Journal of Infrastructure Intelligence and Resilience最新文献

{"title":"An integrated model for selecting bridge structural systems using quality function deployment and analytical hierarchy process","authors":"Saleh Abu Dabous ,&nbsp;Mohammad AL Ayoub ,&nbsp;Mohammed Alsharqawi ,&nbsp;Fatma Hosny","doi":"10.1016/j.iintel.2024.100096","DOIUrl":"https://doi.org/10.1016/j.iintel.2024.100096","url":null,"abstract":"<div><p>Selecting an efficient structural system during the conceptual design of bridge projects is an essential requirement for the project’s success and fulfilling stakeholders’ expectations. This process involves evaluating a broad range of objective and subjective requirements based on multiple technical criteria. Despite its importance, current literature lacks a structured methodology for assisting designers in the selection process of the bridge structural system. Therefore, this research aims to develop a selection model to facilitate the decision-making process, helping evaluate different bridge structural systems during the conceptual design phase. The primary goal is to choose the most optimal design that aligns with both the client’s needs and technical specifications. The proposed methodology begins by identifying client needs and finding their relative importance using an Analytic Hierarchy Process (AHP) questionnaire, followed by determining the technical requirements in bridge conceptual design. A Quality Function Deployment (QFD) model is developed to evaluate bridge structural systems. The main advantage of integrating QFD and AHP is that it reduces the inconsistency and uncertainty in the QFD inputs. The methodology is implemented in a real case study of a bridge project in the United Arab Emirates (UAE), demonstrating improved results in structural system selection compared to traditional methods. While this research focused on the conceptual design phase of bridge projects, future work could extend to other phases of design.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100096"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277299152400015X/pdfft?md5=5327916e8e089d3f85d321248f271b98&pid=1-s2.0-S277299152400015X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638600","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":"Intrinsic self-sensing concrete to energize infrastructure intelligence and resilience: A review","authors":"Xinyue Wang ,&nbsp;Siqi Ding ,&nbsp;Yi-Qing Ni ,&nbsp;Liqing Zhang ,&nbsp;Sufen Dong ,&nbsp;Baoguo Han","doi":"10.1016/j.iintel.2024.100094","DOIUrl":"https://doi.org/10.1016/j.iintel.2024.100094","url":null,"abstract":"<div><p>Under loading and environmental actions, infrastructures undergo continuous aging and deterioration of the constituent materials during their service lifespan. In-situ monitoring the aging and deterioration at material level of infrastructures can provide effective protection and maintenance prior to serious failure, thus enhancing their safety and lifespan as well as resilience. Therefore, self-sensing performance of materials is an important paradigm for updating infrastructures with intelligent digital insights. Concrete, the most widely used engineering material for infrastructure construction, inherently lacks self-sensing property. The incorporation of functional fillers can form a conductive sensory “neural” system inside concrete, thus empowering concrete with the capability to sense stress (or force), strain (or deformation), and damage (e.g., cracking, fatigue) in itself, and also improving (or maintaining) its mechanical properties and durability. The emergence of intrinsic self-sensing concrete has laid a material foundation for realizing in-situ monitoring, contributing to the development of intelligent and resilient infrastructures. This review concisely introduces the significant research progress of research on the composition and preparation, measurement and characterization, performance and control, mechanism and model, and application of intrinsic self-sensing concrete in civil and transportation infrastructures, as well as current challenges and roadmap for its future development.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000136/pdfft?md5=721fc57999551542e849532456d2c413&pid=1-s2.0-S2772991524000136-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140330698","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":"2024-03-08","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":"Few-shot classification for sensor anomalies with limited samples","authors":"Yuxuan Zhang ,&nbsp;Xiaoyou Wang ,&nbsp;Yong Xia","doi":"10.1016/j.iintel.2024.100087","DOIUrl":"10.1016/j.iintel.2024.100087","url":null,"abstract":"<div><p>Structural health monitoring (SHM) systems generate a large amount of sensing data. Data anomalies may occur due to sensor faults and extreme events. Sensor faults can result in low-fidelity measurement data, while data associated with extreme events are crucial for assessing the structural safety condition and should be given special attention. Accurate detection and classification of anomalies can improve the performance of SHM systems. However, most existing classification methods work well only when the number of a-single-class anomalies is sufficient. This study proposes an automatic few-shot classification method for sensor anomalies with limited labeled samples. The most discriminatory shapelet, a new representation of abnormal data, is learned from the standard normal class by maximizing the overall distance, which can locate the prominent abnormal features from 1-h acceleration data. The classification is then learned based on manual feature extraction and deep-learning-based feature extraction by measuring the similarity between the most discriminatory shapelets from the query and support sets. The proposed few-shot classification method is applied to datasets collected from two SHM systems of a long-span bridge and a campus footbridge. Results demonstrate that the proposed method can classify new anomalies with limited samples that differ from the defined anomalies.