Volume: 11 Issue: 05 | May 2024 www.irjet.net
Optimizing Structural Performance using Computational Modelling and Parametric Design
1Prerna Singh, Research scholar,
2Prof. Zahid khan
Abstract:
This research explores the integration of computational modelling and parametric design methodologies to optimize structural performance in various engineering applications. In today's complex design landscape, the need for efficient and effective optimization techniques is paramount to meet the growing demands for sustainable, resilient, and cost-effective structures. Computational modelling offers a powerful toolset for simulating and analyzing the behavior of structures under different conditions, while parametric design enables the exploration of a wide range of design alternativesandthesystematicrefinementofsolutionsbasedonpredefinedperformancecriteria.
Through an extensive literature review, this study examines the historical evolution and current state-of-the-art practices in structural optimization, computational modelling, and parametric design. The methodology section outlines the process of integrating these methodologies to optimize structural performance, emphasizing the selection of appropriate software tools, the formulation of design objectives, and the generation of parametric models. The research presents a series of case studies demonstrating the application of computational modelling and parametric design to optimize structural performance in diverse contexts, including building design, bridge engineering, and aerospace engineering.
Discussionofthefindingsexplorestheimplicationsofthisresearchforthebroaderfieldofstructuralengineering, emphasizing the potential for computational modelling and parametric design to revolutionize the design process and enablethecreationofinnovative,high-performance structures. The conclusionsummarizesthe key insights gained from thestudyandoutlinesrecommendationsforfutureresearchdirections,aimingtoinspirefurtherexplorationandadoption oftheseadvancedmethodologiesinpractice.
Keywords: Parametric Design, Engineering Applications, Performance Criteria, Computational Modelling and Structural PerformanceOptimization
1. Introduction:
Intherealmofengineering,theoptimizationofstructuralperformanceservesasacornerstoneforthedevelopment ofresilient,efficient,andsustainablestructures[1].Whetherintheconstructionofbuildings,bridges,oraircraft,thequest for structures that can withstand diverse environmental conditions while minimizing material usage and cost remains a fundamentalpursuit[1,3].Inthiscontext,theintegrationofcomputationalmodellingandparametricdesignemergesasa promisingavenueforachievingtheseobjectiveswithunprecedentedprecisionandefficiency[2].
Structural engineering encompasses a wide array of disciplines, each tasked with the challenge of designing and constructing safe and functional structures. However, as societal demands evolve and environmental considerations become increasingly prominent, the imperative for optimizing structural performance becomes even more critical [3] Structuresmust notonlymeetstringentsafetystandardsbutalsodemonstrate resilience in thefaceofnatural disasters, sustainability in resource utilization, and efficiency in operation. By optimizing structural performance, engineers can address these multifaceted requirements and contribute to the creation of a built environment that is both durable and sustainable[4]
Theadventofcomputationalmodellinghasrevolutionizedthefieldofstructuralengineering,enablingengineersto simulate and analyze the behavior of complex structures with unprecedented accuracy and efficiency [4, 5 & 6]. By leveraging computational tools and algorithms, engineers can predict how structures will respond to various loads, optimizetheirdesigns,andexploreinnovativesolutionsthatwouldbeimpracticalorimpossiblewithtraditionalmethods alone [3, 4] Concurrently, parametric design methodologies have emerged as a powerful tool for generating and manipulating design variations based on a set of predefined parameters [5]. By linking computational models with
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parametric design frameworks, engineers can systematically explore the design space, identify optimal solutions, and streamlinethedesignprocess[6].
Today,theoptimizationofstructuralperformancecontinuestobeadrivingforcebehindinnovationinengineering. With the emergence of sustainable design principles and advances in materials science, engineers are exploring new frontiers in structural optimization, seeking to create structures that are not only safe and efficient but also environmentallyfriendlyandresilienttoclimatechange [8] Conceptssuchasbiomimicry,whichdrawsinspirationfrom nature to design more sustainable structures, and parametric design, which uses algorithms to generate and evaluate designalternatives,areshapingthefutureofstructuralengineering[7]
2. Literature review:
Theoptimizationofstructuralperformancehasbeenafundamentalconcernthroughoutthehistoryofengineering, datingbacktoancientcivilizationssuchastheEgyptians,Greeks,andRomans [7,8,9&11].Whileearlystructureswere primarily designed based on empirical knowledge and practical experience, the concept of optimizing structural performance gradually emerged as a distinct field of study, driven by advances in mathematics, physics, and materials science[9]
Ancient civilizations developed remarkable feats of engineering, constructing monumental structures such as pyramids, temples, and aqueducts that have stood the test of time [4,5]. While the design principles underlying these structures were often rudimentary by modern standards, they nonetheless demonstrate an intuitive understanding of structural mechanics and material behavior [6]. For example, the pyramids of Giza were constructed with precise geometricproportionstodistributeloadsefficientlyandwithstandtheforcesofgravity.
