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The consideration of alternative sources of material for construction is imperative to reduce the environmental impacts as two-fifths of the carbon footprint of materials is attributed to the construction industry. One alternative material with improved biodegradable attributes which can contribute to carbon offset is bamboo. The commercialisation of bamboo in modern infrastructures has significant potential to address few of the Sustainable Development Goals (SDGs) itemised by the United Nations, namely SDG 9 about industry, innovation and infrastructure. Other SDGs covering sustainable cities and communities, responsible consumption and production and climate action are also indirectly addressed when utilising sustainable construction materials. Being a natural material however, the full commercialisation of materials such as bamboo is constrained by a lack of durability. Besides fracture mechanisms arising from load-induced cracks and thermal modification, the durability of bamboo material is greatly impaired by biotic and abiotic factors, which equally affect its natural rate of degradation, hence fracture behaviour. In first instance, this chapter outlines the various factors leading to the durability limitations in bamboo material due to load-induced cracks and natural degradation based on recent findings in this field from the author's own work and from past literature. Secondly, part of this chapter is devoted to a new approach of processing the surge of information about the varied aspects of bamboo durability by considering the powerful technique of artificial intelligence (AI), specifically the artificial neural network (ANN) for prediction modelling. Further use of AI-enabled technologies could have an impactful outcome on the life cycle assessment of bamboo-based structures to address the growing challenges outlined by the United Nations.

In this chapter, we consider collaborative models of engaged research in comparison to models of team science that include persons with lived experience of the topic area as team members. ‘Co-led’, ‘co-design’ and ‘co-research’ are all terms used in the literature with distinct, but not precise, definitions and approaches. These collaborative models tend to describe methods that allow those with lived experience to be treated differently than other academic members of the research team. Power imbalances between those with lived experiences and researchers persist in such models, in spite of researcher efforts. For example, persons with lived experience are often described as being compensated with gift cards which may be welcomed but can be perceived as diminishing their role and contribution. In contrast, participatory team science involves persons with lived experience as full members of the research team. In the model that we propose, power is balanced through mutual planning and consensus-based decision-making. We contend that using participatory team science advances research through egalitarian consideration of team members' perspectives of the research problem and the designs necessary to knowledge development.