Simulation of lineal roughness index of random curvature fracture profiles

1989 ◽  
Vol 23 (5) ◽  
pp. 759-762 ◽  
Author(s):  
K.K. Ray ◽  
D.K. Das
2021 ◽  
Vol 10 (1) ◽  
pp. 27
Author(s):  
Bilal Ahmad Munir ◽  
Sajid Rashid Ahmad ◽  
Raja Rehan

In this study, a relation-based dam suitability analysis (RDSA) technique is developed to identify the most suitable sites for dams. The methodology focused on a group of the most important parameters/indicators (stream order, terrain roughness index, slope, multiresolution valley bottom flatness index, closed depression, valley depth, and downslope gradient difference) and their relation to the dam wall and reservoir suitability. Quantitative assessment results in an elevation-area-capacity (EAC) curve substantiating the capacity determination of selected sites. The methodology also incorporates the estimation of soil erosion (SE) using the Revised Universal Soil Loss Equation (RUSLE) model and sediment yield at the selected dam sites. The RDSA technique identifies two suitable dam sites (A and B) with a maximum collective capacity of approximately 1202 million m3. The RDSA technique was validated with the existing dam, Gomal-Zam, in the north of Sanghar catchment, where RDSA classified the Gomal-Zam Dam in a very high suitability class. The SE estimates show an average of 75 t-ha−1y−1 of soil loss occurs in the study area. The result shows approximately 298,073 and 318,000 tons of annual average sediment yield (SY) will feed the dam A and B respectively. The SE-based sediment yield substantiates the approximate life of Dam-A and Dam-B to be 87 and 90 years, respectively. The approach is dynamic and can be applied for any other location globally for dam site selection and SE estimation.


Author(s):  
Ernesto Ferreira Nobre Junior ◽  
Arielle Elias Arantes ◽  
Priscilla Oliveira Azevedo

2015 ◽  
Vol 39 (1) ◽  
pp. 268-278 ◽  
Author(s):  
Elói Panachuki ◽  
Ildegardis Bertol ◽  
Teodorico Alves Sobrinho ◽  
Paulo Tarso Sanches de Oliveira ◽  
Dulce Buchala Bicca Rodrigues

Surface roughness of the soil is formed by mechanical tillage and is also influenced by the kind and amount of plant residue, among other factors. Its persistence over time mainly depends on the fundamental characteristics of rain and soil type. However, few studies have been developed to evaluate these factors in Latossolos (Oxisols). In this study, we evaluated the effect of soil tillage and of amounts of plant residue on surface roughness of an Oxisol under simulated rain. Treatments consisted of the combination of the tillage systems of no-tillage (NT), conventional tillage (CT), and minimum tillage (MT) with rates of plant residue of 0, 1, and 2 Mg ha-1 of oats (Avena strigosa Schreb) and 0, 3, and 6 Mg ha-1 of maize (Zea mays L.). Seven simulated rains were applied on each experimental plot, with intensity of 60±2 mm h-1 and duration of 1 h at weekly intervals. The values of the random roughness index ranged from 2.94 to 17.71 mm in oats, and from 5.91 to 20.37 mm in maize, showing that CT and MT are effective in increasing soil surface roughness. It was seen that soil tillage operations carried out with the chisel plow and the leveling disk harrow are more effective in increasing soil roughness than those carried out with the heavy disk harrow and leveling disk harrow. The roughness index of the soil surface decreases exponentially with the increase in the rainfall volume applied under conditions of no tillage without soil cover, conventional tillage, and minimum tillage. The oat and maize crop residue present on the soil surface is effective in maintaining the roughness of the soil surface under no-tillage.


Author(s):  
Gary J. Higgins

Data collected by inertial profilers on new asphalt pavements in Colorado in 2012 were used to analyze the effectiveness of the localized roughness specification in Colorado. For the analyzed projects, data were collected before any corrections were made as well as after diamond grinding had been performed to remove areas of localized roughness. The data indicated that localized roughness features having a half-car roughness index (HRI) lower than 175 in./mi were rarely addressed during correction. However, about half the localized roughness features that had an HRI of 175 to 200 in./mi were successfully addressed during correction. Localized roughness features having an HRI greater than 200 in./mi appeared to be successfully addressed during correction. The analysis indicated a significant difference in the localized roughness locations identified by AASHTO R 54 and the Colorado Department of Transportation (DOT) method of detecting localized roughness. The Colorado DOT procedure specifies a minimum length for a roughness feature that is to be corrected, but AASHTO R 54 does not. This paper shows that collecting accurate profile data and analyzing the data to determine localized roughness locations are not enough. The identified locations must be correctly marked on the pavement in the field so that the feature does not cause localized roughness. This paper presents a procedure not only for collecting accurate data but also for accurately marking the roughness features in the field. It is shown that it is possible to locate and correct localized roughness accurately to the current thresholds as set by AASHTO R 54.


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