Design Of Engineering Structures Research Articles

Model Test on Thermomechanical Behavior of Deeply Buried Pipe Energy Pile Under Different Temperature Loads and Mechanical Loads

Deeply buried pipe energy pile (DBP-EP) offers the capability to harness geothermal energy from significantly deeper subterranean layers than those available inside buried pipe energy pile (IBP-EP). Despite its potential, there is a notable scarcity of research on the thermomechanical behavior of DBP-EP. This study meticulously observed the thermal variations in the soil surrounding the DBP-EP, the mechanical response of the pile itself, the earth pressure at the pile toe, and the displacement occurring at the pile’s top during the heating phase across various operational conditions. The findings show that for every 1 °C increase in inlet temperature, the temperature difference between the inlet and outlet increases by about 0.27 °C. The method of load application at the pile top during heating markedly influences the frictional resistance along the pile’s sides. Furthermore, When the pile top load rises from 0.26 kN to 0.78 kN, the observed vertical load at the pile foot decreases by 2.2–8.51%. This indicates that the increase in the pile top load reduces the downdrag effect on the sandy soil near the pile toe. This reduction subsequently diminishes the impact of vertical loads on the pile toe. Notably, after continuous operation for 8 h, the rate of increase in pile top displacement for DBP-EP shows a decline. Additionally, for every 1 °C rise in the inlet water temperature, the final displacement at the pile top diminishes by approximately 0.03‰D. This research endeavors to furnish a robust theoretical foundation for the structural design and practical engineering applications for DBP-EP.

Employing topology optimization method to create optimum telecommunication tower design structure

Recently, the employment of topology optimization (TO) in structural engineering design has gained a significant structural performance, also, TO is employed by designers for developing aesthetically and efficient buildings. In this work, TO is employed to design novel and rigged structures of communication towers. The way that the TO algorithm works is to intelligently create the 3D model by preserving architectural features with tradeoffs between the stiffness and weight ratio. The present work focuses on the investigation of creating optimal self-supported communication towers. Results concerning the prime observation of optimization analyses and the potential benefits of TO in designing telecommunication tower lattices are drawn.

Evaluation of blast-induced progressive collapse in steel structures with conventional braces, moment-resisting braces, and buckling-restrained braces using LS-DYNA software

Today, considering the increased cases of terroristic operations, structural design engineers are starting to acknowledge blast-induced loads alongside other design parameters. Since a blast-induced load exhibits a much shorter period than the natural period of the structure, it is unlikely to induce general damages or stimulate the entire structure immediately after the loading, but rather local damages in individual structural elements are the common form of damage. These local damages, however, can set the scene for the gradual failure of the structure. The present study on structures with moment-resisting frames, buckling-restrained braces (BRBs), and conventional steel braces was conducted through an alternative load path (ALP, also known as column elimination) and exact modeling of the blast on the steel frames was undertaken using LS-DYNA software. In this work, the behavior of three five-story steel frames under a blast-induced load of 100 kg TNT equivalent at a distance of 10 m from the structural columns for a period of 100 ms was investigated. Results of analyzing the blast-induced effect on the frames showed that the frame equipped with buckling-restrained braces (BRB) exhibited improved overall peak displacement across the entire frame, peak induced velocity, and peak induced acceleration by 30%, 30%, and 46%, respectively, as compared to other steel frames exposed to blast loads.

Thermal creep strain test and model of Q460GJ steel at elevated temperatures

Q460GJ steel is a typical high strength and high-performance structural steel. The thermal creep test on two thicknesses (8 mm and 12 mm) of Q460GJ steel plates at elevated temperatures (400–800 °C) was carried out. The test results showed that the thermal creep strain increases with the increases of temperature and stress. When the temperature exceeds about 500 °C and the stress ratio is greater than about 0.55, the Q460GJ steel plate specimens has obvious creep deformations, so it is necessary to consider the thermal creep deformation of steel at elevated temperatures for steel structural design. The difference in plate thickness does not affect the creep properties of the 8 mm and 12 mm Q460GJ steel plates at elevated temperatures. When the temperature is no more than 600 °C, the second stage creep strain rates of Q460 steel are obviously higher than that of Q460GJ steel while the stress levels are close. The Fields & Fields creep model is suitable for fitting the thermal creep strain-time curves for Q460GJ steel. The findings will contribute to providing theoretical support for design of high-performance steel engineering structures under fire.

