Monitoring Infrastructure Research Articles

Greedy Weighted Stacking of Machine Learning Models for Optimizing Dam Deformation Prediction

Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate the application of ML algorithms, including Boosted Regression Trees (BRT), Random Forest (RF), and Neural Networks (NN), focussing on their combination by Stacking to improve prediction accuracy on concrete dam deformation using radial displacement data from three dams. The methodology involves training first-level models (experts) using those algorithms, and a second-level meta-learner that combines their predictions using BRT, a Linear Model (LM) and the Greedy Weighted Algorithm (GWA). A comparative analysis demonstrates the superiority of Stacking over traditional methods. The GWA emerged as the most suitable meta-learner, enhancing the optimal expert in all cases, with improvement rates reaching up to 16.12% over the optimal expert. Our study addresses critical questions regarding the GWA’s expert weighting and its impact on prediction precision. The results indicate that the combination of accurate experts using the GWA improves model reliability by reducing error dispersion. However, variations in optimal weights over time necessitate robust error estimation using cross-validation by blocks. Furthermore, the assignment of weights to experts closely correlates with their precision: the more accurate a model is, the more weight that is assigned to it. The GWA improves on the optimal expert in most cases, including at extreme values of error, with improvement rates up to 41.74%. Our findings suggest that the proposed methodology significantly advances AI applications in infrastructure monitoring, with implications for dam safety.

Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster’s hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system’s ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures.

An integrated urban water resources management approach for infrastructure and urban planning

Transportation projects are crucial for the overall success of major urban, metropolitan, regional, and national development according to their capacity by bringing significant changes in socio-economic and territorial aspects. In this context, sustaining and development of economic and social activities depend on having sufficient Water Resources Management. This research helps to manage transport project planning and construction phases to analyze the surface water flow, high-level streams, wetland sites for development of transportation infrastructure planning, implementing, maintenance, monitoring and long-term evaluations to better face the challenges and solutions associated with effective management and enhancement to deal with Low, Medium, High levels of impact. A case study was carried out using the Arc Hydro extension within ArcGIS for processing and presenting spatially-referenced Stream Model. Geographical information systems have the potential to improve water resources planning and management. The study framework would be useful for water resources problem solving by enabling decision makers to collect qualitative data more effectively and gather them into the water management process by a systematic framework.

Decision Making for Self-Adaptation Based on Partially Observable Satisfaction of Non-Functional Requirements

Approaches that support the decision-making of self-adaptive and autonomous systems (SAS) often consider an idealized situation where (i) the system’s state is treated as fully observable by the monitoring infrastructure, and (ii) adaptation actions are assumed to have known, deterministic effects over the system. However, in practice, the system’s state may not be fully observable, and the adaptation actions may produce unexpected effects due to uncertain factors. This article presents a novel probabilistic approach to quantify the uncertainty associated with the effects of adaptation actions on the state of a SAS. Supported by Bayesian inference and POMDPs (Partially-Observable Markov Decision Processes), these effects are translated into the satisfaction levels of the non-functional requirements (NFRs) to, therefore, drive the decision-making. The approach has been applied to two substantial case studies from the networking and Internet of Things (IoT) domains, using two different POMDP solvers. The results show that the approach delivers statistically significant improvements in supporting decision-making for SAS.

Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning.

The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between -1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.

Perceived needs of disease vector control programs: A review and synthesis of (sub)national assessments from South Asia and the Middle East.

Systems for disease vector control should be effective, efficient, and flexible to be able to tackle contemporary challenges and threats in the control and elimination of vector-borne diseases. As a priority activity towards the strengthening of vector control systems, it has been advocated that countries conduct a vector-control needs assessment. A review was carried out of the perceived needs for disease vector control programs among eleven countries and subnational states in South Asia and the Middle East. In each country or state, independent teams conducted vector control needs assessment with engagement of stakeholders. Important weaknesses were described for malaria, dengue and leishmaniases regarding vector surveillance, insecticide susceptibility testing, monitoring and evaluation of operations, entomological capacity and laboratory infrastructure. In addition, community mobilization and intersectoral collaboration showed important gaps. Countries and states expressed concern about insecticide resistance that could reduce the continued effectiveness of interventions, which demands improved monitoring. Moreover, attainment of disease elimination necessitates enhanced vector surveillance. Vector control needs assessment provided a useful planning tool for systematic strengthening of vector control systems. A limitation in conducting the vector control needs assessment was that it is time- and resource-intensive. To increase the feasibility and utility of national assessments, an abridged version of the guidance should focus on operationally relevant topics of the assessment. Similar reviews are needed in other regions with different contextual conditions.

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures.

The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments.

The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter's height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of 3.86 dB and standard deviation (SD) of 3.8614 dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure.

