scholarly journals Modal and thermal analysis of Les Arches unstable rock column (Vercors massif, French Alps)

2013 ◽  
Vol 194 (2) ◽  
pp. 849-858 ◽  
Author(s):  
P. Bottelin ◽  
C. Lévy ◽  
L. Baillet ◽  
D. Jongmans ◽  
P. Guéguen
2020 ◽  
Author(s):  
Jérôme Poulenard ◽  
Norine Khedim ◽  
Lauric Cecillon ◽  
Amélie Sailard ◽  
Pierre Barré ◽  
...  

<p>High-elevation ecosystems are considered as systems that have accumulated large amounts of organic carbon in their soils over the past millennia. However, there are still large uncertainties about soil organic matter (SOM) stocks and stability in mountain areas . The fate of SOM in alpine environments is particularly questioned in the context of climate change.</p><p>The aim of this study was to investigate SOM stocks and biogeochemical characteristics of SOM along altitudinal gradients to decipher their climatic and biogeochemical drivers. To do so, we used the soil samples set of the French ORCHAMP long-term observatory network. ORCHAMP is built around multiple altitudinal gradients (ca. 1000m of elevation gain representative of the pedoclimatic variability of the French Alps. Each gradient is made of 5 to 8 permanent plots distributed regularly each 200 m of elevation, from the valley (1000 m a.s.l.) to the mountain top (until 3000 m a.s.l.). We studied 18 elevational gradients, including 105 soil profiles and 350 soil horizons. The biogeochemical stability of SOM was estimated with Rock-Eval® thermal analysis.</p><p>SOM stocks are extremely variable and do not increase with elevation . The size of the thermally labile SOM  pool strongly increases with elevation. The high lability of SOM revealed by Rock-Eval® thermal analysis suggests a generally high vulnerability of SOM to climate change in alpine environments. The mechanisms explaining the maintenance of this SOM pool in alpine environments are still under study. Hypotheses involving complex balances between climate, nature of fresh organic matter, and enzymatic activities will be discussed.</p><p> </p>


Geosciences ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 242
Author(s):  
Pierre Bottelin ◽  
Laurent Baillet ◽  
Aurore Carrier ◽  
Eric Larose ◽  
Denis Jongmans ◽  
...  

Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment’s fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column’s dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column’s dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments.


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