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Arrangements of piezoelectric transducers, such as PZT (lead zirconate titanate), have been widely used in numerous structural health monitoring (SHM) applications. Usually, when two or more PZT transducers are placed close together, significant interference, namely crosstalk, appears. Such an effect is usually neglected in most SHM applications. However, it can potentially be used as a sensitive parameter to identify structural faults. Accordingly, this work proposes using the crosstalk effect in an arrangement of PZT transducers modeled as a multiconductor transmission line to detect structural damage. This effect is exploited by computing an impedance matrix representing a host structure with PZTs attached to it. The proposed method was assessed in an aluminum beam structure with two PZTs attached to it using finite element modeling in OnScale® software to simulate both healthy and damaged conditions. Similarly, experimental tests were also carried out. The results, when compared to those obtained using a traditional electromechanical impedance (EMI) method, prove that the new approach significantly improved the sensitivity of EMI-based technique in SHM applications.

Background. As of today in Russia there are no scientific-technical and experimental-technological reserves on development of systems of structural monitoring of serviceability of products and objects of rocket-space technology (RST) and ground space infrastructure (GSI) with transducers of physical quantities on the basis of macrofiber actuators on the basis of inverse piezo effect necessary for full assessment of reliability of technically complex objects by non-destructive methods. The development of the latest generation of aerospace structures requires the creation of advanced materials that would have high physical and mechanical parameters, meet reliability and durability requirements for specific tasks. Great importance is also given to active materials and structures capable of forming functional shapes and profiles depending on the external influence, i.e. to ensure the fulfillment of one of the principles of the motion control concept – adaptronics. The formation of functional shapes and profiles will provide optimal wing aerodynamic profile, better maneuverability of the aircraft, reduce fuel, energy consumption and provide better remote control in extreme space conditions. The purpose of this article is to create a scientific-technical and experimental-technological reserve for the development of advanced technologies for the production of sensor and actuator elements of structural performance monitoring systems based on piezoceramic materials and technologies for incorporation into the elements of composite structures of RST and GSI products in order to control and manage their characteristics to prevent emergencies. Materials and methods. The paper considers the structure of the system of structural monitoring of serviceability of products and objects of RST and GSI with transducers of physical quantities on the basis of macrofiber actuators based on the inverse piezo effect. Results. As a result of technology implementation, import-independent self-regulating adaptive structures of spacecraft structures and equipment will be created on the basis of controllable properties of materials with response to external influences. Implementation of the technology will make it possible to create an effective system for controlling vibrations of structural elements. The developed piezoactuators will find applications in structural monitoring systems of the aerospace industry in rocket and space technologies and mechanical engineering.

This study investigates the dynamic responses of the Davutpaşa Pedestrian Bridge to pedestrian loads using low-cost and high-cost Global Navigation Satellite Systems (GNSS) along with accelerometers. Traditionally, expensive dual-frequency GNSS receivers were employed for structural monitoring, but recent advancements in low-cost GNSS technology have provided a cost-effective alternative. The study employs signal filtering techniques and Fast Fourier Transform (FFT) to analyse datasets in time and frequency domains. Specifically, the Yule-Walker Design of Infinite Impulse Response (IIR) Band-pass Filter is applied to GNSS measurements to reveal natural frequencies obscured by noise. The accuracy was assessed by comparing frequency values obtained through full-scale monitoring with those from a Finite Element (FE) model. After filtering analysis, it has been determined that the natural frequencies obtained from geodetic GNSS agree with accelerometer frequencies with a maximum difference of 0.12 Hz (3%). In low-cost GNSS receivers, for the A10 antenna, a maximum difference of 0.09 Hz (3%), and for the UBX antenna, a maximum difference of 0.10 Hz (3%) has been obtained, showing compatibility with the accelerometer. As a result, it has been found that with appropriate filtering techniques and selecting sampling intervals suitable for structural dynamics, even rigid structures like pedestrian bridges can have their natural frequencies determined using geodetic equipment.

