Journal of Infrastructure Intelligence and Resilience最新文献_第3页

Partial transfer matrix-based group sparse regularisation for impact force localization and reconstruction 基于部分传递矩阵的群稀疏正则化求解冲击力局部化与重构

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-09-01 Epub Date: 2025-08-20 DOI: 10.1016/j.iintel.2025.100170

Bing Zhang, Xinqun Zhu, Zihao He, Jianchun Li

{"title":"Partial transfer matrix-based group sparse regularisation for impact force localization and reconstruction","authors":"Bing Zhang, Xinqun Zhu, Zihao He, Jianchun Li","doi":"10.1016/j.iintel.2025.100170","DOIUrl":"10.1016/j.iintel.2025.100170","url":null,"abstract":"<div><div>Existing methods for impact force identification are based on full transfer matrix. Constructing and using transfer matrices can be computationally intensive, especially for large-scale complex structures in practice. Partial transfer matrix refers to a subset of the full transfer matrix, potentially reducing computational cost and complexity. In this paper, a partial transfer matrix-based group sparse regularisation method is proposed for the impact force localization and reconstruction. Its robustness and adaptivity with respect to different subsets of full transfer matrix, noise level and number of impact forces are numerically studied using impact forces on a simply supported beam. The number of sensors for impact force identification can be significantly reduced by the proposed method and its localization and time history reconstruction can be determined even with one single sensor configuration. A 10 m long steel-concrete composite bridge model is built in the laboratory. The effectiveness of the proposed method for impact force identification is validated and compared with <em>L</em><sub>1</sub>-norm and <em>L</em><sub>2</sub>-norm regularisation methods numerically and experimentally. Results show that the proposed partial transfer matrix-based group sparse regularisation method has good robustness and identification accuracy and has better performance on the impact force localization and time history reconstruction comparing with <em>L</em><sub>1</sub>-norm and <em>L</em><sub>2</sub>-norm regularisation methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100170"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988669","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

Cascading failures in urban infrastructure systems: A comprehensive review of disaster chain mechanisms 城市基础设施系统的级联故障：灾害链机制的综合综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-09-01 Epub Date: 2025-06-03 DOI: 10.1016/j.iintel.2025.100157

Zheng Lu , Deyu Yan , Huanjun Jiang , Hongjing Xue , Zhao-Dong Xu

{"title":"Cascading failures in urban infrastructure systems: A comprehensive review of disaster chain mechanisms","authors":"Zheng Lu , Deyu Yan , Huanjun Jiang , Hongjing Xue , Zhao-Dong Xu","doi":"10.1016/j.iintel.2025.100157","DOIUrl":"10.1016/j.iintel.2025.100157","url":null,"abstract":"<div><div>Urban engineering systems (UESs) are highly interconnected, forming complex dependencies that render them vulnerable to cascading failures during disasters. While existing studies have explored specific aspects of disaster chains in UESs, a synthesized framework for understanding their interdependencies, data acquisition challenges, and methodological limitations remains underdeveloped. This paper addresses this gap by conducting a systematic review of UES disaster chains, beginning with the definitions of disaster chains from different academic perspectives, common types of urban disaster chains, namely earthquake, flood, fire, freezing and ground subsidence disaster chains, as well as the interdependency of UES. Furthermore, three identification methods of disaster chains are summarized, namely based on historical disaster data, expert experience, and natural language processing (NLP). Moreover, five analysis methods of disaster chains are summarized, including those based on Bayesian networks, complex networks, numerical simulation, scenario simulation and remote sensing, with comparison of their applicability, advantages, limitations and complexity. The benefits and drawbacks of each approach are clearly illustrated. The paper concludes by discussing the limitations in the current literature and suggests that future research may utilize new technologies to facilitate data analyzing process, conduct cross-regional studies, and focus on integrating socio-economic factors for disaster-related decision-making support.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330377","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

Strain signal denoising in bridge SHM: A comparative analysis of MODWT and other techniques 桥梁SHM应变信号去噪：MODWT与其它方法的比较分析

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-09-01 Epub Date: 2025-04-29 DOI: 10.1016/j.iintel.2025.100155

