Model Building Research Articles

Transcending disciplinary boundaries: Nature based solutions (NbSs) for Caribbean coastal resilience

Caribbean Small Island Developing States are experiencing multiple interconnected crises including susceptibility to natural hazards, increased vulnerability to climate change, biodiversity loss and reduced accessibility to development funding. Nature-based Solutions can provide an integrated approach to boost Caribbean coastal resilience as they are economic and practical solutions that capably balance competing interests. This study used a participatory method, Group Model Building, to engage stakeholders across varying disciplines and levels of decision-making to collaboratively identify the underlying causes of low Nature-based Solutions uptake regionally and potential intervention mechanisms. Six overarching factors contributed to low adoption, including data and knowledge limitations, a propensity towards hard engineering, low collaborative engagement, and limited implementation capacity. Some areas identified for intervention were comprehensive data collection, reducing uncertainty surrounding the implementation of Nature-based Solutions, effective collaboration, using case studies and best practices, and knowledge translation and dissemination. The outputs demonstrated a critical role for the engineer in mainstreaming Nature-based Solutions through adopting a systematic and holistic design approach. Concomitant to this is the need for engineers to operate successfully within transdisciplinary teams to enhance transformative communication skills and ensure that solutions not only minimize environmental impact but also support the conservation of biodiversity and ecosystem services.

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Assessing and mitigating building fire risk based on the scenario clusters in Bangladesh

The increasing occurrence of fire hazards in residential buildings in Bangladesh has led to severe property damage, physical injuries, and fatalities, underscoring a critical research gap in effective fire risk management. This study aims to address this gap by identifying key fire risks and proposing mitigation strategies using BIM technology. The research employs a comprehensive survey and scenario clustering methodology to identify these risks and assess the efficacy of various fire-resistant materials. Data collection involved questionnaires distributed to 200 respondents, including building owners, occupants, engineers, and firefighters. These clusters are based on the metrics of fatality numbers and property damage to quantify fire risk. Findings highlight the significant role of wall and ceiling lining materials (RII = 0.919) in contributing to fire risk. Further analysis was conducted using a residential building model developed in Autodesk Revit and simulated in Pyrosim software, assessing various lining materials. Quantitative results demonstrated that fire brick is the most effective material, exhibiting the lowest heat release rate (968 kW/m2), minimum adiabatic surface temperature (28°C), minimum heat transfer coefficient (1.75 W/m2/K), and lowest surface burning rate (7.5e−08 Kg/m2/s). These results suggest that fire brick is the optimal choice for enhancing fire resistance in residential buildings.

Optimizing Energy Efficiency with a Cloud-Based Model Predictive Control: A Case Study of a Multi-Family Building

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention.

Experimental Study on Seismic Performance Evaluation of a Multi-Story Steel Building Model with Rolling-Type Seismic Base Isolation

Critical structures such as hospitals, high-precision manufacturing facilities, telecommunications centers, and fire stations, especially, need to maintain their functionality even during severe earthquakes. In this sense, seismic isolation technology serves as a vital design method for preserving their functionality. Seismic isolators, also known as earthquake isolation systems, are used to reduce the effects of earthquakes on buildings by isolating them from the ground they are located on. By ensuring that less acceleration and force demand is transmitted to the superstructure, both the building and the equipment and the devices in the building are prevented from being damaged by earthquakes. This experimental study aims to conduct vibration tests on a small-scale multi-story steel-building model equipped with a specially designed rolling-type seismic base isolation system. The relationship between the test model and the prototype was achieved by frequency simulation. The tests will be performed on a shake table under six different earthquake accelerations to examine the model’s dynamic behavior. The primary goal is to evaluate the isolation performance of the rolling-type seismic base isolator under seismic loads, with a focus on recording the vibrations at the top of the test building. It has been observed that the isolator placed at the base of the building significantly reduced the peak acceleration and displacement values of the floor motion. Under the most severe earthquake record applied to the shake table, the acceleration at the top of the building with the isolator was found to be reduced by approximately 50%, compared to the non-isolated case.

Controlling Cell Interactions with DNA Directed Assembly.

The creation of complex cellular environments is critical to mimicking tissue environments that will play a critical role in next-generation tissue engineering, stem cell programming, and therapeutic screening. To address this growing need, techniques capable of manipulating cell-cell and cell-material interactions are required that span single-cell to 3D tissue architectures. DNA programmed assembly and placement of cells present a powerful technique for the bottom-up synthesis of living microtissues for probing key questions in cell-cell and cell-material-driven behaviors through its refined control over placement and architecture. This review examines the current state of the art in the programming of cellular interactions with DNA and its applications spanning tissue model building, fundamental cellular biology, and cell manipulation for measurements across a host of applications.

