Bridge Monitoring Research Articles

An unsupervised machine learning approach for real-time damage detection in bridges

The bridge network is progressively aging, with an alarming proportion of bridges over 100 years. This situation engenders substantial risks to the overall reliability of transportation networks, requiring innovative methods for efficient management. Monitoring can provide a direct source of information about structural behavior generating alerts when changes occur. Real-time alerts enable effective infrastructure management and decision-making during damage or anomalous situations. However, monitoring can result in a large amount of data that is often difficult to convert into valuable information in real time. This paper presents an approach for real-time detection of abrupt damage occurrence in bridges using unsupervised anomaly detection algorithms and strain/acceleration measurements. The approach incorporates the separation of measurements into events having the same loading nature and the construction of three feature matrices based on statistical features, time-frequency features, and wavelet spectrum features. It includes the evaluation of five anomaly detection algorithms including Isolation Forest, One-Class Support Vector Machine, Robust Random Cut Forest, Local Outlier Factor, and Mahalanobis Distance. The approach is illustrated with a case study of a steel-bascule-railway bridge, that has experienced a brittle cracking event during monitoring. Results highlight the robustness of One-Class Support Vector Machine, Isolation Forest, and Local Outlier Factor algorithms in promptly detecting abrupt changes across different features. The separation of strain and acceleration data into loading-based events, coupled with the comparison of previous and new event features, provides robust feature matrices for effective damage detection. Enhanced detection and higher scores are particularly attributed to time-frequency domain features during damage occurrence. The presented approach can be used as a base on how to perform real-time anomaly detection within the context of bridge monitoring.

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Traditional bridge health monitoring methods that necessitate sensor installation are not only costly but also time-consuming. In contrast, utilizing smartphone data collected from vehicles as they traverse bridges offers an efficient and cost-effective alternative. This paper introduces a cutting-edge damage detection framework for indirect monitoring of bridge structures, leveraging a substantial volume of acceleration data collected from smartphones in vehicles passing over the bridge. Our innovative approach addresses the challenge of collecting and transmitting high-frequency data while preserving smartphone battery life and data plans through the integration of compressed sensing (CS) into the crowdsensing-based monitoring framework. CS employs random sampling and signal recovery from a significantly reduced number of samples compared to the requirements of the Nyquist–Shannon sampling theorem. In the proposed framework, acceleration signals from vehicles are initially acquired using smartphone sensors, undergo compression, and are then transmitted for signal reconstruction. Subsequently, feature extraction and dimensionality reduction are performed using Mel-frequency cepstral coefficients and principal component analysis. Damage indexes are computed based on the dissimilarity between probability distribution functions utilizing the Wasserstein distance metric. The efficacy of the proposed methodology in bridge monitoring has been substantiated through the utilization of numerical models and a lab-scale bridge. Furthermore, the feasibility of implementing the framework in a real-world application has been investigated, leveraging the smartphone data from 102 vehicle trips on the Golden Gate Bridge. The results demonstrate that damage detection using the reconstructed signals obtained through compressed sensing achieves comparable performance to that obtained with the original data sampled at the Nyquist measurement sampling rate. However, it is observed that to retain severity information within the signals for accurate damage severity identification, the compression level should be limited to 20%. These findings affirm that compressed sensing significantly reduces the data collection requirements for crowdsensing-based monitoring applications, without compromising the accuracy of damage detection while preserving essential damage-sensitive information within the dataset.

Acoustic Emission Monitoring in Prestressed Concrete: A Comparative Study of Signal Attenuation from Wire Breaks and Rebound Hammer Impulses

Acoustic emission monitoring (AEM) has emerged as an effective technique for detecting wire breaks resulting from, e.g., stress corrosion cracking, and its application on prestressed concrete bridges is increasing. The success of this monitoring measure depends crucially on a carefully designed sensor layout. For this, the attenuation of elastic waves within the structure’s material is ideally determined in situ through object-related measurements (ORMs) with a reproducible signal source, typically a rebound hammer. This assumes that the attenuation coefficients derived from rebound hammer tests are comparable to those from wire breaks, thus allowing their results to be directly applied to wire break detection without further adjustments. This study challenges this assumption by analysing attenuation behaviour through an extensive dataset. Employing time-domain and frequency analysis, the research generates attenuation profiles from laboratory experiments and in situ measurements across various girders and bridge structures, extracting the slope and residual standard deviation (RSD). While generally validating this approach, the findings highlight differences in attenuation behaviour from among wire break signals and rebound hammer impulses, whereby the latter potentially underestimates the relevant attenuation of wire breaks by approximately 20%. Consequently, a transfer factor is proposed to adjust ORM results obtained with the rebound hammer for wire break scenarios. It consists of a scaling factor of 1.2 to modify the average attenuation coefficient and a constant term of ±1.0 dB/m to cover a 95% confidence interval, and thus, account for sample scattering. Moreover, the anisotropic attenuation behaviour across different structures was studied, showing that transverse attenuation consistently exceeds the longitudinal, significantly influenced by structural features such as voids. In prefabricated concrete bridges with in situ-cast concrete slabs, transverse signal transmission remains unhindered across multiple elements. Finally, the results provide a valuable reference for the design of sensor layouts in bridge monitoring, particularly benefiting scenarios where direct in situ experiences are lacking.

