Journal of Infrastructure Intelligence and Resilience最新文献

Automatic settlement assessment of urban road from 3D terrestrial laser scan data 利用三维地面激光扫描数据自动评估城市道路沉降状况

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 DOI: 10.1016/j.iintel.2025.100142

Xinchen Zhang , Qian Wang , Hai Fang , Guogang Ying

{"title":"Automatic settlement assessment of urban road from 3D terrestrial laser scan data","authors":"Xinchen Zhang , Qian Wang , Hai Fang , Guogang Ying","doi":"10.1016/j.iintel.2025.100142","DOIUrl":"10.1016/j.iintel.2025.100142","url":null,"abstract":"<div><div>Tunnel construction in urban environments often requires passing beneath existing roads, where excessive soil excavation can lead to road cracking, settlement, or heaving, posing risks to road safety. Traditional road settlement monitoring methods rely on manual measurements, which are time-consuming, labor-intensive, and costly. Some existing approaches also require extensive sensor deployment, complicating installation and maintenance. To address these challenges, this study introduces a LiDAR-based method for efficient and accurate road settlement assessment. The impact of various LiDAR measurement parameters on assessment accuracy and efficiency was analyzed under typical urban road conditions. A comprehensive workflow was developed, incorporating both rough and fine alignment processes. Key steps in the workflow, such as automated identification of matching planes between point clouds, directional alignment, and angle fine-tuning, were automated using advanced algorithms. The proposed method was applied and validated in a region undergoing tunneling works in Singapore. Results demonstrated that the partially automated LiDAR-based approach achieved comparable accuracy to manual point cloud alignment methods while significantly improving efficiency and reducing labor costs. Furthermore, when compared to traditional total station methods, the LiDAR-based technique maintained errors within acceptable limits and enabled broader spatial coverage. Overall, this study highlights the feasibility and potential of LiDAR technology to enhance road settlement monitoring in engineering practice, offering a cost-effective and scalable alternative to traditional methods.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687560","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

Automated Operational Modal Analysis of a steel truss railway bridge employing free decay response 采用自由衰减响应对钢桁梁铁路桥进行自动化运行模态分析

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-03-01 DOI: 10.1016/j.iintel.2025.100145

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

{"title":"Automated Operational Modal Analysis of a steel truss railway bridge employing free decay response","authors":"Francesco Morgan Bono, Antonio Argentino, Lorenzo Bernardini, Lorenzo Benedetti, Gabriele Cazzulani, Claudio Somaschini, Marco Belloli","doi":"10.1016/j.iintel.2025.100145","DOIUrl":"10.1016/j.iintel.2025.100145","url":null,"abstract":"<div><div>The efficiency and resilience of transportation networks depend significantly on the integrity of bridges, which are increasingly threatened by ageing, traffic, and extreme climate events. Traditional visual inspections have notable limitations, necessitating the adoption of more objective methods like Structural Health Monitoring (SHM). This study explores the application of Operational Modal Analysis (OMA) to estimate the modal parameters of railway bridges, specifically using the Covariance-based Stochastic Subspace Identification (SSI-COV) algorithm. The case study involves a steel Warren truss bridge monitored over 20 months. The research demonstrates that SSI-COV, typically requiring stationary random input, can effectively utilise the bridge’s free decay responses following train passages. This approach strongly improves signal-to-noise ratio, which is vice-versa critical for railway bridges ambient vibrations due to the very low input energy, enabling precise modal parameter estimation with shorter time windows and lower-performance sensors. Results were validated against the Peak-Picking (PP) and the Enhanced Frequency Domain Decomposition (EFDD) methods, with SSI-COV identifying three additional natural frequencies and exhibiting lower dispersion in frequency estimates throughout the monitored period. Statistical analysis further indicated that using multiple free decays enhances the accuracy and reduces variability for challenging modes, while dominant modes are reliably estimated with minimal decay data. These findings endorse the combination of SSI-COV and free decays as a robust tool for detailed and long-term bridge monitoring, offering a valuable and potentially low-cost alternative to ambient vibration-based OMA techniques.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 1","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687561","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

Deep learning in crack detection: A comprehensive scientometric review

Journal of Infrastructure Intelligence and Resilience Pub 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":"2025-02-23","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

Modular approach to model order reduction for offshore wind turbines supported by multi-bucket jacket foundation

Journal of Infrastructure Intelligence and Resilience Pub 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":"2025-02-20","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

Positional inaccuracy investigation and innovative connection solution for robotic construction of load carrying structures

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-16 DOI: 10.1016/j.iintel.2025.100141

Cheav Por Chea, Yu Bai, Yihai Fang

{"title":"Positional inaccuracy investigation and innovative connection solution for robotic construction of load carrying structures","authors":"Cheav Por Chea, Yu Bai, Yihai Fang","doi":"10.1016/j.iintel.2025.100141","DOIUrl":"10.1016/j.iintel.2025.100141","url":null,"abstract":"<div><div>Robotic construction of load carrying structures in civil engineering becomes promising with the supports from robotics, computer-vision, and design for manufacturing and assembly. A multi-robot system was developed to demonstrate an automated construction of reciprocal frame structures where mobile robots were used to facilitate the access of robotic arms and a series of programming packages were developed to automate the construction. Furthermore, the AprilTag fiducial marker system was applied as a positioning system to align the mobile robots during construction tasks and to target the structural components. In this context, the key challenges are centred on the understanding of the accuracy and tolerance of the robotic system in positioning and navigation. To this end, experimental methods were developed in this study to understand the observed distances and the accuracy of the positioning system. The optimal observation distance for the positioning system in the robotic system was then determined considering the positional and orientational accuracies of the AprilTag fiducial marker system using a red, green, blue-depth (RGB-D) camera. Moreover, experiments were conducted to study the impact of the barycentre of robotic arms on the precision of the mobile robots and to determine the offset of the mobile robot during the manoeuvre. In consideration of the positional inaccuracies, the magnetic connection approach was creatively implemented using their inherent self-aligning property. The corresponding effective range was also firstly determined, within which the structural components could be installed successfully.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146657","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