Continuous QoS‐compliant orchestration in the Cloud‐Edge continuum

AbstractThe problem of managing multi‐service applications on top of Cloud‐Edge networks in a quality of service (QoS)‐aware manner has been thoroughly studied in recent years from a decision‐making perspective. However, only a few studies addressed the problem of actively enforcing such decisions while orchestrating multi‐service applications and considering infrastructure and application variations. In this article, we propose a next‐gen orchestrator prototype based on Docker to achieve the continuous and QoS‐compliant management of multiservice applications on top of geographically distributed Cloud‐Edge resources, in continuity with CI/CD pipelines and infrastructure monitoring tools. Finally, we assess our proposal over a geographically distributed testbed across Italy.

Landscape review of active vaccine safety surveillance activities for COVID-19 vaccines globally

BackgroundEvidence of COVID-19 vaccine safety relied upon the global vaccine monitoring infrastructure due to shortened clinical development timelines and emergency use licensure. Differences in AVSS capacity between high-income countries (HICs) versus low- and middle-income countries (LMICs) were known prior to the pandemic. ObjectiveTo assess the global landscape of COVID-19 vaccine AVSS activities to identify gaps in safety evidence generation across vaccine products and populations with a focus on LMICs. MethodsA cross-sectional survey was conducted in January 2022 on AVSS activities evaluating adverse events following immunization (AEFI). Data collected included country, targeted population, COVID-19 vaccine product(s), design of surveillance/monitoring activities or study, and AEFIs to be monitored.To supplement these findings, we conducted a literature review of COVID-19 vaccine safety activities published in PubMed through January 2023. Observational activities assessing AEFI, specifically adverse events of special interest (AESI), following routine use of COVID-19 vaccines in medical practice were included; systematic reviews, benefit/risk assessments, clinical trials, and case reports/series were excluded. ResultsThe survey, completed by 34 respondents and compiled with reviews of 7 publicly available Risk Management Plans from five vaccine manufacturers, identified 79 monitoring activities in HICs, 24 in LMICs, and 9 in multiple regions. Most activities in LMICs were planned cohort event monitoring (CEM) studies (n = 18); two multi-national hospital-based sentinel surveillance studies for AESI were ongoing. Activities in LMICs evaluated multiple COVID-19 vaccine products simultaneously and were sponsored by health authorities. The literature review identified 1245 unique citations, of which 379 met inclusion criteria. The majority evaluated vaccines primarily used in high-income countries: Pfizer BioNTech (Comirnaty; n = 303), Moderna (mRNA-1273; n = 164), AstraZeneca (AZD1222; n = 126), and Janssen (Ad26.COV2.S); n = 62); 14 citations assessed vaccines used exclusively in LMICs: Sinovac (CoronaVac), Beijing CNBG (BBIBP-Corv), Bharat (Covaxin), SII (Covashield), and Gamaleya (Gam-Covid-Vac) vaccines. ConclusionsRobust safety evidence for input into benefit/risk assessments is likely unavailable for most COVID-19 vaccines used primarily in LMICs due to emphasis on cohort event monitoring methods. Goals for equitable vaccine access should be coupled with investment and support for building infrastructure and capacity for safety evidence generation to inform policy and regulatory decisions at local levels.

Deep learning techniques to detect rail indications from ultrasonic data for automated rail monitoring and maintenance

The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

Combining UAV-LiDAR and UAV-photogrammetry for bridge assessment and infrastructure monitoring

Combining UAV-LiDAR and UAV-photogrammetry for bridge assessment and infrastructure monitoring

Novel odor sensing intelligence and surveillance capabilities in controller-responder robots

The rise in global travel has led to an increased need for heightened security measures at airports. Despite the best efforts of airport security officers, in the past year, hundreds of kilograms of illegal drugs and thousands of agricultural invasive species have found their way into the country, posing a severe threat to public safety and the environment. Moreover, human threats pose a significant risk to civil aviation, reinforcing the need for advanced security technology. In response to these challenges, NOSI (Novel Odor Sensing Intelligence) and ROSI (Reconnaissance Operations Security Intelligence), intelligence surveillance systems consisting of semi-autonomous controller-responder robots, were developed as a proof of concept to supplement the efforts of security and K-9 (police dogs) operators at airports. NOSI is equipped with multi-channel gas sensors for odor detection, enabling it to identify illegal drugs and invasive species in the baggage handling process, while ROSI is equipped with computer vision to identify individuals already in the government’s database of persons of interest. These coordinated robots also provide travelers with important information pertaining to their journey and allow them to trigger emergency alerts. The robots were tested in a custom-designed test bed that replicated both the behind-the-scenes baggage handling and front-office customer service operations of an airport, thus simulating a realistic airport-like setting. Based on design criteria, NOSI and ROSI demonstrated success rates of 73.4 percent and 69.8 percent, respectively. Improvements in areas of robot stability, sensor accuracy, and feature expansion were documented for further development. In conclusion, the NOSI and ROSI framework can enhance the efficiency and accuracy of airport infrastructure monitoring and supplement the capabilities of human and K9 operators. Overall, this approach can potentially revolutionize operations in various infrastructures and represents the future of human-robot collaboration.

