Journal of Infrastructure Intelligence and Resilience最新文献

{"title":"Automatic settlement assessment of urban road from 3D terrestrial laser scan data","authors":"Xinchen Zhang ,&nbsp;Qian Wang ,&nbsp;Hai Fang ,&nbsp;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}

{"title":"Automated Operational Modal Analysis of a steel truss railway bridge employing free decay response","authors":"Francesco Morgan Bono,&nbsp;Antonio Argentino,&nbsp;Lorenzo Bernardini,&nbsp;Lorenzo Benedetti,&nbsp;Gabriele Cazzulani,&nbsp;Claudio Somaschini,&nbsp;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}

{"title":"Reliability-based safety format for structural fire engineering – Derivation based on the most likely failure point","authors":"Ruben Van Coile,&nbsp;Balša Jovanović,&nbsp;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":"2025-03-01","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}

{"title":"Semi-supervised learning approach for construction object detection by integrating super-resolution and mean teacher network","authors":"Wen-Jie Zhang ,&nbsp;Hua-Ping Wan ,&nbsp;Peng-Hua Hu ,&nbsp;Hui-Bin Ge ,&nbsp;Yaozhi Luo ,&nbsp;Michael D. Todd","doi":"10.1016/j.iintel.2024.100095","DOIUrl":"https://doi.org/10.1016/j.iintel.2024.100095","url":null,"abstract":"<div><p>Deep learning-based object detection methods are utilized for safety management at construction sites, which require large-scale, high-quality, and well-labeled datasets for training. The existing construction datasets are relatively small due to the high expense of labor-intensive annotation, and the varying quality of the construction images also affects the detection performance of the model. To address the limitations of datasets, this study proposes a new method for construction object detection by integrating super-resolution and semi-supervised learning. The proposed method improves the quality of construction images and achieves excellent detection performance with limited labeled data. First, the Real-ESRGAN model is introduced to improve the quality of construction images and make the construction objects visible. The proposed super-resolution method can enhance the texture details of low-resolution images, hence improving the performance of object detection models. Second, the mean-teacher network is adopted to expand the training set, thus avoiding the labor-intensive annotation work. To verify the effectiveness of the proposed method, the method is applied to the state-of-the-art Yolov5 object detection model, and construction images from the Site Object Detection Dataset (SODA) with different labeled data proportions (from 10% to 50% in 10% intervals with an extreme case of 5%) are used as the training set. By comparing with the existing supervised learning method, it is shown that the proposed method can achieve better detection performance. In particular, the method is more effective in enhancing detection performance when the proportion of the labeled data is smaller, which is of great practical value in real-world engineering. The experimental results show the potential of the proposed method in improving image quality and reducing the expense of developing construction datasets.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 4","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000148/pdfft?md5=a1f292ff4e6a45e5e49364629c2b74b7&pid=1-s2.0-S2772991524000148-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140536775","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}

{"title":"Multimodal vortex-induced vibration mitigation and design approach of bistable nonlinear energy sink inerter on bridge structure","authors":"Ruihong Xie ,&nbsp;Kun Xu ,&nbsp;Houjun Kang ,&nbsp;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":"2024-12-01","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}