Prediction of Performance and Emissions Diesel Engines Fueled-Biodiesel Using Artificial Neural Network (ANN) Resilient Backpropagation Algorithm (Rprop)

In order to increase energy security and improve environmental quality, the Indonesian Goverment set a target of 23% renewable energy mix in 2025, one of which is the Mandatory Bioediesel Program. A higher biodiesel blending ratio will affect the performance and emissions of diesel engines because biodiesel is chemically different from diesel oil. Research related to the prediction of diesel engine performance and emissions using Artificial Neural Network (ANN) has been conducted, but the author sees a research opportunity for the implementation of the ANN Resilient Backpropagation (Rprop) algorithm. The data used to create the ANN model prediction was secondary data from previous research. The model designed multi input and multi output (MIMO) with 4 input variables and 7 output variables. Model building done by varying the number of neurons and hidden layers. Model evaluation selected based on the largest coefficient of determination parameter R2 and the smallest RMSE or MAPE. The results showed that the ANN single layer 4-20-7 network architecture is the best model for predicting diesel engine performance and emissions with test data R2 , RMSE and MAPE of 0.962532, 6.699428 and 6.0% respectively, while for overall data testing has a performance of 0.982869, 3.908542 and 4.3%. The results also show that based on the ANN prediction results, the increasing biodiesel ratio can increase NOx emissions and decrease HC, CO and CO emissions2 . In terms of performance, the addition of biodiesel can increase BSFC and BP and decrease BTE. The results also show that the addition of ZnO concentration can reduce emissions while in terms of performance it will increase BTE and reduce BSFC and BP.

Improvements and Heterogeneities of the Global Centroid Moment Tensor Catalog

Abstract Earthquake catalogs are heterogeneous, especially those developed over long time spans. Changes in seismological monitoring, which provides the records on which these catalogs are based, are common. Typically, instruments and networks become more sensitive over time, allowing for the detection and characterization of smaller earthquakes. In pursuit of improvement, new methods for routine data analysis are occasionally introduced, modifying the procedures for catalog compilation. The resulting heterogeneities may not be evident to users, but they should be unveiled and considered in any application of the catalog, especially in statistical seismology, which analyzes large earthquake data sets. The Global Centroid Moment Tensor catalog is considered the most homogeneous database of global seismicity. However, a detailed analysis of its heterogeneities has been lacking. This work reviews changes in the catalog’s development from 1976 to 2023 and reveals how these have caused improvements and heterogeneities in the resulting data. Several periods are distinguished, separated by milestones in the methods employed for moment tensor inversion and catalog compilation, as well as by the advent of global broadband monitoring in 2004. These changes are shown to have caused variations in the catalog’s completeness and in the determinations of centroid depths, scalar seismic moments, and moment tensors. The magnitude of completeness is measured here in detail, both temporally and spatially. It has decreased over the years and shows spatial variations within each period, correlated to regional differences in network monitoring and compilation biases. Moment tensor determinations have been significantly different since 2004, resulting in a different frequency distribution of rake angles and a different dependence of the double-couple component as a function of rake. This work is expected to benefit all future uses of the catalog, enabling better characterization of seismicity properties and improved building and testing of models for earthquake occurrence.

Adapting Group Model Building for Mental Healthcare: A Participatory Co-Design Approach.

Countries around the world are experiencing both a growing need for mental health services and major gaps in the delivery of mental healthcare. The World Health Organisation's 2022 report on mental health emphasised co-designed and systems-level innovations responsive to the voice of those with lived experience. However, evidence for implementing such methodologies is sparse. Group model building (GMB), a participatory systems-based approach, has been used in various fields, but its application is limited within mental healthcare settings. It is recognised as a valuable tool for engaging stakeholders, including those with lived experience, in understanding and redesigning health systems and services. The participatory approach aligns with the mental health sector's shift towards co-design and person-centred care. This study assessed the acceptability of GMB as a method for co-design within mental healthcare, aiming to explore its potential for meaningful collaborative improvement and systems-level insights. Participants from a regional Mental Health and Drug and Alcohol Service (MHDAS) engaged in GMB workshops and focus groups. Eighteen individuals, including lived experience workers and healthcare professionals, provided feedback that informed adjustments to the workshops' design, language, timing and facilitation techniques, enhancing GMB's relevance for mental health co-design. The findings demonstrate the value and acceptability of the adapted GMB process in acute mental healthcare, marking a significant step towards evidence-based, person-centred service improvement. This study highlights the potential effectiveness of participatory methods in identifying and addressing systemic challenges, paving the way for future mental health reforms. Future work will focus on implementing and evaluating co-designed interventions, aiming for more inclusive and effective mental healthcare and advancing innovation and reform in the field.

