Browsing by Author "Durga Prasad, C.D."

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    Assessment of Seismic Liquefaction of Soils Using Swarm-Assisted Optimization Algorithm
    (Springer Science and Business Media Deutschland GmbH, 2021) Thotakura, V.; Durga Prasad, C.D.; Chaudhary, B.; Sunil, B.M.
    Assessment of liquefaction potential of soils due to the earthquake has been carried out in this research using the nature-inspired Metaheuristic swarm-assisted algorithm (PSO). An assessment has been made on the basis of actual field data from the previous research. The field data consists of 59 sets having variables of total stress of soil (⌠o), effective stress of the soil (⌠′o), percentage fines, mean size of soil particles (D50), standard penetration value (SPT), the equivalent dynamic shear stress (Tav/⌠′o), maximum horizontal acceleration at ground surface (a/g) and the earthquake magnitude (M). PSO-based models were developed for both single variable and multivariable linear approaches. The results revealed that for the assessment of liquefaction of soils, the developed PSO models perform good estimations in terms of the errors and convergent solution. And also, with a damping coefficient and varying input variables, there is a significant improvement in the best solution. These developed models can be useful for practicing engineers in the field. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Prediction of ammonia contaminants in the aquaculture ponds using soft computing coupled with wavelet analysis
    (Elsevier Ltd, 2023) Thotakura, T.V.; Sunil, B.M.; Chaudhary, B.; Durga Prasad, C.D.; Gobinath, G.
    Intensive aquaculture practices generate highly polluted organic effluents such as biological oxygen demand (BOD), alkalinity, total ammonia, nitrates, calcium, potassium, sodium, iron, and chlorides. In recent years, Inland aquaculture ponds in the western delta region of Andhra Pradesh have been intensively expanding and are more concerned about negative environmental impact. This paper presents the water quality analysis of aquaculture waters in 64 random locations in the western delta region of Andhra Pradesh. The average water quality index (WQI) was 126, with WQI values ranging from 21 to 456. Approximately 78% of the water samples were very poor and unsafe for drinking and domestic usage. The mean ammonia content in aquaculture water was 0.15 mg/L, and 78% of the samples were above the acceptable limit set by the World Health Organization (WHO) of 0.5 mg/L. The quantity of ammonia in the water ranged from 0.05 to 2.8 mg/L. The results show that ammonia levels exceed the permissible limits and are a significant concern in aquaculture waters due to toxicity. This paper also presents an intelligent soft computing approach to predicting ammonia levels in aquaculture ponds, using two novel approaches, such as the pelican optimization algorithm (POA) and POA coupled with discrete wavelet analysis (DWT-POA). The modified and enhanced POA with DWT can converge to higher performance when compared to standard POA, with an average percentage error of 1.964 and a coefficient of determination (R2) value of 0.822. Moreover, it was found that prediction models were reliable with good accuracy and simple to execute. Furthermore, these prediction models could help stakeholders and policymakers to make a real-time prediction of ammonia levels in intensive farming inland aquaculture ponds. © 2023 Elsevier Ltd