Structural Optimization Approach Research Articles

A simple and efficient approach for pore structure optimization and hydrophilic modification of PTFE nanofiber membrane

Polytetrafluoroethylene (PTFE) membranes have great potential for treating wastewater in harsh environment due to their excellent stability. However, difficulties in the pore structure optimization and functional modification of biaxial stretched PTFE membrane have limited its further applications in liquid separation. Herein, we presented a simple and efficient approach for the preparation and hydrophilic modification of electrospun PTFE nanofiber membranes. This method involved optimizing the pore structure of PTFE nanofiber membranes through adjusting the mass ratio of the spinning carrier polyethylene oxide (PEO) and PTFE, followed by introducing the acetalized polyvinyl alcohol (PVA) on the membrane’s pore surface to improve the hydrophilicity. The average pore size changed from 288.5 to 161.3 nm, while the water contact angle reduced from 135.7° to 50.9°, which not only increased the water permeate flux from 68.56 to 1056.16 L·m−2·h−1, but also achieved high rejection rates of 99.3 % and 97.3 % for carbon (1 μm) and SiO2 (100 nm) particles respectively. Meanwhile, the obtained PTFE nanofiber membrane also exhibited excellent hydrophilic stability after long term exposure to harsh environments (pH=2 HCl, pH=12 NaOH, 1000 ppm NaClO) and 100 friction cycles (5 g weights on 1000 mesh sandpaper). This approach of preparing hydrophilic PTFE nanofiber membranes showed significant potential for practical applications in wastewater treatment.

Structure Design And Optimization Approach For Low RCS Screws

With the rapid development of radar stealth and detection technology, large numbers of screws and rivets have become a major factor that affecting the aircraft’s invisibility. This is because these components have a significant impact on the overall radar cross section of the aircraft, when the electromagnetic scattering characteristic of strong scattering sources is greatly reduced. By analyzing the scattering contribution of the head and body to the screw, it has been obtained that the geometric structure of the head plays a primary role in influencing the overall radar cross section of the screw. According to the scattering mechanism and stealth principle, a shape design method for the low RCS screw is proposed, and then we investigated the scattering characteristic of the octagonal, hexadecagonal, and circular head screws respectively. Furthermore, a structure optimization approach for low RCS screws is introduced which takes the angle of the incident wave into account. This has resulted in the development of a corresponding triangular screw with low detection capabilities. The simulation results show that within the operating frequency band of radar, the triangular head screw has a maximum RCS reduction of approximately 30.1dB compared to a traditional round head screw in [-90°,90°]. On average, there is a reduction of 12.6dB. The simulated results demonstrate the effectiveness and feasibility of the proposed design and optimization method for low RCS screw structures

Hybrid genetic optimization for quantum feature map design

Kernel methods are an import class of techniques in machine learning. To be effective, good feature maps are crucial for mapping non-linearly separable input data into a higher dimensional (feature) space, thus allowing the data to be linearly separable in feature space. Previous work has shown that quantum feature map design can be automated for a given dataset using NSGA-II, a genetic algorithm, while both minimizing circuit size and maximizing classification accuracy. However, the evaluation of the accuracy achieved by a candidate feature map is costly. In this work, we demonstrate the suitability of kernel-target alignment as a substitute for accuracy in genetic algorithm-based quantum feature map design. Kernel-target alignment is faster to evaluate than accuracy and does not require some data points to be reserved for its evaluation. To further accelerate the evaluation of genetic fitness, we provide a method to approximate kernel-target alignment. To improve kernel-target alignment and root mean squared error, the final trainable parameters of the generated circuits are further trained using COBYLA to determine whether a hybrid approach applying conventional circuit parameter training can easily complement the genetic structure optimization approach. A total of eight new approaches are compared to the original across nine varied binary classification problems from the UCI machine learning repository, showing that kernel-target alignment and its approximation produce feature map circuits enabling comparable accuracy to the previous work but with larger margins on training data (in excess of 20% larger) that improve further with circuit parameter training.

