Journal of Infrastructure Intelligence and Resilience最新文献_第2页

Fractal-based numerical simulation of multivariate typhoon wind speeds utilizing weierstrass mandelbrot function

Journal of Infrastructure Intelligence and Resilience Pub 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":"2024-11-22","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

A new method for predicting PM2.5 concentrations in subway stations based on a multiscale adaptive noise reduction transformer -BiGRU model and an error correction method

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-11-17 DOI: 10.1016/j.iintel.2024.100128

Dingyu Chen, Hui Liu

{"title":"A new method for predicting PM2.5 concentrations in subway stations based on a multiscale adaptive noise reduction transformer -BiGRU model and an error correction method","authors":"Dingyu Chen, Hui Liu","doi":"10.1016/j.iintel.2024.100128","DOIUrl":"10.1016/j.iintel.2024.100128","url":null,"abstract":"<div><div>PM2.5 is a significant contributor to air pollution, with a notable impact on human health. Subway stations, with their high pedestrian traffic, present a particular challenge in this regard. By monitoring PM2.5 levels, subway managers can take prompt action, such as optimizing the operation of air purification equipment in stations, to enhance air quality within stations and thereby enhance the passenger experience. This paper proposes an enhanced Transformer-BiGRU prediction model, which incorporates a MSHAM(Multiscale Hybrid Attention Mechanism)comprising a multi-scale convolutional attention mechanism and a VMD decomposition self-attention mechanism. Additionally, a ANR(Adaptive Noise Reduction) module has been integrated into the model to facilitate noise reduction. Finally, the prediction is performed by BiGRU. The resulting error sequence is predicted by BiGRU and the predicted sequence is corrected. In this paper, a dataset of pollutants from Seoul subway stations in South Korea is used to compare with the base model. The model presented in this paper achieves the highest accuracy.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130220","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

Review on optimization strategies of probabilistic diagnostic imaging methods 概率成像诊断方法的优化策略综述

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-10-22 DOI: 10.1016/j.iintel.2024.100127

Ning Li , Anningjing Li , Jiangfeng Sun

{"title":"Review on optimization strategies of probabilistic diagnostic imaging methods","authors":"Ning Li , Anningjing Li , Jiangfeng Sun","doi":"10.1016/j.iintel.2024.100127","DOIUrl":"10.1016/j.iintel.2024.100127","url":null,"abstract":"<div><div>With the continuous development of intelligent infrastructure, structural health monitoring (SHM) and non-destructive testing (NDT) have become major research focuses. Ultrasonic-guided wave imaging technology not only integrates the global impact of damage on structures but also provides intuitive localization and severity characterization of the damage. Probabilistic diagnostic imaging (PDI) methods, which do not require direct interpretation of guided wave signals and can achieve high-quality imaging with sparse arrays, have garnered increasing attention. This paper introduces the principles, general processes, and technical advantages of PDI methods. Based on the process of the PDI, existing optimization strategies are categorized into two types: internal process optimizations, which include sensor layout, damage indices optimization, construction of the distribution weight function, and data fusion; and external process optimizations, which include spurious image suppression, on-site environment detection, and integration of methodologies, each analyzed in detail. With the affirmation of the value of these strategies, this paper also highlights the current issues within these methods and explores potential future developments by integrating emerging technologies such as intelligent sensing, big data, and artificial intelligence. These insights provide valuable reference suggestions for the continued optimization of these methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586147","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

An integrated management system (IMS) approach to sustainable construction development and management 可持续建筑开发和管理的综合管理系统（IMS）方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-10-18 DOI: 10.1016/j.iintel.2024.100126

Ahsan Waqar , Saad Nisar , Muhammad Muddassir , Omrane Benjeddou

{"title":"An integrated management system (IMS) approach to sustainable construction development and management","authors":"Ahsan Waqar , Saad Nisar , Muhammad Muddassir , Omrane Benjeddou","doi":"10.1016/j.iintel.2024.100126","DOIUrl":"10.1016/j.iintel.2024.100126","url":null,"abstract":"<div><div>Construction is significantly contributing to the severe environmental crisis it is facing. The sector consumes over 3 billion tons of raw materials annually, and its activities account for 40% of global CO<sub>2</sub> emissions. Traditional integrated strategies toward fragmented sustainability cannot offer total optimization. In this respect, the present research presents an integrated management system (IMS) containing a composite of metrics for sustainable construction management (SCM). This research was specifically geared to test the relationship between the elements of IMS and SCM from the perspective of the construction industry. A quantitative survey tested through 119 professionals was used for data collection. It is established through structural equation modeling (SEM) that the internal consistency of Cronbach’s Alpha 0.72–0.95 and construct validity was strong. The Fornell-Larcker criterion was realized to affirm good discriminant validity. Crucial results identified the presence of significant impacts for quality management (QM) (β = 0.643, <em>p</em> < 0.001), risk management (RM) (β = 0.53, <em>p</em> < 0.001), and safety management (SM) (β = 0.439, <em>p</em> < 0.001). Therefore, this study further enhances the scalability of IMS so that it is practically applied to improve project quality and safety, along with risk management. Future research could also focus on studying the context of the integration of IMS with SCM and continue to work using objective performance measures to validate these findings.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555002","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