Application Research of BIM Technology in Bridge Monitoring

According to the development situation of BIM technology and the application of bridge monitoring methods, this article proposes the design and application of bridge monitoring platform based on BIM technology, integrating the collection, data analysis, data transmission, safety assessment and prediction of basic bridge data. Then, according to the bridge load, the overall structure and the local data, it conducts the overall prediction and evaluation of bridge safety status, which can provide the method reference for the later maintenance and operation of the bridge.

Thermal deformation monitoring of a highway bridge: combined analysis of geodetic and satellite-based InSAR measurements with structural simulations

Structural Health Monitoring (SHM) of civil engineering structures is experiencing an increasing progress in the last decades. The present work focuses on the static behavior of a highway bridge due to environmental temperature effects. The goal of the present study was to test the applicability of the satellite-based synthetic aperture radar interferometry (InSAR) for deformation monitoring of a large, curved highway bridge and to compare the obtained results with alternative measurement techniques like classical geodesy surveying and with an advanced computer simulation. Such a comparison is quite rare and provides an important insight into the accuracy, efficiency and limitations of the InSAR technique in the context of SHM. Especially interesting was the question whether the InSAR technique is suitable for blind monitoring of a cluster of bridges in the region of interest. The present study shows that a pre-knowledge about each structure can be very important for a reliable interpretation of the InSAR measurement results. The second challenge of the study was to overcome several objective difficulties of combining and comparing quite different monitoring techniques that result from different sampling rates, measurement points and other specific features and sensitivities. Nevertheless, a suitable approach has been developed and implemented in the present study for the InSAR and total station measurements, providing new results and important knowledge about novel SHM techniques.

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.

Review of Ground Penetrating Radar Applications for Bridge Infrastructures

Infrastructure bridges play a crucial role in fostering economic and social development. However, the adverse effects of natural hazard and weather degradation, coupled with escalating rates of traffic, pose a significant threat. The resultant strain on the structure can lead to undue stress, elevating the risk of a critical asset failure. Hence, non-destructive testing (NDT) has become indispensable in the surveillance of bridge infrastructure. Its primary objectives include ensuring safety, optimizing structural integrity, minimizing repair costs, and extending the lifespan of bridges. NDT techniques can be applied to both existing and newly constructed bridge structures. However, it is crucial to recognize that each NDT method comes with its own set of advantages and limitations tailored to specific tasks. No single method can provide an effective and unequivocal diagnosis on its own. Among the various NDT methods, Ground Penetrating Radar (GPR) has emerged as one of the most widely employed techniques for monitoring bridges. In fact, recent technical regulations now mandate the use of GPR for bridge monitoring and characterization, underscoring its significance in ensuring the structural health and longevity of these critical infrastructures. Ground Penetrating Radar (GPR) stands out as one of the most highly recommended non-destructive methods, offering an efficient and timely assessment of the structural conditions of infrastructure. Recognizing the pivotal role of non-destructive testing (NDT) in this context, this paper aims to elucidate recent scientific endeavors related to the application of GPR in bridge engineering structures. The exploration will commence with a focus on studies conducted both at the model level within laboratory settings and on real cases. Subsequently, the discussion will extend to encompass the characterization and monitoring of the bridge’s main elements: slab, beam, and pillar. By delving into these scientific experiences, this paper intends to provide valuable insights into the efficacy and applicability of GPR in assessing and ensuring the structural integrity of bridges. This paper provides a concise survey of the existing literature on the application of Ground Penetrating Radar (GPR) in the assessment of bridges and viaducts constructed with masonry and reinforced concrete, taking into account papers of journal articles and proceedings available on open databases. Various approaches employed in both laboratory and field settings will be explored and juxtaposed. Additionally, this paper delves into discussions on novel processing and visualization approaches, shedding light on advancements in techniques for interpreting GPR data in the context of bridge and viaduct evaluations.