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000069/pdfft?md5=0510fe12562729a914ba390bb6ce1cb9&pid=1-s2.0-S2772991524000069-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089254","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 quantification using long short-term memory (LSTM) auto-encoder and impulse response functions","authors":"Chencho ,&nbsp;Jun Li ,&nbsp;Hong Hao","doi":"10.1016/j.iintel.2024.100086","DOIUrl":"https://doi.org/10.1016/j.iintel.2024.100086","url":null,"abstract":"<div><p>This paper presents an approach for structural damage quantification using a long short-term memory (LSTM) auto-encoder and impulse response functions (IRF). Among time domain responses-based methods for structural damage identification, using IRF is advantageous over the original time domain responses, since IRF consists of information of system properties and is loading effect independent. In this study, IRFs are extracted from the acceleration responses measured from different locations of structures under impact force excitations. The obtained IRFs are concatenated. Moving averaging with a suitable window size is performed to reduce random variations in the concatenated responses. Further, principal component analysis is performed for dimensionality reduction. These selected principal components are then fed to the LSTM auto-encoder for structural damage identification. A noise layer is added as an input layer to the LSTM auto-encoder to regularise the model. The proposed model consists of two phases: (1) reconstruction of the selected “principal components” to extract the features; and (2) damage identification of structural elements. Numerical studies are conducted to verify the accuracy of the proposed approach. The results demonstrate that the proposed approach can accurately identify and quantify structural damage for both single- and multiple-element damage cases with noisy measurements, as well as uncertainties in the stiffness parameters. Furthermore, the performance of the proposed approach is evaluated using the limited measurements from a few sensors.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 2","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000057/pdfft?md5=f3e5252bd85bf26600d9a4445daa485f&pid=1-s2.0-S2772991524000057-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140332871","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":"Towards vision-based structural modal identification at low frame rate using blind source separation","authors":"Shivank Mittal ,&nbsp;Ayan Sadhu","doi":"10.1016/j.iintel.2024.100085","DOIUrl":"10.1016/j.iintel.2024.100085","url":null,"abstract":"<div><p>With increasing availability of cost-effective and high-resolution cameras, their use as a non-contact sensing tool has rapidly progressed for structural health monitoring. The cameras offer unique capabilities to provide full-field measurement with high spatial density at low cost. However, extracting high-density temporal data is challenging, as a high-speed camera increases the monitoring cost with high-rate data processing. Recently, motion magnification (MM) has shown significant success in analyzing low-amplitude motion of structural systems. However, previous studies observed that MM methodology performs poorly at low frame rates for modal identifications. In this paper, the influence of low frame rate on phased-based motion magnification (PMM) has been investigated. A novel technique is proposed by combining PMM with zero mean-normalization cross-correlation tracker to determine vibrational responses, and then the spatial Wigner-Ville spectrum-based time-frequency blind source separation method is explored for modal identification using the extracted vibrational responses obtained from the video data. The experimental data of a lumped mass experimental model and a steel bridge is used to test the accuracy of the proposed method. The original and motion-magnified image response data is compared with accelerometer data for modal identification. The proposed method is able to extract the modal parameters with high accuracy for motion-magnified images, even for low frame rates.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 3","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000045/pdfft?md5=3856b76a2dacf10913cf7351487c87f3&pid=1-s2.0-S2772991524000045-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140467224","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":"Implications of 5G rollout on post-earthquake functionality of regional telecommunication infrastructure","authors":"Ao Du","doi":"10.1016/j.iintel.2024.100084","DOIUrl":"https://doi.org/10.1016/j.iintel.2024.100084","url":null,"abstract":"<div><p>Telecommunication infrastructure (TI) is becoming increasingly vital in modern society, where information exchange is needed in almost all aspects of the built environment, business operations, and people's daily lives. The ongoing 5G rollout will lead to a paradigm shift in regional TI deployment landscape, with increased seismic hazard exposure particularly due to the densely deployed small cells. As TI is known to be vulnerable to seismic hazard impacts yet necessary for post-earthquake emergency response, this study carries out a pioneering effort in quantifying the post-earthquake TI failures and functionality to better support risk mitigation decision-making. We propose a novel seismic risk assessment framework for regional 5G TI, by holistically integrating regional seismic hazard analysis, infrastructure seismic exposure data, electric power infrastructure seismic fragility modeling and network connectivity analysis, as well as wireless TI functionality modeling. The proposed framework is evaluated based on a hypothetical regional infrastructure testbed located in Memphis, Tennessee, subjected to several earthquake scenarios. From a reference heterogeneous 5G TI deployment scenario, the results indicate that significant performance degradation of 5G TI is expected especially after major earthquake events. Enabled by the proposed framework, we further compared the efficacy of several risk mitigation strategies and pertinent implications are provided.