Architecture, Structures and Construction最新文献_第2页

Coupled dynamic interaction analysis of box-girder bridge and high-speed train using an artificial neural network framework 基于人工神经网络框架的箱梁桥与高速列车耦合动力相互作用分析

Architecture, Structures and Construction Pub Date : 2026-09-18 DOI: 10.1007/s44150-026-00219-w

Virajan Verma, Dina N. Al Sheikh, Khair Ul Faisal Wani, K. Nallasivam, Karan Singh

{"title":"Coupled dynamic interaction analysis of box-girder bridge and high-speed train using an artificial neural network framework","authors":"Virajan Verma, Dina N. Al Sheikh, Khair Ul Faisal Wani, K. Nallasivam, Karan Singh","doi":"10.1007/s44150-026-00219-w","DOIUrl":"10.1007/s44150-026-00219-w","url":null,"abstract":"<div><p>This study focuses on the complex dynamic interaction between high-speed trains and curved, thin-walled box-girder bridges, where traditional computational approaches are typically insufficient for lengthy parametric simulations. A thorough numerical model was developed by combining a 38-degree-of-freedom train system and a slab track structure with a thin-walled, curved box-beam finite element model. The resulting coupled dynamic simulation data were utilized to train two different Artificial Neural Network (ANN) models that predicted essential structural reactions based on two significant input variables, train velocity and subgrade suspension system parameters. The parametric analysis yielded substantial results for the dynamic behaviour of the bridge. The model demonstrated that increasing train velocity from 150 km/h to 400 km/h significantly increases all stress resultants such as shear force, bending moment and torsional moment. Changing the subgrade suspension parameters, on the other hand, led to only an insignificant change in the bridge’s dynamic response, implying a minor impact on global behaviour. Both ANN frameworks underwent intensive validation, displaying remarkable predictive performance. Diagnostic plots repeatedly revealed near-perfect correlation coefficients (R nearing 1), confirming the robustness of the Levenberg-Marquardt training procedure, which achieved rapid, stable convergence. Furthermore, the Error Histogram analysis confirmed that the network produces unbiased prediction residuals, indicating that the model is reliable and robust for generalization. The successful implementation of the ANN framework offers structural engineers an accurate, computationally efficient tool for performing quick safety assessments and design optimization on high-speed rail infrastructure.</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic taxonomy and adaptive framework for multi-constraint optimization in steel frame structures 钢框架结构多约束优化的系统分类和自适应框架

Architecture, Structures and Construction Pub Date : 2026-09-18 DOI: 10.1007/s44150-026-00223-0

Bikramjit Singh, Gurpreet Singh Dhanoa, Jagbir Singh

{"title":"A systematic taxonomy and adaptive framework for multi-constraint optimization in steel frame structures","authors":"Bikramjit Singh, Gurpreet Singh Dhanoa, Jagbir Singh","doi":"10.1007/s44150-026-00223-0","DOIUrl":"10.1007/s44150-026-00223-0","url":null,"abstract":"<div><p>A systematic and comprehensive review of optimization algorithms for steel frame structure design under multiple engineering constraints is presented. The study focuses on key design objectives, including weight minimization, cost efficiency, structural stability, and load resistance. A structured review methodology based on PRISMA guidelines is employed to analyse recent studies published between 2020 and 2025, ensuring methodological rigor and unbiased selection of relevant literature.A novel taxonomy is developed to classify optimization techniques into gradient-based, heuristic, nature-inspired, and hybrid approaches. The performance of these algorithmic categories is comparatively evaluated using metrics such as convergence speed, computational cost, and solution accuracy. To standardize the evaluation, a Comparative Performance Index (CPI) is introduced, enabling quantitative comparison across diverse optimization strategies.The analysis reveals a clear transition from conventional methods towards hybrid and AI-driven optimization techniques, driven by their enhanced adaptability and superior performance in handling complex, multi-constraint structural problems. Furthermore, several research gaps are identified, including limited real-world validation, insufficient consideration of dynamic loading conditions, and lack of integration of sustainability metrics.An adaptive optimization framework is proposed to address these limitations by integrating real-time data, AI-based learning mechanisms, and multi-objective constraint handling. The findings provide a structured understanding of current advancements and outline future research directions for intelligent, scalable, and sustainable steel structure optimization.</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44150-026-00223-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hyperparameter-Optimized machine learning approach for compressive strength prediction of metakaolin concrete reinforced with glass fiber 玻璃纤维增强偏高岭土混凝土抗压强度预测的超参数优化机器学习方法

