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        <title>Frontiers in Built Environment | Computational Methods in Structural Engineering section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/built-environment/sections/computational-methods-in-structural-engineering</link>
        <description>RSS Feed for Computational Methods in Structural Engineering section in the Frontiers in Built Environment journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-04-05T03:50:47.50+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2026.1756908</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2026.1756908</link>
        <title><![CDATA[Beyond fragility: physics-driven neural surrogates for seismic resilience prediction of bridges]]></title>
        <pubdate>2026-03-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jacob Atkins</author><author>Donya Hajializadeh</author><author>Waqas Iqbal</author><author>Farahnaz Soleimani</author>
        <description><![CDATA[Traditional fragility-based methods are rigorous, but they can be computationally intensive and difficult to scale to large bridge inventories, particularly when resilience assessments must propagate fragility outputs through functionality and recovery models for time-dependent decision support. This study presents a physics-driven neural surrogate framework that complements fragility-informed workflows by directly predicting a bridge-level seismic resilience index as a continuous system metric. Using pre-1971 concrete box-girder bridges as a case study, we generate a simulation-informed dataset from high-fidelity nonlinear time-history analyses in OpenSees, covering 1,600 bridge-ground motion scenarios. A multilayer perceptron (MLP) model is trained with systematic hyperparameter tuning over loss functions, optimizers, network depth, and regularization. The final MLP achieves over 97% prediction accuracy and outperforms baseline ensemble learning models. By learning directly from physics-based simulations, the proposed surrogate enables rapid and scalable resilience estimation, supporting retrofit prioritization, emergency planning, and resilience-informed design in seismically active regions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2026.1753382</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2026.1753382</link>
        <title><![CDATA[Interpretable machine learning for predicting the bearing capacity of double shear-bolted connections: a data-driven evaluation]]></title>
        <pubdate>2026-02-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Soheila Kookalani</author><author>Hongchen Liu</author><author>Tirtharaj Dash</author><author>Alwyn Mathew</author><author>Ioannis Brilakis</author>
        <description><![CDATA[IntroductionAccurate prediction of the bearing capacity of double shear-bolted connections in structural steel is essential for ensuring safety and efficiency in structural design. This study explores the application of ten machine learning algorithms to enhance prediction accuracy while addressing the interpretability challenges often associated with such models.MethodsModels were tuned with 10-fold crossvalidation and assessed using RMSE, R2 and a20 accuracy index. A comprehensive sensitivity analysis evaluates the influence of input parameters, while advanced interpretability techniques, such as partial dependence plots, accumulated local effects, and Shapley additive explanations, are employed alongside parametric studies to elucidate the decision-making processes of the models.ResultsThese methods facilitate the identification of critical variables that influence bearing capacity predictions at both local and global scales.DiscussionThe study demonstrates that machine learning can be a trustworthy and data-driven complement to conventional mechanics-based approaches, when coupled with rigorous interpretability, advancing both safety and efficiency in steelconnection design. The findings highlight the potential of interpretable machine learning approaches to not only improve predictive precision but also provide actionable insights into complex model behaviours, ultimately advancing structural engineering practices and promoting data-driven design methodologies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1700908</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1700908</link>
        <title><![CDATA[Numerical investigation of a novel steel connection for panelized modular houses]]></title>
        <pubdate>2026-01-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mostafa Elhadary</author><author>Ahmed Bediwy</author><author>Ahmed Elshaer</author>
