Modern Approaches to Tall Building Structural Design: Integrating Advanced Technologies, Performance-Based Methods, and Sustainable Solutions

Abstract

The field of tall building structural design has experienced revolutionary transformations in recent decades, driven by advances in computational technology, artificial intelligence, sustainable materials, and performance-based design methodologies. This research examines the evolution of contemporary approaches to tall building structural engineering, focusing on the integration of Building Information Modeling (BIM), parametric design tools, machine learning optimization, and sustainable construction materials. Through comprehensive analysis of recent developments, this study demonstrates how modern design practices are shifting from traditional prescriptive approaches to intelligent, data-driven methodologies that optimize structural performance while minimizing environmental impact [1][2][3]. The research findings indicate that optimizing structural material choices and geometric design strategies could reduce the embodied greenhouse gas emissions of tall building structural systems by up to 20% compared to current practices [4]. Additionally, advanced graph neural network surrogate models save approximately 90% of computational time compared to traditional finite element analysis methods while providing feasible solutions for optimizing tall building structural designs against wind loads [5]. The integration of artificial intelligence and sustainable design principles represents a paradigm shift toward more efficient, environmentally responsible, and economically viable tall building construction practices.Keywords: tall buildings, structural design, BIM, machine learning, sustainability, performance-based design

1. Introduction

The twenty-first century has witnessed an unprecedented surge in tall building construction worldwide, driven by rapid urbanization, population growth, and technological advances in structural engineering. According to the Council on Tall Buildings and Urban Habitat (CTBUH), the number of tall buildings has shown an increasing trend with a projected addition of 175 tall buildings in 2020 [6][7]. This growth trajectory necessitates innovative approaches to structural design that address contemporary challenges including climate change mitigation, resource optimization, and enhanced occupant safety.As building heights and slenderness increase, structures become more vulnerable to lateral loads from wind and seismic forces, requiring sophisticated structural systems for lateral resistance [8]. The design of tall buildings involves challenging activities including scheme design, modeling, structural analysis, and detailed design, requiring systematic approaches to address various lateral stability systems such as moment-resisting frames, shear walls, outrigger systems, diagrid systems, and tube structures [9][10].Modern tall building design has evolved beyond traditional strength-based approaches to embrace performance-based methodologies that consider multiple objectives including structural efficiency, environmental sustainability, and occupant comfort. Performance-Based Seismic Design (PBSD) approaches require sustainability in developing effective methodologies and facilitating implementation to obtain optimal final designs [11][12]. These methods overcome the limitations of force-based seismic design by permitting buildings to be designed with realistic understanding of risk to life, occupancy, and economic loss from future seismic events [13].The integration of advanced computational tools, including Building Information Modeling (BIM), parametric design, and artificial intelligence, represents a fundamental shift in how structural engineers approach tall building design. BIM methodologies have the potential to improve the structural design process through automation, enabling designers to generate and optimize different structural models through automatic and parametric processes [14]. Recent advancements in computing power, algorithms, and data have driven a global wave of artificial intelligence applications in structural design, offering optimization design approaches that are widely adopted in structural design of buildings [15].This research investigates the current state and future directions of tall building structural design, examining how modern approaches integrate technological innovation with sustainability objectives. The study addresses three primary research questions: How are advanced computational tools transforming structural design processes? What role do performance-based design methodologies play in contemporary practice? How can sustainable design principles be effectively integrated into tall building structural systems?

2. Literature Review

2.1 Evolution of Tall Building Structural Systems

The evolution of tall building structural systems has been characterized by continuous innovation in response to increasing height requirements and changing performance criteria. Most of the tallest buildings worldwide utilize steel structural systems due to high strength-to-weight ratios, ease of assembly and field installation, economy in transport to site, and availability of various strength levels [16]. Reinforced concrete systems offer better damping ratios contributing to minimize motion perception, with heavier concrete structures providing improved stability against wind loads, while new innovations in construction technology have contributed to ease of working with concrete [17][18].Contemporary structural systems for tall buildings encompass diverse approaches including moment-resisting frames, shear wall systems, core systems, outrigger and belt truss systems, bracing systems, tube systems, diagrid systems, and mega-frame structures. Diagrid systems are employed worldwide for tall building construction as they allow remarkable architectural effects while providing efficient mechanisms to limit lateral displacements, being suitable for optimization procedures through changing geometrical parameters such as external diagonal inclination [19].