Architecture, Structures and Construction Pub Date : 2026-09-17 DOI: 10.1007/s44150-026-00216-z

Rima Sahani, Nihar Ranjan Mohanta, Ipsita Mohanty, Debasree, Ankita Sahani, Somya Priyadarsini Sahani, Truptimala Pattnaik

{"title":"Hyperparameter-Optimized machine learning approach for compressive strength prediction of metakaolin concrete reinforced with glass fiber","authors":"Rima Sahani, Nihar Ranjan Mohanta, Ipsita Mohanty,  Debasree, Ankita Sahani, Somya Priyadarsini Sahani, Truptimala Pattnaik","doi":"10.1007/s44150-026-00216-z","DOIUrl":"10.1007/s44150-026-00216-z","url":null,"abstract":"<div><p>This paper introduces an optimized machine learning framework for the prediction of the compressive strength of metakaolin concrete with glass fiber reinforcement based on the comparison between artificial neural network and deep neural network models. The innovative aspect of the proposed framework is the optimization of the hyperparameters of both models using Keras Tuner in order to obtain the best configuration of the networks and their training procedure. A set of 168 concrete mixtures was generated experimentally and was used to train and validate the models. The input features used were the quantity of cement, metakaolin, water, aggregate, superplasticizer, glass fiber, and curing time, and the compressive strength was used as the output. Predictive accuracy of the developed models was determined through mean square error, mean absolute error, and coefficient of determination. Both models exhibited high predictive accuracy and yielded identical outcomes. The developed artificial neural network model obtained mean square error of 11.29, mean absolute error of 2.70, and coefficient of determination of 0.892, whereas deep neural network model, obtained mean square error of 11.69, mean absolute error of 2.64, and coefficient of determination of 0.884. Even though the deep neural network yielded a slightly lower mean absolute error, the artificial neural network outperformed the deep neural network in terms of accuracy. This research framework can be regarded as an accurate and efficient alternative to traditional laboratory experiments for quick evaluation of compressive strength and subsequent design and optimization of sustainable concrete reinforced with metakaolin and glass fibers.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of rubber latex based concrete from the perspective a circular economy 循环经济视角下橡胶乳胶基混凝土研究综述

Architecture, Structures and Construction Pub Date : 2026-09-16 DOI: 10.1007/s44150-026-00218-x

Badrinarayan Rath, T. R. Praveenkumar, Ramu Debnath, Binaya Patnaik, Kavindra Singh Dhami, Keerat Kumar Gupta