Reliability-based safety format for structural fire engineering – Derivation based on the most likely failure point

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-10-18 DOI: 10.1016/j.iintel.2024.100125

Ruben Van Coile, Balša Jovanović, Florian Put

{"title":"Reliability-based safety format for structural fire engineering – Derivation based on the most likely failure point","authors":"Ruben Van Coile, Balša Jovanović, Florian Put","doi":"10.1016/j.iintel.2024.100125","DOIUrl":"10.1016/j.iintel.2024.100125","url":null,"abstract":"<div><div>Designing structures for burnout resistance ensures stability during evacuation and search and rescue operations, limits collateral damage, and enhances post-fire repairability. This represents a significant shift from traditional prescriptive designs that do not evaluate performance under realistic fire conditions. However, given the variability in fire exposure and structural response, it is unclear which input values should be used to ensure a high level of reliability for burnout calculations. This paper introduces a safety format for burnout resistance compatible with the Eurocode and its reliability principles. The format allows users to specify desired reliability levels and prescribes equations for determining design values for load effects and fire load density using predetermined sensitivity weights. A method for calculating default sensitivity weights is outlined, proposing tentative values: 0.65 for resistance effect, −0.40 for load effect, and −0.80 for fire load density, with a default coefficient of variation of 0.30 for resistance effect when case-specific information is lacking. The safety format's performance is verified through case studies of a concrete slab and a numerical evaluation of a steel column, showing satisfactory and conservatively assessed results. Inherent conservatism in the design format may, however, occasionally lead to the undue rejection of designs. Further investigations are necessary to confirm the safety format's conceptualization, default sensitivity weights, and the influence of the adopted compartment fire model.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143130219","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

Quantitative risk analysis of road transportation of hazardous materials in coastal areas 沿海地区危险品公路运输的定量风险分析

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-09-08 DOI: 10.1016/j.iintel.2024.100124

Daijie Chen , Xiyong Bai

{"title":"Quantitative risk analysis of road transportation of hazardous materials in coastal areas","authors":"Daijie Chen , Xiyong Bai","doi":"10.1016/j.iintel.2024.100124","DOIUrl":"10.1016/j.iintel.2024.100124","url":null,"abstract":"<div><p>Given the complex climate conditions in coastal areas and their role as key transportation hubs for hazardous chemicals, this study proposes a method to quantitatively and comprehensively evaluate transportation risks. Initially, accident data were analyzed to identify risk factors from five aspects: human, vehicle, materials, environment, and management, based on system safety theory. Subsequently, a risk analysis model was developed using Decision-making Trial and Evaluation Laboratory, interpretive structural model theory, and Bayesian theory to quantitatively assess accident risk levels. The model was applied to a case involving a hazardous chemical accident on a cross-sea bridge, where Bayesian backward reasoning was used to analyze the sensitivity and importance of risk factors. This approach facilitated the key risk factors affecting the safety of hazardous chemical transportation systems. Notably, the study incorporated scenarios involving hazardous material transport vehicles crossing sea bridges into the risk assessment framework, offering valuable insights for management authorities. It also considered the impact of strong side winds-a factor often overlooked-in hazardous material transport. The validation process demonstrated that the method accurately quantifies the risk of hazardous chemical transportation and identifies the key factors influencing accident occurrence. The research highlights that strong gusts of wind significantly impact safety, and human factors are crucial in the overall risk system.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 4","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000434/pdfft?md5=964f153c00429cfef1e5889999414f17&pid=1-s2.0-S2772991524000434-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274608","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

Multimodal vortex-induced vibration mitigation and design approach of bistable nonlinear energy sink inerter on bridge structure 多模式涡流诱导振动缓解与桥梁结构双稳态非线性能量吸收器的设计方法

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-09-07 DOI: 10.1016/j.iintel.2024.100123

