Risk analysis for the Ancona landslide—II: estimation of risk to buildings

Uzielli, Marco; Catani, Filippo; Tofani, Veronica; Casagli, Nicola

doi:10.1007/s10346-014-0477-x

Cited by 56 publications

(36 citation statements)

References 17 publications

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“…Similarly, the vulnerability of buildings to damage by flood events is linked to the property age, which acts as an indicator of the type of foundation, wall and roof construction (Fedeski and Gwilliam 2007). Models estimating landslide risk also incorporate construction period (Uzielli et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Predicting residential building age from map data

Rosser

Boyd

Long

et al. 2019

Computers, Environment and Urban Systems

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Predicting residential building age from map data The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving thisa per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing crossvalidated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points.

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Section: Introductionmentioning

confidence: 99%

Predicting residential building age from map data

Rosser

Boyd

Long

et al. 2019

Computers, Environment and Urban Systems

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“…Its assessment requires the evaluation of different parameters and factors such as type of element at risk, resistance, and implemented protective measures (i.e., local structural protection) [15]. In physical geography and in engineering geology, most of the studies consider the vulnerability to a hazardous event of a given magnitude as being the degree of loss of elements at risk expressed in a scale ranging from 0 (no loss) to 1 (total loss), e.g., [16][17][18][19][20]. From this definition emerged a wide range of vulnerability assessment models, each study addressing vulnerability in a different way [21].…”

Section: Introductionmentioning

confidence: 99%

Combining Social Vulnerability and Physical Vulnerability to Analyse Landslide Risk at the Municipal Scale

Guillard-Gonçalves¹,

Zêzere²

2018

Geosciences

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In this work residents’ social vulnerability and buildings’ physical vulnerability of the Loures municipality (Portugal) were combined to locate the areas where the vulnerability is the highest, and to analyse the landslide risk. The social vulnerability of Loures was assessed using the Geographic Basis for Information Reference (BGRI) terrain units by combining sensitivity and lack of resilience based on the population and housing Census 2011 data. The physical vulnerability was assessed in a previous study based on an inquiry of a pool of European landslide experts and a sub-pool of landslide experts who know the study area. A matrix approach was used to cross the classes of the social and physical vulnerabilities. Finally, the landslide risk was analysed for each terrain unit considering the combined vulnerability, the buildings’ economic value and the landslide susceptibility for a specific landslide magnitude (3-metre-deep rotational slide). Results show that 0.9% of the population reside in the area of the municipality where 75% of the future landslide should occur, and 0.8% of the buildings of the municipality—which represent a value of EUR 146,170,000—are also located in this dangerous area. This approach is reproducible: the risk analysis can be applied for another magnitude scenario in Loures, and the combined vulnerability can be assessed in any Portuguese municipality thanks to the availability of the data.

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“…This situation is driving geotechnical practice towards a more frequent application of QRA. Examples of QRA applied to landslide management can be found in El-Ramly et al (2003), Kong (2002), Mostyn and Sullivan (2002), Bonnard et al (2004), Lacasse et al (2008); and more recently in Vranken et al (2014), Lu et al (2014) and Uzielli et al (2014). Further application of QRA for landslide management requires addressing its current challenges.…”

Section: Introductionmentioning

confidence: 95%

Development and application of a quantitative risk assessment to a very slow moving rock slope and potential sudden acceleration

et al. 2015

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Development and application of a quantitative risk assessment to a very slow moving rock slope and potential sudden acceleration Abstract The benefits of quantitative risk assessments for landslide management have been discussed and illustrated in several publications. However, there still are some challenges in its application for low-probability, high-magnitude events. These challenges are associated with the difficulties in populating our models for risk calculations, which largely require the input of expert opinion. This paper presents a quantitative risk assessment to a very slow moving rock slope within a dam reservoir in the Province of British Columbia, Canada. The assessment is focused on the risk to the population in the vicinity of the dam and the populated areas downstream. Expert opinions quantified the slope failure probabilities in the order of 10 −3 to 10 −1 per year for the smallest failure scenario considered and less than 10 −6 for a failure of the entire slope. However, these estimations are associated with high levels of uncertainty. Our approach starts with the calculation and assessment of the magnitude and probability of the potential slope failure consequences, minimizing the uncertainties associated with estimated slope failure probabilities. Then, these consequences and failure probabilities are combined to obtain a measure of risk. The uncertainty associated with the slope failure probabilities is managed by the estimation of plausible ranges for these. The calculated risk levels are then presented as ranges of values and assessed against adopted evaluation criteria. The consequence and risk assessment of the rock slope suggest that the risk to the population exposed in the vicinity of the dam and populated areas downstream is under adequate control. The probability of large consequence scenarios is extremely low, in the order of 10 −7 chance of an event causing more than 100 fatalities. We propose an observational technique to assess changes in risk levels and decide when to update the risk management approach or deploy emergency measures. The technique is focused on the detection of changes in the slope deformation patterns that would indicate an increase in the potential failure volumes or an imminent failure. It can be considered an extension to the current early warning system in place, easy to implement and enhanced with the strength of the comprehensive analysis required for a quantitative risk assessment.Keywords Risk assessment . Landslide hazard . Rock slope . Consequence analysis Checkerboard Creek rock slope The Revelstoke Dam, part of a hydroelectric generation project on the Columbia River, has an installed capacity of 2,480 MW. A future expansion is being planned that would increase the capacity by another 500 MW (BC Hydro 2014). The dam, 5 km upstream of Revelstoke, British Columbia, comprises a 1,160-m-long earthfill dam that wraps around a 183-m-long concrete gravity dam and the concrete gravity spillway structure (Taylor and Lou 1983;Salmon 1988). Figure 2 shows the...

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Risk analysis for the Ancona landslide—II: estimation of risk to buildings

Cited by 56 publications

References 17 publications

Predicting residential building age from map data

Predicting residential building age from map data

Combining Social Vulnerability and Physical Vulnerability to Analyse Landslide Risk at the Municipal Scale

Development and application of a quantitative risk assessment to a very slow moving rock slope and potential sudden acceleration

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