        <description><![CDATA[Indigenous communities in Canada, particularly those in remote areas, face a persistent shortage of adequate housing. Modular construction offers a potential solution, yet challenges related to transportation and lifting limit its widespread adoption. This study proposes an innovative steel bolted connection using hollow structural sections (HSS) to improve constructability and performance in modular housing. The connection was experimentally tested and validated using three-dimensional finite element models. A parametric study on one- and two-dimensional prototypes examined the influence of stiffeners, bolt arrangement, bolt number, and plate thickness on the connection’s structural performance. The results showed that the AR1.5 bolt arrangement increased capacity through early bolt bearing but reduced ultimate rotation by 50%, whereas the AR0.6 arrangement shifted failure to the column due to local buckling. Increasing plate thickness from 10 mm to 15 mm increased capacity by up to 7% and ductility by 11%, while increasing the number of bolts from six to ten improved capacity by up to 22%, depending on the arrangement. The addition of a 10-mm stiffener reduced ultimate rotation by approximately 60% due to local buckling. These findings highlight the critical role of bolt configuration and reinforcement techniques in optimizing both strength and deformation capacity, providing guidance for the design of efficient and durable modular housing connections.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1724879</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1724879</link>
        <title><![CDATA[Evaluation of MobileNetV3-Large for crack classification across low- and high-resolution images]]></title>
        <pubdate>2025-12-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liujie Chen</author><author>Haodong Yao</author><author>Ke Gan</author><author>Zanyu Huang</author><author>Jing Zhang</author><author>Ching-Tai Ng</author><author>Jiyang Fu</author>
        <description><![CDATA[IntroductionThis paper evaluates the robustness and generalization ability of five recently developed Convolutional Neural Networks (CNNs), Visual Geometry Group 16 (VGG16), Google Inception Net (GoogLeNet), Mobile Network version 3 Large (MobileNetV3-Large), Efficient Network B0 (EfficientNetB0) and Efficient Network version 2 Small (EfficientNetV2-S), on crack recognition and classification.MethodsThis study proposes a semantic segmentation based on VGG16- U-Net to address the issue of background noise in the images automatically and the transfer learning with fine-tuning is used to improve the performance of the CNNs in the bridge crack image dataset and building crack image dataset (transverse cracks, vertical cracks, oblique cracks and irregular cracks).ResultsThe results indicate that the MobileNetV3-Large has the best performance. For the low-resolution building crack image dataset, the accuracy of the crack recognition reaches 99.58% and the F1-score reaches 99.60%. The accuracy of the classification reaches 94.70% and the Macro-F1 reaches 94.71%. For the higher resolution bridge crack image dataset, the accuracy of the classification reaches 95.70% and the Macro-F1 reaches 95.67%.DiscussionThe results show that the MobileNetV3-Large has the best robustness and generalization ability with a small CNN size and the shortest training time.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1692879</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1692879</link>
        <title><![CDATA[Forecasting bond strength of various FRP bars with different surface characteristics in concrete using machine learning models]]></title>
        <pubdate>2025-11-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ameer M. Salih</author><author>Aso A. Abdalla</author><author>Sardar R. Mohammad Ali</author><author>Tre A. Abdullah</author>
        <description><![CDATA[Fiber-reinforced polymer (FRP) bars are gaining prominence in civil infrastructure due to their high strength-to-weight ratio, corrosion resistance, and low thermal conductivity. The bond strength (BS) between FRP bars and concrete, which is influenced by surface treatments like sand-coating or ribbing, plays a critical role in ensuring structural performance and durability. This study aims to predict the bond strength of different FRP bar types and surface characteristics in concrete using machine learning models. A total of 416 datasets from standard pull-out tests were collected and statistically analyzed, considering variables such as bar type, surface treatment, concrete compressive strength, bar diameter, bonded length, concrete cover, and FRP bar tensile properties. Two machine learning models, Artificial Neural Networks (ANN) and Extreme Gradient Boosting (XGBoost) were developed for bond strength prediction. Model performance was evaluated using Root Mean Squared Error (RMSE), Correlation Coefficient (R), and Scatter Index (SI). XGBoost demonstrated superior performance with lower RMSE and SI, and higher R values in 5-fold cross-validation. Sensitivity analysis identified concrete compressive strength as the most significant input in bond strength prediction. Additionally, main effect plots and Analysis of Variance (ANOVA) tests were conducted to further investigate the relationships between variables. These findings contribute to a more accurate understanding of FRP bar-concrete interactions, facilitating the optimization of structural design.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1693218</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1693218</link>
        <title><![CDATA[Stacked ensemble and SHAP-based approach for predicting plastic rotational capacity in RC columns]]></title>
