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ENHANCE: Exploring how climate change affects coastal cliff recession: modelling and forecasting

Lead Research Organisation: Brunel University London
Department Name: Civil and Environmental Engineering

Abstract

As one of the most developed areas on this planet, the coastal regions have the greatest concentration of assets, abundant developments, critical infrastructures and complex ecosystems. However, due to the global climate change, the coastlines are unprecedentedly threatened by the accelerated occurrence of natural hazards, such as coastal flooding, landslide and tsunami, leading to serious coastal erosion and retreating landward. Some existing researches have confirmed that the coastline retreating rate depends highly on the environmental drivers such as wave action, temperature and rainfall, following a highly complex and non-linear relationship, while the mechanisms of progressive cliff failures remain unclear. ENHANCE will employ the emerging digital technologies of advanced numerical modelling and Artificial Intelligence (AI) to investigate the triggering mechanisms of coastal landslides and mitigation measures. The key climatic factors governing the cliff stability and their inter-dependency relationship will be clarified by the data-driven AI analyses. Then, a coupled continuous-discontinuous modelling will be performed to investigate the undercutting-notch effect on the progressive failure of coastal cliffs, clarifying the large deformation, crack propagation and mass movement. A novel fluid-solid-thermal coupling constitutive model will be developed to investigate the multi-physics responses of coastal slopes under changing climate conditions. The developed numerical modelling platform aims to be scalable for EU/worldwide applications by explicitly considering the temporal and spatial variabilities of geological formation properties. Consequently, an integrated coastal cliff management framework will be proposed for effective decision-makings on urban planning, hazard forecasting and mitigation. The findings will contribute to the UN 2030 Agenda for SDGs (9, 11, 13) and the European Green Deal, featuring great intellectual merits and engineering impacts.

Publications

10 25 50
 
Description (1) The random rock failure process analysis (RRFPA) method was developed to characterize the material spatial variability and uncertainty in rock failure modelling. The variation of rock properties is represented as a function of relative distance, and the influence of material intrinsic heterogeneity on its fracturing behaviour can be appropriately captured; (2) The rock discrete fracture analysis (RDFA) method was proposed to model progressive rock failure. RDFA enables adaptive node adjustments at critical crack tips once the strength criteria are met, effectively simulating the initiation and propagation of zero-thickness cracks.
Exploitation Route The random rock failure process analysis method and the rock discrete fracture analysis method can be applied to assess the risk of natural hazards such as landslides and rockfalls by researchers and geotechnical engineers. Furthermore, they are able to provide data-driven model-based insights to support regulatory decisions in construction and environmental management for local authorities.
Sectors Construction

Environment