Search

With growing urbanization, an increasing number of landslides worldwide are linked to construction works, especially along transportation routes. To get a better understanding of these post‐construction landslides at artificial waterways, archive studies have been conducted, in addition to analysis of historical scientific literature and technical reports. The resultant database is used to describe the occurrence and the characteristics of the landslides at the Mittelland Canal, and to evaluate aspects of conditioning and preparatory factors, potential triggers and applied mitigation measures. The Mittelland Canal has been chosen because it is one of the most important artificial waterways in Europe, and the high number of landslides that occur there is unique. In particular, landslides at the Mittelland Canal were frequently reported in the first half of the 20th century. The susceptibility to landslides is strongly influenced by material characteristics, as a large number of landslides is linked to sites where fine‐grained mudstone is abundant, which quickly decomposes under immersion in water or under dynamic stress. In addition, landslides can be linked to the creation of over‐steepened slopes. Construction works can also be considered another preparatory factor for landslides. In addition, some landslides in World War II appear to be connected to the blast of aerial bombings. Precipitation seems to play a minor role in triggering landslides. The applied mitigation measures were developed recently, marking the importance of the canal as a transportation route. © 2020 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Landslide research relies on landslide inventories for a multitude of spatial, temporal, or process analyses. Generally, it takes high effort to populate a landslide inventory with relevant data. In this context, the present work investigated an effective way to handle vast amounts of automatically acquired digital data for landslide inventories by the use of machine learning algorithms and information filtering. Between July 2017 and February 2019, a keyword alert system provided 4381 documents that were automatically processed to detect landslide events in Germany. Of all those documents, 91% were automatically recognized as irrelevant or duplicates; thereby, the data volume was significantly reduced to contain only actual landslide documents. Moreover, it was shown that inclusion of the document's images into the automated process chain for information filtering is recommended, since otherwise unobtainable important information was found in them. Compared with manual methods, the automated process chain eliminated personal idiosyncrasies and human error and replaced it with a quantifiable machine error. The applied individual algorithms for natural language processing, information retrieval, and classification have been tried and tested in their respective fields. Furthermore, the proposed method is not restricted to a specific language or region. All languages on which these algorithms are applicable can be used with the proposed method and the training of the process chain can take any geographical restriction into account. Thus, the present work introduced a method with a quantifiable error to automatically classify and filter large amounts of data during automated digital data acquisition for landslide inventories. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for ex post assessment of direct landslide costs for transportation infrastructures. The methodology includes tools to compile, model, and extrapolate landslide losses on different spatial scales over time. A landslide susceptibility model enables regional cost extrapolation by means of a cost figure obtained from local cost compilation for a representative case study area. On a local level, cost survey is closely linked with cost modeling, a toolset for cost estimation based on landslide databases. Cost modeling uses landslide disaster management process models (LDMMs) and cost modules to simulate and monetize cost factors for certain types of landslide damage. The landslide susceptibility model provides a regional exposure index and updates the cost figure to a cost index which describes the costs per kilometer of traffic route at risk of landslides. Both indexes enable the regionalization of local landslide losses. The methodology is applied and tested in a cost assessment for highways in the Lower Saxon Uplands, NW Germany, in the period 1980 to 2010. In this 7,400-km large mountain region, 77 km of highway is located in landslide hazard area. Annual average costs of US$52,000 per km of highway at risk of landslides are identified as cost index for a local case study area. The cost extrapolation results in annual average costs for highways in the Lower Saxon Uplands of US$4.02 million. This test application as well as a validation of selected modeling tools verifies the functionality of this methodology. [ABSTRACT FROM AUTHOR]