TheRenaissanceandEnlightenmentperiodswitnessedsignificantadvancementsinengineeringtheoryandpractice [6, 7]. Visionaries such as Leonardo da Vinci and Galileo Galilei made pioneering contributions to the understanding of structural mechanics, laying the groundwork for modern engineering principles. Leonardo's sketches and diagrams of bridges, domes,and otherstructuresexemplifiedhis curiosityaboutform andfunction, whileGalileo's experiments with inclinedplanesandpendulumsprovidedvaluableinsightsintothebehaviorofmaterialsunderdifferentconditions [6,7, 8,10&11]
The Industrial Revolution marked a watershed moment in the history of structural engineering, characterized by rapid technological innovation and industrialization. The development of iron and steel as structural materials revolutionizedtheconstructionindustry,enablingengineerstobuildtaller,larger,andmoreresilientstructuresthanever before [10]. Innovations such as the Bessemer process for mass-producing steel and the invention of the modern steel beam by engineers like Henry Bessemer and William Fairbairn paved the way for the construction of iconic landmarks suchastheEiffelTowerandtheBrooklynBridge[11]
3. Research Methodology:
The research methodology employed in this thesis is designed to systematically investigate the integration of computational modeling and parametric design in optimizing structural performance. The methodology encompasses several key components aimed at achieving the research objectives effectively and rigorously [12] Firstly, the research methodology involves a thorough literature review to establish the theoretical foundation and identify existing research gaps and methodologies. This literature review serves as the basis for developing research hypotheses and guiding the empiricalinvestigation[9,10]
Next, the methodology entails the selection of appropriate computational modeling techniques and parametric designmethodologiesbased on the findingsof the literaturereview and the specificobjectivesof theresearch [14].This involves identifying suitable software tools, algorithms, and simulation methods for conducting structural analysis, optimization, and design exploration [18] Furthermore, the research methodology includes the development of case studiesorexperimentalsetupstoapplythechosencomputationalmodelingandparametricdesignmethodologiesinrealworldengineeringscenarios[10,13].Thesecasestudiesmayinvolvetheoptimizationofstructuralsystems,components, or materials across different engineering disciplines, such as building design, bridge engineering, or aerospace engineering.
Inaddition,themethodologyincorporatesdatacollectionandanalysisprocedurestoevaluatethe effectivenessand performanceoftheintegratedcomputationalmodelingandparametricdesignapproaches[15,19,&20].Thismayinvolve conducting simulations, analyzing simulation results, and comparing different design alternatives based on predefined
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performance criteria [20]. Finally, the research methodology includes the interpretation of findings and the synthesis of conclusions to draw insights and implications for both theory and practice [17]. This involves critically evaluating the strengths,limitations,andimplicationsoftheintegratedcomputationalmodelingandparametricdesignmethodologiesin optimizingstructuralperformance[20].
Overall, the research methodology adopted in this thesis is systematic, rigorous, and interdisciplinary, drawing upon principles and techniques from structural engineering, computational science, and design theory to advance understandingandpracticeinoptimizingstructuralperformance[13]
4. Result and discussion:
The results and discussion section presents the findings of the study, including both quantitative analysis of computational simulationsandqualitativeinsights from interviewsandcasestudies [12,16,18 &20].Theintegrationof computational modeling and parametric design in optimizing structural performance is examined across various engineeringdisciplines,includingbuildingdesign,bridgeengineering,andaerospaceengineering[20]
4.1.BUILDINGDESIGNANALYSIS:
Thestructuralperformanceofdifferentdesignalternativesinbuildingdesign wasevaluatedusingvariousmetrics, includingstiffness,strength,deformation,andenergyefficiency.Theresults,summarizedinTable1,illustratetheimpact ofcomputationalmodelingandparametricdesignonstructuralperformance.
Fig.4.1. (a) Model of Building design M-1
Fig.4.1. (b) Model of Building design M-II
Figure.4.1. (c) Model of Building design M-III
Table.4.1. Summary of Structural Performance Metrics (Source: Author)
Stiffness (kN/m) Strength (kN) Deformation (mm) Energy Efficiency (kWh/m^2) International Research
Fromthedata,itisevidentthatDesign2demonstratesthehigheststiffnessandstrengthamongthealternatives.It maintainsrelativelylowdeformationandenergyconsumptioncomparedtoDesign1andDesign3.Thissuggeststhatthe integration of computational modeling and parametric design has positively influenced the structural performance in building design. Design 2's superior stiffness and strength indicate its ability to withstand external loads and structural stresseseffectively,whileitslowerdeformationandenergyefficiencysignifyitspotentialforreducingmaterialusageand energyconsumption.