Enhancing Data Privacy in Wireless Sensor Networks: Investigating Techniques and Protocols to Protect Privacy of Data Transmitted Over Wireless Sensor Networks in Critical Applications of Healthcare and National Security

The article discusses the emergence of Wireless Sensor Networks (WSNs) as a groundbreaking technology in data processing and communication. It outlines how WSNs, composed of dispersed autonomous sensors, are utilized to monitor physical and environmental factors, transmitting data wirelessly for analysis. The article explores various applications of WSNs in healthcare, national security, emergency response, and infrastructure monitoring, highlighting their roles in enhancing patient care, public health surveillance, border security, disaster management, and military operations. Additionally, it examines the foundational concepts of data privacy in WSNs, focusing on encryption techniques, authentication mechanisms, anonymization techniques, and access control mechanisms. The article also addresses vulnerabilities, threats, and challenges related to data privacy in healthcare and national security contexts, emphasizing regulatory compliance, ethical considerations, and socio-economic factors. Furthermore, it introduces the Diffusion of Innovation Theory as a framework for understanding the adoption of privacy-enhancing technologies in WSNs. Finally, the article reviews empirical studies demonstrating the efficacy of security solutions in preserving data privacy in WSNs, offering insights into advancements in safeguarding sensitive information.

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda.

The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

Persistent scatterer interferometry (PSI) technique for road infrastructure monitoring: a case study of the roadway network of the Nile Delta (Egypt)

A dependable transportation infrastructure is an essential local resource, linking communities and facilitating economic expansion. The Nile Delta, renowned for its high population density, is experiencing subsidence as a result of natural forces exerting pressure on its sediments. Land subsidence exerts a substantial influence on infrastructure, economic, and social progress. This study examines the evaluation of land subsidence in roads experiencing various issues in the Nile Delta. Each year, subsidence results in significant damage and, in certain instances, complete disruption of numerous road networks, with potential durations ranging from a few days to several months. Conventional methods for identifying and monitoring subsidence on a large and intricate road network can be challenging due to the substantial time and resource requirements. Satellite-based monitoring is essential as it can effectively detect ground displacements caused by human activities (such as subterranean diggings and disruption of other infrastructures) or environmental dangers, specifically those associated with crucial hydrogeological incidents. Moreover, it possesses the benefit of efficiently encompassing extensive regions with precision. This study utilized the persistent scatterer interferometry (PSI) of SNAP-StaMPS workflow to employ differential interferometry synthetic aperture radar (DInSAR) processing methods for investigating the area. By utilizing Sentinel-1 data, we successfully acquired a comprehensive understanding of the deformation occurring in the study area, specifically in the roads where subsidence is persistently taking place. The data has demonstrated its value in enhancing our understanding of the safety status of infrastructure and facilitating maintenance actions. The accuracy of the radar interferometry results was confirmed through the use of in situ methods, including Global Navigation Satellite System (GNSS), Global Positioning System (GPS), smart phone, and unmanned aerial vehicle. This approach has demonstrated its capability to consistently detect vertical displacement in critical areas of roads. Furthermore, there is a strong correlation between the PSI interferometry and the in situ measurements when absolute values are taken into consideration. The findings of this study also demonstrate that open-source solutions are more effective, which encompass freely available data and software, offered through the COPERNICUS program, possess significant potential for monitoring vertical displacements in roads.

A Fully Programmable DAQ Board of Vibrational Signals from IEPE Sensors: Hardware and Software Design, Performance Analysis

Vibration analysis is commonly used to assess machinery conditions, earthquake detection, and structural monitoring. Commercially available DAQs (Data Acquisition Systems) feature high costs and limited versatility in terms of end-user hardware/firmware customization, making it difficult to adapt them to the input signal features and add supplementary functionalities. Hence, this research aims to develop a custom acquisition board for detecting vibration signals via IEPE (Integrated Electronic Piezoelectric) sensors, considering the limitations of commercially available systems, and building upon solutions found in the literature. The DAQ board was intended for remote vibration monitoring of infrastructure and machinery for industrial applications, allowing the implementation of predictive maintenance strategies. The proposed DAQ board has two independent and fully configurable channels, which can be set for acquiring signals from IEPE sensors or generic voltage sources. The DAQ board relies on the STM32F401 microcontroller to manage the acquisition from high-speed ADCs, process data, and store them in mass memory (SD card). During acquisition, the DAQ implements a batch acquisition strategy based on a buffer flash memory for temporarily storing ADCs data, which are iteratively poured into mass memory. Also, the board has Bluetooth connectivity to transmit acquired data and receive commands remotely. A prototype of the DAQ board was developed and tested with several waveforms, including vibration signals. The tests showed that the board can acquire vibration signals and compute the FFT onboard. The DAQ demonstrated a good balance between performance, accuracy, flexibility, and cost, making it suitable for several industrial applications and allowing for scalability and integration potential.