Ruihong Xie , Kun Xu , Houjun Kang , Lin Zhao

{"title":"Multimodal vortex-induced vibration mitigation and design approach of bistable nonlinear energy sink inerter on bridge structure","authors":"Ruihong Xie , Kun Xu , Houjun Kang , Lin Zhao","doi":"10.1016/j.iintel.2024.100123","DOIUrl":"10.1016/j.iintel.2024.100123","url":null,"abstract":"<div><div>Large-scale structures, e.g., long-span bridge structures, are prone to induce multi-modal vibrations due to their densely spaced low modal frequencies. Due to the limited frequency bandwidth of linear dynamic absorbers, they are incapable of effectively mitigating vibrations across multiple modes. To this end, the bistable nonlinear energy sink inerter (BNESI) is used to mitigate the multimodal vortex-induced vibration (VIV) of the beam structure. The highly nonlinear equilibrium differential equations of the beam-BNESI system are numerically solved, and the simulated annealing (SA) algorithm is employed to determine the optimal VIV reduction ratio and BNESI parameters. In comparison to the cubic-type nonlinear energy sink inerter (CNESI), BNESI is found to possess more stable equilibrium positions, smaller stiffness coefficients, and higher VIV mitigation efficiency. The selection of design modes has been found to influence the efficiency of multimodal VIV mitigation, with the use of the intermediate modal order as the design mode resulting in the highest efficiency for multimodal VIV mitigation. The performance-based multimodal VIV mitigation design can be realized with three parameters, i.e., inertance ratio, damping coefficient, and stiffness coefficient. Moreover, the performance-based multimodal VIV mitigation approach and models proposed in this study demonstrate a high level of precision.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 4","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421587","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

A review of artificial intelligence in dam engineering

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-09-02 DOI: 10.1016/j.iintel.2024.100122

Wenxuan Cao , Xinbin Wu , Junjie Li , Fei Kang

引用次数: 0

Enhanced operational modal analysis and change point detection for vibration-based structural health monitoring of bridges 增强运行模态分析和变化点检测，用于基于振动的桥梁结构健康监测

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-08-31 DOI: 10.1016/j.iintel.2024.100121

Serge L. Desjardins , David T. Lau

{"title":"Enhanced operational modal analysis and change point detection for vibration-based structural health monitoring of bridges","authors":"Serge L. Desjardins , David T. Lau","doi":"10.1016/j.iintel.2024.100121","DOIUrl":"10.1016/j.iintel.2024.100121","url":null,"abstract":"<div><div>One of the most promising uses of vibration-based structural health monitoring (VBSHM) in bridge damage detection is the tracking of modes through long-term repeated or continuous operational modal analysis (OMA). Any shifts in modal parameters over time can signal structural damage. However, in real-world applications, noise and environmental uncertainties introduce variability in the data, potentially obscuring damage-related changes. To address this, it is essential to establish and understand the temporal trends and behavior of the estimated modal parameters, enabling accurate interpretation of the engineering data. This paper presents a detailed study focusing on data-driven techniques to improve the OMA results by determining the causes of modal variability and establishing modal models to filter out these known causes of variability. It explores the use of data continuously collected over a period of one month in November 2017 on the Confederation Bridge in eastern Canada. Operational modal analysis is conducted to extract modal frequencies and mode shapes, revealing correlations with environmental and operational factors such as wind, temperature and vehicular traffic. A novel approach using the residuals from regression modal models for damage detection is proposed, utilizing a change point detection algorithm. Results indicate the potential to detect shifts in modal frequencies corresponding to damage scenarios, at lower levels than was previously possible, highlighting the feasibility of using enhanced modal features for sensitive damage identification. Overall, the paper contributes to advancing the understanding of variability in vibration-based structural health monitoring and presents a promising practical technique for improving damage detection results using enhanced operational modal estimates in realistic field applications of a real-world structure.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 4","pages":"Article 100121"},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442385","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

Automatic PAUT crack detection and depth identification framework based on inspection robot and deep learning method 基于检测机器人和深度学习方法的 PAUT 裂纹自动检测和深度识别框架

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-08-30 DOI: 10.1016/j.iintel.2024.100113

Fei Hu , Hong-ye Gou , Hao-zhe Yang , Huan Yan , Yi-qing Ni , You-wu Wang

{"title":"Automatic PAUT crack detection and depth identification framework based on inspection robot and deep learning method","authors":"Fei Hu , Hong-ye Gou , Hao-zhe Yang , Huan Yan , Yi-qing Ni , You-wu Wang","doi":"10.1016/j.iintel.2024.100113","DOIUrl":"10.1016/j.iintel.2024.100113","url":null,"abstract":"<div><div>Orthotropic steel bridge decks (OSD) are widely acclaimed for their lightweight, high load-carrying capacity, and adaptability, making them a popular choice in steel structure bridges. However, the complex nature of their structure makes them susceptible to fatigue cracking, posing significant safety concerns. To address the issues above, this study employs a robot equipped with an ultrasonic phased array probe to automate the detection of internal cracks within Orthotropic Steel Decks (OSD). A Deep Convolutional Generative Adversarial Network (DCGAN) is utilized to augment the training dataset of Phased Array Ultrasonic Testing (PAUT) images. The YOLO series algorithms are applied and compared for crack localization, with YOLO v7-tiny exhibiting the highest accuracy and speed. Integrating attention mechanisms into the YOLO v7-tiny algorithm to facilliate rapid and high-precision crack detection. Analyzing the echo region with an echo intensity bar enabled the identification of crack depth, with an identification error within 5%.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535895","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