{"title":"Vibration reduction technique of shield construction in water-rich karst strata","authors":"Jing-Rui Peng ,&nbsp;Hua Zhou ,&nbsp;Jing-Yi Hao ,&nbsp;Yan-Ning Wang","doi":"10.1016/j.iintel.2024.100111","DOIUrl":"10.1016/j.iintel.2024.100111","url":null,"abstract":"<div><p>In shield tunneling within karst formations, the vibrational effects often impact the safety of surrounding residents and buildings. The study of construction vibration mitigation measures holds significant importance. Based on the shield tunneling project in the Huang-Shang section of the Xuzhou Metro Line 6, this paper studies the causes, propagation characteristics and influencing factors of ground vibration caused by shield construction. Three effective mitigation measures were identified: (1) Optimization adjustment of shield tunneling parameters; (2) Grouting with mixed bentonite; (3) Layout of vibration reduction boreholes. Each mitigation measure was individually tested for its impact on ground vibration. The comprehensive application of the three measures in shield tunnel construction was analyzed to assess their combined effectiveness. The integration of actual engineering measurements indicates that boreholes provide the best damping effect. Furthermore, the application of multiple mitigation measures resulted in an overall 60% reduction in ground vibration, significantly mitigating the impact on residential structures on the ground. This study provides valuable references for vibration reduction measures in other engineering projects.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 4","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000306/pdfft?md5=9651f42ae91f31613b990f48b6f67b9b&pid=1-s2.0-S2772991524000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129107","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}

{"title":"Enhanced operational modal analysis and change point detection for vibration-based structural health monitoring of bridges","authors":"Serge L. Desjardins ,&nbsp;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":"2024-12-01","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}

{"title":"Quantitative risk analysis of road transportation of hazardous materials in coastal areas","authors":"Daijie Chen ,&nbsp;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":"2024-12-01","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}

{"title":"A systematic literature review of unmanned underwater vehicle-based structural health monitoring technologies","authors":"Joel Friesen Waldner ,&nbsp;Ayan Sadhu","doi":"10.1016/j.iintel.2024.100112","DOIUrl":"10.1016/j.iintel.2024.100112","url":null,"abstract":"<div><p>The structural health of underwater infrastructure such as bridges, dams, and pipelines are constantly degrading due to aging, fatigue, unexpected loads, and environmental wear and tear. Historically, these structures have been inspected by human divers; however, the need for safe and cost-effective monitoring has fostered the development of unmanned underwater vehicles (UUVs) capable of performing subsea surveillance. This paper provides a concise and systematic review of emerging technologies and methodologies for deploying underwater vehicles to perform inspections. Literature is classified into two main groups: advancements to UUV designs and capabilities and advancements to instrumentation for underwater structural health monitoring. After a systematic review, the existing challenges to UUV development and implementation are discussed. Finally, recommendations for future areas of research are outlined. This systematic literature survey aims to provide researchers and practitioners with a holistic outlook on the current state and future trends of UUV-based infrastructure inspection.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 4","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000318/pdfft?md5=7ec3a7a00799411ba7c6543b0bd3df9f&pid=1-s2.0-S2772991524000318-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232391","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}

{"title":"Structural damage identification based on dual sensitivity analysis from optimal sensor placement","authors":"Tengrun Qi,&nbsp;Zhilong Hou,&nbsp;Ling Yu","doi":"10.1016/j.iintel.2024.100110","DOIUrl":"10.1016/j.iintel.2024.100110","url":null,"abstract":"<div><p>Structural damage identification (SDI) methods using incomplete modal information can avoid the extension for unmeasured degrees of freedom, but the absence of essential damage information often leads to the failure of SDI. To address this problem, a novel SDI method based on dual sensitivity analysis and optimal sensors placement technique is proposed in this study. Firstly, in the optimal sensor placement technique, an improved eigenvector sensitivity method combined with weighted modal kinetic energy is proposed, which enables the acquisition of eigenvector information related to damage sensitivity, and incorporates it into the modal strain energy sensitivity matrix to obtain the dual sensitivity analysis matrix. Then, the sparsity of structural damage is considered, and the L1 sparse regularization is selected and introduced into the dual sensitivity analysis damage equation for better SDI results. Finally, to assess the effectiveness of the proposed method, a series of numerical simulations and experimental verifications were carried out under different structural damage scenarios. The results indicate that the proposed method can efficiently localize and quantify the structural damage with minimal modal information in one single step.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 3","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277299152400029X/pdfft?md5=53f6a3a7596efb165c45cda6c77b79d6&pid=1-s2.0-S277299152400029X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843906","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}