        <pubdate>2025-10-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andrei-Odey Kadhim</author><author>Iolanda-Gabriela Craifaleanu</author><author>Eugen Lozincă</author>
        <description><![CDATA[The accurate estimation of plastic rotational capacity in reinforced concrete (RC) elements is essential for performance-based seismic design and structural safety assessments. In this study, an extensive experimental database, comprising 258 rectangular and 151 circular RC column specimens, was compiled based on open data available and used to train machine learning models for predicting this parameter. Three algorithms, i.e. Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were implemented and optimized using grid search within a nested cross-validation framework. The predictive performance was evaluated by averaging the coefficient of determination (R2) across five outer folds, while final accuracy was assessed on the test set using both R2, the Mean Absolute Error (MAE), the root mean squared error (RMSE), and the mean absolute percentage error (MAPE). Model interpretability was improved using SHAP (SHapley Additive exPlanations) analysis, which quantified the influence of input parameters on predictions. Finally, a stacking ensemble model was developed to integrate the strengths of the individual regressors. The proposed methodology demonstrates increased accuracy and robustness in predicting the plastic rotational capacity of both circular and rectangular RC columns, providing a valuable tool for seismic assessment and structural reliability analysis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1661712</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1661712</link>
        <title><![CDATA[Wind tunnel testing to study turbulent wind field effect on wind load and wind-induced response of TV tower]]></title>
        <pubdate>2025-10-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Daqiao Xia</author><author>Cheng Pei</author>
        <description><![CDATA[For steel-constructed TV towers, complex aerodynamic profiles and low damping are typical characteristics—two attributes that render wind-induced response and wind load critical considerations in their design. Additionally, their wide distribution across diverse terrains exposes these structures to varied wind conditions, further complicating wind-resistant design efforts. To explore how wind field parameters affect wind load and wind-induced response, this study took a 240-m-high TV tower as the engineering background, simulated different turbulent wind fields in a wind tunnel, conducted force measurement tests using a high-frequency dynamic balance (with the model segmented into seven sections to improve accuracy), calculated via the equivalent static wind load (ESWL) method (considering the first three modes), and verified with the complete quadratic combination (CQC) method. Results within the tested range show that mean wind force decreases with increasing turbulence intensity, while the root mean square (RMS) of wind force increases correspondingly; conversely, the RMS of the tower’s wind-induced response decreases as turbulence intensity rises. These findings highlight the need to comprehensively consider mean and fluctuating wind effects and their impact on structural response in the wind-resistant design of steel TV towers.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1672716</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1672716</link>
        <title><![CDATA[Data-driven models for human–structure interaction based on MLP and NARX neural networks]]></title>
        <pubdate>2025-10-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Daniel Mena-Sanchez</author><author>Natividad Garcia-Troncoso</author><author>Wilfredo Alfonso</author><author>Albert R. Ortiz</author><author>Daniel Gomez</author>
        <description><![CDATA[Structural design often neglects the dynamic effects induced by human activities. Excessive vibrations in structures such as pedestrian bridges, grandstands, slabs, and stairways have highlighted the analysis as dynamic systems of humans interacting with structures. This phenomenon, commonly referred to as “human–structure interaction” (HSI), is investigated in this study using experimental records obtained from a cantilever steel frame specially constructed to represent a variety of structures susceptible to the HSI phenomenon. This study aims to develop and evaluate artificial neural network (ANN) models capable of representing subjects in the passive condition of HSI using only simple anthropometric parameters. Two models—Nonlinear Auto-Regressive with eXogenous input (NARX) and MultiLayer Perceptron (MLP) —are implemented and compared with a conventional Mass-Spring-Damper (MSD) model. The results show that the ANN models significantly outperform the MSD model, achieving lower Normalized Mean Square Error (NMSE) values both in time-response prediction (20.23% for NARX and 25.07% for MLP vs. 30.19% for MSD) and frequency-response prediction (16.00% for NARX and 17.05% for MLP vs. 26.01% for MSD). These findings demonstrate that the proposed ANN-based models can predict the dynamic response of individual subjects using only simple anthropometric parameters such as mass and height. This approach provides a practical and efficient tool for modeling HSI in civil engineering applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1648231</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1648231</link>
