Interaction processes in a straight compound channel with rigid and flexible emergent floodplain vegetation

2004 ◽  
pp. 347-352
Author(s):  
I Schnauder
Keyword(s):  
Compound Channel ◽  
Floodplain Vegetation ◽  
Interaction Processes

scholarly journals Effects of Floodplain Vegetation on Momentum Transfer in Compound Channel

2011 ◽  
Vol 67 (2) ◽  
pp. I_549-I_557 ◽  
Author(s):  
Fumiaki HASEGAWA ◽  
Takuya YAMAMOTO ◽  
Fatima JAHRA ◽  
Yoshihisa KAWAHARA
Keyword(s):  
Momentum Transfer ◽  
Compound Channel ◽  
Floodplain Vegetation

Large-scale vegetation mapping of the Pinega river valley (the surroundings of Golubino village, Arkhangelsk oblast)

2018 ◽  
pp. 19-39
Author(s):  
M. A. Makarova
Keyword(s):  
Plant Communities ◽  
Indicator Species ◽  
Dominant Species ◽  
Large Scale ◽  
Vegetation Mapping ◽  
River Valley ◽  
Floodplain Forests ◽  
Floodplain Vegetation ◽  
Wet Meadows ◽  
Arkhangelsk Oblast

Geobotanical survey of floodplain natural complexes near gypsum outcrops in the Pinega river valley was done in 2015. Large-scale geobotanical map of the key polygon (scale 1 : 30 000) was composed. Typological units of vegetation were selected on the basis of the composition of dominant species and groups of indicator species. Homogeneous and heterogeneous territorial units of vegetation (serial series, combinations, environmental series) were used. 53 mapped unit types (25 homogeneous types and 28 heterogeneous types) were recognized. The floodplain vegetation consists of 17 homogeneous types of plant communities, 3 series, 14 combinations and 6 ecological series. The sites of old floodplain forests, such as willow forests with Urtica sondenii rare in the Arkhangelsk region and oxbow wet meadows with Scolochloa festucacea were identified.

scholarly journals Predicting Excavation-Induced Tunnel Response by Process-Based Modelling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Linlong Mu ◽  
Jianhong Lin ◽  
Zhenhao Shi ◽  
Xingyu Kang
Keyword(s):  
Soil Properties ◽  
Urban Areas ◽  
Field Measurements ◽  
Inverse Modelling ◽  
Machine Learning Techniques ◽  
Interaction Patterns ◽  
Support Structures ◽  
Interaction Processes ◽  
Excavation Support ◽  
Artificial Neural Network Ann

Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.

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