{"title":"A review of rubber latex based concrete from the perspective a circular economy","authors":"Badrinarayan Rath, T. R. Praveenkumar, Ramu Debnath, Binaya Patnaik, Kavindra Singh Dhami, Keerat Kumar Gupta","doi":"10.1007/s44150-026-00218-x","DOIUrl":"10.1007/s44150-026-00218-x","url":null,"abstract":"<div><p>Concrete is the most widely used construction material worldwide; however, its susceptibility to cracking and durability-related deterioration has encouraged the development of innovative modification techniques. Among these, rubber latex has emerged as a promising polymeric admixture for enhancing the performance of concrete. This review critically evaluates the influence of natural rubber latex (NRL), styrene–butadiene rubber (SBR) latex, and other synthetic latexes on the fresh, mechanical, durability, and microstructural properties of concrete from a sustainable and circular economy perspective. Unlike previous reviews that focused primarily on individual performance aspects, this study provides an integrated assessment of engineering performance, durability enhancement, and sustainability benefits. The reviewed literature indicates that optimum rubber latex dosages generally improve workability by 15–35%, compressive strength by up to 20%, flexural strength by up to 42.8%, and split tensile strength by up to 25%. Significant reductions in chloride ingress (up to 22%) and water sorptivity (up to 60%) have also been reported. These improvements are primarily attributed to polymer film formation, pore refinement, enhanced interfacial transition zone (ITZ) characteristics, and reduced permeability. Among the investigated latexes, SBR generally demonstrates more consistent improvements in strength and durability than NRL. The scientific value of this review lies in its comprehensive synthesis of dispersed research findings and its evaluation of rubber latex-modified concrete within a circular economy framework. Improved durability and service-life extension can reduce maintenance requirements and support sustainable infrastructure development. However, existing studies remain limited by insufficient long-term field validation, lack of standardized mix-design procedures, and limited life-cycle assessment data.</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Civil engineering in the age of artificial intelligence: state of the art of breakthrough, innovation, and intelligent design 人工智能时代的土木工程：突破、创新和智能设计的艺术状态

Architecture, Structures and Construction Pub Date : 2026-09-15 DOI: 10.1007/s44150-026-00222-1

Najmadeen Mohammed Saeed, Hogr Zainaddeen Hassan

{"title":"Civil engineering in the age of artificial intelligence: state of the art of breakthrough, innovation, and intelligent design","authors":"Najmadeen Mohammed Saeed, Hogr Zainaddeen Hassan","doi":"10.1007/s44150-026-00222-1","DOIUrl":"10.1007/s44150-026-00222-1","url":null,"abstract":"<div><p>This article provides a structured systematic review and assessment of Artificial intelligence (AI) applications in structural, transportation, and environmental engineering, drawing on peer-reviewed literature to identify implementation trends, performance standards, and research gaps, by exploring the applications of AI in predictive analytics, workflow automation, and sustainable infrastructure, with a focus on structural, transportation, and environmental engineering, since existing reviews remain domain-specific or lack methodological rigor. This study presents a novel examination of the multidisciplinary integration of artificial intelligence (AI) into civil engineering, with a focus on its applications, challenges, and future prospects. AI has enabled breakthroughs by employing techniques like machine learning, deep learning, and genetic algorithms. Emerging trends like AI-driven material improvements and autonomous systems are improving the sustainability, efficiency, and adaptability of buildings. Prospects like interdisciplinary collaboration and hybrid AI models are critically analyzed, but so are obstacles including adoption hesitancy, security concerns, and data limitations. Through case examples and visual aids, this paper provides practical insights into the transformative effects of AI, making it a valuable resource for academics and practitioners. By addressing problems and synthesizing current implementations, this article offers a comprehensive and detailed state of the art for using AI to support resilient and sustainable civil engineering practices.</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A study on crack characterization, damage severity, and propagation prediction for reinforced concrete beams using deep learning and machine learning techniques 利用深度学习和机器学习技术研究钢筋混凝土梁的裂缝表征、损伤严重程度和传播预测

Architecture, Structures and Construction Pub Date : 2026-09-15 DOI: 10.1007/s44150-026-00220-3