Seismic topology optimization considering first-passage probability by incorporating probability density evolution method and bi-directional evolutionary structural optimization

This contribution focuses on addressing the challenging problem of dynamic-reliability-based topology optimization (DRBTO) of engineering structures involving uncertainties by synthesizing the probability density evolution method (PDEM) and the bi-directional evolutionary structural optimization (BESO) approach. The considered optimization problem aims at minimizing the first-passage probability under the constraint of material volume. Generally, the double-loop essence of DRBTO involving dynamic reliability evaluation and topology searching makes the computational efforts prohibitively large. To this end, the PDEM is adopted to efficiently assess the first-passage probability of structures under earthquake actions. In particular, by reformulating the first-passage probability under the framework of the PDEM, the sensitivity of the first-passage probability is derived. To further improve the efficiency, a strategy taking advantage of important representative points (IRPs) is employed to achieve a robust estimate of sensitivity of the first-passage probability. The adjoint variable method (AVM) for the sensitivity analysis of transient response considering given modal damping ratios is incorporated to considerably improve the computational efficiency when the reliability sensitivity analysis in terms of multiple design variables is needed. To drive the topology towards the optimum, the above highly efficient reliability assessment and sensitivity analysis are embedded into BESO. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed method, illustrating significant improvement in computational efficiency compared to direct implementation. Additionally, the necessity of introducing seismic reliability in topology optimization is also discussed based on the numerical results.

Identification of naphthalimide-derivatives as novel PBD-targeted polo-like kinase 1 inhibitors with efficacy in drug-resistant lung cancer cells

Targeting polo-box domain (PBD) small molecule for polo-like kinase 1 (PLK1) inhibition is a viable alternative to target kinase domain (KD), which could avoid pan-selectivity and dose-limiting toxicity of ATP-competitive inhibitors. However, their efficacy in these settings is still low and inaccessible to clinical requirement. Herein, we utilized a structure-based high-throughput virtual screen to find novel chemical scaffold capable of inhibiting PLK1 via targeting PBD and identified an initial hit molecule compound 1a. Based on the lead compound 1a, a structural optimization approach was carried out and several series of derivatives with naphthalimide structural motif were synthesized. Compound 4Bb was identified as a new potent PLK1 inhibitor with a KD value of 0.29 μM. 4Bb could target PLK1 PBD to inhibit PLK1 activity and subsequently suppress the interaction of PLK1 with protein regulator of cytokinesis 1 (PRC1), finally leading to mitotic catastrophe in drug-resistant lung cancer cells. Furthermore, 4Bb could undergo nucleophilic substitution with the thiol group of glutathione (GSH) to disturb the redox homeostasis through exhausting GSH. By regulating cell cycle machinery and increasing cellular oxidative stress, 4Bb exhibited potent cytotoxicity to multiple cancer cells and drug-resistant cancer cells. Subcutaneous and oral administration of 4Bb could effectively inhibit the growth of drug-resistant tumors in vivo, doubling the survival time of tumor bearing mice without side effects in normal tissues. Thus, our study offers an orally-available, structurally-novel PLK1 inhibitor for drug-resistant lung cancer therapy.

Adaptive scaled boundary finite element method for two/three-dimensional structural topology optimization based on dynamic responses

This paper presents an efficient solution for designing the topology of structures that can withstand dynamic loads. The method, called image/stereolithography (STL)-based adaptive scaled boundary finite element (SBFE), is a novel approach to topology optimization (TO) that is particularly effective for designing two/three-dimensional structures. The SBFE-based TO algorithm adopts a bi-evolutionary structural optimization (BESO) approach, which utilizes image/STL-based procedures to automatically adapt meshes and minimize mechanical compliance under varying volume fractions in the time domain. A key advantage of this approach is that it uses images of TO responses, rather than specific error functions, to guide the automatic adaptive mesh schemes. The method also incorporates a quadtree/octree mesh refinement to address the problem of hanging nodes, ensuring fast mesh convergence during the optimization process. Furthermore, a high-order implicit time integration technique provides a close approximation of equivalent static loads, allowing for the accurate mapping of displacement fields under actual dynamic responses at each time step. The effectiveness of this method is demonstrated through a variety of numerical examples and benchmarks.