Yun-Xia Xia , Ru-Kai Xu , Yi-Qing Ni , Zu-Quan Jin

{"title":"Strain signal denoising in bridge SHM: A comparative analysis of MODWT and other techniques","authors":"Yun-Xia Xia , Ru-Kai Xu , Yi-Qing Ni , Zu-Quan Jin","doi":"10.1016/j.iintel.2025.100155","DOIUrl":"10.1016/j.iintel.2025.100155","url":null,"abstract":"<div><div>Accurate denoising of strain signals is critical for early damage detection in bridge structural health monitoring (SHM). However, signals denoising methods often struggle with the non-stationary and broadband noise encountered in real-world environments. This study provides the first comprehensive comparison of various denoising techniques specifically tailored for bridge strain signals, emphasizing the maximal overlapping discrete wavelet transform (MODWT) for its capacity to handle complex noise profiles. We rigorously compare MODWT with time-domain (moving average filter, finite impulse response filter, empirical mode decomposition), frequency-domain (bandpass filter, Fourier mode decomposition), and other wavelet-based (discrete wavelet transform) approaches. Uniquely, this study employs three datasets from two distinct bridge types (masonry arch and steel bowstring) and evaluates performance using both expert assessments and quantitative metrics (signal-to-noise ratio, peak signal-to-noise ratio, root mean square error, and correlation coefficient). Our findings demonstrate that MODWT exhibits a distinct advantage in high-intensity white noise environments, a common scenario in real-world bridge monitoring, offering valuable guidance for engineers in selecting appropriate denoising strategies. The results not only validate MODWT as a promising preprocessing technique but also offer critical insights into the limitations of existing methods, paving the way for the development of more adaptive and robust denoising solutions in bridge SHM.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115297","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 normalization for the continuous monitoring of a steel truss bridge: A case study from the Italian railway line 钢桁架桥梁连续监测数据归一化：以意大利铁路线为例

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-09-01 Epub Date: 2025-09-15 DOI: 10.1016/j.iintel.2025.100171

Antonio Argentino, Luca Radicioni, Francesco Morgan Bono, Lorenzo Bernardini, Lorenzo Benedetti, Gabriele Cazzulani, Claudio Somaschini, Marco Belloli

{"title":"Data normalization for the continuous monitoring of a steel truss bridge: A case study from the Italian railway line","authors":"Antonio Argentino, Luca Radicioni, Francesco Morgan Bono, Lorenzo Bernardini, Lorenzo Benedetti, Gabriele Cazzulani, Claudio Somaschini, Marco Belloli","doi":"10.1016/j.iintel.2025.100171","DOIUrl":"10.1016/j.iintel.2025.100171","url":null,"abstract":"<div><div>Structural health monitoring is recognized as a powerful tool to assist bridge management. Continuous long-term monitoring of bridge structures presents several challenges, including the need for effective system design, robust sensors deployment, efficient data management, and comprehensive data analysis and interpretation. In the field of operational modal analysis, automatic tracking of bridge frequencies over time has been shown to be significantly influenced by temperature fluctuations. This effect is also observed in low-frequency sampled signals. To address these issues, the authors present a double-step procedure to effectively mitigate the influence of temperature on the estimated modal parameters and raw signals from displacement, strain and rotation transducers. The procedure is based on multiple linear regression, taking the measured temperatures as inputs, followed by low-pass filtering operations applied to the residuals through moving averages, leading to the creation of minimum detectable anomaly curves. The latter allow to establish quantitative relationships between filtering window lengths and detectable damage thresholds at specified confidence levels. The case study involves a railway steel truss bridge, where more than a year of data was collected through a permanent monitoring system. The monitoring layout includes a variety of sensors deployed to measure the structural response, as well as environmental and operational variables. A 15-month dataset demonstrates how temperature compensation effectively reduces signal variability, which is crucial for enhancing early-stage anomalies detection.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100171"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104594","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

Deep learning in crack detection: A comprehensive scientometric review 裂纹检测中的深度学习：科学计量学综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-09-01 Epub Date: 2025-02-23 DOI: 10.1016/j.iintel.2025.100144

Yingjie Wu , Shaoqi Li , Jingqiu Li , Yanping Yu , Jianchun Li , Yancheng Li

{"title":"Deep learning in crack detection: A comprehensive scientometric review","authors":"Yingjie Wu , Shaoqi Li , Jingqiu Li , Yanping Yu , Jianchun Li , Yancheng Li","doi":"10.1016/j.iintel.2025.100144","DOIUrl":"10.1016/j.iintel.2025.100144","url":null,"abstract":"<div><div>Cracks represent one of the common forms of damage in concrete structures and pavements, leading to safety issues and increased maintenance costs. Therefore, timely crack detection is crucial for preventing further damage and ensuring the safety of these structures. Traditional manual inspection methods are limited by factors such as time consumption, subjectivity, and labor intensity. To address these challenges, deep learning-based crack detection technologies have emerged as promising solutions, demonstrating satisfactory performance and accuracy. However, the field still lacks comprehensive scientometric analyses and critical surveys of existing works, which are vital for identifying research gaps and guiding future studies. This paper conducts a bibliometric and critical analysis of the collected literature, providing novel insights into current research trends and identifying potential areas for future investigation. Analytical tools, including VOSviewer and CiteSpace, were employed for in-depth analysis and visualization. This study identifies key research gaps and proposes future directions, focusing on advancements in model generalization, computational efficiency, dataset standardization, and the practical application of crack detection methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739718","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