Stormwater Quality and Long-Term Efficiency Capturing Potential Toxic Elements in Sustainable Urban Drainage Systems—Is the Soil Quality of Bio-Swales after 10–20 Years Still Acceptable?

Sustainable urban drainage systems (SuDS) or nature-based solutions (NBSs) are widely implemented to collect, store and infiltrate stormwater. The buildup of pollutants is expected in NBSs, and Dutch guidelines advise monitoring the topsoil of bio-swales every 5 years. In the Netherlands, almost every municipality has implemented bio-swales. Some municipalities have over 300 bio-swales, and monitoring all their NBSs is challenging due to cost and capacity. In this study, 20 locations where bio-swales with ages ranging between 10 and 20 years old were selected for a field investigation to answer the following question: is the soil quality of bio-swales after 10 years still acceptable? Portable XRF instruments were used to detect potential toxic elements (PTEs) for in situ measurements. The results showed that for copper (Cu), zinc (Zn) and lead (Pb), 30%, 40% and 25% of the locations show values above the threshold and 5%, 20% and 0% above the intervention threshold, meaning immediate action should be taken. The results are of importance for stakeholders in (inter)national cities that implement, maintain, and monitor NBS. Knowledge of stormwater and soil quality related to long-term health risks from NBS enables urban planners to implement the most appropriate stormwater management strategies. With these research results, the Dutch guidelines for design, construction, and maintenance can be updated, and stakeholders are reminded that the monitoring of green infrastructure should be planned and executed every 5 years.

Internet of things (IoT)-based structural health monitoring of laboratory-scale civil engineering structures

Rapid advances in the Internet of Things (IoT) domain have made it a crucial technology for the real-time structural health monitoring (SHM) of civil engineering infrastructures. The availability of quick and accurate vibration data is essential for SHM, and such data can be obtained through IoT devices mounted on the structures. This study proposes a real-time damage prediction and localization approach using a low-cost "do-it-yourself" wireless sensor node with IoT capabilities for SHM. The proposed sensor node comprised a microcontroller (NODE MCU ESP8266) and a 6-axis accelerometer (MPU6050). The IoT devices track the real-time frequency of the laboratory-scale structure indirectly via measurement of acceleration-time history, and their results are compared with conventional industry-standard accelerometers. Promising results, with a <6% average difference from the conventional accelerometer (difference ranging from 1.3 to 14.3%), provided an innovative SHM for vibration-based real-time SHM using the IoT paradigm. The performance of the proposed methodology was validated numerically and experimentally on two laboratory-scale structures, and the potential of IoT technology for enhancing the efficiency of SHM was demonstrated. The proposed method thus can enable the early detection of damages in infrastructures such as buildings and bridges and thus can reduce the likelihood of accidents via continuous SHM.

Bridge Monitoring Strategies for Sustainable Development with Microwave Radar Interferometry

The potential of a coherent microwave radar for infrastructure health monitoring has been investigated over the past decade. Microwave radar measuring based on interferometry processing is a non-invasive technique that can measure the line-of-sight (LOS) displacements of large infrastructure with sub-millimeter precision and provide the corresponding frequency spectrum. It has the capability to estimate infrastructure vibration simultaneously and remotely with high accuracy and repeatability, which serves the long-term serviceability of bridge structures within the context of the long-term sustainability of civil engineering infrastructure management. In this paper, we present three types of microwave radar systems employed to monitor the displacement of bridges in Japan and Italy. A technique that fuses polarimetric analysis and the interferometry technique for bridge monitoring is proposed. Monitoring results achieved with full polarimetric real aperture radar (RAR), step-frequency continuous-wave (SFCW)-based linear synthetic aperture, and multi-input multi-output (MIMO) array sensors are also presented. The results reveal bridge dynamic responses under different loading conditions, including wind, vehicular traffic, and passing trains, and show that microwave sensor interferometry can be utilized to monitor the dynamics of bridge structures with unprecedented spatial and temporal resolution. This paper demonstrates that microwave sensor interferometry with efficient, cost-effective, and non-destructive properties is a serious contender to employment as a sustainable infrastructure monitoring technology serving the sustainable development agenda.