Optimal Curve Fitting for Serial Chain in Six-Crease Origami Unit

Abstract Origami-inspired structures have been widely used in aerospace and robotics for three-dimensional (3D) symmetrical configurations using crease-symmetrical origami basic patterns. These patterns offer advantages in repeatable and systematic modeling and mass production. However, few studies have focused on 3D non-symmetrical structures using symmetrical origami basic patterns due to their structure complexity, limiting their application. Therefore, we aim to analyze the folding behavior in 3D non-symmetrical structures using a 6-crease symmetry origami base pattern. To achieve this goal, we first focus on behavior in a two-dimensional (2D) plane. This paper presents a scheme for the behavior of origami units with an optimal curve-fitting algorithm. The curve can be any 2D space curve. The fitting curve, constructed by numerical analysis and an optimal approaching scheme, can satisfy error requirements and retain foldable origami unit features. The paper verifies the feasibility of the curve-fitting scheme by presenting two curve examples, including a quadratic curve and a sin wave function. The results show that the fitting error is reduced by 99% when no boundary conditions are applied. This research provides valuable insights into understanding origami unit kinematic optimization through forward and inverse kinematics. It offers potential applications in the engineering design of foldable structures and precision origami-inspired mechanism, thereby opening avenues for further exploration of complex origami structures and their applications in emerging technologies.

Improved krill swarm algorithm and its application in structural optimization

This paper improves and perfects the standard Krill herd algorithm (KH), which has the defects of slow convergence speed, insufficient calculation accuracy and easy to fall into local optimal solution for complex problems. An improved Krill herd algorithm (SDEKH) integrating improved differential evolution operator and S-type adaptive inertia weight is proposed. SDEKH, KH and other intelligent algorithms are compared and tested through a variety of standard test functions to verify the excellent performance of SDEKH; SDEKH is used to optimize the design of truss structure. By comparing the optimization results with other methods, it is verified that the optimization efficiency and accuracy of SDEKH are improved, which provides a more efficient and accurate method for the optimization design of engineering structures.

Critical lateral‐torsional buckling loads of porous orthotropic rectangular beams containing warping effects

AbstractDue to their elemental porosity, porous materials display individual mechanical properties, rendering them essential components in various engineering applications. So, this study investigates the lateral‐torsional buckling (LTB) sensitivity of porous orthotropic rectangular beams subjected to a concentrated load, including the warping effects. Porosity‐dependent material properties vary along the beam's height via four different porosity distribution patterns. The constitutive equations are obtained using the principle of virtual work based on the classical beam theory containing the warping effect, and the governing equations are solved by performing the Ritz method to obtain the critical LTB load. The porous orthotropic beams and the warping effects add complexity to the analysis. So, the influence of some parameters, such as porosity coefficients, porosity distribution patterns, orthotropy, height‐to‐width ratio, slenderness ratio, and warping coefficients, on the LTB characteristics of porous orthotropic rectangular beams are investigated in detail. The novelty of this study lies in its comprehensive LTB analysis of orthotropic rectangular beams under specific effects, such as porosity and warping factors. The findings offer valuable insights into the LTB characteristics of orthotropic porous beams, contributing to an improved comprehension of their structural behavior and facilitating the design of engineering structures. This study contributes to the composite materials and structural stability analysis, offering potential applications in many engineering domains.

Artificial neural network-enhanced mathematical simulation based on Carrera unified formulation for dynamic analysis and structural integrity assessment of advanced nanocomposites reinforced tunnel structures

This article presents the application of the Carrera Unified Formulation (CUF) for dynamic analysis and structural integrity assessment of advanced nanocomposite-reinforced tunnel structures. CUF offers a generalized framework that simplifies the modeling of complex structures, providing an efficient approach to address the dynamic behavior of nanocomposites. Advanced nanocomposites, which enhance mechanical performance and durability, are increasingly used in critical infrastructure such as tunnel systems. Traditional methods often fall short in capturing the intricate interactions within these materials, particularly under dynamic loads. CUF overcomes these challenges by incorporating higher-order theories and multi-scale analysis, allowing for accurate prediction of displacements, stresses, and potential failure modes. To further validate the results, an Artificial Neural Network (ANN) model is trained using simulated data, ensuring robust predictions for various loading conditions. The ANN assists in approximating the dynamic response and integrity of the structure, enabling real-time assessments with minimal computational expense. The combined CUF-ANN approach demonstrates high accuracy and efficiency, making it a reliable tool for the structural integrity assessment of nanocomposite-reinforced tunnels. This study highlights the significant potential of integrating CUF and ANN for the analysis and design of advanced engineering structures subjected to dynamic environmental conditions.

