Civil Infrastructure Monitoring Research Articles

A refined output-only modal identification technique for structural health monitoring of civil infrastructures

The increasing installation of structural health monitoring systems has raised the need for efficient and rapid data interpretation algorithms. Operational modal analysis is currently one of the most used techniques for extracting modal properties, and some attempts have been made to automate this procedure. However, automated techniques often require manual calibration of hyperparameters and show inconsistencies across different case studies. This paper proposes a refined version of the automated frequency domain decomposition (AFDD) using the modal assurance criterion (MAC) to obtain natural frequencies and mode shapes. Sensitivity analyses were conducted on the ambient vibration response of the Yonghe cable-stayed bridge in China, accounting for influential factors including noise levels, acceleration record length, sensor layouts. A novel approach is then introduced to interpret the results of the sensitivity analyses. The approach consists in plotting the result of each analysis in a stabilization diagram and then using a Gaussian mixture model that clusters the poles into core and outliers. This allows to identify regions where the MAC thresholds are optimal. By comparing the results of all the sensitivity analyses it was possible to define a single optimal MAC threshold, avoiding the need for fixing a value based on the user’s experience. Three substantially different case studies were analyzed to extensively test the methodology: the Yonghe cable-stayed bridge, the PolyU footbridge in Hong Kong, and Moletta Tower in the Circus Maximus archeological site in Italy. The analysis compared the proposed AFDD algorithm to the traditional frequency domain decomposition and covariance-driven stochastic subspace identification. Specifically, the efficiency in identifying close frequencies, weakly excited modes, spurious peaks, and complex modes was evaluated for each method, which highlighted the robustness of the proposed optimized AFDD. The analysis showcases peculiar characteristics and drawbacks of each method when trying to identify complex vibrational modes of the specific case studies. It was found that the proposed AFDD procedure performs better than traditional methods despite it may misidentify complex modes due to the constraints of a narrower modal domain and similar geometries.

A Drone-Based Structure from Motion Survey, Topographic Data, and Terrestrial Laser Scanning Acquisitions for the Floodgate Gaps Deformation Monitoring of the Modulo Sperimentale Elettromeccanico System (Venice, Italy)

The structural deformation monitoring of civil infrastructures can be performed using different geomatic techniques: topographic measurements with total stations and levels, TLS (terrestrial laser scanning) acquisitions, and drone-based SfM (structure from motion) photogrammetric surveys, among others, can be applied. In this work, these techniques are used for the floodgate gaps and the rubber joints deformation monitoring of the MOSE system (Modulo Sperimentale Elettromeccanico), the civil infrastructure that protects Venice and its lagoon (Italy) from high waters. Since the floodgates are submerged most of the time and cannot be directly measured and monitored using high-precision data, topographic surveys were performed in accessible underwater tunnels. In this way, after the calculation of the coordinates of some reference points, the coordinates of the floodgate corners were estimated knowing the geometric characteristics of the system. A specific activity required the acquisition of the TLS scans of the stairwells in the shoulder structures of the Treporti barrier because many of the reference points fixed on the structures were lost during the placement of elements on the seabed. They were replaced with new points whose coordinates in the project/as-built reference system were calculated by applying the Procrustean algorithm by means of homologous points. The procedure allowed the estimation of the transformation parameters with maximum residuals of less than 2.5 cm, a value in agreement with the approximation of the real concrete structures built. Using the obtained parameters, the coordinates of the new reference points were calculated in the project reference system. Once the 3D orientation of all caissons in the barrier was reconstructed, the widths of the floodgate gaps were estimated and compared with the designed values and over time. The obtained values were validated in the Treporti barrier using a drone-based SfM photogrammetric survey of the eight raised floodgates, starting from the east shoulder caisson. The comparison between floodgate gaps estimated from topographic and TLS surveys, and those obtained from measurements on the 3D photogrammetric model, provided a maximum difference of 1.6 cm.

Suppression of Rayleigh fading induced errors in φOTDR by different pulse widths for improving the reliability of civil infrastructure monitoring

Rayleigh fading is a widely observed phenomenon in the many fields, such as wireless communication and optical imaging. It is also the main factor limiting the performance of the phase-sensitive optical time domain reflectometry (φOTDR). The low SNR at the fading points results in a large measurement error, severely affecting the reliability of civil infrastructure monitoring. The proposed method involves changing the pulse width during measurements to suppress the impact of fading. Experimental result shows that the number of fading points is greatly reduced by ∼ 96 % and the measurement error is reduced by more than 5 times. Unlike existing methods, this approach requires no hardware modifications, making it applicable to almost all current phase-based φOTDR systems. The versatility and effectiveness of this method make it an excellent candidate for infrastructure monitoring and related fields.