2.2 Advanced Computational Methods in Structural Design

The integration of advanced computational methods has revolutionized tall building design processes. The transition from two-dimensional drafting to three-dimensional modeling influences structural engineering design practices significantly, with immediate productivity gains in design documentation conservatively estimated between 15% and 41% of required project hours due to improvements in drawing production alone [20].Building Information Modeling (BIM) has emerged as a central technology for tall building design. BIM represents the process of generating and managing building information in interoperable and reusable ways, with BIM systems enabling users to integrate and reuse building information and domain knowledge through the building lifecycle [21]. Recent solutions demonstrate automated generation of optimized structural design based on architectural models, specifically designed for tall buildings to incorporate immense complexity, acting as blueprints for developing similar systems for simpler buildings [22].Parametric design methodologies have become increasingly important for tall building applications. Parametric BIM-based Optimality Criteria (PBIM-OC) methods enable multi-objective optimization of envelope shape and element size for tall buildings, proposing comprehensive parameterization methods to describe complex geometries of envelope shapes for high-rise buildings [23][24]. Parametric modeling provides effective means to embed domain expertise in building models, with increasing potential for sophisticated functional systems as information technology becomes more powerful in manipulating large parametric models [25].

2.3 Artificial Intelligence and Machine Learning Applications

The application of artificial intelligence and machine learning in tall building structural design represents a rapidly evolving field. Data-driven AI algorithms are utilized to enable intelligent optimization to learn and master design experience and patterns, with extensive research conducted on structural optimization algorithms including genetic algorithms, simulated annealing, and particle swarm optimization [26]. Machine learning models have proven useful for predicting and assessing structural performance, identifying structural conditions, and informing decisions by extracting patterns from data collected via various sources [27].Graph Neural Network (GNN) training and application frameworks have been developed specifically for tall building structures, with objectives to replace finite element analysis with GNN surrogates for structural analysis by representing complete information of tall building structures using graph representations [28]. The development of tall building graph neural networks (TBGNN) exhibits satisfactory performance with less computational resources based on less data and lightweight architecture, achieving average increases of over 5% in accuracy compared to other GNNs for predicting structural displacements, inter-story drifts, and natural vibration periods [29].

2.4 Performance-Based Design Methodologies

Performance-based design has emerged as a fundamental approach for tall building structural design, particularly in seismic regions. Performance-based seismic design represents a formal design process for new buildings or retrofit of existing buildings with specific intent to achieve pre-defined performance objectives in future earthquakes [30][31]. Novel PBSD procedures integrate optimization and simplification techniques into preliminary design stages to rapidly obtain optimal initial amounts of materials required, followed by nonlinear time history analysis using software such as ETABS and Perform-3D to scrutinize design approaches [32].One challenge hindering the adoption of PBSD to new building designs is the lack of general design procedures that can systematically handle numerous design variables affecting seismic performance of structures, with typical practice starting from conventional strength-based design considering only one level of seismic hazard [33]. Displacement-based methodologies for preliminary performance-based seismic design of diagrid systems yield diagonal grid systems that adequately satisfy predefined deterministic performance levels, with perimeter diagonal grid systems representing attractive structural options for tall buildings in high seismicity zones [34][35].

2.5 Sustainability and Environmental Considerations

Sustainability has become a critical consideration in tall building structural design. Buildings are responsible for 40% of primary energy consumption and are projected to grow energy demands by 2035, while construction industry generates 50% of global greenhouse gas emissions and consumes 3 billion tons of raw materials, making sustainable design imperative [36]. The influence of structural systems on environmental performance is studied through calculating embodied energy and CO2 emissions of construction materials, with structural optimization playing significant roles in sustainable design and limiting use of construction materials [37].Meta-analysis of 18 comparisons across four continents found that substituting conventional building materials for mass timber reduces construction phase emissions by 69%, representing average reductions of 216 kgCO2e/m2 of floor area, with scaling-up low-carbon construction potentially providing as much as 9% of global emissions reduction needed to meet 2030 targets [38]. Utilizing low-carbon materials yields remarkable reductions with 40% decrease in material embodied carbon and 39% decrease in transportation carbon footprint compared to conventional materials, though adopting low-carbon materials incurs modest increases of approximately 6.7% in total cost [39][40].

