The optimization of water distribution networks (WDN) has evolved, requiring approaches that seek to reduce capital costs and maximize the reliability of the system simultaneously. Hence, several evolutionary algorithms, such as the non-dominated sorting-based multi-objective evolutionary algorithm (NSGA-II), have been widely used despite the high computational costs required to achieve an acceptable solution. Alternatively, energy-based methods have been used to reach near-optimal solutions with reduced computational requirements. This paper presents a method to combine the domain knowledge given by energy-based methods with an evolutionary algorithm, in a way that improves the convergence rate and reduces the overall computational requirements to find near-optimal Pareto fronts (PFs). This method is divided into three steps: parameters calibration, preprocessing of the optimal power use surface (OPUS) results, and periodic feedback using OPUS in NSGA II. The method was tested in four benchmark networks with different characteristics, seeking to minimize the costs of the WDN and maximizing its reliability. Then the results were compared with a generic implementation of NSGA-II, and the performance and quality of the solutions were evaluated using two metrics: hypervolume (HV) and modified inverted generational distance (IGD+). The results showed that the feedback procedure increases the efficiency of the algorithm, particularly the first time the algorithm is retrofitted.

Several researchers have proposed methodologies for addressing the problem of designing optimal water distribution systems. Metaheuristic approximations are studied the most due to the vast solution space. In search of reducing computational time, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been tested with retrofitting from the Optimal Power Use Surface (OPUS) methodology. A previous study demonstrates how OPUS significantly improves the results since it seeks to reduce energy losses in the network, in order to approximate minimum-cost designs using fewer hydraulic executions. However, more research is still needed to determine applicable hydraulic criteria that allow an enhanced comprehension of optimal designs. Therefore, this paper aims to understand the characteristics of near-optimal solutions using designs from the retrofitted OPUS/NSGA-II Pareto fronts of four distinct networks (Hanoi, Balerma, Fossolo, and Modena). Moreover, fractal characteristics of the networks’ energy dissipation, flow, and diameter distribution have been analyzed for this purpose. In this way, outcomes suggest that the hydraulic gradient line box dimension in optimal designs approaches a value of two, demonstrating that objects resemble a single-plane surface. These promising results propose fractal analysis as a practical design criterion due to its hydraulic significance and low computational cost.

Sewage systems are essential for the efficient functioning of cities. Wastewater contains solids and organic matter that pose health risks, making it necessary to optimize the sewage system design. In recent years, optimization tools have been introduced to minimize costs while still complying with regulations. Despite this, traditional designs still dominate, but the use of optimization methodologies can significantly reduce construction costs. For this reason, this research will compare the construction costs of a sewage system designed optimally and one designed using traditional methods, to determine the cost difference between the two. In a sector of Bogota, Colombia, a sewerage system was already built and designed according to Colombian laws using traditional methodologies. The information from this area was used to implement the UTOPIA program, created by the Universidad de los Andes. This program uses the Shortest Path Problem with the Bellman-Ford algorithm to design the network and minimize costs. The results show that the optimized system was about 15% cheaper than the traditional one, and it ensured that all pipelines met the design restrictions. Optimized sewage systems are a useful alternative for ensuring universal access to safe drinking water, increasing sewerage coverage, and reducing problems associated with inadequate design.

Sewer deterioration is a major concern for water utilities, as failures in sewer systems can have severe consequences for public health and the environment. Sewer asset management is widely used to improve long-term system performance and reduce failure risks. This approach relies heavily on data, typically derived from closed-circuit television inspections and summarized using a general pipe condition score. However, this score provides limited insight because it does not explicitly represent the individual defects observed during inspections. Recent studies have highlighted the benefits of analyzing sewer condition at the defect level rather than relying solely on aggregated condition scores. Defect-level approaches allow a more detailed representation of pipe condition and can improve the development of deterioration and prediction models. Nevertheless, several aspects remain underexplored, particularly the properties and characteristics of individual defects, which may play an important role in understanding deterioration processes. Given the challenges associated with data management and the large number of variables related to sewer deterioration, several initiatives have attempted to structure and organize these data; however, many frameworks are not publicly available and often lack a universal structure and consistent terminology. This paper proposes an open, structured data framework for defect-level sewer condition modeling, supported by an entity–relationship diagram. The framework defines data requirements through a state-of-the-art literature review of previous studies and technical guidelines on sewer deterioration. The review included 77 studies on factors influencing sewer pipe condition, three international guidelines on the identification and characterization of defects, and several studies on sewer system failures. The resulting structure provides a flexible framework that can be adapted to different contexts and data availability, offering a practical resource for researchers and water utilities seeking to model sewer pipe condition at the defect level.

In urban areas with a flat terrain, pumping stations must be included to elevate wastewater and avoid extreme excavation depths. These systems are characterized by high operational costs due to the pump’s power consumption. The present work presents a methodology for the optimal design of sewer networks including pumping stations, whose objective function is to minimize the construction and operation costs of the system. The methodology was tested on three sewer benchmark networks using two cost functions proposed in the literature. In all the sewer benchmarks, the cost achieved in the present work was compared with the best costs reported in the literature.