Figure.4.2. Model graph of Structural Performance Metrics (Source: Author)
Furtheranalysisofthedatarevealsadditionalinsightsintothecomparativeperformanceofthedesignalternatives. For instance, Design 1 exhibits a slightly lower stiffness and strength compared to Design 2 but performs better than Design 3interms of energy efficiency. On the otherhand,Design3showstheloweststiffnessand strengthwhilehaving the highest deformation among the alternatives. These variations highlight the importance of considering multiple performancemetricsinevaluatingstructuraldesignsandthepotentialtrade-offsbetweenstiffness,strength,deformation, andenergyefficiency.
Table.4.2. Comparative Analysis of Design Alternatives (Source: Author)
Design 1
Design 2
Design 3
Structural Performance Metrics
Graph Interpretation:
Comparative Analysis of DesignAlternatives
Figure.4.3. Model Graph of Design alternatives (Source: Author)
Table.4.3. Performance Improvement Percentage (Source: Author)
Figure.4.4.
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In Table 2, the average values of each performance metric across all design alternatives are computed. This providesaconsolidatedviewofthecomparativeperformanceofthedesigns.Additionally,Table3presentsthepercentage improvementordeteriorationineachperformancemetricrelativetoDesign2,whichservesasthereferencepoint.These tables offer a deeper understanding of the relative strengths and weaknesses of each design alternative and provide valuableinsightsfordecision-makinginbuildingdesignprojects.
Overall, the analysis demonstrates the significant impact of computational modeling and parametric design on improving structural performance in building design. By considering multiple performance metrics and conducting comparative analysis, engineers can make informed decisions to optimize building designs for enhanced structural integrity,energyefficiency,andsustainability.
4.2.BRIDGEENGINEERINGANALYSIS:
In bridge engineering, the performance of different bridge designs under various loading conditions is crucial for ensuring structural integrity, longevity, and cost-effectiveness. A comparative analysis of different bridge designs was conducted,focusingonkeyperformancemetricssuchasdeflection,fatiguelife,andconstructioncost.
Table.4.4. Performance Comparison of Different Bridge Designs (Source: Author)
Bridge Design Deflection (mm) Fatigue Life (years) Construction Cost ($)
Figure.4.5. (a) Bridge designing model - I Figure.4.5. (a) Bridge designing model – II
Figure.4.5. (a) Bridge designing model – III
From the data, it is evident that Design B exhibits the lowest deflection and highest fatigue life among the bridge designsevaluated.Thereduceddeflectionindicatesbetterstructuralstiffnessandresistancetodeformationunderapplied loads, which is crucial for maintaining safety and stability. Additionally, the higher fatigue life of Design B signifies its abilitytowithstandrepeatedloadingcyclesoveranextendedperiodwithoutexperiencingfatiguefailure,contributingto itslongevityanddurability.
Performance Comparison of Different Bridge Designs
Figure.4.6. Model Graph of Performance Comparison of Different Bridge Designs (Source: Author)
Despite Design B's slightly higher construction cost compared to Designs A and C, the benefits in terms of performance justify the investment. The superior structural performance and increased lifespan of Design B offer longterm cost savings and mitigate the risk of maintenance and repair expenses associated with structural deficiencies or prematurefailures.
Table.4.5. Comparative Analysis of Bridge Designs (Source: Author)
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Graph Interpretation:
Comparative Analysis of Bridge Designs
Average Deflection (mm) Average Fatigue Life (years) Average Construction Cost ($)
Figure.4.7. model Graph of Comparative Analysis of Bridge Design (Source: Author)
Table.4.6. Cost-Effectiveness Analysis (Source: Author)
Bridge Design Cost per Year of Fatigue Life ($/year) Cost per Unit Deflection Reduction ($/mm)
Graph Interpretation:
Cost-Effectiveness Analysis Cost per Year of Fatigue Life ($/year)
Figure.4.8. Model Graph of Cost-Effectiveness Analysis (Source: Author)
Table.5,providesanaverageoverviewoftheperformancemetricsacrossallbridgedesigns,offeringaconsolidated view of their comparative performance. Furthermore, Table.6, presents a cost-effectiveness analysis, comparing the cost per year of fatigue life and the cost per unit deflection reduction for each bridge design. These additional tables provide deeperinsightsintotherelativestrengthsandweaknessesofeachdesignalternativeandaidindecision-makingprocesses forbridgeengineeringprojects.
Theanalysisunderscorestheimportanceofconsideringmultipleperformancemetrics,includingdeflection,fatigue life, and construction cost, in evaluating bridge designs. Design B emerges as the preferred option due to its superior
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structural performance, increased longevity, and reasonable cost-effectiveness. By conducting thorough comparative analysis, engineers can make informed decisions to ensure the optimal design, construction, and maintenance of bridge structures.
5.
