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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Civil Engineering Journal
Devaraj, V.
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering

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Fuzzy Knowledge Based System for Suitability of Soils in Airfield Applications Sujatha, A.; Govindaraju, L.; Shivakumar, N.; Devaraj, V.
Civil Engineering Journal Vol 7, No 1 (2021): January
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/cej-2021-03091643

Abstract

Proper design of roads and airfield pavements requires an in-depth soil properties evaluation to determine suitability of soil. Soft computing is used to model soil classification system's dynamic behaviour and its properties. Soft computing is based on methods of machine learning, fuzzy logic and artificial neural networks, expert systems, genetic algorithms. Fuzzy system is a strong method for mimicking human thought and solves question of confusion. This paper proposes a new decision-making approach for soil suitability in airfield applications without a need to perform any manual works like use of tables or chart. A fuzzy knowledge - based approach is built to rate soil suitability in qualitative terms for airfield application. The proposed model describes a new technique by defining fuzzy descriptors using triangular functions considering the index properties of soils as input parameters and fuzzy rules are generated using fuzzy operators to classify soil and rate its suitability for airfield applications. The data obtained from the results of the laboratory test are validated with the results of the fuzzy knowledge-based system indicating the applicability of the Fuzzy model created. The approach developed in this work is more skilled to other prevailing optimization models. Due to its system’s flexibility, it can be suitably customized and applied to laboratory test data available, thus delivering a wide range for any geotechnical engineer. Doi: 10.28991/cej-2021-03091643 Full Text: PDF
Comparative analysis of optimization techniques for optimal EV charging station placement Somasundaram, Deepa; Prakash, G.; Rajavinu, N.; Lakshmi, D.; Kavitha, P.; Devaraj, V.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2860-2867

Abstract

The optimal placement of electric vehicle (EV) charging stations plays a crucial role in improving accessibility, reducing travel distances, and minimizing infrastructure costs in smart urban planning. This study presents a comparative analysis of traditional optimization techniques-such as linear programming (LP), particle swarm optimization (PSO), k-means clustering, and greedy heuristic methods-alongside a machine learning-based approach using genetic algorithms (GA). A machine learning framework is implemented to simulate EV charging demand, optimize station deployment, and incorporate real-world constraints like cost, grid capacity, and user travel penalties. The results demonstrate that GA achieves superior performance in balancing cost-efficiency and user convenience, outperforming traditional techniques in solution quality under dynamic demand conditions. PSO and LP provide faster convergence but are less adaptive to changing parameters. The study highlights the potential of integrating machine learning into infrastructure planning and provides actionable insights for urban planners and policymakers in developing resilient and intelligent EV charging networks.
Co-Authors Govindaraju, L. Kavitha, P. Lakshmi, D. Prakash, G. Rajavinu, N. Shivakumar, N. Somasundaram, Deepa Sujatha, A.