Evolutionary Algorithms Emerge as Best Tools for Greener, Cheaper Flyover Design

Designing an urban flyover has always been a balancing act. A structure that is too short may fail to relieve congestion, one that is too long consumes land and money, and every additional tonne of concrete and steel carries a carbon price. A new study published in Frontiers in Built Environment tackles this dilemma head-on by pitting nine optimization algorithms against one another on a real flyover design problem, with the explicit goal of finding designs that are simultaneously short, affordable, and low in construction-related carbon emissions.

The research, conducted by Jaya Rajkumar Ramchandani and Suddhasheel Ghosh of Jawaharlal Nehru Engineering College at MGM University in Chhatrapati Sambhaji Nagar, India, formulates flyover design as a three-objective minimization problem. The objectives are flyover length, construction cost expressed in Indian rupees at 2023 base-year prices, and construction-related carbon emissions measured in tonnes of carbon dioxide equivalent. Rather than treating environmental impact as an afterthought to be assessed once a design is complete, the framework builds emissions directly into the optimization loop, drawing emission factors from the IPCC 2019 greenhouse gas inventories, Indian Standards for steel, and Maharashtra state road development project reports.

The case study is grounded in a real infrastructure context: the announced 25-kilometre Shendra–Waluj corridor in Maharashtra, which includes an 8.5-kilometre double-decker metro section. Megaprojects of this scale involve substantial public investment and long-term environmental consequences, which makes the choice of optimization algorithm a strategic decision rather than a technical footnote. The researchers modeled a prestressed concrete box-girder flyover with piers and abutments, loaded according to Indian Roads Congress Class A and 70R live load classes in seismic Zone III, with traffic assumptions of 45,000 vehicles per day growing at six percent annually.

At the heart of the framework are 29 design variables, ranging from clear span lengths of 20 to 45 metres and roadway widths of 7.5 to 15 metres, to deck slab thicknesses, girder depths, bearing types, skew angles, and load distribution factors. The bounds for each variable were established from Indian Road Congress codes, AASHTO specifications, and analysis of existing flyovers in Aurangabad. A variable-importance ranking revealed that clear span length, roadway width, pier height, and slab thickness exert the greatest influence on cost and environmental outcomes, while reinforcement diameter and foundation depth play more moderate roles.

Evaluating thousands of candidate designs with full finite element analysis would be computationally prohibitive, so the team built Radial Basis Function surrogate models for each objective. These models interpolate exactly at training points and approximate smoothly between them, allowing the optimizers to explore the design space at a fraction of the cost. Validation using 500 designs generated by Latin Hypercube Sampling, with ten-fold cross-validation, yielded mean absolute errors of 2.1 percent for length, 3.4 percent for cost, and 4.0 percent for carbon emissions, with coefficients of determination above 0.95. The surrogates achieved efficiency ratios of 95 to 98 percent relative to full finite element evaluation, meaning the entire optimization could run on a standard engineering office computer in under an hour.

Nine algorithms were compared under identical conditions: NSGA-II, NSGA-III, a genetic algorithm, differential evolution, CMA-ES, a classical evolution strategy, a stochastic ranking evolution strategy, Nelder-Mead, and pattern search. Each ran ten times with different random seeds, a population of 100 where applicable, up to 500 generations, and a budget of 50,000 function evaluations. Performance was judged on the hypervolume indicator, which measures the volume of objective space dominated by a Pareto front; the inverted generational distance, which measures closeness to a reference front; spacing, which measures how evenly solutions are distributed; computational time; and the fraction of solutions satisfying IRC and AASHTO constraints.

The results were decisive for the evolutionary multi-objective algorithms. NSGA-III achieved the highest hypervolume at 0.847, narrowly ahead of NSGA-II at 0.839, and both substantially outperformed every single-objective method adapted through weighted-sum scalarization. NSGA-III also produced the most evenly spread front, with a spacing metric of 0.0127 versus 0.0156 for NSGA-II, and generated 134 non-dominated solutions against NSGA-II’s 127. Statistical tests, including Wilcoxon signed-rank and Friedman tests with p-values below 0.05 and Cliff’s delta effect sizes, confirmed that the superiority of the two algorithms was not a product of random variation. Notably, the stochastic ranking evolution strategy was withdrawn from the final comparison due to constraint-handling inconsistencies.

The trade-off structure of the Pareto front itself tells a compelling story. Designs in the 800 to 1000 metre range can be built for 45 to 55 crore rupees with moderate emissions of 35 to 42 tonnes of CO2 equivalent, but they require more piers and denser construction. Designs longer than 1400 metres cost less per kilometre, between 32 and 42 crore rupees, yet emissions climb to 48 to 58 tonnes because of greater land take and material consumption. The balanced region between 1100 and 1400 metres forms the knee of the front, where all three objectives reach reasonable values. For the Aurangabad case, a compromise solution of roughly 1250 metres, 42 crore rupees, and about 40 tonnes of CO2 equivalent was selected using knee-point detection combined with expert weighting. Compared with standard IRC-compliant baseline designs, optimized solutions promise cost reductions of 8 to 12 percent, material savings of 10 to 15 percent, construction schedules shortened by 5 to 7 months, and carbon emission reductions of 18 to 22 percent.

Sensitivity analysis added an important practical dimension. When parameter bounds were varied by plus or minus 20 percent, the multi-objective evolutionary algorithms kept hypervolume variations under 5 percent, while Nelder-Mead’s final solutions shifted by up to 18 percent when span length bounds changed. This robustness matters because real projects rarely have perfectly certain boundary conditions. The study also offers scenario-based guidance: NSGA-II or NSGA-III for comprehensive trade-off exploration, CMA-ES with weighted sums when objective priorities are clear, Nelder-Mead or pattern search for rapid refinement of existing designs, and differential evolution for large infrastructure programs where many structures must be optimized and simplicity aids deployment. For the full 25-kilometre corridor, the authors recommend a hybrid strategy: explore the design space with NSGA-II, present the Pareto front to stakeholders, then refine the preferred design with CMA-ES using stakeholder-derived weights.

The authors acknowledge limitations, including the focus on bound-constrained optimization rather than the full complexity of geometric compatibility and safety-factor constraints, and they point toward future work on uncertainty quantification, hybrid algorithms, integration with multi-criteria decision-making methods such as TOPSIS and AHP, and transfer learning from previous flyover designs across cities. They also note practical barriers to adoption, including stakeholder resistance to non-traditional design parameters, procurement limitations, and regulatory approvals. All data and code are deposited in a DOI-bearing Zenodo repository to support reproducibility. As cities worldwide grapple with the twin demands of mobility and climate responsibility, the message of this study is clear: no single algorithm dominates every scenario, but with the right tool matched to the right project context, flyover design can become measurably cheaper, faster to build, and significantly greener.

Subject of Research: Multi-objective optimization algorithms for sustainable flyover design

Article Title: Comparative analysis of multi-objective optimization algorithms for sustainable flyover design framework

Article References: Ramchandani, J. R., & Ghosh, S. (2026). Comparative analysis of multi-objective optimization algorithms for sustainable flyover design framework. Frontiers in Built Environment, 12, Article 1915058. https://doi.org/10.3389/fbuil.2026.1915058

Image Credits: AI Generated

DOI: 10.3389/fbuil.2026.1915058

Keywords: multi-objective optimization, NSGA-II, NSGA-III, flyover design, radial basis function surrogate models, carbon emissions, Pareto front, sustainable infrastructure, structural engineering, differential evolution, CMA-ES, Aurangabad