Civil engineering articles from across Nature Portfolio

Transport has always played a major role in shaping society. By enabling or restricting the movement of people and goods, the presence or absence of transport services and infrastructure has historically been determining for cultures to connect, for knowledge to be shared, and for societies to evolve and prosper, or, in contrast, for societies to decay and fail. Since the beginning of the twenty-first century, transport has been going through a revolution worldwide. One of the primary goals for the transport sector is clear: it needs to be decarbonized and become more sustainable. At the same time, technological advances are shaping the transport sector toward smart services and societies. The Special Collection showcases some of the latest advances in research towards sustainable and technology-enabled transport.

Wildfires have increased in frequency and intensity due to climate change and have had severe impacts on the built environment worldwide. Moving forward, models should take inspiration from epidemic network modeling to predict damage to individual buildings and understand the impact of different mitigations on the community vulnerability in a network setting.

Learning from the 2023 Kahramanmaraş Earthquake Sequence offers valuable insights into disaster recovery. Carmine Galasso and Eyitayo Opabola delve into the intricacies of the “Build Back Better” (BBB) concept, underscoring the importance of recovery and reconstruction efforts toward a future that is not only more resilient but also more sustainable and equitable.

Recent high-profile concrete material failures, including the collapse of parts of public buildings in the UK, have highlighted the need for a greater understanding of the durability of concrete. Here, John Provis explores the need to recognise the complexity of concrete when planning both the research and application of this key construction material.

The implementation of particle-tracking techniques with deep neural networks is a promising way to determine particle motion within complex flow structures. A graph neural network-enhanced method enables accurate particle tracking by significantly reducing the number of lost trajectories.