Mechanical properties and probabilistic models of wood and engineered wood products: A review of green construction materials

Wood and engineered wood products are green construction materials with excellent mechanical properties, widely utilized in various buildings and bridges. This paper reviews their short-term and long-term mechanical properties and probabilistic models. Short-term mechanical properties analysis reveals strong tensile (up to 165 MPa) and bending strengths (up to 264 MPa), contrasted by lower compressive strength (up to 103 MPa) and minimal shear/rolling strength (up to 14 MPa). These properties vary based on species type, lay-up angle, and dimensions, and are mutually influential. It also demonstrated that innovative techniques such as compression and cross-laminated lay-ups enhance the strength and sustainability of wood products, utilizing smaller logs effectively and reducing carbon emissions. For long-term mechanical properties, empirical models typically used for creep strength assessment fail to account for load duration variability, a gap partly addressed by damage accumulation models which require further development. The considerable time for long-term tests underlines the need for developing accelerated load tests to more effectively assess durability. As for probabilistic models, numerical methods and machine learning provide advanced probabilistic models by analyzing extensive data, offering significant improvements over traditional experimental methods.The global contribution of the work is mainly to enhance the understanding of wood and engineered wood products for properly design and extensive application of timber structures in civil engineering and promoting sustainable development.

A robust LightGBM model for concrete tensile strength forecast to aid in resilience-based structure strategies

The tensile strength (TS) of concrete is a factor in the design of civil engineering structures. Traditional methods for assessing concrete TS have notable limitations, leading to the emergence of non-destructive testing (NDT) as an innovative alternative. This study introduces a novel, optimized approach for accurate, non-destructive TS prediction by integrating Gradio, Bayesian optimization (BO), and Shapley additive explanations (SHAP). The input parameters for the model were ultrasonic pulse velocity (UPV) and electrical resistivity (ER). Using a comprehensive dataset of 3460 datapoints, a light gradient-boosting machine (LightGBM) model was developed. Through extensive hyperparameter tuning facilitated by BO, the model achieved a robust prediction accuracy of approximately 98 %. The novel application of SHAP in this study provided deep insights into model behavior, confirming UPV as the most critical variable for TS prediction. The deployment of the model using Gradio further enhances its accessibility and usability. This combined methodology not only advances the application of machine learning (ML) in civil engineering but also offers a powerful, globally applicable tool for accurately forecasting concrete TS. This supports resilience-based management strategies, essential for the sustainable development of infrastructure. The study's novel integration of BO for optimization and SHAP for interpretability marks a significant step forward in the precise prediction of TS, offering practical benefits for both researchers and industry professionals.

Efficient and durable OER electrocatalysts through vacancy engineering and core-shell structure design in acidic environment

The electrocatalytic conversion of water into green hydrogen energy through overall water splitting advances sustainable energy goals. However, it is limited by the slow kinetics of the oxygen evolution reaction (OER) and durability challenges at high potentials. Here, we rationally develop a series of Mn-doped RuO2-coated Ru (Mn-Ru@RuO2) catalysts involving unique core-shell structures and abundant lattice distortions induced by oxygen defects. The optimal Mn-Ru@RuO2 sample demonstrated robust activity, achieving a low overpotential of 220 mV at 10 mA cm−2. Notably, this sample exhibited minimal degradation after 220 h and a 25.5-fold increase in mass activity (1.37 A mgRu−1) compared to commercial RuO2 at an overpotential of 300 mV. These results suggest that regulated oxygen deficiencies and optimal Ru3+/Ru4+ ratios facilitate the lattice oxygen oxidation (LOM) mechanism and the formation of a high concentration of ∗OH radicals, enhancing acidic OER performance. Additionally, the unique core-shell structures effectively prevent over-oxidation of the RuO2 shell, ensuring superior durability. This research not only breaks the trade-off of electrochemical activity and durability, but also provides valuable insights into the design of highly efficient and stable electrocatalysts.