Influence of the modal damping ratio calculation method in the analysis of dynamic events obtained in structural health monitoring of bridges

This paper includes a review of the several calculation methods available to calculate the modal damping ratio from dynamic events obtained in structural health monitoring of bridges. A comparative analysis among methods involving the logarithmic decrement, bandwidth applied over the spectrum, adjustment to the theoretical curve and random decrement technique is included. Additionally, an alternative calculation method based on signal spectra reduction has been formulated, whose main advantage lies in the fact that it directly allows the study of multiple-degrees-of-freedom systems, which describe typical structures, without the need for applying filters; this method gives extremely precise values despite simplification of the calculations. First, a comparative analysis is carried out on a set of theoretically simulated waves. In these cases, although there are differences in detail, the obtained results are reasonably close to the theoretical values. Second, an analysis of the registered accelerograms during a test campaign of a real structure, the viaduct “The Arches of Alconétar”, is performed. It has been observed that in this case, unlike what happens with the theoretical simulations, the obtained results significantly vary depending on the selected calculation method. For this reason, it is important to know which method has been used to calculate the modal damping ratio in structural health monitoring systems, as well as to be cautious in setting thresholds.

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle-bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

Quantifying the Impact of Environment Loads on Displacements in a Suspension Bridge with a Data-Driven Approach.

Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge displacement is a crucial parameter reflecting the bridge's health condition. Due to the simultaneous bearing of multiple environmental loads on suspension bridges, determining the impact of different loads on displacement is beneficial for the better understanding of the health conditions of the bridges. Considering the fact that extreme gradient boosting (XGBoost) has higher prediction performance and robustness, the authors of this paper have developed a data-driven approach based on the XGBoost model to quantify the impact between different environmental loads and the displacement of a suspension bridge. Simultaneously, this study combined wavelet threshold (WT) denoising and the variational mode decomposition (VMD) method to conduct a modal decomposition of three-dimensional (3D) displacement, further investigating the interrelationships between different loads and bridge displacements. This model links wind speed, temperature, air pressure, and humidity with the 3D displacement response of the span using the bridge monitoring data provided by the GNSS and Earth Observation for Structural Health Monitoring (GeoSHM) system of the Forth Road Bridge (FRB) in the United Kingdom (UK), thus eliminating the temperature time-lag effect on displacement data. The effects of the different loads on the displacement are quantified individually with partial dependence plots (PDPs). Employing testing, it was found that the XGBoost model has a high predictive effect on the target variable of displacement. The analysis of quantification and correlation reveals that lateral displacement is primarily affected by same-direction wind, showing a clear positive correlation, and vertical displacement is mainly influenced by temperature and exhibits a negative correlation. Longitudinal displacement is jointly influenced by various environmental loads, showing a positive correlation with atmospheric pressure, temperature, and vertical wind and a negative correlation with longitudinal wind, lateral wind, and humidity. The results can guide bridge structural health monitoring in extreme weather to avoid accidents.

Research on Bridge Integrity Assessment and Early Warning Monitoring Methods Based on Bearing Reaction Force

Traditional bridge monitoring and damage identification techniques typically rely on full-bridge coverage of sensors, such as displacement or strain sensors. However, this approach proves economically unfeasible for the vast numbers of small- and medium-span continuous beam bridges. In response to the need for rapid damage identification and integrity assessment of continuous beam bridges, a novel bridge safety monitoring method relying solely on bearing reaction forces is proposed. Firstly, the analytical expressions for the bearing reaction influence lines of a three-span continuous beam bridge under damage conditions were derived. Secondly, a rapid structural damage localization method based on the bearing reaction influence lines was proposed. Finally, feasibility and applicability were confirmed through numerical simulations and experimental validation. Additionally, the discussion includes the implementation of the warning classification and threshold setting using data from bearing force monitoring. The research demonstrates that utilizing a limited amount of bearing reaction force information can not only identify damage areas in a “non-full-coverage” manner, but also facilitates early warning and the integrity assessment of bridges. In the future, there is potential for large-scale application in medium- and small-span continuous beam bridges.