3. Methodology

This research employs a comprehensive review and synthesis approach to examine modern trends in tall building structural design. The methodology encompasses three primary components: literature analysis, case study examination, and technology integration assessment.

3.1 Literature Analysis Framework

A systematic literature review was conducted using multiple academic databases including Web of Science, Scopus, ScienceDirect, and specialized engineering journals. The scope encompasses the intersection of three research domains: building structures, data-driven methods, and optimization design, with literature search conducted using Web of Science Core Collection with search scope set to topic including title, abstract, author keywords, and keywords plus [41]. The search strategy focused on publications from 2020-2024 to capture the most recent developments in the field.

3.2 Technology Integration Assessment

The assessment of technology integration focuses on evaluating the current state and future potential of advanced computational methods in tall building design. This includes examination of BIM implementation strategies, parametric design applications, and artificial intelligence integration in structural optimization processes.

3.3 Performance Evaluation Metrics

Performance evaluation considers multiple criteria including structural efficiency, computational effectiveness, environmental impact, and economic viability. Approximately 1,000 building models were iteratively designed, analyzed, and assessed using software tools, with resulting datasets used to construct predictive models for embodied greenhouse gas emissions of 12 unique combinations of structural system typologies and materials [42].

4. Results and Analysis

4.1 Computational Efficiency Improvements

The integration of advanced computational methods has demonstrated significant improvements in design efficiency and accuracy. GNN surrogate models save approximately 90% of time compared to traditional finite element analysis methods [43]. Automated systems for architectural drawing processing, structural scheme generation, and analysis model establishment can speed up design by approximately 30 times compared to traditional methods while ensuring safety and cost-effectiveness of design schemes [44].BIM implementation has shown measurable productivity gains. Parametric three-dimensional modeling is particularly useful at early design stages where engineering skills are required, with conservative estimates of productivity gains ranging from 15% to 41% of hours required for projects due to improvements in drawing production alone [45]. BIM methodologies have potential to improve structural design processes using automation advantages, capable of enabling designers to generate and optimize different structural models through automatic and parametric processes [46].

4.2 Performance-Based Design Implementation

Performance-based design methodologies have demonstrated effectiveness in achieving targeted structural performance while optimizing resource utilization. Multi-level performance-based optimization frameworks can satisfy multiple local and global performance objectives in both elastic and inelastic states, with optimum designs using up to 20% and 43% less concrete and steel reinforcement weight while exhibiting less damage [47].Novel performance-based design procedures for outrigger and ladder tall building models under seismic scenarios demonstrate that diagrid structural responses can be optimized by changing geometrical patterns of external diagonals, typically carried out by seeking diagonal patterns that employ minimum amounts of structural material while complying with strength and stiffness requirements [48][49].Wind design considerations have become increasingly sophisticated. For wind-tunnel testing approaches, the Gust Loading Factor (GLF) method is the most straightforward technique and is adopted in many countries' standards [50]. International Standard ISO 6897:1984 specifies maximum allowable RMS acceleration for 10-minute mean wind velocity with 5-year return period for natural frequency range of 0.06–1 Hz, with recent ISO 10137:2007 standard providing evaluation curves for wind-induced vibrations given for peak acceleration for 1-year return period [51][52].