Augmented Space Integral Approach for Structural Reliability-Based Optimization

An efficient, fully decoupled approach is proposed to solve the structural reliability-based design optimization (RBDO) problem. The proposed approach utilizes augmented space integral and importance sampling (ASI-IS) to efficiently evaluate a functional relationship between the probability of failure and the design parameters, namely the so-called failure probability function. ASI-IS allows for the avoidance of both the intractable density fitting task associated with augmented space methods and the time-consuming repeated reliability evaluations associated with surrogate model approaches. The resulting functional relationship can be used to completely decouple the original RBDO problem into a deterministic one. Then, an iteration mechanism is constructed with gradually smaller design domains to enhance the efficiency of the optimization process. Furthermore, a sample reuse algorithm is proposed to improve the performance of the proposed approach by collecting the samples generated in previous iterations and reusing them in the current iteration in order to produce a better estimator of the failure probability function. Numerical and engineering examples, including a turbine blade and an aircraft inner flap, are given to demonstrate the efficiency and feasibility of the proposed approach.

A method for generating case-specific vehicle models from a single-view vehicle image for accurate pedestrian injury reconstructions

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84–165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety.

Structural design against brittle fracture: Optimizing energy release rate and experiment

Despite substantial advancements in optimizing structural designs for stiffness, the field of design against fracture is still in its early stages. This paper introduces a fundamental and standardized paradigm for structural design against brittle fracture, achieved through the minimization of the energy release rate G within the framework of linear elastic fracture mechanics (LEFM). By leveraging the Griffith criterion expression and the total potential energy in LEFM, we propose an efficient approach for structural optimization that enhances the toughness of architected materials. The energy release rate G is evaluated a synergistic application of finite element analysis and finite difference methods, yielding a precision of 0.6% compared to benchmarks in published handbooks. We demonstrate significantly reduced G values compared to standard stiffness and stress-oriented topology optimization, which directly contributes to increased toughness. To comprehensively analyze the fracture process encompassing damage, crack initiation, propagation, and failure, we employ post-processing phase field fracture modeling on the optimally designed architected materials. By performing multi-objective optimization of the stiffness and energy release rate G, the toughness and extreme load-bearing capacity can be simultaneously improved. The resulting perforated architecture effectively outperforms homogeneous structures in both toughness and peak load. Additionally, we extend our analysis to various configurations, such as non-center cracks, pure shear loading, and adherence to the standard 3D compact tension ASTM E-399-72. The designed structures are additively manufactured and experimentally validated, demonstrating a toughness increase of over 10 times compared to stress-based design. These contributions offer promising prospects for advancing the design of future engineering structures and materials with a favorable balance of critical fracture resistance characteristics.

An improved proportional topology optimization method combining a polarized material interpolation scheme and Heaviside threshold function

An improved proportional topology optimization (IPTO) method is proposed in this work. The main improvement of this method is that the conventional solid isotropic material with penalization (SIMP)-based material interpolation scheme is replaced by a polarized material interpolation scheme, and the Heaviside threshold function is adopted based on the original proportional topology optimization (PTO) method. By using this approach, the minimum compliance problem can be solved without requiring the numerical derivation of the sensitivity function. To verify the feasibility and effectiveness of the proposed method, two-dimensional (2D) and three-dimensional (3D) cantilevers and L-bracket beams are used as examples. The 2D results obtained by the IPTO method are compared with those obtained by the PTO and SIMP methods. Numerical examples demonstrate that IPTO can acquire better objective function values and more ideal topology structures compared to PTO and SIMP. Furthermore, IPTO offers significant advantages over PTO and SIMP in terms of convergence speed and the ability to suppress intermediate density elements. Additionally, this method enables topology optimization design under multiple working conditions. Therefore, it provides an effective approach for structural topology optimization in research and engineering applications. With appropriate adjustment, this method can also be applied to composite material design and heat conduction design.