In‐fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data

AbstractDrive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality of connected and autonomous vehicles (CAVs) into drive‐by damage detection by introducing In‐Fleet SHM. To the authors’ knowledge, this is the first study that proposes using CAVs for SHM application in civil engineering structures. Each In‐Fleet CAV could automatically collect the vehicle's persistent and temporal data by the embedded sensors and transmit them to edge computing systems for analysis. These persistent data include type and model and temporal parameters encompassing position, speed, heading, and vertical acceleration of CAVs. Knowing the persistent and temporal data of the passing vehicles over the transportation infrastructures enables the identification of the dynamic parameters of the bridge from the vehicles’ vertical acceleration response using drive‐by techniques and, on the other hand, reconstruction of the finite element model of the passing vehicles over the supporting bridges in a near real‐time manner. In contrast to the drive‐by SHM, In‐Fleet monitoring has an expanded spatial and temporal coverage, enabling continuous near real‐time monitoring of highway bridges of the transportation network. The accuracy and resolution of the identified modal components in In‐Fleet SHM are enhanced due to the crowdsensing nature of the collected data. Furthermore, by offering a unique set of characteristics, this method fills the crucial gap in implementing Industry 4.0 technologies and digital twins for SHM of bridges.

Laser-upgraded coal tar for smart pavements in road and bridge monitoring applications

The implementation of an intelligent road network system requires many sensors for acquiring data from roads, bridges, and vehicles, thereby enabling comprehensive monitoring and regulation of road networks. Given this large number of required sensors, the sensors must be cost-effective, dependable, and environmentally friendly. Here, we show a laser upgrading strategy for coal tar, a low-value byproduct of coal distillation, to manufacture flexible strain-gauge sensors with maximum gauge factors of 15.20 and 254.17 for tension and compression respectively. Furthermore, we completely designed the supporting processes of sensor placement, data acquisition, processing, wireless communication, and information decoding to demonstrate the application of our sensors in traffic and bridge vibration monitoring. Our novel strategy of using lasers to upgrade coal tar for use as a sensor not only achieves the goal of turning waste into a resource but also provides an approach to satisfy large-scale application requirements for enabling intelligent road networks.

Online Bridge Structural Condition Assessment Based on the Gaussian Process: A Representative Data Selection and Performance Warning Strategy

Data-driven methods have now been widely used in structural health monitoring of civil infrastructures thanks to the rapid development of sensor technologies with massive structural and operational condition data. One main issue of data-driven methods is that the computational time increases with the number of monitoring data used, which limits their applications for online structural condition assessment. Focusing on bridge structural health monitoring, this paper proposes a representative data selection strategy for online performance assessment based on Gaussian process models. The proposed method can effectively reduce the required monitoring data size for training, allowing the bridge performance assessment to be conducted in a real-time manner. The method is developed in a probabilistic manner, allowing associated uncertainty of bridge monitoring data to be rigorously considered. A probabilistic warning index is proposed for bridge condition assessment and anomaly detection. The proposed method is validated using synthetic data and applied to structural condition assessment of two full-scale bridges, illustrating the feasibility for real implementations.

Structural Health Monitoring of Long-Span Continuous Girder Bridge: System Implementation and Data Analysis

Long-span bridges are the key component of the human transportation system, linking communities over vast obstacles. To ensure the safe operation of long-span bridges, structural health monitoring (SHM) is perhaps the most effective solution. This study takes a 430[Formula: see text]m four-span continuous girder bridge as an example and systematically presents an implementation example of the SHM system on the bridge regarding monitoring items, sensor placement, and sensor parameters. The monitoring data including operational load and bridge response are analyzed and the statistical rules of these monitoring data are presented. Besides, the modal parameters of the girder are tracked from long-term vibration monitoring data. Furthermore, the correlations between structural temperature and bridge response are analyzed, and the correlation formulas between bridge modal frequency, static responses, and structural temperature are finally given. The analysis results reveal significant seasonal variations in the responses of the continuously monitored girder bridge, offering valuable insights for data-driven assessment and early warning systems for bridges.

Monitoring climatic impacts on the moisture uptake of the first Swiss wildlife bridge made of wood

Wildlife bridges represent a major component of any sustainable strategy to counteract the negative consequences of cutting the natural habitat of wildlife into disconnected parts by motorways and rail. This is especially true for a small and densely populated country such as Switzerland with diverse wildlife scattered through its territory. Up to now all wildlife bridges in Switzerland have been made of concrete with steel reinforcement. The wildlife bridge under investigation here is the first one in Switzerland made of wood-based materials. The idea of building a wildlife bridge by using wood-based materials represents besides a challenging technological endeavor also an ecological progress regarding embodied energy. A further advantage which resulted after realizing the construction, was the short interruption time it needed for installation on a motorway in operation. The most urgent question with respect to the wood-based elements is their moisture uptake when subjected to weather conditions. The present paper reports on a long-term monitoring of this wooden wildlife bridge over a period of approximately 2 years. Different kind of sensors, data from a nearby meteorological station, data regarding hourly number of different kind of vehicles passing beneath the wildlife bridge as well as lab measurements have been used to enable a robust and reliable statement on wooden wildlife bridges subjected to Swiss flatland weather conditions.