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 1","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000033/pdfft?md5=08f1505d87bec07c525c7b0327c6ffa2&pid=1-s2.0-S2772991524000033-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694829","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":"Ecological network analysis and optimization of resilience and efficiency for electric power systems design","authors":"Bharadwaj Somu ,&nbsp;Enrico Zio","doi":"10.1016/j.iintel.2024.100083","DOIUrl":"10.1016/j.iintel.2024.100083","url":null,"abstract":"<div><p>The simultaneous increase in natural disasters and human dependence on critical infrastructures for essential services such as water, electricity, etc., places ever-increasing demands on the reliable, safe, resilient design and operation of these infrastructures, with a trade-off between continuity of supply (safety and resilience) and quality of supply (reliability and efficiency) at limited cost. With this in mind, a new methodology for the analysis of electric power systems inspired by natural ecosystems is proposed here and applied to representative systems from literature. Information theory is used to quantify the results of the ecological network analysis (ENA) performed. The analysis shows that electric power systems are more efficient than reliable and vulnerable to disasters. A flow matrix is constructed from the available IEEE systems data, quantified and analyzed using information theory, and finally validated by contingency analysis and SCOPF analysis. The original network configurations are compared to random generated topologies. Comparisons are also made with ENA-inspired configurations. The latter show significantly fewer violations in each contingency scenario compared to the original configurations, further supporting the use of ENA to balance power system efficiency and resilience. Thus, ENA can be used to develop power systems with balanced efficiency and resilience.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 1","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991524000021/pdfft?md5=6a7c5da757d015787dd1f073a57fa8a3&pid=1-s2.0-S2772991524000021-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456019","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":"Control of seismic induced response of wind turbines using KDamper","authors":"Haoran Zuo ,&nbsp;Xunyi Pan ,&nbsp;Kaiming Bi ,&nbsp;Hong Hao","doi":"10.1016/j.iintel.2024.100082","DOIUrl":"10.1016/j.iintel.2024.100082","url":null,"abstract":"<div><p>Earthquake-induced vibrations of wind turbines may compromise structural serviceability and safety. Most previous studies adopted passive control devices to mitigate the seismic responses of wind turbines. However, their control effectiveness is heavily dependent on the mass ratio between control devices and wind turbines, and they were typically housed at the tower top or within the nacelle. The restricted space within the hollow tower and the nacelle imposes considerable challenges for the implementation of such devices, rendering the application of large-scale control devices unfeasible for structural vibration control of wind turbines. To this end, this paper integrates a negative stiffness element within a conventional tuned mass damper (TMD), termed KDamper, to mitigate vibrations of wind turbine towers under seismic loads. Specifically, the widely used NREL 5 MW wind turbine is selected as a prototype structure and its tower is modelled as a multiple-degree-of-freedom system. Then KDamper is incorporated into the developed model and its parameters are optimized based on the <em>H</em>₂ criterion. Subsequently, the control effectiveness of KDamper is investigated and compared with TMD in the frequency domain, and the control performances in terms of the effectiveness and robustness of KDamper are further examined under a series of earthquake records. Results show that KDamper has superior control effectiveness and robustness than TMD, indicating it has considerable potential for application in improving wind turbine performances against earthquake hazards.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 1","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277299152400001X/pdfft?md5=ebed026d6d0a94994b7a061c881d41de&pid=1-s2.0-S277299152400001X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395547","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":"Identifying and estimating causal effects of bridge failures from observational data","authors":"Aybike Özyüksel Çiftçioğlu ,&nbsp;M.Z. Naser","doi":"10.1016/j.iintel.2023.100068","DOIUrl":"10.1016/j.iintel.2023.100068","url":null,"abstract":"<div><p>This paper presents a causal analysis aimed at identifying and estimating causal effects with regard to bridge failures under extreme events. Observational data on about 299 bridge incidents were used to conduct this causal investigation and examine bridges’ performance. As causal investigations can also deliver counterfactual assessments of parallel worlds, a causal analysis can serve as a high-merit methodology to evaluate the performance of critical bridges. Our findings quantify the causal impacts of various factors spanning the characteristics of bridges, traffic demands, and incident type (i.e., fire, high wind, scour/flood, earthquake, and impact/collision). More specifically, our analysis reveals high causal effects related to the used structural system, construction materials, and demand served.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"3 1","pages":"Article 100068"},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991523000439/pdfft?md5=99c0fa84cbc6713a8fe4c4d18727f3a6&pid=1-s2.0-S2772991523000439-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139019337","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}