        <title><![CDATA[Comprehensive analysis of a single-story single-bay RC frame with varied reinforcement detailing]]></title>
        <pubdate>2025-08-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ammar T. Al-Sayegh</author>
        <description><![CDATA[This study examines various reinforcement detailing approaches in Single-Story-Single-Bay (SSSB) reinforced concrete (RC) frames using the design tool SAP 2000 and nonlinear finite element analysis (NLFEA) with ABAQUS. A finite element model of the SSSB was developed in ABAQUS, adjusted based on experimental data from prior tests. The Concrete Damaged Plasticity (CDP) model simulated the behavior of concrete, while steel reinforcement bars were modelled as bilinear elastoplastic materials. After calibration, the peak lateral load and displacement values from the FEA models closely matched experimental results. The Control Model (CM) served as a basis for new models: (i) Half Diameter of Stirrups in the Beam (HDB), (ii) Half Diameter of Stirrups in both Beam and Columns (HDBC), and (iii) Double Spacing of Stirrups in Beam and Columns (DSBC). This analysis evaluated the effect of key design parameters, specifically the transverse reinforcement ratio (ρt), on SSSB’s load-carrying capacity. Results showed HDB had a 10.2% increase in lateral load compared to CM, while HDBC and DSBC demonstrated decreases of 15.8% and 15.5%, respectively, relative to experimental values.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1612575</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1612575</link>
        <title><![CDATA[Responsible AI in structural engineering: a framework for ethical use]]></title>
        <pubdate>2025-07-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vagelis Plevris</author><author>Haidar Hosamo</author>
        <description><![CDATA[The integration of Artificial Intelligence (AI) into structural engineering holds great promise for advancing analysis, design, and maintenance. However, it also raises critical ethical and governance challenges—including bias, lack of transparency, accountability gaps, and equity concerns—which are particularly significant in a discipline where public safety is paramount. This study addresses these issues through eight fictional but realistic case studies that illustrate plausible ethical dilemmas, such as algorithmic bias in predictive models and tensions between AI-generated recommendations and human engineering judgment. In response, the study proposes a structured framework for responsible AI implementation, organized into three key domains: (i) Technical Foundations (focusing on bias mitigation, robust validation, and explainability); (ii) Operational and Governance Considerations (emphasizing industry standards and human-in-the-loop oversight); and (iii) Professional and Societal Responsibilities (advocating for equity, accessibility, and ethical awareness among engineers). The framework offers actionable guidance for engineers, policymakers, and researchers seeking to align AI adoption with ethical principles and regulatory standards. Beyond offering practical tools, the study explores broader theoretical and institutional implications of AI, including risks associated with model drift, the need for lifecycle oversight, and the importance of cultural and geographic adaptability. It also outlines future challenges and opportunities, such as incorporating AI ethics into engineering education and considering the ethical impact of emerging technologies like quantum computing and digital twins. Rather than offering prescriptive answers, the study aims to initiate an essential dialogue on the evolving role of AI in structural engineering, equipping stakeholders to manage its benefits and risks while upholding trust, fairness, and public safety.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1618329</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1618329</link>
        <title><![CDATA[Comparative study of NLFE models for simulating settlement-induced damage in masonry façades: macro- and simplified micro-models]]></title>
        <pubdate>2025-06-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alfonso Prosperi</author><author>Michele Longo</author><author>Paul A. Korswagen</author><author>Giorgia Giardina</author><author>Jan G. Rots</author>