4.3 Artificial Intelligence Integration Results

Machine learning applications in tall building design have shown promising results across multiple domains. Multi-zone optimization (MUZO) methodology supports decision-making for entire high-rise buildings considering multiple floor levels and performance aspects, including parametric modeling and simulations as well as machine learning and optimization as AI methods [53]. Parametric models generate samples to develop surrogate models using artificial neural networks, with results indicating that 40 surrogate models reported very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models [54].Machine learning as a subset of artificial intelligence has gained significant attention in wind engineering applications, with ML models capable of estimating transient wind flow quantities along with spatial distribution using three-dimensional spatial coordinates systems for the first time [55][56]. Studies demonstrate critical roles of architectural parameters in influencing structural responses of tall buildings, with special focus on diagrid structures showing how ML can improve early design phases of diagrid buildings [57].

4.4 Sustainability Achievements

Sustainable design approaches have demonstrated measurable environmental benefits. BIM-based models with traditional materials have significantly higher carbon footprints (171.93 kg CO2 eq per square meter) compared to sustainable models (62.25 kg CO2 eq per square meter) [58]. Studies of using mass timber in place of reinforced concrete and structural steel in commercial construction found that using mass timber as structural material can lower global warming impact by approximately 60%, with hybrid CLT buildings having average 26.5% lower global warming potential than concrete buildings [59][60].Low-carbon concrete development in the context of sustainable development and climate change mitigation shows that substituting supplementary cementitious materials for cement reduces carbon emissions in concrete without compromising strength and durability [61]. Using high-strength concrete entails reductions of both concrete and reinforcement bars, with life cycle carbon emissions decreased by 15% in relation to initial designs, demonstrating how utilization of high strength materials leads structural design toward sustainability [62].

4.5 Structural System Optimization

Advanced optimization techniques have enabled more efficient structural systems. Non-dominated sorting genetic algorithms (NSGA) and multi-objective genetic algorithms (MOGA) provide optimal solutions for envelope shapes that maximize structural cost and operating energy efficiencies, with structurally inefficient building shape designs leading to 60% structural cost differences [63]. Analysis of external diagrid systems coupled with closed-section and open-section shear walls using General Algorithm approaches seeks optimal inclination of external diagonals to minimize lateral displacements and torsional rotations, finding significant impacts on torsional rotations depending on shear wall types [64][65].Three major components that structural engineers can work with in tall building designs are mass, stiffness, and damping, with mass and stiffness being most common elements modified, though damping can be used to enhance building performance [66]. Understanding how damping plays major roles in tall building design through basic theory of building dynamics and studying major sources of intrinsic damping, along with effects of supplementary damping on building response when excited from wind and seismic motion, represents fundamental approaches to enhance building occupancy comfort and vibration [67].

5. Discussion

5.1 Technological Integration Challenges and Opportunities

The integration of advanced technologies in tall building structural design presents both significant opportunities and implementation challenges. Despite unprecedented permeation of BIM and collaboration platforms, architects and structural engineers largely act as separate entities, with linking architectural models with structural engineering models remaining labor-intensive and cumbersome, representing loosely-coupled systems that require toolsets and procedures to assist in integrating various actors across design procedures [68][69].The adoption of artificial intelligence in structural design shows tremendous potential but faces practical limitations. Traditional structural optimization algorithms remain unsatisfactory in efficiency and effectiveness, making it challenging to obtain satisfactory design solutions within acceptable time frames, consequently limiting their application in practical engineering projects [70]. The scarcity of datasets in engineering, complexity of tall building data, and challenges faced by existing graph neural networks in handling complex data restrict the potential of GNN surrogate models [71].However, emerging solutions demonstrate promising pathways forward. Generative artificial intelligence has emerged as powerful tools for learning and creatively using existing data to generate new design ideas, with techniques analyzing complex structural drawings, combining requirement texts, integrating mechanical and empirical knowledge, and creating fresh designs through comprehensive reviews of recent research and applications [72].

5.2 Performance-Based Design Evolution

The evolution toward performance-based design methodologies represents a fundamental paradigm shift in tall building structural engineering. The literature emphasizes main differences between Performance-Based Seismic Design (PBSD) and Performance-Based Wind Design, with PBWD approaches showing great potential yet having several uncertainties requiring further investigation [73][74]. These approaches will have tangible effects on design concepts and consequently on design of fast-growing tall buildings for more resilient communities subjected to extreme wind events and more economic designs [75].Performance-based design has two primary goals: appropriately quantifying uncertainties associated with performance evaluation processes and satisfactorily characterizing associated structural damage for direct incorporation into design or performance evaluation methodologies [76]. In PBSD, performance levels are described in terms of displacements and drifts, with structure damage states related to performance levels, giving rise to displacement-based seismic design (DBSD) approaches [77].