Environmental impacts and BIM: a Portuguese heritage case study

PurposeThis paper aims to compare the possible environmental impacts of different rehabilitation solutions proposed to a case study of a Portuguese vernacular building, bearing in mind the need to search for less impactful practices in accordance with global agendas and, simultaneously, the importance of preserving heritage assets.Design/methodology/approachThe two proposed solutions differ from each other in the selection of materials, varying from the exclusive use of conventional contemporary materials to the use of locally sourced and natural-based materials, considering the Portuguese and local scenario. Results obtained from Life Cycle Assessment procedures are the basis of comparison, which were developed using a Building Information Model (BIM)-integrated analytical plugin.FindingsProducing Heritage Building Information Model (HBIM) models was essential to the analysis, and therefore is a vital part of the used methodology; nevertheless, the modelling of heritage buildings itself plays an important part in preserving heritage assets once it provides precise documenting and managing tools. The results depict variation in values for different indicators in both cases, whose interpretation might need further development to get to deeper conclusions. It has been found that the software tools are still not well adapted to analyse heritage or historic buildings, and therefore adaptations had to take place to overcome these limitations.Originality/valueThe study distinguishes itself by involving the production of HBIM models and the use of BIM-integrated Life Cycle Assessment plugins as well as its analyses, to validate this approach.

Information Technologies for Calculating Anthropogenic Heat Flux in Urban Areas

Modern megalopolis face challenges in effectively managing energy resources and minimizing the impact of human activities on the environment. One of the significant aspects of anthropogenic impact is the heat flow generated by buildings, transport and industry. This article presents a set of programs for assessing anthropogenic heat flow (AHF) caused by heat loss from buildings during the heating season. The study is based on a spatial geometric model of the city constructed using OpenStreetMap data. The thermophysical properties of enclosing structures and specific characteristics of thermal energy consumption are obtained from building codes. The proposed method includes the stages of buildings modeling, filtering, supplementing information from Yandex Maps and State Information System of Housing and Communal Services, eliminating collisions, assigning specific characteristics and calculating AHF. The method is implemented through scripts for the Rhinoceros platform, known for its wide functionality and visual programming environment Grasshopper. The proposed approach allows us to effectively analyze and visualize anthropogenic heat flow in cities, which is a key step in developing strategies for sustainable energy management and reducing negative environmental impacts.

Modeling and scenario building for climate change adaptation planning: The case of large mining in Chile

The mining sector in Chile is a strategic industrial sector for the country. Existing evidence shows that it faces several serious climate change threats; precipitation and flooding, droughts, heatwaves, among others. This scene put pressure on the government and the industry to develop efficient adaptation plans. To date, adaptation plans are designed under the rather linear paradigm of predict-then-act or the impact-lea approach as they are characterized in the IPCC Assessment Report 5 (AR5). Literature and the IPCC reports have identified the limits of those planning paradigms showing the relevance of adaptation barriers or enabling conditions, which should be considered as intrinsic part of the planning problem.In methodological terms, planning for climate change risks implies an enriched adaptation plan problem, previously characterized only by an operative climate risk management, that must be described at the beginning of the decision-making process. In this study, our objective is to contribute to the climate change adaptation planning of large-scale mining in Chile. The study is based on a climate change adaptation planning approach that overcomes the limitations of the current paradigm. In doing so, we start from the understanding that what emerges as an object of analysis from adding to the climate risks their enabling managing conditions is a social system. The social system whose function is that social climate risk management takes place. Therefore, we call it the Social Management System for Adaptation to Climate Change (SMSACC). As such the SMSACC should be the adaptation planning key object.In the first place we modeled that system applying a qualitative system methodology and then we developed it into a mathematical model based on graph theory, in particular the signed digraphs. This allows us to simulate two types of intervention on the enlarged object of analysis of the large mining adaptation plan. On the one hand, we carried out a future scenario analysis based on prospective tools which enables us to understand the system's answer to different future behavior of its environment, including climate change. On the other hand, we simulate different strategic interventions options on the system, which facilitates understanding the system's reaction under different public policy approaches.The modeling and simulation results provided an insightful understanding of the dilemma of social adaptation management of large mining in Chile, and as such they are useful input for the planning process.