        <description><![CDATA[Damage assessment for masonry structures subjected to settlement is crucial for ensuring structural safety, guiding repairs, and preserving the built environment. Non-linear finite element modelling offers an effective approach for this purpose, though balancing model complexity, computational cost, and predictive reliability remains a key challenge. This study addresses the absence of a systematic comparison between macro- and simplified micro-modelling strategies for such analyses, clarifying their respective strengths, limitations, and sensitivity to key parameters. The performance and accuracy of semi-coupled NLFEM models are compared in simulating the response of a 1/10th scaled masonry façade under settlement, available from prior research. The two approaches considered are: simplified micro-modelling, where bricks are represented as expanded blocks with non-linear interfaces for mortar joints and their contact edges, and macro-modelling, where masonry is homogenised into an equivalent orthotropic composite material. The macro-models employ two well-established constitutive models, the Total Strain Rotating Crack Model (TSRCM) and the Engineering Masonry Model (EMM), to capture the non-linear cracking behaviour of masonry. Sensitivity analyses assess the influence of base interface models and the interface’s tangential stiffness. The results show how the selection of the modelling approach depends on the analysis objective: The macro-model with the Engineering Masonry Model best predicts damage severity, deviating by only 10% from the experiment, further improved by calibrating the minimum head-joint tensile strength. While all models yield similar predictions for vertical displacements of the façade, the TSRCM better captures overall and horizontal displacements, whereas the simplified micro-model more accurately represents the crack pattern. The EMM-based macro-models are the most computationally efficient, with TSRCM requiring 1.5 times the CPU time of EMM, and the micro-model requiring twice as much. The analysis also shows that the TSRCM-based macro-model is more sensitive to variations in the type of base interface models and base interface tangential stiffness, convergence criteria, incremental-iterative procedure, and analysis settings, whereas the EMM macro-model and the simplified micro-model are less affected. By identifying the strengths and limitations of each modelling approach, this study supports informed modelling choices for a more reliable assessment of settlement damage, contributing to the effective protection of existing masonry structures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1565348</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1565348</link>
        <title><![CDATA[Research on a risk assessment model for dense urban cable channels based on fuzzy mathematics]]></title>
        <pubdate>2025-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yongjie Nie</author><author>Daoyuan Chen</author><author>Shuai Zheng</author><author>Xiaowei Xu</author><author>Xilian Wang</author><author>Zhensheng Wu</author>
        <description><![CDATA[With the acceleration of urbanization, the safe and stable operation of dense urban cable channels is of great importance to the guarantee of urban power and communication systems. Cable channels face many sources of risk that bring great challenges to urban power supplies. Most existing risk assessment methods are based on accurate mathematical models, which require clear and deterministic boundaries of assessment indicators. These methods have difficulty in dealing with the fuzziness and uncertainty of cable channel risk factors, such as the challenge of determining the degree of aging of cable insulation or the degree of influence of external environmental factors that cannot be simply quantified. This paper presents a risk assessment model of a dense urban cable passage based on fuzzy mathematics. The model combines a membership function with a fuzzy comprehensive evaluation method to analyze and classify the risk factors of a dense urban cable passage. Eight risk factors were identified, including external damage, facility defects, and non-standard cable laying, and the importance of each factor was evaluated by constructing a membership matrix based on historical data and expert scoring methods. A typical dense cable trench and cable tunnel in actual operation in a region of China Southern Power Grid are analyzed, and the risk level is calculated by MATLAB 2021a programming. The results show that the model can effectively assess the level of risk and clearly show the impact of individual risk factors on the overall risk. For example, in the cable trench risk assessment, the model accurately identifies that external damage and cable overheating risk factors lead to moderate risk, and the remaining six factors are low risk. In the cable tunnel assessment, the corresponding risk level of each risk factor is also accurately determined. This indicates that the evaluation method based on fuzzy mathematics can not only quantify the uncertainty of risk factors but also improve the rationality of the evaluation results and provide a scientific decision basis for the safety management and maintenance of cable channels. The model has significant advantages over traditional evaluation methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2025.1561429</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2025.1561429</link>
        <title><![CDATA[Geometric characterization of locally corroded surfaces in steel bridge girders]]></title>
        <pubdate>2025-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tao Zhang</author><author>Michael Vaccaro</author><author>Arash Zaghi</author><author>Amvrossios Bagtzoglou</author>
