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The objective of this research was to prepare a rockfall susceptibility map. Explorations were conducted in the Dubračina River basin (Croatia). The input data included a geological map, an orthophoto and a 1-m digital terrain model (DTM). After a talus inventory was prepared, the seed cell concept was applied to define the rockfall source areas. The contributing factors (predictors) of rockfalls were evaluated by the chi-squared test. The analysis confirmed the following predictors: CORINE land cover, lithology, slope, aspect, distance from a spring, distance from a road, distance from a fault, distance from a stream, and distance from the rock-soil geological boundary. A matrix pairwise comparison of the predictor ratings was used to define the most significant contributing factors. The predictors that affected the susceptibility map in the share of 86.3% were the slope (61.6%), lithology (13.4%), CORINE land cover (6.2%), and distance from the rock-soil geological boundary (5.1%). Two susceptibility maps were prepared: one using all nine contributing factors and another using the four most significant factors. The analysis showed that both maps were good, with the same areas under the receiver operating characteristic (ROC) curves. The map prepared with only four contributing factors can be considered a better map due to its more precise spatial definition of critical areas. It can be concluded that geological map, 1-m DTM and orthophoto provide enough data to prepare reliable rockfall susceptibility map. The application of the bivariate statistical zonation method called the "frequency ratio method" was proven to be successful. This research demonstrates that the application of the seed cell concept can be useful to speed up the process of rockfall source area detections in large research regions. [ABSTRACT FROM AUTHOR]

Featured Application: Modulus of elasticity for grain-supported carbonate rocks is successfully estimated by using simple estimation models. The methodology and models described in this paper can help engineers in the early design stages when they need a quick and easy estimate. The determination and estimation of elastic behaviour are essential in engineering practise, especially in quarrying, mining, construction, and all engineering professions that perform operations dealing with rock materials. Young's modulus, or modulus of elasticity, is the most important property describing the deformability of rock material. In this paper, grain-supported carbonates from Croatia are described and their elastic modulus and significant physical and mechanical properties are determined. The analysis of the collected data was performed in the R statistical environment. Estimation models based on multiple linear regression and the regression tree model were created. The methodology of model development and evaluation in R environment is described in detail. According to the more stringent coefficients (RMSECV and adjusted R2) used to evaluate the success of the estimation, simple regression tree models were found to perform well for the preliminary estimation, while more complex models based on Bagging performed very well. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for estimating rockfall threats in carbonate rock cuts along railways in Croatia. The assessment is based on the Rockfall Hazard Rating System (RHRS). In general, the RHRS versions developed for Tennessee and Colorado are combined and modified to comply with Croatian carbonate rock mass properties, specific geological environments and values measured in the investigation area. The modifications are related to the classification parameters and corresponding adjustments of their classes. The exponential scoring of parameter classes is retained, but the calculation of final rockfall threat classes is modified. The proposed methodology is developed and applied to 366 cut segments along railways with a total length of 74.888 km. All rock cuts are located in the Dinaric karst area of Croatia, where carbonate rocks are locally susceptible to rockfalls, which mostly include sliding, toppling and ravelling. Rockfall threat is calculated for each cut segment with 15 different scoring models. The optimal scoring model is chosen according to the verification results. The verification is based on three different verification parameters: preliminary rockfall threat assessment, rockfall scars and cut stability, which are defined by the Regional Maintenance Administration of Croatian Railways. An ordinal logistic regression is performed to determine the threshold values for each rockfall threat class. The established methodology is used to detect the most vulnerable railway sections, which are selected for detailed inspection and reclamation. The research also enables a proactive approach of rockfall management along linear infrastructure in Croatia but also worldwide in similar geological environments. [ABSTRACT FROM AUTHOR]

In this paper, for the first time, a regional-scale 1:100,000 landslide-susceptibility map (LSM) is presented for Sisak-Moslavina County in Croatia. The spatial relationship between landslide occurrence and landslide predictive factors (engineering geological units, relief, roughness, and distance to streams) is assessed using the integration of a statistically based frequency ratio (FR) into the analytical hierarchy process (AHP). Due to the lack of landslide inventory for the county, LiDAR-based inventories are completed for an area of 132 km2. From 1238 landslides, 549 are chosen to calculate the LSM and 689 for its verification. Additionally, landslides digitized from available geological maps and reported via the web portal "Report a landslide" are used for verification. The county is classified into four susceptibility classes, covering 36% with very-high and high and 64% with moderate and low susceptibility zones. The presented approach, using limited LiDAR data and the extrapolation of the correlation results to the entire county, is encouraging for primary regional-level studies, justifying the cost-benefit ratio. Still, the positioning of LiDAR polygons prerequires a basic statistical analysis of predictive factors. [ABSTRACT FROM AUTHOR]