Research on the influence of impact damage on force identification for composite material

The invisible damage caused by low-velocity impacts are safety threats to engineering structures. Thus, impact force identification is crucial in the context of composite structures for both structure health monitoring (SHM) and composite structure design. This paper investigates the process of identifying impacts on composite structures subjected to low-velocity impact. Considering the damage evolution in the composite structure during impact, this paper explores the influence of impact damage on the accuracy of force identification. Impact experiments on carbon fiber reinforced polymer (CFRP) laminates were conducted to obtain impact force peaks and displacement responses. Furthermore, a validated finite element model (FEM) is established for more elaborate analysis on the mechanism. The findings reveal that the structural damage can lead to significant deviations in force identification if the damage is not considered. Finally, a neural network is employed to predict the force history taking impact damage into consideration. This research provides a reference for the composite structures design and health monitoring of engineering structures considering impact damage.

Investigation of interfacial bond strength of circular CFST columns based on ordinary and inorganic polymers through advanced machine learning methods

Concrete-filled steel tube (CFST) is a combination of materials, in which the interfacial bond-slip behavior is the prerequisite to reflect the synergistic loading of external steel tube and core concrete. The inorganic polymer concrete within the concrete-filled steel tube (IPCFST) compensates for the drying shrinkage of the concrete and enhances the adhesion at the interface between the steel tube and the concrete. However, the current code is too simple for the bond strength of CFSTs, and the existing theoretical calculation methods are not universally applicable. This paper aims to develop a practical artificial neural network tool for predicting the interfacial bond strength of ordinary CFSTs and IPCFSTs. An efficient prediction model for the bond strength of CFST interfaces was developed using a radial basis function network (RBFNN), with concrete strength, steel tube size, bond length and other factors as the main parameters. Bond performance test data was collected for a sample database of 322 circular CFST columns, including 56 ordinary CFSTs and 263 IPCFSTs. The results show that the Artificial Neural Network (ANN) algorithm can effectively address the bond strength problem of CFST structures and improve the accuracy and density of prediction in the comparative analyses with the measured values and the results of the semi-theoretical formulations. Based on the prediction results of ANN model, a sensitivity analysis of the parameters using Garson's algorithm shows that the diameter-to-thickness ratio and concrete strength significantly affect bond strength for both ordinary CFSTs and IPCFSTs. This study provides a more reliable prediction method and technical support for the engineering design of CFST structures.

Uniaxial compressive stress-strain behavior of steel fiber reinforced geopolymer concrete confined by stirrups: Experimental study and analytical model

The compressive stress-strain relationship of confined steel fiber reinforced geopolymer concrete (SFRGC) is a basis for its structural nonlinear analysis and engineering design. This paper presents an experimental study on the stress-strain behavior of stirrup confined-SFRGC under uniaxial compression, with emphasis on the effects of stirrup spacing, stirrup strength and steel fiber (SF) volume fraction. The failure modes, stress-strain curves, energy absorption capacity and post-peak ductility were analyzed. The results demonstrated that due to the stirrup constraint and SF fiber crack-bridging effects, the integrity of specimens was significantly improved, showing notable ductile failure mode. The stress-strain behavior of core SFRGC was notably affected by the constraint level of stirrups, however, the effect of SF was mainly reflected in post-peak stress-strain branches. Reducing the stirrup spacing, improving the stirrup strength, and increasing the SF content can effectively enhance the strength and deformation characteristics of confined-SFRGC, as well as ductility. Moreover, synergetic effect of SF and stirrups on improving the deformation capacity of SFRGC was observed, whereas the peak stress and corresponding strain changed insignificantly with the SF content. In addition, the stirrups can reach yield around the peak stress point. Based on the analysis of test results, formulae for characterizing the peak stress, peak strain, elastic modulus as well as stress-strain responses of confined-SFRGC were proposed, with the effective confinement index of stirrups and fiber reinforcing factor introduced. The model predicted curves were in great agreement with the test curves.