Level of social security expenditures and economic growth rate based on econometric regression modelling: New evidence from OECD countries

This research article examines the relationship between the level of social welfare expenditure and economic growth rates, based on unbalanced panel data from 38 OECD countries covering the period from 1985 to 2022. Four hypotheses are formulated regarding the impact of social expenditure on economic growth rates. Through multiple iterations of regression model building, employing various combinations of dependent and independent variables, and conducting tests for stationarity and causality, compelling empirical evidence was obtained on the negative influence of social welfare spending on economic growth rates. The study takes into account both government and non-governmental expenditures on social welfare, a novelty in this field. This approach allows for a detailed examination of the effects of different components on economic growth and provides a more comprehensive understanding of the relationships. The findings indicate that countries with high levels of social welfare spending experience a slowdown in economic growth rates. This is associated with increasing demands on social security systems, their growing inclusivity, and the escalating required levels of financing, which are increasingly covered by debt sources. The research highlights the need to strike a balance between social expenditures and economic growth rates and proposes a set of measures to ensure economic growth outpaces the indexing of social expenditures. The abstract underscores the relevance of the study in light of the widespread recognition of the necessity to combat inequality, poverty, and destitution, and calls on OECD countries’ governments to pay increased attention to social policy in order to achieve sustainable and balanced economic growth.

Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components-the so-called agents-discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment.

Semantic Enrichment of a BIM Model Using Revit: Automatic Annotation of Doors in High-Rise Residential Building Models Using Machine Learning

Semantic Enrichment of a BIM Model Using Revit: Automatic Annotation of Doors in High-Rise Residential Building Models Using Machine Learning

An Algorithm for Simplifying 3D Building Models with Consideration for Detailed Features and Topological Structure

To tackle problems such as the destruction of topological structures and the loss of detailed features in the simplification of 3D building models, we propose a 3D building model simplification algorithm that considers detailed features and topological structures. Based on the edge collapse algorithm, the method defines the region formed by the first-order neighboring triangles of the endpoints of the edge to be collapsed as the simplification unit. It incorporates the centroid displacement of the simplification unit, significance level, and approximate curvature of the edge as influencing factors for the collapse cost to control the edge collapse sequence and preserve model details. Additionally, considering the unique properties of 3D building models, boundary edge detection and face overlay are added as constraints to maintain the model’s topological structure. The experimental results show that the algorithm is superior to the classic QEM algorithm in terms of preserving the topological structure and detailed features of the model. Compared to the QEM algorithm and the other two comparison algorithms selected in this paper, the simplified model resulting from this algorithm exhibit a reduction in Hausdorff distance, mean error, and mean square error to varying degrees. Moreover, the advantages of this algorithm become more pronounced as the simplification rate increases. The research findings can be applied to the simplification of 3D building models.

A Influência de Brises e Cortinas na Eficiência Energética de Edifícios de Escritório

The construction sector is responsible for a significant portion of energy consumption and greenhouse gas emissions. In this sense, it is extremely important that initiatives are developed to reduce the energy consumption of buildings, contributing to the sustainability of these buildings. Among the alternatives for reducing energy consumption is the possibility of inserting shading devices into building windows. In this context, this work aims to analyze the influence of the use of shading devices on windows of an office building model geographically located in Passo Fundo - RS. To this end, 24 computer simulations were carried out using the Energy Plus software in a model of an office building considering its windows without any shading device and, subsequently, with the installation of two different types of sun shades and curtains that act according to the internal temperature of the building. environment and with a pre-defined agenda. Single and double glazing were also tested. The results revealed positive influences of shading devices in the office building, reducing electrical energy consumption by up to 4.64% in relation to the reference case. Therefore, the use of sunshades and curtains can help reduce energy consumption in the building, maintaining the thermal comfort of the environment.