Developing a BIM based digital twin system for structural health monitoring of civil infrastructure

Abstract The utilization of building information modeling (BIM) within digital technology facilitates the creation of three-dimensional representations for monitoring data in large-scale civil infrastructure. In response to the need for intelligent structural management, this study establishes a structural health monitoring (SHM) system and foundational framework based on digital twins. This framework integrates information from various sources and facilitates collaborative efforts for structural operation and maintenance. Additionally, the SHM system integrates actual monitoring measurements and early warning mechanisms to consolidate multi-source monitoring data with BIM. Through real-time analysis, the system provides insights into the operational status of bridges, capturing geometric, physical, and performance evolution characteristics. To construct the system, engineering challenges are initially digitized, with appropriate sensors deployed on real bridge structures to monitor dynamic (acceleration) and static (strain, displacement) physical information during bridge operation. Subsequently, through wireless communication and data storage technologies, the monitored physical data serves as input for mode identification and early warning algorithms, facilitating the acquisition of structural performance information. Finally, three-dimensional display technology enables real-time calculation and rendering of BIM models, fostering the exchange and interaction of monitoring and BIM information, thus enhancing the intelligence of SHM system.

Assessment of different optimal sensor placement methods for dynamic monitoring of civil structures and infrastructures

The optimal sensor placement is a critical aspect in the structural health monitoring of structures, as the number and layout of sensors directly impact the quality and usefulness of collected data. When developing a monitoring system, it is of paramount importance to use the minimum number of sensors to gain the maximum amount and quality of information. However, in the case of a limited number of sensors, their location on the structure becomes crucial. This study aims to assess the potential of several optimal sensor placement methods for the development of cost-efficient and well-dimensioned dynamic structural health monitoring systems to be implemented in a wide range of civil engineering structures and infrastructures. Indeed, the construction stock consists of a great variety of civil engineering structures and infrastructures that significantly differ from each other in terms of typology, historical period, construction techniques, materials, etc. Some of the most widely used optimal sensor placement methods are selected and applied in real case studies, and the obtained sensor configurations are discussed and compared. The results illustrated in this paper will contribute to defining good practice in the optimal sensor placement procedure for typical civil engineering structures and infrastructures.

Acoustic Emission Detection in Noisy Environments using Linear Prediction

In non-destructive testing, acoustic emission testing is an established technique with numerous applications in academia and the industry. In nearly all applications, the detection of relevant acoustic emission signals in continuous structure-borne sound recordings is a crucial step that forms the basis for subsequent analyses and should therefore neither miss any relevant acoustic emissions nor detect too many irrelevant events. That is because, in its most prevalent form, acoustic emission testing utilizes ultrasound signals that require large amount of storage, which might be an economically limiting factor especially for structural health monitoring of large civil infrastructures. In these monitoring applications, the signal-to-noise ratio is also expected to be worse compared to more controlled laboratory conditions, as they can be often found, for example, in materials research. We therefore propose the use of linear prediction, a time series forecasting technique, to extract relevant acoustic emissions from continuous recordings. The proposed methodology utilizes the residual between the signal predicted from a linear combination of previous time steps and the actual measurement to detect any anomalous events. Since the linear predictor can be initialized using the environmental noise only, no specific knowledge of the acoustic emission signals of interest is required beforehand and hence can automatically adapt to each measurement environment. We compare our approach with a simple amplitude-based detection as it is commonly implemented in commercially available acoustic emission systems. Especially for low signal-to-noise ratios, an increase in the area under the receiver operating characteristic curve of up to 0.7 compared to the amplitude-based detection is found.