A great deal of signals coming from structural monitoring scenarios are sparse in the frequency domain, suggesting the application of Compressive Sensing (CS) techniques in order to reduce the quantity of transmitted information. CS can recover data vectors starting from a subset of the original vector entries, thus allowing to recover a previously sampled signal with much less samples than those suggested by the Nyquist-Shannon theorem, and much less than what is commonly used in dynamic identification of structures. A CS technique, specifically Basis Pursuit, is applied for the dynamic identification study of a 30-meter-high lightning rod, consisting of a steel monotubular pole, where possible issues had been raised concerning fatigue damage due to resonant response with the first and second modes of vibration. An experimental measurement campaign was carried out to estimate damping coefficients useful for structural verifications. The ambient response was collected using triaxial accelerometers positioned at the top and at an intermediate height, which transmit data via WiFi to a nearby workstation. Different sampling frequencies for the compressed records are utilized for the dynamic identification of the structure, comparing modal frequencies and damping ratios with values obtained from the original records to find the best trade-off between data reduction and accuracy of modal parameters. Despite the usual challenges inherent in identification problems, further complicated by the low damping levels of the structure under consideration, the comparisons demonstrate a very good approximation. Fundamental frequencies are accurately estimated, while the slight discrepancies in the damping coefficients are associated with the intrinsic uncertainties of this parameter. Regarding the structural aspect of the case study, the outcomes of the analysis indicate very low damping values, pointing to potential criticality, particularly in the second mode of vibration. Moreover, the solid approximation achieved with the CS technique marks a significant advancement in applying IoT solutions for structural monitoring, emphasizing a significant reduction in data flow without affecting data quality. This may lead to several benefits, including simpler installation and maintenance, lower costs, and decreased energy consumption.

This study develops a structural stability monitoring method for an implant structure (i.e., a single-tooth dental implant) through deep learning of local vibrational modes. Firstly, the local vibrations of the implant structure are identified from the conductance spectrum, achieved by driving the structure using a piezoelectric transducer within a pre-defined high-frequency band. Secondly, deep learning models based on a convolutional neural network (CNN) are designed to process the obtained conductance data of local vibrational modes. Thirdly, the CNN models are trained to autonomously extract optimal vibration features for structural stability assessment of the implant structure. We employ a validated predictive 3D numerical modeling approach to demonstrate the feasibility of the proposed approach. The proposed method achieved promising results for predicting material loss surrounding the implant, with the best CNN model demonstrating training and testing errors of 3.7% and 4.0%, respectively. The implementation of deep learning allows optimal feature extraction in a lower frequency band, facilitating the use of low-cost active sensing devices. This research introduces a novel approach for assessing the implant’s stability, offering promise for developing future radiation-free stability assessment tools.

The box girder’s condition significantly impacts the safety and overall performance of the entire ship because it is the primary stress component of the hull construction. This work used experimental research on the long-span hull box girder based on IFEM (Inverse Finite Element Method) technology to ensure the structural safety of the hull box girder. Due to the limitations of conventional experiments in this technical field, such as their reliance on finite element data and lack of input from physical tests, numerous research methods combining the strain sensing data from physical tests with the strain data from virtual sensors were conducted. The strain fields of the top plate, side plate, and bottom plate were each reconstructed in turn, and the verifier measuring points in the physical model test were used to assess the accuracy of the reconstruction results. The findings demonstrate that the top plate, side plate, and bottom plate reconstructions had relative errors of 0.24–7.86%, 0.75–8.13%, and 3.31–2.52%, respectively. This enables the reconstruction of the strain field of the long-span hull box girder using physical test data and promotes the use of iFEM technology in the field of structural health monitoring of large marine structures.

Ports play a significant role in the economic and social activities of the areas they serve. However, the adverse effects of the harsh and highly corrosive environments that ports operate in, along with phenomena related to the climate crisis and insufficient maintenance practices, increase port infrastructure's susceptibility to rapid degradation. This degradation can be aesthetic, functional, or structural and often leads to loss of serviceability at either a component (local and national) level or a global level. Moreover, many port structures have reached the limit of their lifetime, thus introducing the concept of extending their lifespan as a financially attractive alternative to constructing new facilities. Therefore, port operators pursue monitoring the structural integrity of the structures through an inspection plan aiming to reduce their maintenance and rehabilitation costs and ensure the safety of both the port infrastructure itself and human lives. Optimising monitoring approaches to enable damage detection and condition assessment can be achieved through the employment of Non-Destructive Testing (NDT) and Remote Sensing (RS) techniques. The current surge of using Unmanned Aerial Vehicles (UAVs) for both RS and NDT monitoring practices has proved promising since UAVs provide improved accessibility, increased inspection speed, and reduced safety hazards. The present paper is focused on investigating and evaluating the recent advances in UAV-driven port infrastructure monitoring. For this purpose, a comprehensive review of UAVs applications combined with NDT Infrared Thermography (IRT) and Ground Penetrating Radar (GPR) or RS Close Range Photogrammetry (CRP) and Light Detection and Ranging (LiDAR) is carried out to assess the potential and the limitations of the UAV-based monitoring approaches. This research provides valuable information on enhancing management strategies by upgrading port monitoring practices.