Random‐positioned‐sampling effect on probabilistic seismic demand modeling of modularized suspended buildings with free‐standing objects and architectural function regioning

AbstractThe definition of the targeted engineering demand parameters (EDPs) is important to probabilistic seismic demand modeling (PSDM), which produces probability density functions of EDPs conditioned on seismic intensity measure (IM). The targeted EDPs are usually defined at the group level to account for multiple components/units. Thus, they are affected by the considered range of units, i.e., the sample positions. For instance, the maximum peak floor acceleration (PFA) within the whole building differs from the maximum among only the important positions related to seismic loss. Additional uncertainties are induced in the PSDM of PFA if the sample positions vary when architectural function and non‐structural elements change. In this study, the aforementioned influence is termed random‐positioned‐sampling (RPS) effect, and it is investigated by targeting a modularized suspended building, which features the tuning mechanism, multiple major modes, uneven response envelopes, and notable non‐structural‐object‐structure interactions (NSOSI). Results show that the RPS effect lowers the maximum‐based group‐level EDP and increases the dispersion within the EDP sample sets, indicating that conventional PSDMs without considering the RPS effect are biased. The significance of the influence is positively correlated to the position‐wise coefficient of variation of EDP but negatively correlated to the density of sample positions. The combined influence of the NSOSI and the RPS effect is two‐sided for PSDM. The NSOSI amplifies the RPS effect via enlarging position‐wise dispersion of EDP, whereas, the RPS effect waives part of the detrimental scattered contributions from NSOSI. Overall, the IM performance is handicapped, even with IM optimization. However, it can be compensated if architectural function region information is acquired beforehand since the sample positions are restrained.

Regional/ethnic differences in ocular axial elongation and refractive error progression in myopic and non-myopic children.

To determine the regional and ethnic differences in ocular axial elongation and refractive error progression in myopic and non-myopic children. A retrospective analysis of 15 longitudinal clinical and population-based studies was conducted in the UK, Sweden, Australia (classified as European), China, and Vietnam (classified as East Asian) between 2005 and 2021. A total of 14,593 data points from 6208 participants aged 6-16 years with spherical equivalent from +6 to -6 D were analysed. Progression was annualised from longitudinal axial length and cycloplegic spherical equivalent (SE) refraction. Generalised estimating equation models including main effects and interactions were used for model building. Age and region-specific estimates for myopes and non-myopes and confidence intervals are reported. Factors affecting axial elongation and SE progression in children included being myopic, followed by age, region/ethnicity and sex. The magnitude of regional/ethnic differences was dependent on myopia and age. Axial elongation and SE progression were lower in European compared with East Asian children, but differences were reduced with increasing age and differences in axial elongation were larger in myopes than non-myopes. Age-specific regional/ethnic differences indicated that axial elongation for a 6-year-old East Asian myopic child was greater than a European child by 0.15 mm/year (0.58 vs. 0.43 mm/year) and by 0.09 mm/year (0.35 vs. 0.26 mm/year) for a 10-year-old myope. SE progression was lower in a 6-year-old European myope by 0.48 D/year and at 10 years of age by 0.34 D/year compared with an East Asian myope. There are regional/ethnic differences in age-specific refractive and axial growth patterns in both myopic and non-myopic eyes, with more marked differences in younger East Asian children who demonstrated a higher axial growth and greater negative SE shift than their non-Asian peers. Regional/ethnic differences in progression reflect environmental and ethnic variations. Age and region/ethnicity-specific estimates could contribute as a reference for future comparisons.

Effects of turbulence intensity and integral length scale on wind-induced vibrations of a three-dimensional aeroelastic square cylinder

Despite a wealth of prior research on the effects of free-stream turbulence (FST) on the aerodynamics of square cylinders, there is limited knowledge about their aeroelasticity such as characteristics of wind-induced vibrations that vary with the FST properties. This paper focuses on the investigation of the effects of FST properties including longitudinal turbulence intensity and integral length scale on the wind-induced vibrations of a three-dimensional (3D) aeroelastic square cylinder with an aspect ratio of 10 (a super-tall building model). Through wind tunnel testing, displacements of the aeroelastic model are measured in smooth flow and four turbulent flows to experimentally investigate its characteristics of fluid-solid interaction, including along- and across-wind vibrations, especially vortex-induced vibration and galloping. The effects of the turbulence intensity with a higher-level than previous studies are also investigated. The results show that the root-mean-square (RMS) displacements of the along-wind responses increase with the increase of turbulence intensity and are not affected by integral length scale. For the across-wind responses, in the non-lock-in region, the RMS displacements generally increase with the increase of turbulence intensity. In the lock-in region, the RMS displacements remain unchanged with the increase of turbulence intensity, while increase with the increase of integral length scale. This paper aims to improve the comprehension of the effects of FST on the wind-induced vibrations of 3D square cylinders and to furnish valuable insights for the wind-resistant design of slender structures, such as supertall buildings.