Dynamic monitoring of a masonry arch rail bridge using a distributed fiber optic sensing system

AbstractMasonry arch bridges are an integral part of the European transportation infrastructure. Regular inspections are critical to ensure the safe operation of these bridges and also to preserve historical heritage. Despite recent advancements in assessment techniques, monitoring masonry arch bridges remains a difficult and important research topic. This paper describes a proof-of-concept study carried out on a masonry arch rail bridge in Gavirate, Italy, to investigate the dynamic responses of the bridge to train-induced moving loads. The dynamic measurements are obtained by a distributed fiber optic sensing system that enables a novel inspection of the integrity of masonry arch bridges. The focus of this field study is to quantify the dynamic strain induced by train moving loads and reveal the masonry arch bridge’s dynamic behaviors through the use of an innovative distributed fiber optical sensing-based technique. The results may provide a useful guideline for the application of distributed fiber optical sensing to monitoring masonry arch bridges.

SINGULARITIES OBSERVED IN THE CALCULATION OF THE MODAL DAMPING RATIO OF A LONG‐SPAN METAL ARCH BRIDGE

AbstractWithin the recent studies that are being carried out in the structural health monitoring of bridges, it has been observed that the results of the modal damping ratio can be influenced by some ambient variables and significantly depend on the calculation method that is adopted. In this paper, the main conclusions obtained in the study of the dynamic behaviour of a real structure, the viaduct “Arcos de Alconétar”, in Spain, whose main span is a long metal arch, is reviewed. With this aim, the results obtained from the instrumentation installed during the initial months after its traffic opening are analysed.

Strategy for vertical deformation of railway bridge monitoring using polarimetric ground-based real aperture radar system

The health monitoring of infrastructure is vital for ensuring the safety and structural integrity of bridges. Recently, ground-based real aperture radar (GB-RAR) systems have been successfully utilized in the dynamic and static monitoring of bridges. In this study, a comprehensive and innovative approach is presented to monitor the vertical deformation of a long-span metallic railway bridge and a reinforced concrete Shinkansen bridge in Japan using a polarimetric GB-RAR system. Distinct from conventional signal processing procedures, the proposed method omits the coherent scatterer selection step. Instead, polarization analysis is employed to evaluate the properties of scatterers and identify those corresponding to bridge sections requiring monitoring, while considering the structural characteristics of the bridge. Simultaneously, the signal-to-noise ratio for monitoring is enhanced by combining co-polarization responses from scatterers. Furthermore, the radar look angle is determined by accounting for the spatial configuration of the survey and the polarization orientation angle. Lastly, vertical deformation is assessed by projecting line of sight deformation in the vertical direction. The findings reveal the dynamic responses of the two bridges under diverse loading conditions, which include the transit of a low-speed train and a high-speed Shinkansen bullet train. The results demonstrate that the polarimetric GB-RAR interferometry technique, coupled with the developed algorithms, can be effectively applied to monitor any type of bridge with unparalleled spatial and temporal resolutions.

Structural health monitoring of a lenticular truss bridge: a comprehensive study

The combined use of finite element modeling and structural health monitoring is becoming increasingly relevant in bridge maintenance. A growing number of new and old structures is instrumented with different kinds of sensors driven by dedicated hardware and software, while numerical models are used to simulate the response of the structures under various scenarios. This paper presents the monitoring of the Smithfield Street Bridge, one of the oldest and most iconic bridges in the city of Pittsburgh (Pennsylvania, USA). The bridge was instrumented by an independent party, not involved with the research presented here, with strain, displacement, and rotation wireless sensors. A detailed finite element model of one of the spans of the bridge was developed to calculate the strains induced by standardized trucks under pristine and simulated damage conditions. In addition, the model enabled to determine the sensitivity necessary to detect relevant structural changes in the bridge. The results of the numerical analyses were then compared with the results of a test in which a truck of known weight crossed the bridge multiple times. Finally, the data relative to 3 years of uninterrupted monitoring were processed and analyzed to identify eventual anomalies.