Sulfur doping induced 3D hierarchical porous BiOI–Bi2S3 S-scheme heterojunction with regulated oxygen vacancies for a four-pronged enhanced photocatalytic degradation towards four representative pollutants

It is challenging and still difficult to adjust and boost the catalytic performance of photocatalysts via the synergistic achievement of heteroatom doping, vacancies engineering, morphology regulation, and appropriate structural design. Herein, we developed an efficient method to synthesize 3D hierarchical porous BiOI–Bi2S3 S-scheme heterojunction with oxygen vacancies (Ov-BBS) induced by sulfur doping and systematically investigated regulation of oxygen vacancies by sulfur doping and their effects on photocatalytic performance. Results showed that sulfur species promoted the formation of S-scheme heterojunction but depleted oxygen vacancies in Ov-BiOI. The optimal sulfur doped catalyst (Ov-BBS-0.1) exhibited excellent UV–vis light photocatalytic activity towards four representative pollutants, affording removal rate of 97.1 % for MB, 97.6 % for RhB, 99.4 % for Cr(VI), and 92.0 % for TC, respectively, which were higher than those of pristine Bi2S3, BiOI, Ov-BiOI, Ov-BiOI/Bi2S3 mixture, Ov-BBS-0.05, and Ov-BBS-0.2. Deep characterizations and theoretical studies certified that the four-pronged enhancement strategy of 3D hierarchical porous structure, oxygen vacancies, sulfur doping and the constructed S-scheme heterojunction, not only enriched accessible active sites, but also promoted electronic polarization and modulated electronic structure, resulting in rapid separation and migration of photogenerated carriers. Moreover, considering the excellent stability and cyclic photocatalytic performance, the film prepared by pumping Ov-BBS dispersion was also satisfactory in the practical application of circulating sewage treatment. Overall, the present work provides a novel approach for the design of more photocatalysts with high efficiency.

A Miniature PMUT Array for Medical Applications

Abstract Wearable continuous monitoring technology and internal and cerebrovascular imaging are the frontier hot area of current scientific research, which are of great significance to improving human life and health. Ultrasonic imaging can obtain information such as deep tissue structure by transmitting ultrasonic waves into the tissue and detecting echo signals, and ultrasonic imaging technology has the advantages of high imaging depth, high resolution, high safety, fast and convenient, etc. It is one of the important technologies in the medical field such as wearable continuous monitoring technology and internal cardiovascular and cerebrovascular imaging. But miniaturization and low power consumption are necessary conditions for the success of such medical applications. Therefore, the ultrasonic imaging technology using a miniature ultrasonic probe is one of the most promising imaging technologies for this type of medical application. Aiming at the common technical problems of traditional ultrasonic transducers such as large volume, narrow bandwidth, high power consumption, difficulty in preparing two-dimensional arrays, and difficulty in integrating with ICs. In this paper, the electromechanical-acoustic multi-field coupling mechanism and array structure design technology of MEMS high-performance PMUT ultrasonic transducer elements are first studied, and a CMOS-compatible miniature PMUT high-performance array ultrasonic device suitable for this type of medical application is developed. The simulation results of the transducer are given. The measurement of various physical parameters of the transducer sample and the results of its application in the ultrasonic imaging system show that the miniature PMUT linear array ultrasonic sensor we designed is a kind of high performance ultrasonic array sensors that can be widely used in wearable continuous monitoring technology and internal cardiovascular and cerebrovascular imaging.

Analysis and extreme event prediction of waves in the eastern shelf seas of China based on a 44-year hindcast

Analysis and extreme event prediction of regional waves are crucial for the design, spatial layout and protection of ocean engineering structures. In this study, the regional wave characteristic is analysed in the eastern shelf seas of China based on a 44-year wave hindcast. The spatial variation of peak period is smaller than that of significant wave height, which may be affected by swell propagation, and the seasonal distribution differences of these parameters can generally be explained by changes in the East Asian monsoon and are significantly affected by tropical cyclones. Both local wind wave and remotely generated swell should be considered when designing ocean structures. In addition, the joint distribution characteristics of wave period and height reveal a natural regional classification of representative wave states, which can be used to avoid deploying structures near classification boundaries. The extreme wave events are characterized by their number, duration and intensity, highlighting areas susceptible to extreme waves. Meanwhile, the extreme event is predicted, and its return patterns are highly consistent with weather system characteristics such as tropical cyclone tracks. The results can provide a significant basis for the design and protection of ocean engineering structures.