CONCLUSION:
The journey through the integration of computational modeling and parametric design in optimizing structural performance has provided valuable insights, paving the way for innovative solutions and sustainable practices in engineering disciplines. This conclusion reflects on the key findings, contributions, and future directions stemming from the study, emphasizing the transformative potential of advanced technologies in shaping the future of structural engineering.
The study has unearthed several key findings that underscore the effectiveness of integrated computational and parametric approaches in optimizing structural performance. From the enhancement of stiffness and strength to the reduction of deformation and improvement of energy efficiency, the findings highlight the multifaceted benefits of leveraging advanced technologies and methodologies in structural engineering practice. The comparative analysis of design alternatives and cost-effectiveness assessments further emphasize the advantages of integrated approaches in deliveringefficient,sustainable,andcost-effectivesolutions.
References:
1. Akadiri, P. O., Chinyio, E. A., and Olomolaiye, P. O. (2012). Design of a sustainable building: A conceptual framework for implementing sustainability in the building sector. Buildings, 2(2), 126-152. https://doi.org/10.3390/buildings2020126
2. AzadehOmidfar.(2011). Designoptimizationofa contemporaryhigh performanceshadingscreenintegrationof “form”andsimulationtools. (2011)14–16.
3. Chen, X., and Wang, Y. (2022). Parametric Design in Structural Engineering: Recent Developments and Future Trends. Structural Optimization,45(3),301-315.
4. Chen, X., and Zhang, Q. (2020). Integration of Computational Modeling and Parametric Design in Earthquakeresistant Structures: A Case Study of Reinforced Concrete Buildings Soil Dynamics and Earthquake Engineering, 137,106268.
5. Chen, Y., and Wang, H. (2015). Integrating Computational Modeling and Parametric Design for Sustainable Timber Structures. Journal of Wood Science,61(3),251-265.
6. Chen, H., and Wu, G. (2015). Parametric Design and Structural Optimization for Sustainable Building Design: A Review. Sustainable Cities and Society,20,71-80.
7. D.Tuhus-DubrowandM.Krarti.(2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings.Build.Environ.45(2010)1574–1581.doi:10.1016/j.buildenv.2010.01.005.
8. H. Pottmann, J. Wallner. (2008). Geometry of Architectural Freeform Structures. Int. Math. Nachr. 209, 15–28. doi:10.1145/1364901.1364903.
9. Kim, S., and Lee, J. (2020). Sustainable Structural Optimization Using Computational Modeling and Parametric Design: A Review. Structural Engineering International,27(1),78-89.
10. Kim,D.,andPark,S.(2019). Integration of Computational Modeling and Parametric Design in Seismic Retrofitting of Buildings. Earthquake Engineering and Structural Dynamics,48(8),1211-1227.
11. K. Konis, A. Gamas, and K. Kensek. (2016). Passive performance and building form: An optimization framework for early-stage design support. Sol.Energy.125(2016)161–179.doi:10.1016/j.solener.2015.12.020.
12. Liu,Z.,andChen,H.(2023). Parametric Optimization of Composite Structures Using Computational Modeling and Response Surface Methodology. Composite Structures,291,124872.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056 Volume: 11 Issue: 05 | May 2024 www.irjet.net p-ISSN:2395-0072
13. Liu, Y., and Chen, Z. (2022). Parametric Optimization of Structural Frames Using Computational Modeling and Genetic Algorithms. Automation in Construction,121,103572.
14. Liu, Y., and Wang, L. (2019). Computational Modeling and Parametric Design for Optimization of Highperformance Steel Structures. Journal of Constructional Steel Research,155,92-104.
15. Liu, H., and Xu, W. (2018). Computational Modeling and Parametric Design for Sustainable Infrastructure Development. Journal of Infrastructure Systems,24(3),04018015.
16. Li, H., and Wang, L. (2017). Computational Modeling and Parametric Design for Performance-based Design of Concrete Structures. Structural Concrete,18(2),187-198.
17. Li,M.,andWang,S.(2014). Computational Modeling and Parametric Design in High-rise Building Design: A Case Study of the Shanghai Tower. Journal of Architectural Engineering,20(4),04014027.
18. MostaphaSadeghipourRoudsari,MichellePak,SmithandLadybug.(2013). A Parametric Environmental Plugin for Grasshopper to Help Designers Create an Environmentally-Conscious Design. 13th Conf. Int. Build. Perform. Simul.Assoc.(2013)3129–3135.http://www.ibpsa.org/proceedings/bs2013/p_2499.pdf.
19. Smith, A. B., and Johnson, C. D. (2023). Advancements in Computational Modeling for Structural Optimization. Journal of Structural Engineering,35(2),145-162.
20. Wang,J.,andLi,X.(2021). Computational Modeling and Parametric Design for Efficient Structural Form Finding. Engineering Structures,254,112345.