Investigation of Issues in Data Anomaly Detection Using Deep-Learning- and Rule-Based Classifications for Long-Term Vibration Measurements

Structural health monitoring (SHM) systems are widely used for civil infrastructure monitoring. Data acquired from the SHM systems play an important role in assessing structural integrity and determining further maintenance activities. Considering that sensors in the SHM systems are installed in a harsh environment for long-term measurements, some sensors can malfunction and produce faulty data. As a large amount of measured data is often desired to be automatically processed and can adversely affect structural assessments, identifying such abnormal data is important. This paper provides critical investigations of the automated detection of data anomalies using existing deep-learning-based classification in conjunction with a simple rule-based approach. The issues investigated in this study include (1) the presence of ambiguous data that cannot be categorized as an anomaly class in the literature, (2) information loss during the conversion of time-series data into images for the deep-learning-based approach, and (3) additional issues, such as misclassification by trained models and requirements of the threshold section in the rule-based approach. The results of these key investigations can be utilized to develop an effective anomaly detection process.

COMPARATIVE ANALYSIS OF WIRED AND WIRELESS SENSORS IN STRUCTURAL HEALTH MONITORING

Structural Health Monitoring (SHM) systems play a critical role in ensuring the safety and longevity of civil engineering infrastructure. This paper presents a comparative analysis of wired and wireless sensor technologies within SHM applications, drawing insights from scholarly literature, expert interviews, and case studies. The significance of SHM in safeguarding infrastructure durability is emphasised, considering the substantial investment and long service lifespans associated with civil engineering structures. Thematic analysis was utilized and revealed key performance criteria such as reliability, flexibility, environmental adaptability, cost-effectiveness, and maintenance requirements. Wired sensors are lauded for their reliability and accuracy, particularly in critical infrastructure projects, while wireless sensors offer greater flexibility and ease of deployment, especially in remote monitoring scenarios. Environmental adaptability remains crucial for both sensor types, with fiber optic sensors demonstrating effectiveness in harsh conditions. Expert interviews further enrich the understanding of sensor performance, highlighting opportunities for advancements in wireless sensor technology and data analytics. The paper concludes with recommendations for future research and development efforts to address existing constraints and meet the evolving needs of SHM in civil infrastructure monitoring. Overall, this comparative analysis provides valuable insights for guiding advancements in sensor technology and SHM procedures to ensure the continued safety and integrity of essential infrastructure.

An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those existing systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge, which is important for accurate and precise SHM. Therefore, there is still a need to improve existing systems and practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework that integrates wireless IoT sensing and communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT sensing and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of structural behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical structural components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with IoT-enabling sensing data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management.

AI-Driven Sensing Technology: Review.

Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.

3D printed self-sensing cementitious composites using graphite and carbon microfibers

Cementitious materials possessing self-sensing properties conferred through the incorporation of conductive fillers have attracted significant interest in the research community due to their potential application to structural health monitoring of civil infrastructure. A key advantage of cementitious sensors is that they can be organically embedded within cementitious structures to provide extremely robust monitoring capabilities. Of interest to this paper, the integration of cementitious sensors could be critical in empowering the 3D printing of cementitious composites by enabling real-time monitoring of additive manufacturing. Such integration could be achieved by 3D printing self-sensing cementitious nodes during the fabrication process. 3D printed self-sensing cementitious sensors could also be topologically engineered to achieve new opportunities in sensing of existing and new concrete and other cementitious structures. This paper presents one of the earliest studies, if not the first, on the 3D printing of self-sensing functionalized composites. The self-sensing cementitious composite sensors are fabricated using a cementitious matrix doped with graphite (G) and carbon microfibers. Various mixtures are studied, in particular G-only, G with milled carbon microfibers (MCMF), G with chopped carbon microfibers (CCMF), and G with MCMF and CCMF. A study of the percolation curves reveals that it is possible to attain electrical percolation using these conductive particles to create strain-sensitive sensors by leveraging the boosted piezoresistive effect. A study of strain sensing performance conducted on various specimens fabricated using different mixture types reveals the optimal concentration of dopants under different types of conductive particles. It is found that the optimal mixture that yields optimal performance also improves the Young’s Modulus of the specimens by 42.2% compared to cement-only specimens. Overall, this optimal mixture composed of 10% weight-to-cement G (10 wt% G), 0.250 wt% MCMF, and 0.125 wt% CCMF yielded a static gauge factor of 622, a resolution of 167 με , an accuracy of 19.24 με , and a Young’s Modulus of 798 MPa.

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.

Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

Structural health monitoring (SHM) of civil infrastructure is significant for sustainable development. This review aims to identify the factors influencing sustainable civil infrastructure SHM implementation and analyze the properties, paths, and conditions under which they have an impact. The method adopted is a coding study based on Grounded Theory. First, the SHM implementation process in the literature is categorized through open coding to obtain an implementation framework that contains phase and activity levels. Second, based on this framework, a synthesis is conducted to categorize the influencing factors in dimensions of contents and properties through open coding and axial coding. Finally, selective coding is used to extract the factors that interacted across activities to propose a scheme of influencing factor relationships. The main findings of the synthesis are shown as follows: (1) sensor work scheduling and data transmission are promising endeavors to balance economic and environmental sustainability, while social sustainability is mainly in terms of safety and user experience; (2) the success of sustainable civil infrastructure SHM requires a collaborative technical and organizational effort; (3) since the influencing factors at different phases may interact with each other, the implementation process should emphasize forward-looking and holistic thinking.

Advanced Fiber Beam Finite Element Model for Neural Network Training in Vibration-Based Bridge Monitoring

Recent advancements in civil infrastructure monitoring have witnessed the increasingly high-performance sensor technologies and data-driven algorithms, opening up new possibilities for assessing structural conditions. In recent years, there has been a growing interest in leveraging the potential of Artificial Intelligence for civil infrastructure monitoring. One promising approach is the use of computational models to train and test data-driven algorithms aiming to tackle damage detection problems. To enhance the effectiveness of such procedures based on simulated data, this study proposes a high-performance beam finite element model for training a neural network model able to predict the dynamic response of the structure and for generating various damage scenarios. Compared to 2D and 3D finite element models, the advanced fiber beam model offers superior computational efficiency while accurately capturing the nonlinear behavior of structural elements. Specifically, a force-based beam finite element based on a damage-plasticity model is implemented to describe damage and degradation of materials in reinforced concrete girders. Through the simulation of the dynamic structural response under withe noise excitation, a neural network model representing the structure in the undamaged conditions is obtained. The prediction error of such network model is investigated as a suitable measure for the definition of a damage indicator able to detect the presence of damage (concrete cracks and reinforcement yielding). The integration of an advanced fiber beam model, accurate constitutive law and neural network models shows promising potential in the monitoring of existing bridges.

A Comparative Analysis of Optimization Algorithms for Finite Element Model Updating on Numerical and Experimental Benchmarks

Finite Element Model Updating (FEMU) is a common approach to model-based Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM) of civil structures and infrastructures. Its application can be further utilized to produce effective digital twins of a permanently monitored structure. The FEMU concept, simple yet effective, involves calibrating and/or updating a numerical model based on the recorded dynamic response of the target system. This enables to indirectly estimate its material parameters, thus providing insight into its mass and stiffness distribution. In turn, this can be used to localize structural changes that may be induced by damage occurrence. However, several algorithms exist in the scientific literature for FEMU purposes. This study benchmarks three well-established global optimization techniques—namely, Generalized Pattern Search, Simulated Annealing, and a Genetic Algorithm application—against a proposed Bayesian sampling optimization algorithm. Although Bayesian optimization is a powerful yet efficient global optimization technique, especially suitable for expensive functions, it is seldom applied to model updating problems. The comparison is performed on numerical and experimental datasets based on one metallic truss structure built in the facilities of Cranfield University. The Bayesian sampling procedure showed high computational accuracy and efficiency, with a runtime of approximately half that of the alternative optimization strategies.

Detection of cracks in concrete using near-IR fluorescence imaging

Structural health monitoring of civil infrastructure is a crucial component of assuring the serviceability and integrity of the built environment. A primary material used in the construction of civil infrastructure is concrete, a material that is susceptible to cracking due to a variety of causes, such as shrinkage, creep, overloading, and temperature change. Cracking reduces the durability of concrete structures, as it allows deleterious environmental agents to penetrate the surface, causing such damage as corrosion of steel reinforcement and delamination of the concrete itself. Conventional crack detection techniques are limited in scope due to issues relating to pre-planning, accessibility, and the need for close proximity to the test surface. Contactless optical image monitoring techniques offer the opportunity to overcome these limitations and have the potential to detect cracks at a distance. Concrete has been reported to have a near-infrared (Near-IR) fluorescence line at a wavelength of 1140 nm when excited with red light. This work investigates the use of fluorescence imaging for the detection of cracks in cementitious surfaces using shallow angle incidence excitation red light. Light oriented at a shallow angle does not excite interior surfaces of cracks, which appear as darker features in images of fluorescing concrete. Artificial cracks with widths of 0.2–1.5 mm were readily imaged using a near-IR camera at distances of 0.5 and 1.3 m. An additional concrete sample with a 0.08 mm wide crack was produced using a flexure apparatus and was also imaged. It is worth noting that the 0.08 mm crack was detected despite its width being below the 0.1 mm pixel resolution of the camera, with the aid of digital image enhancement algorithms.

