Development of an Evidence-based Geotechnical Asset Management Policy for Network Rail, Great Britain

Power, Christopher; Mian, Juliet; Spink, T. W.; Abbott, Simon; Edwards, Michael

doi:10.1016/j.proeng.2016.06.112

Cited by 32 publications

(30 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It would be expected that most of the failures would occur in slopes of classes "C" and mainly "D". However, for rock slopes such behavior is not observed as reported on Power et al (2016). In fact, the number of failures for each EHC class is almost constant from classes "A" to "D", particularly when compared with soil cuttings.…”

Section: Resultsmentioning

confidence: 62%

“…A possible explanation for the lower performance, namely for classes "C" and "D" of rock cutting slopes could be related with the EHC classes being assumed as representative of the real stability condition of each slope. Indeed, analyzing the number of slope failures by EHC class for rock slopes there are some indications that the classification attributed to each rock slope could lack of some accuracy as reported in the work of Power et al (2016), which used the same source of information. It would be expected that most of the failures would occur in slopes of classes "C" and mainly "D".…”

Section: Resultsmentioning

confidence: 95%

“…To fit the proposed models for stability condition identification, from this point referred to as EHC (Earthwork Hazard Category (Power et al 2016)), of rock and soil cutting slopes two database were compiled respectively. Both databases, containing information collected during routine inspections and complemented with geometric, geological and geographic data of each slope, were gathered by Network Rail workers and are concerned with the railway network of the UK.…”

Section: Data Characterizationmentioning

confidence: 99%

“…Both databases, containing information collected during routine inspections and complemented with geometric, geological and geographic data of each slope, were gathered by Network Rail workers and are concerned with the railway network of the UK. For each slope a class of the EHC system was defined by the Network Rail Engineers based on their experience/algorithms (Power et al 2016), which will be assumed as a proxy for the real stability condition of the slope for year 2015. The EHC system comprises 4 classes 1 ("A", "B", "C" and "D") where "A" represents a good stability condition and "D" a bad stability condition.…”

Section: Data Characterizationmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction

Tinoco

Correia

Cortez

et al. 2018

Sustainable Civil Infrastructures

View full text Add to dashboard Cite

This study aims to develop a tool able to help decision makers to find the best strategy for slopes management tasks. It is known that one of the main challenges nowadays for every developed or countries undergoing development is to keep operational under all conditions their transportations infrastructure. However, due to the network extension and increased budget constraints such challenge is even more difficult to accomplish. Keeping in mind the strong impact of a slope failure in the transportation infrastructure it is important to develop tools able to help minimizing this situation. Accordingly, and in order to achieve this goal, the high flexible learning capabilities of Artificial Neural Networks (ANNs) were applied in the development of a classification tool aiming to identify the stability condition of a rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspections activities (visual information) to feed them. For that, it was followed a nominal classification strategy and, in order to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, SMOTE (Synthetic Minority Over-sampling Technique) and Oversampling. The achieved results are presented and discussed, comparing the achieved performance for both slope types (rock and soil cuttings) as well as the effect of the sampling approaches. An input-sensitivity analysis was applied, allowing to measure the relative influence of each model attribute.

show abstract

Section: Resultsmentioning

confidence: 62%

Section: Resultsmentioning

confidence: 95%

Section: Data Characterizationmentioning

confidence: 99%

Section: Data Characterizationmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction

Tinoco

Correia

Cortez

et al. 2018

Sustainable Civil Infrastructures

View full text Add to dashboard Cite

show abstract

“…While substantial research has been carried out in the field of landslide risk assessment (Dai et al 2002;Lee and Jones 2004;Fell et al 2005), comparatively little attention has been given to the application of landslide risk assessment frameworks to transport infrastructure slopes (Gavin et al 2016). Studies in the area (Fell et al 2008;Corominas et al 2014;Power et al 2016) consider vulnerability in a relatively simple manner. Vulnerability indices typically vary depending on the element type, landslide type and the predicted landslide intensity.…”

Section: Introductionmentioning

confidence: 99%

Fragility curves for rainfall-induced shallow landslides on transport networks

2018

View full text Add to dashboard Cite

Many of the earthworks assets on rail transport networks were constructed in the 1800's and have thus operated for periods far in excess of their expected service life. Incidences of failure, particularly shallow planar landslides are increasing, in part due to the effect of more intense and longer duration rainfall events. Network owners have difficulty in targeting scarce resources to reduce risk across networks. This paper proposes a methodology for developing fragility curves for rainfall induced landslides on transport networks. Fragility curves provide the probability of exceedance of different limit states for a given hazard considering a range of magnitudes. In this paper the vulnerability of slopes as expressed by a loss of performance, is quantified for rainfall events of various intensities and duration. The approach expands upon probabilistic slope stability analysis and provides a rational logical framework for considering how vulnerable a slope is to rainfall induced failure.

show abstract

Fragility analysis of slopes exposed to seismic hazard employing surrogate modeling techniques

Cabanzo,

Tinoco,

Sousa

et al. 2023

ce papers

View full text Add to dashboard Cite

Natural disasters may lead to failure of critical assets part of railway systems. Slope failure often results in major economic consequences. Due to the economic importance that railways systems have, it is pertinent to ensure good performance and long‐term. The objective of the present research is to propose tools that may allow obtaining the failure probability of soil slopes under a seismic event by employing surrogate models to ease the computational cost of considering the uncertainties associated with geotechnical and geometrical properties of a case study located in Portugal. The presented methodology and derived fragility curves, using peak ground acceleration (PGA) as the intensity measure (IM), can be used to assess slope performance under a seismic event for different safety levels, thus providing useful information for prioritizing assets and taking preventive actions to maintain the desired performance of the railway system.

show abstract

Development of an Evidence-based Geotechnical Asset Management Policy for Network Rail, Great Britain

Cited by 32 publications

References 2 publications

Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction

Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction

Fragility curves for rainfall-induced shallow landslides on transport networks

Fragility analysis of slopes exposed to seismic hazard employing surrogate modeling techniques

Contact Info

Product

Resources

About