| description abstract | The wastewater management sector is grappling with a multitude of intricate challenges, including but not limited to insufficient infrastructure, financial constraints, rapid urbanization, population booms, and emerging environmental contaminants. These issues necessitate an urgent pivot toward sustainable wastewater management strategies that harmonize ecological, economic, and societal imperatives—a vision aligned with the United Nations’ sustainable development goals. To extend the reach of wastewater networks globally, software tools play a pivotal role in the efficient design of sewer systems, given the inherent complexity of manual design processes. This research paper presents a comprehensive review of the available literature on sewer network design software and selects the most used options—Bentley SewerGEMS, Urbano CANALIS, and Mike+ SWMM—for in-depth analysis. Furthermore, a novel software solution, INSINCE, based on machine learning principles, is included, and evaluated. The assessment and comparison of results encompass critical parameters such as network length, velocity distribution, depth profiling, and hydraulic design obtained from these four software applications. Qualitative aspects including user-friendliness, cross-platform integration, and software capabilities are also discussed. This study underscores the pivotal role of software selection in achieving optimal sewer system designs, offering invaluable insights to enhance wastewater management practices. The research on sewer network design software, including Bentley SewerGEMS, Urbano CANALIS, Mike+ SWMM, and the novel InSINCE, offers crucial insights for practitioners and engineers in the wastewater management sector. These findings underscore the pivotal role of software selection in achieving optimal sewer system designs, with InSINCE showing promise in streamlining design processes through machine learning and optimization techniques. The study highlights the importance of considering critical parameters such as network length, velocity distribution, and hydraulic design in software evaluation. Practitioners can leverage this research to make informed decisions on software choices, ultimately enhancing wastewater management practices. Furthermore, the identified areas for improvement, such as optimal layout selection and life cycle cost analysis, provide valuable guidance for future software development, emphasizing the potential for integrating advanced machine learning algorithms to elevate software functionalities. Overall, this research serves as a valuable resource for stakeholders seeking sustainable solutions in urban wastewater management. | |
