Xiao-Hui Qi

Shallow landslides are a major natural hazard in the UK, causing more than £10M of economic losses annually and posing clear threats to life. Rainfall is an essential control on shallow landslide risk, but network operators currently use generic ‘on-off’ warnings based on localised rainfall intensity and duration, giving no strategic information about where the hazard is highest or evolving the quickest for any particular rainfall event. A new procedure has been developed to automatically evaluate the stability of slopes across regional scales in response to spatially and temporally variable rainfall events, quantified by rainfall radar data updated every 5 minutes. A physically based programme, TRIGRS, was used to assess the factor of safety (FS) of slopes in response to spatially variable rainfall radar data. The dynamic FS maps produced provide early warning and informed decision-making for the management of regional to national-scale infrastructures. Sensitivity analyses were performed to investigate the effect of the soil thickness, shear strength parameters, hydraulic parameters, and antecedent rainfall on the FS during a known landslide-causing rainfall event in Glen Croe, western Scotland. The results specifically highlight the slope where the failure occurred as a high-hazard area 1.0 hour before the event. The dynamic FS maps were particularly sensitive to soil thickness, the saturated hydraulic diffusivity and Gardner’s unsaturated conductivity, suggesting that more effort should be put into improving network-scale datasets of these parameters. Ultimately, the ability developed here to account for near-real-time spatial variabilities in rainfall data and slope responses measured through the relative change in FS values provides a potentially transformative new tool for network operators to proactively mitigate the impacts of specific storms as they develop.

Data analytics is indispensable in geotechnical and geological engineering, particularly the analysis of geo-data, which is often spatially varying or even temporally varying. With the fast develop...

Geological information such as geological interfaces is important for the design of underground excavation and supporting measures. This in turn requires a method to predict accurately the locations of geological interfaces for the gap areas between boreholes. This study presents a generalized additive model (GAM) to predict the location of the geological interfaces. The performance of the GAM method is evaluated using both simulated data and borehole data for the determination of rockhead in two different geological formations in Singapore. The results show that the GAM method can provide a reasonable confidence interval (CI) of the mean trend and the prediction interval (PI) in the sense that the 95% CI covers about 95% of the actual mean curve while the 95% PI covers around 95% of testing data. Furthermore, the geological complexity can be well reflected as the prediction uncertainty in the geologically complex area is larger than that in the geologically regular area. More importantly, the users can impose prior information or personal judgment regarding the shape of the geological profile on the model. This is an important feature to enable further improvement in the accuracy of the prediction. Highlights