Comparison of two 2-D numerical models for snow avalanche simulation.

Snow avalanches are gravitational processes characterised by the rapid movement of a snow mass, threatening inhabitants and damaging infrastructure in mountain areas. Such phenomena are complex events, and for this reason, different numerical models have been developed to reproduce their dynamics over a given topography. In this study, we focus on the two-dimensional numerical simulation tools RAMMS::AVALANCHE and FLO-2D, aiming to compare their performance in predicting the deposition area of snow avalanches. We also aim to assess the employment of the FLO-2D simulation model, normally used in water flood or mud/debris flow simulations, in predicting the motion of snow avalanches. For this purpose, two well-documented avalanche events that occurred in the Province of Bolzano (IT) were analyzed (Knollgraben, Pichler Erschbaum avalanches). The deposition area of each case study was simulated with both models through back-analysis processes. The simulation results were evaluated primarily by comparing the simulated deposition area with the observed one through statistical indices. Subsequently, the maximum flow depth, velocity and deposition depth were also compared between the simulation results. The results showed that RAMMS::AVALANCHE generally reproduced the observed deposits better compared to FLO-2D simulation. FLO-2D provided suitable results for wet and dry snow avalanches after a meticulous calibration of the rheological parameters, since they are not those typically considered in avalanche rheology studies. The results showed that FLO-2D can be used to study the propagation of snow avalanches and could also be adopted by practitioners to define hazard areas, expanding its field of application.

Practical back-analysis methodology to predict underground train-induced building vibrations.

The vehicle-track interaction leads to the vibrating source for the prediction of train-induced building vibrations. To prevent modeling difficulties in the source part, this study proposes a practical back-analysis methodology for calculating underground train-induced building vibrations. The methodology combines the advantages of field measurements and numerical simulations. The fundamental concept of the hybrid methodology is to first create a virtualized moving source at the rail surface and subsequently modify it until the numerical predictions are consistent with the field measurements at the same locations. These locations are frequently selected at the ground surface or near the building foundation. Finally, this imaginary force can be used to predict the vibrations of buildings. The practicality of the hybrid methodology is verified by comparing the predicted vibrations of buildings with field test results. As an application of the proposed method, the transmission laws and characteristics of the vibrations in buildings are analyzed.

Ergonomics evaluation of lawn mower operator's working posture using JACK software and kinect interface.

BACKGROUND: Nowadays, real-time motion tracking devices are widely used for ergonomic assessment of several manual quotidian activities. The real-time tracking of human activities makes it easier to observe the exposure of work-related musculoskeletal disorders (WMSDs) in the human body. OBJECTIVE: This study aims to determine the suitability of a real-time motion tracking device (Kinect v1 interfaced with a commercial ergonomic assessment software, JACK) for real-time ergonomic evaluation of the strenuous operation of the manual lawn mower. METHOD: The lawn mower operators perform various strenuous activities while operating the manual lawn mower for long intervals of time, which causes WMSDs in the entire body of the operators. These working operators' activities have been captured using Kinect v1 interfaced with JACK, to address the ergonomic issues responsible for the whole-body WMSDs. The forces acting on the lower back, Rapid Upper Limb Assessment score and static strength have been predicted using JACK. RESULTS: This study proves the exposure of the operators towards the whole-body WMSDs while operating the manual lawn mower. CONCLUSION: The findings provide a quick and straightforward approach for performing the real-time ergonomic evaluation of any operation, which can help the industrial staff estimate the risk of level WMSDs.

Modelling the dynamics of a large rock landslide in the Dolomites (eastern Italian Alps) using multi-temporal DEMs.