B. Narendra Kumar, Akkala Prasanna Divya Sai

{"title":"A study on crack characterization, damage severity, and propagation prediction for reinforced concrete beams using deep learning and machine learning techniques","authors":"B. Narendra Kumar, Akkala Prasanna Divya Sai","doi":"10.1007/s44150-026-00220-3","DOIUrl":"10.1007/s44150-026-00220-3","url":null,"abstract":"<div><p>Cracks in reinforced concrete (RC) beams pose serious implications on the structural safety, durability, and life cycle, making the detection and assessment of such defect’s indispensable for the purpose of structural health monitoring. The paper presents a unified deep learning approach for the purposes of assessing the crack severity, cracking types and predicting crack progression using the experimentally created image database. The RC beams specimens (100 × 100 × 500 mm) with GGBS (ground granulated blast furnace slag) and steel slag were subjected to progressive loading until failure to create images of flexural, shear and torsional cracks. The process of image processing was used to extract the properties of the cracks and to assess the crack severity index (SI). ResNet18, VGG16, MobileNet, and DenseNet are the four transfer learning models that were used for crack type classification, and ResNet18 was found to be the most efficient model for classification with an accuracy of 0.923 ± 0.006, which is better than all other pretrained models. For crack growth prediction, three algorithms are employed, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR). Random Forest Regression (RFR) is proven to be the best one among all the three regression algorithms with MAE of 0.017 mm and R² score of 0.9988, better than SVR and GBR. The proposed system provides a comprehensive solution by integrating crack detection, severity assessment, classification, and growth prediction.</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance and sustainability assessment of mortar incorporating waste crumb rubber as partial sand replacement 废橡胶渣部分替代砂砂浆的性能及可持续性评价

Architecture, Structures and Construction Pub Date : 2026-09-12 DOI: 10.1007/s44150-026-00217-y

Twana Othman M. Amin, Dler Ali Ahmad, Bzhar Muheddin Mohammed, Hersh F Mahmood, Musa Moetasam Zorab, Soran Abdrahman Ahmad, Bayan Abdullah, Hiba Adnan, Zainab Ahmed

{"title":"Performance and sustainability assessment of mortar incorporating waste crumb rubber as partial sand replacement","authors":"Twana Othman M. Amin, Dler Ali Ahmad, Bzhar Muheddin Mohammed, Hersh F Mahmood, Musa Moetasam Zorab, Soran Abdrahman Ahmad, Bayan Abdullah, Hiba Adnan, Zainab Ahmed","doi":"10.1007/s44150-026-00217-y","DOIUrl":"10.1007/s44150-026-00217-y","url":null,"abstract":"<div><p>A waste tire is one of the waste solids that make a great rate of the produced solid waste in all community. One of the solutions that used to recycle or reuse of the waste tires is to crash it to the small parts and used them as partial replacement of the sand or coarse aggregate in the mortar or the concrete since the construction field considered as one of the zones that capable to recycle or reuse the waste materials. This article deals with the usage of the waste crumb rubber as partial replacement of the sand in the mortar with eight different rate including 0, 5, 10, 15, 20, 30, 40, and 50% to investigate the effect of each used rate on the fresh, mechanical durability, thermal conductivity, elevated temperature resistance, dry shrinkage, bond strength and water absorption ability of the mortar. At the result obtained that with the usage of the waste crumb rubber as partial replacement of sand in the mortar, increase the flowability of the mortar also increase thermal conductivity and water absorption capacity while decrease the compressive strength, flexural strength and bond strength due to the luck in the bond between rubber granular and other part of the mortar mix compare to the bond between sand granular and other granular in the mortar mix. By using 5% of crumb rubber decrease compressive strength, flexural strength by 16.9% and 17.8% compare to the control mix. The dry shrinkage capacity of the mortar will decrease, since the crumb particle have the ability of extension during attracting by the mix composition compare to the sand particles which make the modified mortar to be suitable for using as plastering especially in these zones which has wide range in temperature change in the condition when used crumb rubber not passed 10%.</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Burning behavior of the polyurethane foam acoustic insulation responsible of the kiss nightclub fire 接吻夜总会火灾的聚氨酯泡沫隔音材料的燃烧行为

Architecture, Structures and Construction Pub Date : 2026-08-28 DOI: 10.1007/s44150-026-00213-2

Rodrigo Almeida Freitas, João Paulo Correia Rodrigues

引用次数: 0

Modeling construction waste drivers in Northern Indian Urban housing: A Delphi-ISM-MICMAC analysis 印度北部城市住宅建筑垃圾驱动因素建模：delphiism - micmac分析

