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Paper ID:IJSREM62786

Case Study & Design Underground Sewerage Network on SewerGems Software for Expansion of Solapur City

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62786

Volume:

10 |

5 |

May 2026

Pages:

1--

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62786

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SHUBHAM JADHAV

SHUBHAM JADHAV

CIVIL ENVIRONMENTAL ENGINEERING

ANANTRAO PAWAR COLLEGE OF ENGINEERING & RESEARCH , PARVATI, PUNE.

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Dr. SUNIL B. THAKARE

CIVIL ENVIRONMENTAL ENGINEERING

ANANTRAO PAWAR COLLEGE OF ENGINEERING & RESEARCH , PARVATI, PUNE.

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PRPG

Prof. RANJITSING P. GAIKWAD

CIVIL ENVIRONMENTAL ENGINEERING

ANANTRAO PAWAR COLLEGE OF ENGINEERING & RESEARCH , PARVATI, PUNE.

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Abstract

Rapid urban expansion in Solapur City has significantly increased wastewater generation in newly developed areas, while the absence of a properly designed underground sewerage network and the inadequacy of the existing system have led to frequent overflows, environmental pollution, and public health risks. This study presents a case study and design of an underground sewerage network for Solapur’s expansion areas using SewerGEMS software to develop a hydraulically efficient, cost-effective, and regulatory-compliant system capable of handling present and projected wastewater loads. Essential data such as topography, ground levels, groundwater table, road alignment, population projections, and water supply details are collected, and sewage generation is estimated using per capita consumption and peak factor analysis as per CPHEEO guidelines. The sewer network is modeled in SewerGEMS by incorporating manholes, gravity sewers, junctions, and outfall points, followed by steady-state and peak flow simulations to evaluate velocity, flow depth ratio (d/D), hydraulic grade line (HGL), and surcharge conditions. The proposed work focuses on hydraulic optimization, validation of design parameters, and comparison with conventional manual design methods. The expected outcomes include an optimized network layout with adequate pipe diameters, self-cleansing velocities, minimized excavation depth, reduced overflow risk, improved hydraulic reliability, and enhanced design accuracy, thereby supporting sustainable and future-ready urban wastewater management for Solapur City.

KEYWORDS

Keywords: Underground Sewerage Network Design SewerGEMS Hydraulic Modeling Urban Wastewater Management Population Forecasting Sewage Load Estimation Hydraulic Grade Line Analysis Peak Flow Factor Gravity Sewer Optimization CPHEEO Standards Sustainable Urban Infrastructure

Cite This Article

SHUBHAM JADHAV, Dr. SUNIL B. THAKARE, Prof. RANJITSING P. GAIKWAD, "Case Study & Design Underground Sewerage Network on SewerGems Software for Expansion of Solapur City," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1--, May 2026. https://doi.org/10.55041/IJSREM62786

Full Article/Conclusion

The study successfully developed a hydraulically efficient underground sewerage network design for the New Vidi Gharkul area of Solapur City using SewerGEMS modeling. The proposed system satisfies CPHEEO design criteria by maintaining adequate self-cleansing velocity, permissible flow depth ratio (d/D), and a stable hydraulic grade line (HGL) under peak flow conditions, with no observed surcharge or overflow risks. The software-based approach enhanced design accuracy, optimized pipe sizing and slopes, reduced manual calculation errors, and ensured the network can accommodate projected population growth for sustainable urban sanitation. Future work may include dynamic rainfall–runoff interaction analysis, integration of GIS-based asset management and IoT-enabled real-time monitoring systems, along with cost optimization, life-cycle assessment, and climate resilience evaluation to further improve the long-term performance and adaptability of the sewerage infrastructure.

References

1.       Sundra R. Tatiparthi et al., “Real-Time detection of sewer water levels and blockages using UHF-RFID sensors” Water Research, Elsevier, Volume 278,2025, Page No. 123380. https://doi.org/10.1016/j.watres.2025.123380 2.       Meita Rumbayan et al., “Integration of smart water management and photovoltaic pumping system to supply domestic water for rural communities” Results in Engineering, Elsevier, Volume 25, 2025, Page No.103966. https://doi.org/10.1016/j.rineng.2025.103966 3.       Gangani Dharmarathne et al., “A review of machine learning and internet-of-things on the water quality assessment: Methods, applications and future trends” Results in Engineering, Elsevier, Volume 26, 2025, Page No. 105182. https://doi.org/10.1016/j.rineng.2025.105182 4.       Haiyuan Yao et al., “Blockage detection in pipelines based on linear frequency modulated acoustic reflection method” Developments in the Built Environment, Elsevier, 2025. https://doi.org/10.1016/j.dibe.2025.100723 5.       Muntadher Alsabah et al., “A comprehensive review on key technologies toward smart healthcare systems based IoT: technical aspects, challenges and future directions” Artificial Intelligence Review, Springer, 2025. https://doi.org/10.1007/s10462-025-11342-3 6.       Bo Liu et al., “A Comprehensive Review of Machine Learning Approaches for Flood Depth Estimation” International Journal of Disaster Risk Science, Springer,2025. https://doi.org/10.1007/s13753-025-00639-0 7.       Arati Reddy Nilap et al., “Storm water runoff studies in built‑up watershed areas using curve number and remote sensing techniques” Discover Sustainability, Springer, 2025.https://doi.org/10.1007/s43621-025-00828-3 8.       Chukwuebuka C. Okafor et al., “Artificial intelligence in environmental research: bibliometric, text mining and content analysis” Discover Artificial Intelligence, Springer,2025.https://doi.org/10.1007/s44163-025-00289-7 9.       Xiaojie Wang et al., “Numerical modelling of the impact of drainage system clogging on urban flood processes” Journal of Environmental Management, Elsevier, Volume 388,2025,PageNo.125969. https://doi.org/10.1016/j.jenvman.2025.125969 10.    Qing Wei et al., “Data-driven interpretation of overflow pollution mechanism of urban drainage system using automated machine learning model” Journal of Water Process Engineering, Elsevier, Volume 77, 2025, Page No.108482. https://doi.org/10.1016/j.jwpe.2025.108482

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Publication Date

5/7/2026

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Review Rounds: 2

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