Environmental factors affecting the corrosion behaviour of reinforcing steel: I. The early stage of passive film formation in Ca(OH)2 solutions

2010 ◽  
Vol 52 (12) ◽  
pp. 3875-3882 ◽  
Author(s):  
S.M. Abd El Haleem ◽  
E.E. Abd El Aal ◽  
S. Abd El Wanees ◽  
A. Diab
Keyword(s):  
Environmental Factors ◽  
Passive Film ◽  
Corrosion Behaviour ◽  
Early Stage ◽  
Film Formation ◽  
Reinforcing Steel ◽  
Factors Affecting

Review of Certain Environmental Factors Affecting the Production of Pink and Chum Salmon

1958 ◽  
Vol 15 (5) ◽  
pp. 1103-1126 ◽  
Author(s):  
W. P. Wickett
Keyword(s):  
Environmental Factors ◽  
Preliminary Data ◽  
Chum Salmon ◽  
Environmental Changes ◽  
Early Stage ◽  
Factors Affecting ◽  
Stream Discharge ◽  
Ocean Conditions

The relation between stock and numbers of spawners is obscured by annual environmental changes. Stream discharge at the time the spawners are migrating upstream, at the time when the eggs are in the early stage of incubation, and extreme discharge during the period eggs and alevins are in the gravel can impose an 8-fold variation in the stock resulting from a given number of spawners in one area. Ocean conditions soon after the fry enter the sea have been observed to increase or decrease survival by a factor of 3. The density of spawners that produces the greatest numbers of fry is related to the average permeability of the stream bottom. Preliminary data indicate that more spawners could be used to advantage in most areas of the coast.

scholarly journals From Exploratory Tunnel to Base Tunnel: Hard Rock TBM Performance Prediction by Means of a Stochastic Approach

2020 ◽  
Vol 53 (12) ◽  
pp. 5473-5487 ◽  
Author(s):  
Andrea Rispoli ◽  
Anna Maria Ferrero ◽  
Marilena Cardu
Keyword(s):  
Performance Prediction ◽  
Early Stage ◽  
Tunnel Boring Machine ◽  
Stochastic Approach ◽  
Critical Issue ◽  
Utilization Factor ◽  
Tbm Performance ◽  
Factors Affecting ◽  
Tunnel Section ◽  
Input Variables

AbstractTunnel boring machine (TBM) performance prediction is often a critical issue in the early stage of a tunnelling project, mainly due to the unpredictable nature of some important factors affecting the machine performance. In this regard, deterministic approaches are normally employed, providing results in terms of average values expected for the TBM performance. Stochastic approaches would offer improvement over deterministic methods, taking into account the parameter variability; however, their use is limited, since the level of information required is often not available. In this study, the data provided by the excavation of the Maddalena exploratory tunnel were used to predict the net and overall TBM performance for a 2.96 km section of the Mont Cenis base tunnel by using a stochastic approach. The preliminary design of the TBM cutterhead was carried out. A prediction model based on field penetration index, machine operating level and utilization factor was adopted. The variability of the parameters involved was analysed. A procedure to take into account the correlation between the input variables was described. The probability of occurrence of the outcomes was evaluated, and the total excavation time expected for the tunnel section analysed was calculated.

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