29 research outputs found
TIME AND COST OPTIMIZATION BY MSP SOFTWARE
No full textIn the construction project, time and cost are the most important factors to be considered in the planning of every project. The aim of project is to finish the projects on time, within budget and to achieve other project objectives. It is a difficult task undertaken by project managers in practice, which include constantly measuring progress, evaluation of plans, and corrective actions should be taken whenever required. Optimization is a systematic effort made to improve profit margins and obtain the best results under given circumstances. There is a Systematic planning and programming with effective management is necessary for timely completion of the project. There is availability of various tools and techniques for optimization. Optimizing performance of the different techniques adopted at one stage of the construction process may not be beneficial if the methods used are not to up the efficient level. In this approach we have studied various factors which affect the cost of projects. again in this approach we have studied various techniques and various materials used for cost optimization. Also the need of optimization is discussed
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No full textNot AvailableInformation on soil hydraulic properties is a prerequisite in irrigation management decisions and crop planning. Such information
on soils of the black soil region (BSR) occupying 7.7 × 107 ha of India is sparse. Soil profile information for 49 representative sites
(244 samples) was collected and used for analysis. Ten different functions were evaluated for their efficacy to describe soil water retention
characteristics (SWRC) of the BSR soils. Campbell model fitted to measured SWRC data with relatively lower root mean square error
(RMSE ¼ 0.0214 m3 · m−3), higher degree of agreement d ¼ 0.9653, and lower absolute error on average (MAE ¼ 0.0165 m3 · m−3).
The next best description was by van Genuchten (VG) function with RMSE (0.0249 m3 · m−3), dð0.9489Þ, and MAE (0.0868 m3 · m−3).
Pedotransfer functions (PTF) were developed to predict field capacity (FC) and permanent wilting point (PWP) using nearest neighbor (kNN)
algorithm and artificial neural networks (ANN). Four levels of input information used for point PTF development include (1) textural data
(data on sand, silt, and clay fraction-SSC), (2) level 1+bulk density data (SSCBD), (3) level 2+organic matter (SSCBDOM), and (4) level 1
+organic matter (SSCOM). The RMSE of predictions by kNN PTFs ranged from 0.0346 to 0.0611 m3 · m−3 with an average of
0.0483 m3 · m−3. The ANN PTFs performed with an average RMSE of 0.0550 m3 · m−3 and a range of 0.0367 to 0.0905 m3 · m−3.
Relatively better estimates of FC=PWP were obtained using SSCBD-based PTF. Accuracy of FC and PWP estimates obtained by using
analytical functions was relatively greater than the estimates by kNN and ANN PTFs. Campbell and VG functions were relatively more
accurate. The study demonstrated the efficacy of kNN technique vis-a-vis neural regression with the additional benefit of appending the
development data as and when desired. The proposed PTFs could be useful in making irrigation management decisions for BSR soils of
India. Identification of the most suitable SWRC function for the study soils will help in crop modeling/water balance studies of the region.Not Availabl
Development of Nateglinide Loaded Graphene Oxide-Chitosan Nanocomposites: Optimization by Box Behnken Design
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Synthesis and Characterization of Poly(acrylic acid)/modified Bentonite Superabsorbent Polymer
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No full textNot AvailableThe present study documents the biological properties
of the black soil region (BSR) of India in terms of culturable
microbial population. Besides surface microbial
population, subsurface population of individual
soil horizons is described to improve the soil information
system. An effort has been made to study
the depth-wise distribution and factors (bioclimates,
cropping systems, land use, management practices
and soil properties) influencing the microbial population
in the soils of the selected benchmark spots representing
different agro-ecological sub-regions of BSR.
The microbial population declined with depth and
maximum activity was recorded within 0–30 cm soil
depth. The average microbial population (log10 cfu g–1)
in different bioclimates is in decreasing order of SHm >
SHd > SAd > arid. Within cropping systems, legumebased
system recorded higher microbial population
(6.12 log10 cfu g–1) followed by cereal-based system
(6.09 log10 cfu g–1). The mean microbial population in
different cropping systems in decreasing order is legume
> cereal > sugarcane > cotton. Significantly higher
(P < 0.05) microbial population has been recorded in
high management (6.20 log10 cfu g–1) and irrigated
agrosystems (6.33 log10 cfu g–1) compared to low management
(6.12 log10 cfu g–1) and rainfed agrosystems
(6.17 log10 cfu g–1). The pooled analysis of data inclusive
of bioclimates, cropping systems, land use, management
practices, and edaphic factors indicates that
microbial population is positively influenced by clay,
fine clay, water content, electrical conductivity, organic
carbon, cation exchange capacity and base saturation,
whereas bulk density, pH, calcium carbonate and
exchangeable magnesium percentage have a negative
effect on the microbial population.Not Availabl