Large multimodal model assisted underground tunnel damage inspection and human-machine interaction 大型多模态模型辅助地下隧道损伤检测和人机交互

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-09-01 Epub Date: 2025-04-01 DOI: 10.1016/j.iintel.2025.100154

Yanzhi Qi , Zhi Ding , Yaozhi Luo

{"title":"Large multimodal model assisted underground tunnel damage inspection and human-machine interaction","authors":"Yanzhi Qi , Zhi Ding , Yaozhi Luo","doi":"10.1016/j.iintel.2025.100154","DOIUrl":"10.1016/j.iintel.2025.100154","url":null,"abstract":"<div><div>Artificial Intelligence is playing an increasingly important role in tunnel inspection as a core driver of the new generation of engineering. Traditional methods are difficult to directly generate human linguistic information and lack valid messages extracted from different modalities. This paper proposes Damage LMM, a multimodal damage detection model that can handle images or videos as well as text inputs, to realize fast damage identification and human-computer interaction. The visual instruction database is first created from real damage data collected using different visual sensors and captions extracted by a regional convolutional neural network. The basic language model is then fine-tuned into a specialised Damage LMM, which enhances user instructions by integrating virtual prompt injection and system messages. Finally, the enhanced prompts are processed through the tuned multimodal model to generate a detailed visual description of the damage. The performance of the method is evaluated using a real tunnel dataset, and the results show that it has better robustness and accuracy than other models in multimodal data, with an accuracy of 0.93 for the in-domain image data and a contextual correlation of 0.94. The proposed method can effectively identify tunnel defects and realize multimodal user interaction functions with a moderate number of markers and a short delay time, which will greatly help engineers to quickly obtain effective information and assess the degree of damage at the tunnel inspection site.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 3","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903728","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

Few-shot learning with large foundation models for automated segmentation and accessibility analysis in architectural floor plans 在建筑平面图中进行自动分割和可访问性分析的大型基础模型的少量学习

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-06-01 Epub Date: 2024-12-17 DOI: 10.1016/j.iintel.2024.100137

Haolan Zhang, Ruichuan Zhang

{"title":"Few-shot learning with large foundation models for automated segmentation and accessibility analysis in architectural floor plans","authors":"Haolan Zhang, Ruichuan Zhang","doi":"10.1016/j.iintel.2024.100137","DOIUrl":"10.1016/j.iintel.2024.100137","url":null,"abstract":"<div><div>This paper presents a novel approach for extracting accessibility features from 2D raster floor plans by integrating few-shot learning techniques with the Segment Anything Model (SAM) and GPT-4. The proposed method addresses the limitations of existing deep learning-based floor plan analysis, which often require extensive annotated datasets and struggle with the variability of raster floor plans. Furthermore, there is a lack of research on extracting accessibility features from 2D raster floor plans, which remain one of the most common formats for storing architectural plans post-design and construction. Our approach, GPT-integrated Multi-object Few-shot SAM (GMFS), leverages similarity maps and cluster-based point sampling to generate accurate visual prompts for SAM, enabling the segmentation of rooms and doors using only five reference samples. The segmented masks are then classified using GPT-4, enhancing the semantic richness of the floor plan analysis. We validated GMFS using the CubiCasa and Rent3D datasets, demonstrating impressive performance in segmentation and classification. A detailed case study further showcased the practical application of our approach in calculating accessible means of egress and wheelchair clear space, which are critical features for accessibility compliance. The results highlight the effectiveness and adaptability of our approach in real-world scenarios, underscoring its potential to improve building accessibility and safety analysis in the architecture, engineering, and construction (AEC) industry.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642796","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

Bayesian mixture of factor analyzers for structural damage detection under varying environmental conditions 基于贝叶斯混合因子的结构损伤检测

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-06-01 Epub Date: 2025-01-06 DOI: 10.1016/j.iintel.2025.100140