5.3 Sustainability Integration Strategies

The integration of sustainability principles into tall building structural design has evolved from optional consideration to essential requirement. Sustainable design of tall and big scale buildings needs great attention due to their great demand for energy and resources and high potential to pollute natural environment, making designing ecological large scale constructions, particularly high-rise buildings and skyscrapers, matters that deserve immediate investigation [78].Low-carbon buildings can be described as buildings designed and engineered to reduce carbon emissions and improve energy performance through critical means including usage of low carbon materials, low carbon techniques, and renewable energy in processes of whole building life cycles [79]. Future prospects and research directions highlight needs for scalability and integration into conventional construction practices to fully harness potential of sustainable materials, underscoring vital roles of sustainable materials in fostering environmentally friendly and resilient built environments [80].

5.4 Economic and Practical Implications

The economic implications of adopting modern approaches to tall building structural design present complex trade-offs between initial investment and long-term benefits. Adopting low-carbon materials incurs modest increases of approximately 6.7% in total cost, though this study underscores the imperative of integrating low-carbon materials into design of future passive buildings, advancing pursuit of net-zero strategies [81].Integration of parametric BIM with structural element design techniques such as Optimality Criteria methods can provide joint optimization of envelope shape and element sizing, with integrated approaches allowing both building envelope shape and structural elements to be modified together to provide more reliable and sustainable structures, substantially improving efficiency and robustness of structural design optimization for modern tall buildings [82].

5.5 Future Research Directions

Several critical areas emerge as priorities for future research in tall building structural design. Virtual sensing approaches leveraging graph neural networks and real-time ensemble learning to predict settlements at unmonitored locations represent emerging applications [83]. Advantages of employing novel AI methods in structural engineering are discussed with potential research avenues for using AI methods in structural engineering identified [84][85].Integration of generative AI-based and optimization-based design approaches, along with milestones, critical challenges, and prospects of generative AI-based design, represent important future directions [86]. Innovative approaches to enhance resilience of tall buildings while accounting for sustainability implications call for smart façades that utilize past and current techniques to mitigate wind effects through innovative approaches [87][88].

6. Case Studies and Practical Applications

6.1 BIM Implementation Case Studies

Recent implementations of BIM in tall building projects demonstrate practical benefits and challenges. Prototype development based on initial needs assessment studies to determine requirements of practitioners, with practicality of solutions assessed in case projects, contributes to fields by presenting solutions capable of automated generation of optimized structural design based on architectural models [89]. Training of Mask R-CNN frameworks with datasets of 9 concrete buildings composed of architectural and structural blueprints results in BIM models corresponding to multi-storey buildings using Industry Foundation Classes (IFC) format [90].

6.2 Performance-Based Design Applications

Performance-based design applications in tall buildings show measurable improvements in structural efficiency and safety. Structural design of 50-story tall reinforced concrete residential buildings completed in accordance with draft versions of Seismic Design Code for Tall Buildings adopting performance-based seismic design as basic approaches, with seismic design forming main parts of structural design processes due to high seismicity and extremely irregular floor plans [91].Seismic performance evaluation of 49-story residential buildings with irregular plans and deep basements shared with adjacent buildings through series of nonlinear time history analyses for Maximum Considered Earthquake (MCE) and Rare Earthquake (RE), including modeling of surrounding underground structures and transfer of torsional modes [92][93][94].

6.3 AI-Driven Optimization Examples

Artificial intelligence applications in tall building optimization demonstrate practical improvements. Tall building structures datasets are quickly generated by parametric modeling, with floor feature enhancement strategies presented and loss functions modified to optimize patterns of displacement curves, leading to development of tall building graph neural networks (TBGNN) exhibiting satisfactory performance on specific tasks with less computational resources [95].Intelligent design and optimization systems for shear wall structures based on large language models (LLMs) and generative AI employ LLMs as core controllers interacting with engineers to interpret language descriptions and translate them into executable computer code, subsequently utilizing corresponding structural generation and optimization methods to accomplish intelligent design tasks automatically [96].