Physics-trained artificial intelligence framework to detect chloride induced degradation in concrete

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-10 DOI: 10.1016/j.iintel.2025.100139

Parth Patel , Abhinav Gupta , Saran Srikanth Bodda , Harleen Kaur Sandhu

{"title":"Physics-trained artificial intelligence framework to detect chloride induced degradation in concrete","authors":"Parth Patel , Abhinav Gupta , Saran Srikanth Bodda , Harleen Kaur Sandhu","doi":"10.1016/j.iintel.2025.100139","DOIUrl":"10.1016/j.iintel.2025.100139","url":null,"abstract":"<div><div>Numerous critical infrastructures in the United States, including bridges, dams, and nuclear plants, are aging and prone to concrete degradation, compromising their performance and structural integrity. One of the leading causes of degradation is chloride-induced corrosion, where chloride ions diffuse into the concrete, leading to reinforcement corrosion, spalling, and cracking. Detecting chloride degradation at an early stage is crucial for ensuring the safety of these vital structures. However, the visible signs of degradation, such as spalling and cracking, often appear only after significant damage has occurred. Degradation occurs gradually over many years, making it impractical to collect real-time non-destructive testing (NDT) data over extended periods while allowing the structure to continue deteriorating. To overcome this challenge, an integrated structural health monitoring framework is proposed that combines advanced finite element modeling, sensor data, and deep learning techniques. This framework follows a multi-step approach to simulate chloride degradation over the service life of the structure. Subsequently, finite element analyses are performed to numerically simulate non-destructive testing at various stages of degradation to generate corresponding sensor data. By leveraging these simulated data and insights, a physics-driven artificial intelligence framework is developed. The proposed framework offers a state-of-the-art solution to mitigate the challenges associated with long-term degradation monitoring by utilizing high-fidelity simulations and data-driven techniques to achieve detection of chloride-induced concrete damage.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563491","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 survey of generative models for image-based structural health monitoring in civil infrastructure

Journal of Infrastructure Intelligence and Resilience Pub Date : 2025-01-10 DOI: 10.1016/j.iintel.2025.100138

Gi-Hun Gwon, Hyung-Jo Jung

{"title":"A survey of generative models for image-based structural health monitoring in civil infrastructure","authors":"Gi-Hun Gwon, Hyung-Jo Jung","doi":"10.1016/j.iintel.2025.100138","DOIUrl":"10.1016/j.iintel.2025.100138","url":null,"abstract":"<div><div>Accurately assessing and monitoring the condition of structures is essential for ensuring the safety and integrity of civil infrastructure. Over the past decade, image-based structural health monitoring technologies have emerged as powerful tools to enhance efficiency and improve the objectivity of structural evaluations. The integration of deep learning technologies with these monitoring systems has significantly improved the efficiency and reliability of structural condition diagnostics. Of particular interest are specifically Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models, which have gained increasing attention due to their versatility in data generation and ability to address fundamental challenges in structural monitoring. While image-based structural health monitoring encompasses both damage detection and structural response measurements, this review primarily focuses on local-level monitoring applications such as damage detection, where generative models have demonstrated particular effectiveness in addressing challenges like limited data availability and environmental variations. This paper provides a comprehensive analysis of these generative models, examining their underlying concepts, mechanisms, and applications in image-based structural health monitoring. Key applications are reviewed, including structural damage detection, data augmentation for training, and emerging areas such as image quality enhancement and domain generalization. Our analysis presents the current state of generative models in structural monitoring, identifying critical challenges and promising future research directions. This systematic review serves as a foundational resource for researchers and practitioners in the field, offering insights into current achievements and potential advancements.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776268","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

Journal of Infrastructure Intelligence and Resilience Pub 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":"2025-01-06","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

Few-shot learning with large foundation models for automated segmentation and accessibility analysis in architectural floor plans

Journal of Infrastructure Intelligence and Resilience Pub 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":"2024-12-17","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

Intelligent control of structural vibrations based on deep reinforcement learning

Journal of Infrastructure Intelligence and Resilience Pub Date : 2024-12-15 DOI: 10.1016/j.iintel.2024.100136

Xuekai Guo, Pengfei Lin, Qiulei Wang, Gang Hu

{"title":"Intelligent control of structural vibrations based on deep reinforcement learning","authors":"Xuekai Guo, Pengfei Lin, Qiulei Wang, Gang Hu","doi":"10.1016/j.iintel.2024.100136","DOIUrl":"10.1016/j.iintel.2024.100136","url":null,"abstract":"<div><div>This paper explores the application of Deep Reinforcement Learning (DRL) in structural vibration control, aiming to achieve effective control of the dynamic response of building structures during natural disasters such as earthquakes. A DRL-based control strategy is proposed, and dynamic interaction between the OpenSees environment and the deep reinforcement learning environment is realized. By adjusting the parameters in the reward function, the control preference of the DRL algorithm for different metrics can be effectively modified. Additionally, an intelligent structural vibration control platform based on DRL has been developed to simplify the design process of DRL algorithms. Case studies conducted on the platform demonstrate that DRL can effectively suppress structural responses in both single-layer and multi-layer complex structures. Meanwhile, comparisons with PID and LQR algorithms that are based on linear analysis design, reveal the stability advantages of DRL in handling structural dynamic responses characterized by high nonlinearity, time delay, and large actuator output intervals.</div></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"4 2","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146656","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