Advancing civil infrastructure assessment through robotic fleets

Modern civil engineering structures, instrumented with Internet-of-Things-enabled smart sensors and actuators, are considered cyber-physical systems that integrate physical processes with computational and communication elements. This short communication aims to portray a milestone in the field of monitoring and inspection of civil infrastructure, collaboratively conducted by autonomous, robotic devices orchestrated in robotic fleets. It is expected that robot-based civil infrastructure assessment will revolutionize structural maintenance of the deteriorating building stock, which is increasingly exacerbated by the effects of climate change and develops into a major societal challenge.

EVALUATING A NADIR AND AN OBLIQUE CAMERA FOR 3D INFRASTRUCTURE (CITY) MODEL GENERATION

Abstract. The analysis of Earth’s surface is strongly associated with the creation of three dimensional representations. In light of this, researchers involved in any realm of research as, geological, hydrological, ecological planning, city modelling, civil infrastructure monitoring, disaster management and emergency response, require 3D information of high fidelity and accuracy. For many decades, aerial photos or satellite data and photogrammetry provided the necessary information. In recent years, high-resolution imagery acquired by Unmanned Aerial Vehicles (UAV) has become a cost-efficient and quite accurate solution. In this framework, an infrastructure-monitoring project, named called “PROION”, focuses among others on the generation of very fine and highly accurate 3D infrastructure (city) model. The specific study evaluates a high-resolution nadir camera and an oblique camera for the creation of a 3D representation of the Patras University Campus. During the project, two identical flights over a part of the campus were conducted. The flights were performed with a vertical take-off and landing (Vtol) fixed wind UAV equipped with PPK receiver on-board. Based on the conducted flights, many data sets have been evaluated regarding the accuracy and fidelity. It was proved that both nadir and oblique cameras produced very accurate 3D representations of the University campus buildings. The RMSE error of the nadir imagery is almost two times higher than the respective error of the oblique imagery reaching 30cm.

On the use of domain adaptation techniques for bridge damage detection in a changing environment

AbstractStructural Health Monitoring of civil infrastructures often suffers from the limited availability of damage labelled data. The work here seeks to overcome this issue by using Transfer Learning approaches, in the form of Domain Adaptation, for leveraging information from a source structure with determined health‐state labels to make inferences on an unlabeled monitored structure. The idea is to exploit source data to train a Machine Learning algorithm and achieve improved early‐stage damage detection capabilities across a population of bridges. To account for differences in the underlying distributions of each structure, Transfer Learning is seen as a strategy enabling population‐level bridge SHM. In this paper, the natural frequencies obtained from multiple vibration measurements are extracted to characterise different domains during pristine and abnormal conditions. Such damage‐sensitive features are aligned via Domain Adaptation and used to train a standard classifier within a shared feature space. The methodology is validated on the heterogeneous population composed of the Z24 and S101 bridges. The results prove the effectiveness to successfully exchange damage labels, thus increasing available information for health‐state inference for SHM applications with sparce datasets.

Smart textile embedded with distributed fiber optic sensors for railway bridge long term monitoring

The Structural Health Monitoring (SHM) of large civil infrastructures has become a priority due to economic advantages and early detection of structural failure. Distributed Fiber Optics (DFO) sensors are becoming widely used for SHM due to their immunity to Electromagnetic interference and the ability to collect multiple data points from a single fiber cable. However, its reliability for a long monitoring period is still an area of interest. This paper studied the long-term performance of DFO embedded into textiles for faster installation procedures. The aim was to understand if the textile distributed fiber sensor experience any signal degradation after multiple weather seasons. To perform this investigation a railway bridge was used as a case study. The data was collected using Brillouin Optical Time Domain Reflectometry (BOTDR) and Brillouin Optical Time Domain Amplification (BOTDA). Additionally, a temperature compensation technique was investigated to remove the cross-relation between temperature and strain. The final results show the sensor’s effectiveness for long-term monitoring, where the data quality remained constant with an average standard deviation of 0.455 for the BOTDR and 0.208 for the BOTDA approaches.