        <description><![CDATA[The aging of steel bridge girders is often compounded by corrosion at girder ends due to leaking deck joints. With 6.8% of U.S. bridges in poor condition, there is an urgent need for accurate yet efficient methods to assess the residual load-bearing capacity of corroded girders. Traditional assessment methods often represent corrosion as uniform section loss or rely on simplified surface representations, compromising the accuracy of the residual capacity estimation. To address these limitations, this paper proposes a novel approach for characterizing the geometry of locally corroded steel surfaces by decomposing the corroded region into high-frequency (fine surface textures) and low-frequency (global shape) components using multilevel Lanczos filters. Validated using 3D scans collected from a 57-year-old in-service bridge, our case study shows that each high-frequency component can be modeled as a stationary random field using a Hole-Gaussian autocorrelation function, with correlation lengths inversely proportional to the cutoff frequencies of the Lanczos filters. The low-frequency component is accurately characterized by a bivariate Lagrange polynomial fitted via a 4 × 4 coefficient matrix, with average volume errors of less than 1% and normalized root mean square errors under 10% for most surfaces. The technique results in a manage set of parameters that can be used to investigate the effects of corrosion damage on the behavior of corroded steel members.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2024.1524027</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2024.1524027</link>
        <title><![CDATA[Integrating feedback control for improved human-structure interaction analysis]]></title>
        <pubdate>2025-02-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Santiago A. Lopez</author><author>Daniel Gomez</author><author>Albert R. Ortiz</author><author>Sandra Villamizar</author>
        <description><![CDATA[The human body, composed of interconnected subsystems with complex dynamic behavior, is often oversimplified or neglected by structural designers and building codes. Human-induced loads, whether passive (e.g., standing, sitting) or active (e.g., walking, dancing, jumping), considerably impact the dynamic response of structures such as grandstands, slender slabs, and pedestrian bridges, highlighting the necessity for their consideration in design. This study introduces three closed-loop control models to represent the human-structure interaction (HSI) effect: a Proportional Integral (PI) controller, the Pole Placement control algorithm (PP), and the Linear Quadratic Regulator with an Observer (LQR + L). While well-established in robotics and automation engineering, these control algorithms represent a novel and transformative approach when applied to HSI. They offer an intuitive and effective framework for modeling the dynamic feedback mechanisms inherent in HSI. The model parameters are obtained using global optimization and curve fitting methods, followed by experimental validation on a test structure. The results of this study indicate that feedback controllers accurately predict the experimental structural response for different subjects. These findings highlight the importance of incorporating HSI effects into structural design, promising the design of safer and more comfortable structures in human-occupied environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2024.1492235</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2024.1492235</link>
        <title><![CDATA[Enhancing the assessment of in situ beam–column strength through probing and machine learning]]></title>
        <pubdate>2024-12-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jin Terng Ma</author><author>Luke Lapira</author><author>M. Ahmer Wadee</author>
        <description><![CDATA[Beam–columns are designed to withstand the concurrent action of both axial and bending stresses. Therefore, when assessing the structural health of an in situ beam–column, both of these load effects must be considered. Probing, having been shown recently to be an effective methodology for predicting the in situ health of prestressed stayed columns under axial compression, is applied currently for predicting the in situ health of beam–columns. Although probing stiffness was sufficient for predicting the health of prestressed stayed columns, additional data are required to predict both the moment and axial utilisation ratios. It is shown that the initial lateral deflection is a suitable measure considered alongside the probing stiffness measured at various probing locations within a revised machine learning (ML) framework. The inclusion of both terms in the ML framework produced an almost exact prediction of both the aforementioned utilisation ratios for various design combinations, thereby demonstrating that the probing framework proposed herein is an appropriate methodology for evaluating the structural strength reserves of beam–columns.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2024.1488236</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2024.1488236</link>
        <title><![CDATA[Identification of the factors influencing the liquid sloshing wave height in a sloped bottom tank under horizontal excitation using PCA approach]]></title>
