overall, this research emphasized the potential environmental and financial benefits of grey water separation, collection and treatment systems taking into consideration the entire urban water cycle.

The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. AI has been efficient at tackling ill-defined problems, learning from experience, and handling uncertainty and incomplete data. Although significant research was carried out in this domain, very few review studies have assessed the potential of AI in solving the diverse SWM problems. This systematic literature review compiled 85 research studies, published between 2004 and 2019, analyzing the application of AI in various SWM fields, including forecasting of waste characteristics, waste bin level detection, process parameters prediction, vehicle routing, and SWM planning. This review provides comprehensive analysis of the different AI models and techniques applied in SWM, application domains and reported performance parameters, as well as the software platforms used to implement such models. The challenges and insights of applying AI techniques in SWM are also discussed.