Latest advances in topographic data acquisition techniques have greatly enhanced the possibility to analyse landscapes in order to understand the processes that shaped them. High-resolution Digital Elevation Models (DEMs), such as LiDAR-derived ones, provide detailed topographic information. In particular, if multi-temporal DEMs are available, it is possible to carry out a detailed geomorphic change detection analysis. This analysis may provide information about the dynamics of large landslides and may thus, be useful for landslide risk assessments. However, LiDAR-derived DEMs are mostly available only as post-event surveys. The technique is relatively recent, and local or national authorities only started widespread surveys in the last decade. Therefore, it is of a certain interest to analyse the effectiveness of DEMs derived from technical cartography to produce reliable volumetric estimates related to large landslides. This study evaluates the use of a multi-source DEM of Difference (DoD) analysis for the investigation of a large landslide -Le Laste-, which occurred on November 12, 2014 on Mount Antelao (eastern Italian Alps). The landslide initiated as a 365,000 m3 rockslide close to the summit of the mountain and transformed into a debris avalanche during its runout. The comparison of pre- and post-event DEMs allowed for the identification and quantification of erosion and deposition areas, and for the estimation of landslide volume. A sound back-analysis of the landslide with the 3D numerical model DAN3D was based on this comparison and on seismic records of the event. These seismic records proved to be remarkably useful, as they allowed for the calibration of the simulated landslide velocity. This ensured the reliability of the model notwithstanding the topographic datasets, intrinsic uncertainties. We found that using a pre-event DEM derived from technical cartography tends to slightly overestimate the volume with respect to the use of the more accurate LiDAR-derived DEM. In recent years, the landslide risk around Mt. Antelao has been increasing alongside the ever-growing population and human activities in the area. Sediment accumulations produced by the Le Laste landslide significantly amplified the debris flow hazard by providing new sediment sources. Therefore, it is crucial to delineate the distribution of this material to enable an adequate debris flow hazard assessment. The material properties derived from the back-analysis of the Le Laste landslide can be used to simulate the runout of possible future events, and to generate reliable hazard zone maps, which are necessary for effective risk mitigation.

MLVA Genotyping Characteristics of Human Brucella melitensis Isolated from Ulanqab of Inner Mongolia, China.

Brucellosis is a serious public health problem in Ulanqab, which is a region located in the middle of the Inner Mongolia Autonomous Region adjacent to Shanxi and Hebei provinces. The disease is prevalent in both the latter provinces and Ulanqab with the highest prevalence of brucellosis occurring in Inner Mongolia. The MLVA-16 scheme is a genotyping tool for assessing genetic diversity and relationships among isolates. Moreover, this genotyping tool can also be applied to epidemiological trace-back investigations. This study reports the occurrence of at least two B. melitensis biovars (1 and 3) in Ulanqab, encompassing 22 and 94 isolates, respectively. B. melitensis biovar 3 was the predominant biovar in the area examined. Panel 1 (MLVA-8) identified three genotypes (42, 63, and 114), with genotype 42 (n = 101) representing 87% of the tested strains. MLVA-11 identified eight genotypes (116, 111, 297, 291, and 342-345) from 116 of the analyzed isolates. All of these isolates were identified as belonging to the East Mediterranean group. Genotype 116 (n = 94) was the predominant genotype and represented 81% of the isolates. The isolates pertaining to this genotype were distributed throughout most of Ulanqab and neighboring regions. The MLVA-16 scheme showed the presence of 69 genotypes, with 46 genotypes being represented by single isolates. This analysis revealed that Ulanqab brucellosis cases had epidemiologically unrelated and sporadic characteristics. The remaining 23 genotypes were shared (between a total of 70 isolates) with each genotype being represented by two to eight isolates. These data indicate that these cases were epidemiologically related. MLVA genotyping confirmed the occurrence of a multipoint outbreak epidemic and intrafamilial brucellosis. Extensive genotype-sharing events were observed among isolates from different regions of Ulanqab and from other provinces of China. These findings suggest either a lack of control of animal movement between different regions or the circulation of contaminated animal products in the market. Our study is the first comprehensive genotyping and genetic analysis of B. melitensis in Ulanqab. We believe that this study will help to improve the effectiveness of brucellosis control programs.

Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network / 西安交通大学学报·英文版

Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relatiouships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data. This is of practical significance to the appraisal of stability and informatiomzation design of the surrounding rock.