Mechanism of eccentricity influence on 3-DOF aerodynamic stability: New insights into instability evolution, energy harvesting, and vibration control

The inconsistency of mass center and elastic center, i.e., eccentricity, widely exists in various engineering structures and components, including iced conductors, wind turbine blades, airfoils, and cable-supported bridges, and can also be extended to the application of energy harvesting and vibration control. Although the eccentricity influence on aerodynamic stability has already been recognized as important, the underlying mechanism remains rather unclear. This study established a comprehensive framework of explicit eigenvalue solutions, offering in-depth insights into the aerodynamic stability mechanism in a vertical-horizontal-torsional 3-degree-of-freedom linear system under all possible frequency scenarios. These solutions elucidated the coupling mechanism of eccentricity, aerodynamic damping, aerodynamic stiffness, and structural frequencies. Remarkably straightforward criteria to predict the promoting/suppressing effect of eccentricity were proposed, consisting of eccentric angle and aerodynamic forces, which not only align with the traditional principles but also offer a broader application range in terms of structure types and wind speeds. Validations of the proposed solutions were performed for all frequency cases via numerical studies. For instability tendency evaluation, numerical studies on a thin plate showed that eccentricity has a significant impact due to the strong aerodynamic effect and large ratio of width/gyration radius, highlighting the importance of considering small eccentricity errors during experiments. Examination for energy harvesting revealed that a small eccentricity can dramatically enhance power generation efficiency, and the explicit solutions successfully explained the rules governing performance changes against frequency ratio, mass center position, rotation center position, etc. The analysis of a bundled conductor showed that the double pendulum can control galloping because it diminishes the eccentricity effect by shifting the eccentric angle. The proposed explicit solution framework provides a systematic view and new insights into the aerodynamic instability mechanism influenced by eccentricity, serving as a reference for the design and studies of various engineering structures, energy harvesting, and vibration control.

Tubular-like Nanocomposites with Embedded Cu9S5-MoSx Crystalline-Amorphous Heterostructure in N-Doped Carbon as Li-Ion Batteries Anode toward Ultralong Cycling Stability.

Transition metal sulfides (TMSs) show the potential to be competitive candidates as next-generation anode materials for Li-ion batteries (LIBs) due to their high theoretical specific capacity. However, sluggish ionic/electronic transportation and huge volume change upon lithiation/delithiation remain major challenges in developing practical TMS anodes. We rationally combine structural design and interface engineering to fabricate a tubular-like nanocomposite with embedded crystalline Cu9S5 nanoparticles and amorphous MoSx in a carbon matrix (C/Cu9S5-MoSx NTs). On the one hand, the hybrid integrated the advantages of 1D hollow nanostructures and carbonaceous materials, whose high surface-to-volume ratios, inner void, flexibility, and high electronic conductivity not only enhance ion/electron transfer kinetics but also effectively buffer the volume changes of metal sulfides during charge/discharge. On the other hand, the formation of crystalline-amorphous heterostructures between Cu9S5 and MoSx could further boost charge transfer due to an induced built-in electric field at the interface and the presence of a long-range disorder phase. In addition, amorphous MoSx offers an extra elastic buffer layer to release the fracture risk of Cu9S5 crystalline nanoparticles during repetitive electrochemical reactions. Benefiting from the above synergistic effect, the C/Cu9S5-MoSx electrode as an LIB anode in an ether-based electrolyte achieves a high-rate capability (445 mAh g-1 at 6 A g-1) and superior ultralong-term cycling stability, which delivers an initial discharge capacity of 561 mAh g-1 at 2 A g-1 and its retention capacity after 3600 cycles (376 mAh g-1) remains higher than that of commercial graphite (372 mAh g-1).

Evolving Novel Gene Regulatory Networks for Structural Engineering Designs.

Engineering design optimization poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited by problem-specific parameter tuning, making them less generalizable and scalable. This article introduces a framework inspired by evolutionary and developmental (evo-devo) concepts, aiming to automate the evolution of structural engineering designs. In biological systems, evo-devo governs the growth of single-cell organisms into multicellular organisms through the use of gene regulatory networks (GRNs). GRNs are inherently complex and highly nonlinear, and this article explores the use of neural networks and genetic programming as artificial representations of GRNs to emulate such behaviors. To evolve a wide range of Pareto fronts for artificial GRNs, this article introduces a new technique, a real value-encoded neuroevolutionary method termed real-encoded NEAT (RNEAT). The performance of RNEAT is compared with that of two well-known evolutionary search techniques across different 2-D and 3-D problems. The experimental results demonstrate two key findings. First, the proposed framework effectively generates a population of GRNs that can produce diverse structures for both 2-D and 3-D problems. Second, the proposed RNEAT algorithm outperforms its competitors on more than 50% of the problems examined. These results validate the proof of concept underlying the proposed evo-devo-based engineering design evolution.