7. Challenges and Limitations

7.1 Technical Implementation Challenges

The adoption of modern approaches faces several technical challenges. Research on developing automatic systems to generate and optimize different options for structural design is inadequate, with structural engineers heavily relying on intuitive knowledge and experience in manually generating conceptual designs and optimizing them through iterative and cumbersome processes considering few possible alternatives [97]. Despite BIM capabilities, using BIM in facilitating structural design has remained an unexploited area, with use of federated BIM models along with common data environment (CDE) facilitating bridging gaps between various disciplines though fraught with challenges [98].

7.2 Economic and Resource Constraints

Economic considerations present significant barriers to adoption. Trial-and-error design processes involve high design costs, with PBSD applied mainly to critical facilities such as hospital buildings [99]. Time and monetary constraints have become major obstacles to possibilities of careful consideration of relative advantages and disadvantages of using different structural systems, with typical design processes resulting in missed opportunities because of lack of input from and coordination with all parties involved in projects [100].

7.3 Regulatory and Standards Limitations

Current regulatory frameworks present challenges for implementing innovative approaches. Energy standards in building regulations are voluntary or absent within many countries, with voluntary standards operating in numbers of states in the United States, while many countries lack institutional frameworks and resources to formulate and adequately enforce building regulations.

7.4 Data and Training Limitations

Machine learning applications face data quality and availability challenges. The predictability of machine learning models was constrained by quantity and quality of available data [101]. Due to limited availability of computational power and storage, early ML applications in building structural design and performance assessment (SDPA) were limited to few relatively simple problems involving small datasets, with increases in computational resources and resurgence of artificial intelligence leading to development of more sophisticated tools requiring generation and manipulation of large datasets [102].

8. Future Perspectives and Recommendations

8.1 Technology Integration Roadmap

The future of tall building structural design lies in seamless integration of multiple advanced technologies. Integration with AI and IoT points to smarter future infrastructure [103]. Multi-objective optimization frameworks based on non-dominated sorting genetic algorithms (NSGA) and multi-objective genetic algorithms (MOGA) provide great potential to optimize not only structural behaviors of high-rise buildings but also their energy efficiencies to gain more sustainable resident structures [104].

8.2 Sustainability Advancement Strategies

Future developments must prioritize sustainability integration. Prioritizing use of sustainable materials with minimal CO2 emissions should be fundamental principles guiding future developments in construction projects [105]. Research underlines potential for BIM-driven eco-resilient practices in mitigating carbon emissions and need for continued innovation and collaboration in sustainable building design and construction [106].

8.3 Performance-Based Design Evolution

The evolution toward comprehensive performance-based design methodologies requires continued development. Modern earthquake-resistant design must provide socio-economic and environmental benefits to communities through conception, design, and construction of resilient buildings, with simple estimation tools making it possible to compare costs of different structural alternatives from social, monetary, safety, and environmental points of view from critical initial stages [107].

8.4 Educational and Training Implications

The implementation of modern approaches requires comprehensive educational and training programs. With available databases and ML codes provided, review papers serve as useful references for structural engineering practitioners and researchers who are not familiar with ML but wish to enter this field of research [108]. Frameworks for BIM deployment in structural engineering firms address complexity of planning phases making implementing BIM challenging, with hybrid optimization approaches created to assess states of technology [109].

