PHANI KUMAR VADDI
@gecgudlavalleru.ac.in
Sr.Gr.Assistant Professor & Mentor (ASA)
Gudlavalleru Engineering college
EDUCATION
M.Tech
RESEARCH INTERESTS
soil stabilization, soil structure interaction, neural network, adopts neuro fuzzy inference system.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
V Phani Kumar and Ch. Sudha Rani
IOP Publishing
Abstract Safe bearing capacity (SBC) may be estimated for cohesive soil using the IS Code it is essential to conduct shear tests in order to identify the angle of internal friction and cohesion, SPT tests in order to determine the N-value of the soil, as well as tests in order to determine the dry density of the soil and the relative density of the soil. The SBC of soil is estimated as per settlement criteria using laboratory evaluations of its essential properties. This study suggests the use of ANFIS to predict the Safe Bearing Capacity of cohesive soil as a function of foundation depth, foundation depth, dry density, liquid limit, plasticity index, percentage fine fraction, width/length ratio (in case of square/rectangular footing), and N-Value. An attempt is in the present work with inputs such as foundation depth, dry density, liquid limit, plasticity index, percentage fine fraction, width/length ratio, and N-Value and output as safe bearing capacity. Each input parameter has a different number of membership functions and gaussbell is used in five ANFIS models. The most accurate model, MODEL-IV, with an RMSE of 9.762 and an R2 of 0.9485. For cohesive soil, the results indicate that ANFIS can be used to predict the SBC.
Ch Naga Bharath, Siva Shanmukha Anjaneya Babu Padavala, V Phani Kumar, and N Hari Pavan
IOP Publishing
Abstract Clayey soils swell on absorbing water and shrinks on drying, any constructions built on them are subjected to the differential settlements due to the loss of support from soil. Stabilization of clays with various additives have considerable successes. Lime, cement, CaCl2, fly ash (FA), ground granulated blast furnace slag (GGBS), pond ash and other chemical reagents have been effective in stabilizing expansive soils and improving their characteristics like strength. This paper presents the comparison for strength and compressibility characteristics of clays when treated with FA and GGBS. In this investigation, FA content of 10%, 15%, 20%, 25% and GGBS of 10%, 15%, 20%, 25% were been used with the soil. The strength was observed to be high at 20% of FA & 20% of GGBS when compared with normal clayey soil. The time required for 90% consolidation and compression index were considerably reduced for both FA and GGBS at addition of 20% to soil.
D. N. V. S. L. S. Indira, M. Sobhana, A. H. L. Swaroop, and V Phani Kumar
IEEE
Totally 54% of India's land area is deemed arable, making it the world's largest agrarian economy. Soil infertility owing to over fertilization, as well as a lack of access and awareness of contemporary agricultural practices, are the different factors that contribute to low agricultural production. The main purpose of this research work is to develop a machine learning-based recommendation system to increase agricultural productivity. A variety of datasets were used in this study to design and develop advanced models to estimate the crop, recommend fertiliser, and identify plant disease. An algorithm called MobileNet uses an image of a leaf to identify the disease present in a plant. The XGBoost model predicts a suitable crop based on the local soil nutrients and rainfall. Random Forest [RF] model was used to propose fertilizer and develop ideas for improving soil fertility depending on nutrients present in the soil. When compared to other approaches, the proposed model delivers a high level of accuracy. Moreover, this article suggests the farmer to increase the crop yield by entering the input values and local soil conditions, wherein the model suggests recommended crop for that soil with an accuracy of 99%.
V. Phani Kumar and K. Saikiran
Springer Singapore
The high rise structures are proposed for residential and commercial purposes. They may easily effect by seismic as well as wind loads, so the buildings get deformed and collapsed easily. To avoid these problems we consider p-delta effect in designing. As the number of stories increases p-delta effect becomes very important. The P-Δ effect is relevant in structural engineering problems, especially in civil engineering, where we’re dealing with large structures with proportionally decreasing small moments of inertia as they continue to be extended in absolute height. When designing structures, we may consider that they’re immune to lateral deformation, and may therefore not account for their behaviour when sudden buckling or beam-column-like behaviour is introduced. In this study pdelta (P-Δ) effect on high-rise building studied for the analysis of G+29 RCC framed building and models were done by ETABS2016. Seismic and wind loads are applied to model as per IS-1893 (2002) and IS-875 (PART-III). The displacements, storey drifts, Bending Moments and Shear Forces are compared to the different models by considering with and without P-delta effect and by providing shearwalls at different locations.