Molecular structure prediction, experimental and theoretical properties, and biological activities of (E)-5-nitro-3-(phenylimino) indolin-2-one- in-vitro against 60 lethal tumour cell lines

In this study an isatin derivative (E)-5-nitro-3-(phenylimino) indolin-2-one (5NPI) has been synthesized and examined utilizing quantum chemical calculations of DFT approach for structural optimization, electronic and vibrational characteristics. Functional groups have been detected using FT-IR spectra and compared with simulated spectra. To model UV–vis absorption and identify electronic characteristics in solvents and gas phase, TD-DFT computations have been performed. The GIAO technique was used to measure chemical shifting in NMR in several different solvents. Theoretical parameters coincide perfectly with experimental data. HOMO and LUMO band gaps reflect chemical activity and have satisfactory charge exchange inside the molecule. RDG analysis, topological and NBO investigations have been used to study weak intermolecular interactions. Reactive regions of the 5NPI compound have been determined using MEP and Fukui functions. NLO behavior has been computed using hyperpolarizability parameters. The anticancer activities of the compound 5NPI were evaluated from the National Cancer Institute (NCI), USA and subjected to in-silico analysis such as drug-likeness, ADMET and molecular docking. The compound 5NPI has been docked with 3RUX, 8ADA, 3TFU and 6TOJ proteins and showed moderate binding affinity as −7.14, −6.42, −6.68 and −8.41 kcal/mol respectively. Compound 5NPI is used as a lead molecule in the treatment of TB along with various tumors.

Optimizing structural topology design through consideration of fatigue crack propagation

This paper presents an advanced approach for structural topology optimization by incorporating fatigue crack propagation analysis. The extended finite element method (X-FEM) is employed to model initial crack propagation, while the Paris model serves as the basis for simulating fatigue crack growth. The proposed methodology aims to optimize the structural design by minimizing compliance while considering volume and fatigue constraints. The proposed method employs the developed bi-directional evolutionary structural optimization (BESO) algorithm. The accuracy of the proposed technique is validated through the solution of benchmark problem and is further demonstrated in its effectiveness and robustness by examining several numerical examples. The optimization process considers various crack conditions, including the absence of cracks, horizontal and vertical cracks of different lengths. The optimized topologies obtained through the proposed algorithm clearly demonstrate the impact of crack presence, crack direction, and crack length on the material distribution. Furthermore, the convergence histories of the objective function, represented by mean compliance, highlight the influence of crack length on the stiffness and converged compliance of the structure. The results demonstrate its ability to adapt the material distribution based on fatigue cracks propagation conditions and achieve optimal topologies that balance structural integrity and performance.

Blended structural optimization of steel joints for Wire-and-Arc Additive Manufacturing

Metal Additive Manufacturing, and in particular Wire-and-Arc Additive Manufacturing (WAAM) has proved to be a great and efficient alternative to the common subtractive manufacturing processes to realize complex-shape members and connections for construction. With reference to complex spatial structures such as gridshells, the connections play a crucial role both in the design and the construction processes. Novel computational design tools allow designers to conceive optimized joint solutions while minimizing the material use, resulting in difficult geometries to be fabricated with conventional methods. The present work aims at providing an innovative integrated design and fabrication framework to efficiently apply topology optimization algorithms to improve the efficiency of steel joints in complex spatial structures. The framework is based on the so-called “blended” structural optimization approach aimed at incorporating manufacturing constraints, basic principles of conceptual structural design and structural requirements into topology optimization to design ready-to-fabricate complex steel joints. The designs are suitable for fabrication with the WAAM process. The approach is applied to a case study to re-design the joint connections of the world’s renowned British Museum gridshell rooftop structure. From a catalogue of various designs for each unique joint, one selected design is optimized and then checked for structural requirements in terms of strengths and stiffness. Then the optimized joint is validated for fabrication with WAAM process.

An adaptive B-spline representation of topology optimization design for Additive Manufacturing

Topology optimization is a renowned structural optimization approach used to compute the optimal topology for the enhancement of structural performance. It has been in common practice in different engineering fields such as automobile and aerospace. However, there still exist gaps between topology optimization and its engineering operations, which considerably impedes topology optimization’s applications. One of these gaps is how to make (especially finite element-based) topology optimization results machine-readable, which means, how to transform these into computer-aided design (CAD) models, which are ready-to-use models for manufacturing. In the proposed work the authors adopted a unique methodology that plugs the gap between topology optimization and manufacturing. This methodology seamlessly integrates the structural analysis tool, boundary description tool and standard CAD geometry representation; equipped with the Hausdorff distance approach for 3D printing/additive manufacturing of optimal structures. Initially, we extract the skeleton of the optimal design (in the form of a points cloud) using the level set method, followed by an interpolation technique with shape preservation property, the extracted skeleton is made dense. Incorporating the shape and curvature information of the data set, an adaptive B-spline approximation is devised for fitting a smooth curve through the points cloud. This approach interprets topology optimization results as a parametric CAD model with minimum possible control points and minimum approximation error. The CAD-based optimal designs are then used for additive manufacturing. The numerical results exhibit in detail the validity and accuracy of the proposed method.