Binbin Li , Yulong Zhang , Zihan Liao , Zhilin Xue

{"title":"Bayesian mixture of factor analyzers for structural damage detection under varying environmental conditions","authors":"Binbin Li , Yulong Zhang , Zihan Liao , Zhilin Xue","doi":"10.1016/j.iintel.2025.100140","DOIUrl":"10.1016/j.iintel.2025.100140","url":null,"abstract":"<div><div>Variations of structural dynamic parameters (e.g., frequencies and damping ratios) can be caused by potential structural damages and environmental effects (e.g., temperature, humidity). It is of critical importance to distinguish them for a reliable vibration-based damage detection. A variational Bayesian mixture of factor analyzers (VB-MFA) is proposed in this paper for the probabilistic modeling of measured natural frequencies. It contains multiple factor analyzers to accommodate the nonlinear effect of environmental factors on the natural frequencies. The variational Bayes with automatic relevance determination prior empowers it to automatically determine the number of analyzers and the dimension of latent factors in each analyzer. In addition, the predictive marginal likelihood of natural frequencies is proposed as a damage index, which naturally considers the uncertainties in latent factors and estimated parameters. The method is verified in two case studies: a laboratory eight-story shear-type building model and the Z24-Bridge, both subjected to temperature variations. It shows that better performance has been achieved comparing to the conventional factor analysis and mixture of factor analyzers. The VB-MFA is capable to model the nonlinear effect of environmental effect on natural frequencies, and improves the accuracy of vibration-based structural damage detection.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552763","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

Modular approach to model order reduction for offshore wind turbines supported by multi-bucket jacket foundation 多筒导管基础支撑海上风力机模型降阶的模块化方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-06-01 Epub Date: 2025-02-20 DOI: 10.1016/j.iintel.2025.100143

Zhaofeng Shen , Yue Chen , Pengfei Li , Jun Liang , Ying Wang , Jinping Ou

{"title":"Modular approach to model order reduction for offshore wind turbines supported by multi-bucket jacket foundation","authors":"Zhaofeng Shen , Yue Chen , Pengfei Li , Jun Liang , Ying Wang , Jinping Ou","doi":"10.1016/j.iintel.2025.100143","DOIUrl":"10.1016/j.iintel.2025.100143","url":null,"abstract":"<div><div>Offshore wind turbines (OWTs) supported by multi-bucket jacket foundations (MBJF) provide a cost-effective solution for offshore wind energy production when water depth exceeds 50 m. However, numerical simulation of their dynamic behaviors towards high accuracy and efficiency becomes challenging due to the intricate structural configuration. To tackle it, this paper introduces a model order reduction framework for OWTs with MBJF. The framework strategically decomposes the structure into five substructures, whose reduced-order models (ROMs) are individually constructed and then assembled into a ROM for the entire OWT structure with fixed boundary conditions. The parameters of the assembled ROM on soil are subsequently calibrated through a model updating process, to ensure the alignment of modal parameters and structural displacements between ROM and full-order model (FOM). The results show that Young's moduli of both tower and jacket dominate the frequencies of global bending modes while Young's modulus of the blade dominates the frequencies of blade bending modes. Among the support parameters, the combined T-Z soil spring stiffness plays a critical role, affecting the frequencies of global motion and bending modes. The proposed model order reduction framework provides a robust methodology towards accurate and efficient simulation of structural dynamics for OWTs supported by MBJF.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681861","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

Fractal-based numerical simulation of multivariate typhoon wind speeds utilizing weierstrass mandelbrot function 基于weierstrass mandelbrot函数的多变量台风风速分形数值模拟

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-06-01 Epub Date: 2024-11-22 DOI: 10.1016/j.iintel.2024.100135

Kang Cai , Mingfeng Huang , Qiang Li , Qing Wang , Yi-Qing Ni

{"title":"Fractal-based numerical simulation of multivariate typhoon wind speeds utilizing weierstrass mandelbrot function","authors":"Kang Cai , Mingfeng Huang , Qiang Li , Qing Wang , Yi-Qing Ni","doi":"10.1016/j.iintel.2024.100135","DOIUrl":"10.1016/j.iintel.2024.100135","url":null,"abstract":"<div><div>This paper proposes a fractal-based technique for simulating multivariate nonstationary wind fields by the stochastic Weierstrass Mandelbrot function. Upon conducting a systematic fractal analysis, it was found that the structure function method is more suitable and reliable than the box counting method, variation method, and R/S analysis method for estimating the fractal dimension of the stochastic wind speed series. Wind field measurement at the meteorological gradient tower with a height of 356 m in Shenzhen was conducted during Typhoon Mandelbrot (1983). Significant non-stationary properties and fractal dimensions of typhoon wind speed data at various heights were analyzed and used to demonstrate the effectiveness of the proposed multivariate typhoon wind speed simulation method. The multivariate wind speed components simulated by the proposed fractal-based method are in good agreement with the measured records in terms of the fractal dimension, standard deviation, probability density function, wind spectrum and cross-correlation coefficient.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146658","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