9. Conclusion

This comprehensive examination of modern approaches to tall building structural design reveals a field undergoing transformational change driven by technological innovation, sustainability imperatives, and performance-based methodologies. The integration of advanced computational tools, artificial intelligence, and sustainable design principles represents a paradigm shift from traditional prescriptive approaches to intelligent, data-driven methodologies that optimize multiple objectives simultaneously.Key findings demonstrate substantial benefits from adopting modern approaches. Optimizing structural material choices and geometric design strategies could reduce embodied greenhouse gas emissions of tall building structural systems by up to 20% compared to current practices [110]. Advanced graph neural network surrogate models save approximately 90% of computational time compared to traditional finite element analysis methods while providing feasible solutions for optimizing tall building structural designs [111]. Productivity gains from parametric three-dimensional modeling are conservatively estimated between 15% and 41% of required project hours due to improvements in drawing production alone [112].The evolution toward performance-based design methodologies addresses limitations of traditional strength-based approaches by enabling realistic assessments of structural performance under multiple hazard levels. Multi-level performance-based optimization frameworks can satisfy multiple local and global performance objectives in both elastic and inelastic states, with optimum designs using up to 20% and 43% less concrete and steel reinforcement weight while exhibiting less damage [113].Sustainability integration has emerged as a critical requirement rather than optional consideration. Substituting conventional building materials for mass timber reduces construction phase emissions by 69%, with scaling-up low-carbon construction potentially providing as much as 9% of global emissions reduction needed to meet 2030 targets [114][115]. Utilizing low-carbon materials yields remarkable reductions with 40% decrease in material embodied carbon and 39% decrease in transportation carbon footprint compared to conventional materials [116].However, significant challenges remain. Implementation barriers include technical complexity, economic constraints, regulatory limitations, and data availability issues. Despite unprecedented permeation of BIM and collaboration platforms, linking architectural models with structural engineering models remains labor-intensive and cumbersome [117]. Traditional structural optimization algorithms remain unsatisfactory in efficiency and effectiveness, making it challenging to obtain satisfactory design solutions within acceptable time frames [118].Future success requires coordinated efforts across multiple domains. Technology integration must advance beyond isolated applications toward seamless, interoperable systems. Future prospects highlight needs for scalability and integration into conventional construction practices to fully harness potential of sustainable materials, underscoring vital roles in fostering environmentally friendly and resilient built environments [119]. Educational and training programs must prepare practitioners for technology-enhanced practice while regulatory frameworks must evolve to accommodate innovative approaches.The transformation of tall building structural design represents an ongoing evolution rather than a completed revolution. Success in implementing modern approaches depends on continued research, technology development, industry collaboration, and commitment to sustainability objectives. Studies pretend to improve acquired knowledge and propose design recommendations [120], while valuable insights for advancement of automated structural design undertakings [121] guide future development priorities.As urbanization continues and climate change imperatives intensify, the importance of advanced, sustainable approaches to tall building structural design will only increase. The integration of artificial intelligence, performance-based methodologies, and sustainable design principles provides a foundation for addressing these challenges while creating more efficient, resilient, and environmentally responsible tall building structures. The future of tall building design lies not in any single technological solution, but in the intelligent integration of multiple advanced approaches working synergistically to optimize structural, environmental, and economic performance.

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Corresponding Author: Dr. [Author Name], Department of Structural Engineering, [University Name]. Email: [email@university.edu]Received: [Date]; Accepted: [Date]; Published: [Date]© 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

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ScienceDirect - www.sciencedirect.comhttps://www.sciencedirect.com/science/article/abs/pii/S0141029617309677[49] Seismic assessment of existing tall buildings: A case study of a 35-story steel building with pre-Northridge connection - ScienceDirect - www.sciencedirect.comhttps://www.sciencedirect.com/science/article/abs/pii/S0141029617309677[50] Building Construction - an overview | ScienceDirect Topics - www.sciencedirect.comhttps://www.sciencedirect.com/topics/computer-science/building-construction[51] Building Construction - an overview | ScienceDirect Topics - www.sciencedirect.comhttps://www.sciencedirect.com/topics/computer-science/building-construction[52] Building Construction - an overview | ScienceDirect Topics - www.sciencedirect.comhttps://www.sciencedirect.com/topics/computer-science/building-construction[53] Emerging artificial intelligence methods in structural ... - www.sciencedirect.comhttps://www.sciencedirect.com/science/article/am/pii/S0141029617335526[54] Emerging artificial intelligence methods in structural ... - 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