        <pubdate>2024-12-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wenhao Ren</author><author>Zuowei Zhong</author>
        <description><![CDATA[The dynamic behavior of liquid storage tanks represents a pivotal research area concerning structural safety and reliability. Notably, sloped bottom tanks exhibit heightened sloshing with reduced liquid mass compared to rectangular counterparts. This study adopts a hybrid approach that seamlessly integrates the linear potential-flow theory, renowned for its analytical rigor in fluid dynamics modeling, with principal component analysis (PCA), a potent technique for dimensionality reduction and feature extraction. The hybrid methodology initially employs the linear potential-flow theory to simulate the fundamental fluid dynamics within sloped bottom tanks subjected to horizontal excitation. Subsequently, PCA is applied to the simulated data, identifying key components of liquid sloshing wave height variations. Through the analysis of these principal components, an accurate model of the maximum sloshing wave height is derived, achieving a close correlation with ANSYS simulation results, exhibiting a correlation coefficient of 0.98 and a mean absolute error of 2.5%. This approach uniquely facilitates the evaluation of the intricate interplay between multiple factors, including tank geometry and excitation frequency, on the dynamic characteristics of liquid sloshing waves in sloped bottom tanks. The findings emphasize the significant influence of tank height and tilt angle, with a sensitivity analysis indicating a 4.07% increase in maximum wave height per degree increase in tilt angle under specified experimental conditions. This comprehensive methodology not only enhances understanding of the complex liquid sloshing phenomenon but also provides precise theoretical and practical guidance for fluid sway control strategies. Future investigations will further expand the scope and elucidate the fundamental mechanisms governing liquid sloshing dynamics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2024.1497123</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2024.1497123</link>
        <title><![CDATA[Validation of vibration reduction in barge-type floating offshore wind turbines with oscillating water columns through experimental and numerical analyses]]></title>
        <pubdate>2024-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Payam Aboutalebi</author><author>Aitor J. Garrido</author><author>Julieta Schallenberg-Rodriguez</author><author>Izaskun Garrido</author>
        <description><![CDATA[Floating offshore wind turbines (FOWTs) are highly susceptible to vibrations caused by wind and sea wave oscillations, necessitating effective vibration reduction strategies to ensure stability and optimal performance. This study investigates the effectiveness of a barge-type FOWT integrated with oscillating water columns (OWCs) in reducing oscillations, particularly in rotational modes. A hybrid FOWT-OWCs system was designed, and its vibration mitigation capabilities were assessed through both numerical simulations and experimental tests. The numerical approach focused on controlling airflow in the OWCs, while the experimental tests validated these results under similar conditions. A strong agreement between the simulations and experiments was observed, particularly in reducing platform pitch oscillations, even under irregular wave conditions. The open OWC-based platform outperformed the closed design, reducing pitch angle oscillations from 17.51° to 14.38° for waves with a 10-s dominant frequency. Benchmark tests confirmed this trend, with the open moonpool-based platform achieving a reduction from 18.41° to 12.23°. These findings demonstrate the potential of OWCs to improve the stability and performance of FOWTs, with experimental validation providing confidence in the numerical predictions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2024.1415032</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2024.1415032</link>
        <title><![CDATA[A framework for computer vision for virtual-realistic multi-axial real-time hybrid simulation]]></title>
        <pubdate>2024-08-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>W. Saeger</author><author>P. Miranda</author><author>G. Toledo</author><author>C. E. Silva</author><author>A. Ozdagli</author><author>F. Moreu</author>
        <description><![CDATA[Real-time hybrid simulation has gained popularity over the last 20 years as a viable and cost-effective method of testing dynamic systems that cannot be tested using traditional methods. The emergence of multi-axial Real-time Hybrid Simulation (maRTHS) has led to an increase in the allowable fidelity of the numerical and experimental substructures. The testing community can now replicate multiple-degree-of-freedom (MDOF) responses of both substructures and thus can perform more representative tests. However, with this increased fidelity of the substructures comes an increased complexity of controlling these components. Specifically, multi-axial hydraulic actuator assemblages require nonlinear coordinate transformations to derive plant displacements as the force transducers