As we accelerate into the 21st century, railway infrastructure faces both opportunities and challenges. On the one hand, the latest advances in technology, through digitalisation by integrating new revolutionary data technologies of Internet-of-Things (IoT), artificial intelligence (AI), digital twins, automation and material science, promise new heights in safety and performance. On the other hand, we may be at increased uncertainty from physical stressors (e.g. climate change induced natural disasters). Furthermore, aged track designed 100 years ago (e.g. the UK’s rail networks are based largely on a Victorian infrastructure, dating back to the mid-19th century) are not designed for today’s vehicle speed and frequency, comes with a hefty maintenance bill: Network Rail spends half of its £9bn budget on repairs and renewal each year. Not to mention the indirect social-economic costs because of the interruption caused by the repair/maintenance activates, which also raise environmental and health issues linked to fuel consumption and air and noise pollution. As such, the ability to predict and repair the damages of the railway earthwork in a cost-effective and time-efficient manner to ensure a safer, more reliable, efficient, sustainable and resilience railway earthwork infrastructure offers a great value not just to transport network operators but also to utilities, insurance companies and government agencies.
A key element of maintaining stable capacity and reducing maintenance costs of railway network is to monitor and react to changes in the earthwork. Embankments are at the risk of serious deformation caused either by the dynamic loads from the moving vehicles, geohazards or both, and as such pose a threat to not only safety, but comfortable rail operations. The appropriate analysis of data on embankment deformation can lead to timely interventions and prevent derailment and large maintenance outages. Current best practice is making increasing use of new generation inspection and operating technology to obtain more and more data on the condition of the track, earthworks, and operations. With the increasing complexity as well as volume of data, traditional analysis techniques are no longer viable to convert the massive accessible data into useable information. Big Data analytics with its associated statistical analysis techniques therefore are urgently needed in railway engineering for a better plan of the maintenance and capital program. Furthermore, on the railway earthwork repairing side, current best practice is to use chemical grouting for reinforcing/stabilising infrastructure related subsidence. However, this technique is often costly and requires many injection wells with an increasing of the PH of groundwater to high alkaline levels and thus can cause serious environmental problems and contribute to ecosystem disturbance. Biopolymers, naturally occurring polymers formed by the action of microorganisms, can be added to soil to improve its strength and reduce the potential for cracking. The biopolymers mix with water in the soil to form gels which bind with soil particles giving the soil greater strength. Biopolymers are already utilised in cosmetics and food as thickening agents so they are relatively cheap and safe. They also do not require significant amounts of energy to produce and therefore they are not associated with high carbon dioxide emissions like other potential soil binders (e.g. cement and lime).
This fellowship will seek to conduct cross-disciplinary research that connects the geotechnical engineering to the life science with the aid of the knowledges and tools from computer science. The fellowship has the ambitious vision to develop the underpinning technology for ground improvement through the cross fertilization of soil mechanics and the soil science. We will accomplish this by:
Improved Railway Embankment Deformation Risk Analysis System: further understanding of the rates of railway earthwork asset degradation, long term performance of railway earthwork under the changing climate as well as the elevated vehicle speed, frequency and weight, in order to develop a railway embankment deformation forecast system through the combination of data-driven and model-based solutions, and
Develop Biopolymer Reinforced Railway Embankment Method: understanding the biopolymer reinforced soil mechanical behaviour under long-term cyclic loads as well as meteorological stressors, and the interaction mechanism between reinforcement and the soil ecosystem through a systematic laboratory, field tests and numerical simulations.