Structural Optimization Design of Microfluidic Chips Based on Fast Sequence Pair Algorithm.

The market for microfluidic chips is experiencing significant growth; however, their development is hindered by a complex design process and low efficiency. Enhancing microfluidic chips' design quality and efficiency has emerged as an integral approach to foster their advancement. Currently, the existing structural design schemes lack careful consideration regarding the impact of chip area, microchannel length, and the number of intersections on chip design. This inadequacy leads to redundant chip structures resulting from the separation of layout and wiring design. This study proposes a structural optimization method for microfluidic chips to address these issues utilizing a simulated annealing algorithm. The simulated annealing algorithm generates an initial solution in advance using the fast sequence pair algorithm. Subsequently, an improved simulated annealing algorithm is employed to obtain the optimal solution for the device layout. During the wiring stage, an advanced wiring method is used to designate the high wiring area, thereby increasing the success rate of microfluidic chip wiring. Furthermore, the connection between layout and routing is reinforced through an improved layout adjustment method, which reduces the length of microchannels and the number of intersections. Finally, the effectiveness of the structural optimization approach is validated through six sets of test cases, successfully achieving the objective of enhancing the design quality of microfluidic chips.

A Problem-Independent Machine Learning (PIML) enhanced substructure-based approach for large-scale structural analysis and topology optimization of linear elastic structures

Structural analysis for structures composed of highly heterogeneous materials which often involves the solution of large-scale linear algebraic equations is very time-consuming even within the linear elastic regime. Furthermore, the tremendous computational cost of iterative large-scale finite element analysis also prevents the widespread use of topology optimization as a powerful design tool especially when the desired design resolution is very high. In order to break the bottleneck hindering the efficient solutions of large-scale structural analysis and design optimization problems, in the present article, a general machine learning (ML) enhanced substructure-based framework is proposed. The essential idea is resorting to the classical substructure-based finite element analysis approach and establishing an implicit mapping between the parameters characterizing the material distribution within a substructure and the corresponding condensed stiffness matrix/numerical shape functions through offline trained deep neural networks. In contrast to most of the existing ML enhanced approaches, the proposed framework is truly independent of the forms of structural geometry, boundary condition and external load, and can be applied to solve various boundary value problems governing by the same type of partial differential equation once the offline training is completed. Armed with modern artificial intelligence techniques, the proposed approach in some sense revives the classical substructure approach in finite element analysis. Compared with the traditional paradigm, it can achieve 104–105 times solution efficiency for tested large-scale examples with satisfactory accuracy. The effectiveness of the approach was also validated for the non-adjoint topology optimization problems, i.e., 3D compliant mechanism design. Finally, to demonstrate the proposed approach’s capability in dealing with extremely large-scale three-dimensional problems, a three-dimensional topology optimization problem with about 109 design variables and 3×109 degrees of freedom (Dofs) is solved on a laptop without resorting to any parallel computing techniques.

Arylidene Meldrum's Acid: A Versatile Structural Motif for the Design of Enzyme-Responsive "Covalent-Assembly" Fluorescent Probes with Tailor-Made Properties.