Architecture, Structures and Construction Pub Date : 2026-08-28 DOI: 10.1007/s44150-026-00214-1

Shivani Singhal, Puneet Sharma, Rashmi Kumari

{"title":"Modeling construction waste drivers in Northern Indian Urban housing: A Delphi-ISM-MICMAC analysis","authors":"Shivani Singhal, Puneet Sharma, Rashmi Kumari","doi":"10.1007/s44150-026-00214-1","DOIUrl":"10.1007/s44150-026-00214-1","url":null,"abstract":"<div><p>Despite growing awareness of the need for responsible consumption of resources, construction sites across India continue to be the significant source of preventable waste. Thus this study identifies, prioritize and maps the directional dependencies among the major construction waste generating factors in Northern Indian urban housing sector. Following a literature-based identification of 40 waste drivers, a sequential Relative Importance Index (RII), Interpretive Structural Modeling (ISM) and MICMAC approach was employed with a 10-member expert Delphi panel to establish a multi-level hierarchy and structural interrelationships. Frequent design changes, lack of supervision on site, poor planning and coordination, inadequate site management and lack of environmental awareness emerge as foundational factors with high driving power. A targeted mitigation framework is proposed based on these key systematic drivers. This research develops a novel, context specific hierarchical model tailored to the urban residential sector of Northern India, transforming linear rankings into a dynamic structural framework.</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148838251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven prediction models for curved thin-walled box-girder bridge modal analysis: transitioning from linear regression to Gaussian processes 曲线薄壁箱梁桥模态分析的数据驱动预测模型：从线性回归到高斯过程的过渡

Architecture, Structures and Construction Pub Date : 2026-08-25 DOI: 10.1007/s44150-026-00212-3

Virajan Verma, Khair Ul Faisal Wani, K. Nallasivam, Sandeep Singh, Bhargav P. Majmundar, Karan Singh

{"title":"Data-driven prediction models for curved thin-walled box-girder bridge modal analysis: transitioning from linear regression to Gaussian processes","authors":"Virajan Verma, Khair Ul Faisal Wani, K. Nallasivam, Sandeep Singh, Bhargav P. Majmundar, Karan Singh","doi":"10.1007/s44150-026-00212-3","DOIUrl":"10.1007/s44150-026-00212-3","url":null,"abstract":"<div><p>This paper introduces a data-driven surrogate model for forecasting the multi-output free vibration response of simply supported, single-cell thin-walled curved box-girder bridges, combining a validated 1D thin-walled beam finite element script with machine learning regression methodologies. A computationally efficient one-dimensional finite element approach was created in MATLAB to capture the vertical, lateral, torsional, distortional, and linked warping modes that dictate the structural dynamics of curved box sections. After validation with 3D ANSYS models and experimental data, a parametric dataset comprising 441 structural configurations was created by systematically altering continuous geometric parameters: span lengths ranging from 30 to 75 m and horizontal radii of curvature from 40 to 360 m under baseline rigid end-diaphragm conditions. Five machine learning frameworks: Linear Regression (LR), Regression Trees (RT), Random Forest (RF), Support Vector Machines (SVM) and Gaussian Process Regression (GPR) were trained and assessed utilizing fivefold cross-validation inside this defined design envelope. The results demonstrate that kernel-based methods had enhanced predictive accuracy; the GPR model attained an interpolation correlation of R<sup>2</sup> = 1.00 and a Root Mean Square Error (RMSE) of 0.0089, surpassing the Regression Tree method (R<sup>2</sup> = 0.81). Global Sensitivity Analysis showed Span Length (L) as the key parameter affecting the frequency response of the lower order global modes (Mode 1) and Radius of Curvature as the primary parameter affecting the frequency response of the higher order linked states (Modes 3, 4 and 9).</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0