Mansoura Engineering Journal Mansoura Engineering Journal

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

Optimization algorithms are becoming more prevalent in geotechnical engineering, particularly for tackling challenges involving nonlinearity, uncertainty, and intricate soil–structure interactions. Despite this, most current review studies are primarily descriptive and do not provide a systematic, criteria-driven comparison of optimization methodologies. This study presents a systematic review and a structured comparative evaluation of classical, metaheuristic, surrogate-based, and artificial intelligence–based optimization techniques used in geotechnical engineering. The methods under review are assessed using a unified framework that considers convergence efficiency, data efficiency, interpretability, uncertainty quantification capabilities, constraint handling, scalability, and practical maturity. To augment the literature synthesis and ensure methodological soundness, a benchmark case study on shallow foundation optimization is presented. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Gaussian Process Regression–based Bayesian Optimization (GPR-BO) are employed to address an identical constrained footing design problem subject to settlement and bearing capacity constraints. Although all methods converge to similar optimal designs, the surrogate-based GPR-BO achieves equivalent solution quality with significantly fewer function evaluations and improved computational efficiency, while also providing probabilistic predictions and uncertainty quantification. This systematic comparison and quantitative case study collectively demonstrate that optimization methods in geotechnical engineering are not interchangeable, underscoring the benefits of data-efficient, uncertainty-aware optimization frameworks for dependable, sustainable geotechnical design.

Design Optimization and Material Innovation for the 463L Air Cargo Pallet

A Hybrid Numerical–Evolutionary Framework for Optimization of Underground Blast Design

Underground coal mine blasting is often associated with excessive flyrock, overbreak, and high explosive consumption, primarily due to inefficient distribution of blast energy in confined environments. This study develops a coupled LS-DYNA–Genetic Algorithm (GA) optimization framework for underground blasting based on the principle of controlled energy transfer. The reported performance indicators should be interpreted within the operational and geomechanical constraints of the studied coal mine, where conservative blasting practice governs the achievable advance and excavation response. The geomechanical properties of the Hojedk coal mine were characterized and incorporated into a three-dimensional explicit dynamic model to simulate stress-wave propagation, damage evolution, and fragmentation. A GA was subsequently employed to optimize key blast design parameters, including burden, spacing, charge distribution, and a normalized energy-consumption index, under multi-objective constraints aimed at minimizing blast-induced damage while maximizing excavation efficiency. The optimized blast pattern demonstrates a significant improvement in performance compared to the existing design, with a 19% increase in advance per cycle, a 25.6% reduction in explosive consumption per round, a 37.4% reduction in physical powder factor, a decrease in flyrock velocity of up to a 63.5% reduction in flyrock velocity, and an approximately 50% reduction in overbreak. Numerical results indicate that the improved performance is primarily attributed to enhanced energy redistribution toward the free face and effective stress-wave attenuation through the incorporation of relief mechanisms. The proposed coupled numerical–evolutionary approach provides a robust and transferable methodology for optimizing underground blast design, offering substantial benefits in terms of safety, energy efficiency, and operational cost reduction in underground mining environments. The framework is therefore particularly suited for constrained underground environments where safety and stability govern blasting design decisions.

Improved SA algorithm for masonry layout optimization of building infill walls

This paper proposes a hybrid optimization algorithm that fuses multiple methods to address the weak global exploration ability, frequent local optima, and poor engineering adaptability in masonry layout optimization of building infill walls. The method builds a multilayer cooperative framework. It first uses the Genetic Algorithm to create a diverse population. It then applies Simulated Annealing to perform probabilistic jumping optimization. After that, it introduces sparse A search to verify topological feasibility. It finally relies on a cooperative mechanism of Adaptive Whale Optimization and iterative local search to explore the solution space in depth. Experiments on the simultaneous localization and mapping–building information modeling coupled dataset and the building information modeling component multimodal dataset show that the algorithm reaches a standard block utilization rate of 98.76 percent. It also keeps the cutting loss rate as low as 2.79%and achieves a peak stagger-joint compliance rate of 97.11%. In irregular wall scenarios, it reduces cost by up to 33.87%. The results show that this algorithm improves the optimization quality and engineering applicability of masonry layout and provides reliable technical support for precise construction and efficient material use of building infill walls.

A practical tool for economical design of I-shaped steel beams: development and application for parametric study

Steel beams are efficient structural elements widely used in industrial buildings due to their remarkable strength and ductility characteristics. Despite numerous studies exploring the optimization of steel beam for cost-effective designs, the utilization of optimization within the construction industry remains rare. This scarcity can be attributed to the complexities associated with applying optimization algorithms and the limited understanding of the structural behavior of optimized designs. In light of these challenges, this study aims to leverage the immense computational power of artificial intelligence (AI), specifically the Evolutionary Algorithm (EA), to develop an innovative AI-driven spreadsheet-based tool for cost optimization of I-shaped steel beams. Additionally, a parametric study investigates the influence of various design variables on the optimized cost. The EA within the Solver tool of MS Excel is used to perform the optimization. Design variables are subject to strength and serviceability-related constraints in accordance with AISC 360–22. The effectiveness of the developed optimization approach is demonstrated by optimizing four steel beam design examples from the literature. It is found that up to 51% of the beam cost can be optimized by keeping the beam depth and steel grade as variables. The parametric study reveals that the optimal range for the beam depth varies depending on the steel grade used. Further, the trends obtained for beam cost with respect to other variables such as flange and web slenderness ratio, and beam depth provide valuable insights for structural engineers undertaking steel beam optimization in the future.