on the actuators are not capable of performing this task directly. Recently, benchmark problems have been provided to the RTHS community in the form of virtual simulations. Virtual simulation refers to a fully virtual testing methodology where numerical and physical components are represented virtually. This approach enables the RTHS community to evaluate various control algorithms without the need to recreate physical components. This project aims to demonstrate the capability of computer vision-based displacement tracking in a realistic virtual simulation of the experimental substructure in avoiding excess nonlinear coordinate transforms. The tracking algorithm utilizing the Lucas-Kanade optical flow method is tested in the virtual simulation environment which is set up using real-time 3D creation engine, Unreal Engine 4 (UE4), and computer graphics software, Blender. This environment interfaces with MATLAB/Simulink, more specifically “Simulation Tool for v-maRTHS benchmark” developed for multi-axial tests. The result of this study establishes a novel framework for applying computer vision-based tracking algorithms and sensing in v-maRTHS simulations using simulated cameras within virtual simulation environments. A computer vision displacement tracking algorithm is developed and optimized to work in tandem with a MIMO PI controller to reduce tracking time delays within 31.25 milliseconds while tracking the nodal displacement and rotation of the frame within a normalized RMSE of 1.24 and 1.10 respectively.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2024.1463682</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2024.1463682</link>
        <title><![CDATA[Reliability analysis of mooring chains for floating offshore wind turbines]]></title>
        <pubdate>2024-08-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Guangming Li</author><author>Tianguo Pan</author><author>Ruming Feng</author><author>Liyun Zhu</author>
        <description><![CDATA[As offshore wind farms move into deeper waters, around 80 m, the high costs necessitate replacing bottom-fixed turbines with floating offshore wind turbines, which require mooring systems to maintain stability within design limits. Data from previous projects in China indicate that mooring systems can constitute about 20% of the total investment. Thus, reducing mooring system costs can significantly benefit the development of next-generation floating wind farms. This paper discusses the reliability analysis of mooring chains for floating wind turbines to optimize inspection plans and strategies, thereby saving on maintenance costs over their design lifetime. A case study on S-N curve based fatigue reliability analysis is conducted using both Monte Carlo Simulation and First Order Reliability Method (FORM), with consistent results from both methods. Additionally, three sensitivity analysis cases identify key parameters for the fatigue reliability analysis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbuil.2024.1394952</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbuil.2024.1394952</link>
        <title><![CDATA[Robust decentralized adaptive compensation for the multi-axial real-time hybrid simulation benchmark]]></title>
        <pubdate>2024-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>María Quiroz</author><author>Cristóbal Gálmez</author><author>Gastón A. Fermandois</author>
        <description><![CDATA[Real-time hybrid simulation (RTHS) is a powerful and highly reliable technique integrating experimental testing with numerical modeling for studying rate-dependent components under realistic conditions. One of its key advantages is its cost-effectiveness compared to large-scale shake table testing, which is attained by selectively conducting experimental testing on critical parts of the analyzed structure, thus avoiding the assembly of the entire system. One of the fundamental advancements in RTHS methods is the development of multi-dimensional dynamic testing. In particular, multi-axial RTHS (maRTHS) aims to prescribe multi-degree-of-freedom (MDOF) loading from the numerical substructure over the test specimen. Under these conditions, synchronization is a significant challenge in multiple actuator loading assemblies. This study proposes a robust and decentralized adaptive compensation (RoDeAC) method for the next-generation maRTHS benchmark problem. An initial calibration of the dynamic compensator is carried out through offline numerical simulations. Subsequently, the compensator parameters are updated in real-time during the test using a recursive least squares adaptive algorithm. The results demonstrate outstanding performance in experiment synchronization, even in uncertain conditions, due to the variability of reference structures, seismic loading, and multi-actuator properties. Notably, this achievement is accomplished without needing detailed information about the test specimen, streamlining the procedure and reducing the risk of specimen deterioration. Additionally, the tracking performance of the tests closely aligns with the reference structure, further affirming the excellence of the outcomes.]]></description>
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