Latent cyclic C-nucleophiles are recently proving their value in the field of reaction-based fluorescent probes, far beyond their primary utility in organic synthesis. They are typically used to introduce a Michael acceptor moiety acting as a recognition/reaction site for analyte to be detected or as a kinetic promoter of fluorogenic cascade reactions triggered by a reactive species. C-nucleophiles bearing a further reactive handle offer an additional opportunity for tuning the physicochemical/targeting properties or providing drug-releasing capabilities to these probes, through the covalent attachment of ad hoc chemical moiety. In order to implement such strategy to fluorogenic enzyme substrates based on the "covalent-assembly" principle, we have explored the potential of some functionalized derivatives of barbituric acid, piperidine-2,4-dione and Meldrum's acid. Our investigations based on the rational design and validations of enzyme-responsive caged precursors of fluorescent pyronin dyes and 7-(diethylamino)coumarin-3-carboxylic acid, led to identify a versatile candidate suitable for this late-stage structural optimization approach. This Meldrum's acid derivative enables to either enhance water solubility or achieve the reversible conjugation of a targeting ligand, while promoting in situ formation of fluorophore upon enzymatic activation. This study opens the way to novel multifunctional fluorescence imaging probes and optically modulated small conjugate-based theranostics.

Optimization and fabrication of MEMS based piezoelectric acoustic sensor for wide frequency range and high SPL acoustic application

This paper reports finite element model (FEM) simulation and fabrication of a square shaped diaphragm along with microtunnel for MEMS acoustic sensor which can be used for measurement of wide operational frequency range and high sound pressure level (SPL) 100 dB–180 dB measurement in launching vehicle and aircraft. The structure consists of a piezoelectric ZnO layer sandwiched between two aluminum electrodes on a thin silicon diaphragm. There is a microtunnel in the structure which relates the cavity to the atmosphere for pressure compensation. The microtunnel decides the lower cut-off frequency of device. Analytical and simulation approaches are used to optimize microtunnel dimension and simulation approach for diaphragm structure optimization. The change in displacement, stress, sensitivity and resonance frequency due to different diaphragm sizes with diaphragm thickness variation is also analyzed. The optimized diaphragm structure of 1750 × 1750 μm2 and microtunnel of 100 μm wide and 24 μm deep have been fabricated using bulk micromachining technique. The fabricated device response has been tested using LDV and sensitivity measurement system.

(Non-)linear stiffness customisation of metallic additive manufactured springs

ABSTRACT Additive manufacturing (AM) facilitates the fabrication of compliant mechanisms through its free-form and design customisation capabilities. Specifically, the properties of kinetic mechanisms such as springs can be extended with regards to their inherent (non-)linear stiffness functions. This allows for the customisation of AM springs according to user preferences. By combining the design synthesis approach of building blocks with the structural optimisation approach for AM, it is possible to define and customise spring stiffness functionalities. The optimisation process employs an automated computational framework based on a genetic algorithm scheme, which has been demonstrated through randomised and reference case studies. This framework enables the attainment of linear, progressive (stiffening), and degressive (softening) stiffness curves. The manufacturability of the springs has been validated through laser powder bed fusion using stainless-steel material 17–4 PH (H900). The springs have resulted in an accuracy error of maximum 6.48% and precision error of maximum 5% through compression testing.

Use of Real Coded Genetic Algorithm as a Pre-Dimensioning Tool for Prestressed Concrete Beams

In project practice, the search for optimal solutions is based on the traditional process of trial and error, which consumes much time and does not guarantee that solutions found are the optimal solutions for the problem. Many studies have been developed in recent years with the aim of solving problems in various fields of structural engineering with the aid of intelligent algorithms; however, when it comes to the optimization of structural designs, the approaches considered by the authors involve a large number of variables and constraints, making the implementation of optimization techniques difficult and consuming significant processing time. This research aims to evaluate the efficiency of intelligent algorithms when associated with structural optimization approaches that are simpler to implement. Therefore, a Genetic Algorithm in Real Coding was built to serve as an auxiliary tool for pre-dimensioning prestressed concrete beams. With this, the problem becomes simpler to implement, as it depends on a smaller number of variables, leading to less processing time consumption. Simulations were performed to calibrate the Genetic Algorithm and find the optimal solution later. The solution found by the algorithm was compared with the real solution of a project that had already gone through a traditional optimization process. Even in these circumstances, the proposed Genetic Algorithm was able to find, in 210 s, a more economical solution. Our studies found that even with more straightforward approaches, intelligent algorithms can help in the search for optimal solutions to structural engineering problems; in addition, using real coding in fact proved to be a great strategy due to the nature of the problem, making the implementation of the algorithm simpler and ensuring answers with little processing time.