A systematic review and comparative assessment of optimization algorithms in geotechnical engineering with a benchmark foundation design case study

Genetic-Algorithm-Based Approach for Wind Turbine Foundation Optimisation

Designers traditionally perform the preliminary sizing of foundations based on engineering judgement, relying on parameters such as superstructure loads and the characteristics of the supporting soil. This process must adhere to strict guidelines for wind turbine foundations to ensure structural stability and compliance with regulatory standards. This study proposes a computational model based on genetic algorithms to optimise the dimensions of wind turbine foundations. The fitness function combines two normalised objectives, namely concrete volume and bending moment, while structural and geotechnical requirements are imposed as constraints. The model was validated using six real-world case studies, achieving consistent reductions in concrete volume compared with the original designs, with an average reduction of 19.5%. Each case was run 10 times to assess the consistency of the solutions obtained. The results demonstrate the effectiveness of the proposed approach in identifying more material-efficient foundation geometries while satisfying the adopted design constraints. It should be emphasised that the reported savings refer specifically to concrete volume reduction and should not be interpreted as total foundation cost savings, since reinforcement design and detailing are outside the scope of the present model. This study is restricted to foundations with circular cross-sections, thereby opening avenues for future research aimed at extending the optimisation framework to alternative geometric configurations and incorporating reinforcement design.

A Simulation Optimization Decision Framework for Composite Tunnel Support Design Using FEM, Particle Swarm Optimization, and Multi-Criteria Decision-Making

Tunnel support design in rock masses requires controlling deformation while maintaining structural safety, constructability, and material efficiency under variable geotechnical conditions. This study presents a Simulation–Optimization–Decision Framework (SODF) for preliminary and comparative tunnel support design. The framework integrates FEM-based numerical modeling in PLAXIS 2D, Particle Swarm Optimization (PSO), and a Multi-Criteria Decision-Making (MCDM) model to evaluate composite support systems composed of shotcrete and steel sets. Deformation at the tunnel crown was used as the primary optimization response, while structural feasibility was verified through axial force–bending moment and axial force–shear force interaction envelopes with a minimum safety factor of FS≥1.5. Two case studies were analyzed using GSI values of 40, 45, 50, and 55. Case 1 considered Erm=500,000 kN/m2, with an additional condition of 250,000 kN/m2 at GSI=40, whereas Case 2 considered Erm=545,000 kN/m2, with an additional condition of 300,000 kN/m2 at GSI=45. Three PSO configurations were evaluated to compare the search intensity, the diversity of feasible solutions, and the computational effort. The results showed that feasible configurations were mainly concentrated between 10 and 14 cm of shotcrete thickness in Case 1 and between 10 and 12 cm in Case 2, with frequent selection of 254 mm and 305 mm steel sets. The MCDM analysis indicated that the lowest-deformation alternative does not necessarily coincide with the most suitable engineering alternative when material cost, shotcrete applicability, steel-set maneuverability, and local availability are considered.

Mansoura Engineering Journal Mansoura Engineering Journal

Deterministic, Reliability-Based and Risk-Based Design Optimization of Earth Slopes Using ANN-Based Surrogate Modeling

Expanding road and railway networks in developing countries is essential for economic growth and reducing regional disparities. However, such projects often face significant geotechnical stability and risk management challenges. This study presents a three-phase optimization framework consisting of: (1) deterministic, probabilistic, and risk analyses; (2) Random Variable (RV)-based optimization; and (3) Random Field (RF)-based refinement. Soil variability is modeled using both RV and RF approaches. Two Artificial Neural Network (ANN) surrogate models are employed to improve computational efficiency by predicting failure probabilities and associated costs. Two application cases are presented to demonstrate the integrated framework for optimizing earth slopes in transportation infrastructure using Deterministic Design Optimization (DDO), Reliability-Based Design Optimization (RBDO), and Risk Optimization (RO). The application cases further demonstrate the model’s potential for early-stage roadway design under limited geotechnical data. Overall, the framework supports more reliable, cost-effective, and sustainable infrastructure development.

A review of advances in sensitivity analyses in structural optimization

A Literature Review on the Comparative Analysis of Response Surface Methodology and the Taguchi Method in Concrete Optimization

This review paper provides a comprehensive comparison between response surface methodology (RSM) and the Taguchi method (TM) in the context of concrete mix design optimization. Existing studies often examine these methods independently, lacking a systematic comparison focused on their relevance to sustainable and cost-effective concrete production. This paper addresses this gap by analyzing the theoretical principles, practical implementations, and performance outcomes of both methods across a range of case studies. RSM is shown to be effective for modeling complex variable interactions and optimizing multiple responses, making it suitable for high-performance, environmentally conscious designs. Conversely, TM’s emphasis on robustness and minimal experimental runs makes it particularly valuable in large-scale industrial applications, where reduced experimentation lowers material usage and costs. The review highlights how each method supports sustainability RSM through detailed optimization of eco-friendly materials, and TM through resource efficiency. Finally, the paper explores emerging trends, including the integration of RSM and TM with artificial intelligence, to enhance the development of durable, high-strength, and sustainable concrete solutions.