Ancient masonry constructions and historical buildings, such as cathedrals, are exposed to considerable risks attributed to factors like ageing and long-term exposure to both dynamic and static variations in loading conditions. In this study, an innovative and promising monitoring approach was applied to assess the structural integrity of the vault in the central nave of the Turin Cathedral. Specifically, the outcomes obtained from Acoustic Emissions (AE) are correlated with the insights derived from the Thrust Network Analysis (TNA) conducted on the structure. This analysis considers the structural elements introduced early in the twentieth century to mitigate horizontal forces. Acoustic Emission (AE) is a commonly employed technique in structural monitoring to detect and analyze elastic waves generated by crack formation, providing valuable information about structural damage. The Thrust Network Analysis (TNA) is an approach that applies Heyman's principles to represent stress in masonry vaults. This method models the stresses as a discrete network of forces, achieving equilibrium with gravitational loads. In this context, the results obtained by TNA analysis are strictly correlated with AE localization results.

An extended hybrid method for the analysis of the symmetric structural behavior of segmental tunnel linings is proposed. Their deformations cannot be determined by the traditional hybrid methods when the convergences are close to the serviceability limit states (SLSs). Two real-scale tests are employed for the validation of the proposed method. It involves structural analysis that is based on transfer relations. They represent analytical solutions of the linear theory of thin circular arches, and they contain the symmetric mode of rigid-body displacements. The method is termed hybrid because it is based on two types of input, namely the external loading and experimental data of displacements monitored during the test. It is termed extended because, additionally, the vertical and horizontal convergences are employed to produce more accurate structural deformations than obtained by the traditional hybrid method. The latter is found to be unsuitable for structural analysis after sudden failure has occurred in the vicinity of segmental interfaces. At the respective load steps, the structures were in convergence-related serviceability class C, referring to endangered serviceability. The local failures occurring at these load steps resulted in a rapid increase in the structural deformations and fast attainment of the SLS. If, in convergence-related serviceability class C, local failures at segmental interfaces are detected, the strengthening of the structure is necessary. This cannot be delayed to the attainment of serviceability class D, i.e., to reaching the SLS.

Concrete structures are susceptible to several factors during their life cycles, which can cause various problems. This situation can be avoided in many cases if Structural Health Monitoring (SHM) is used. However, such monitoring is often expensive due to the large number of sensors and the use of data acquisition systems (DAQ). In this context, self-sensing cement composites (SSCCs), monitoring themselves without sensors by incorporating conductive fillers in their composition, provide a better piezoresistive effect. In this research, the Arduino platform and analog to digital converters (ADC) modules were tested, and a prototype of a low-cost DAQ was developed for monitoring SSCCs. The devices were analyzed from the compression tests on the mortar specimens, and the results were compared to measurements obtained from the reference equipment (professional DAQ). The results showed a good sensitivity and an adequate correlation between both devices in some cases. It showed that the Arduino platform has the potential to be used in the experimental monitoring of SSCCs, however it needs to be combined with an ADC module.

While constructing and documenting civil structures, large machines, and industrial facilities, one can encounter a situation where relevant control points are hardly accessible. The instruments with appropriate surveying equipment available on the market provide relatively standard measurements. The limitations mentioned above may transfer into an increased working time (or financial effort) that must be considered while performing the prescribed measuring works. One of the possible solutions (assuming financial capabilities) is utilizing a video-total station (a scan station) with additional supporting equipment. Another possibility would be employing a terrestrial laser scanner (TLS) or close-range photogrammetry. However, such technologies demonstrate significant limitations, especially in the industrial environment. Regarding that, the authors propose an original measuring set collaborating with a free electronic total station. The main working principle is a known surveying 3D-polar method that can determine XYZ coordinates. The solution presented in the paper facilitates the performance of inventory works, consisting of dimensioning civil structures and rooms with difficult access. Such situations can often be encountered in industrial plants or while documenting architectural or other engineering structures. The device can also be used for dimensioning ventilation ducts, elevator shafts, and other similar facilities. Depending on the configuration of the measuring equipment and the target shapes, the final accuracy may reach a sub-millimeter or millimeter level. Hence, the solution can successfully be applied in civil engineering, industrial surveying, and industrial metrology.

Self-awareness has been successfully utilized to create adaptive behaviors in wireless sensor nodes. However, its adoption can be daunting in scenarios, such as structural health monitoring, where the monitored environment is too complex for it to be accurately modeled by a sensor node. This article addresses this challenge by proposing a novel and lightweight anomaly-aware monitoring method for structural health monitoring that can be directly executed by a sensor node. Instead of modeling the complete structure, the proposed anomaly-aware monitoring method uses the vibration measurements of the sensor node to identify local deviations in the dynamic response of the monitored structure. The self-awareness module can then use this information to guide the dynamic behavior of the sensor node, replacing more resource-intensive structural models. We use data from multiple public benchmark structures to evaluate different features and propose an unsupervised feature selection method. Additionally, we evaluate different anomaly detection algorithms comparing their ability to detect local structural damages, also taking into account their memory and energy cost. The proposed method has been implemented in a commercial sensor node, and deployed in a scaled structure where various damage scenarios were simulated to validate the proposed method, where it was able to successfully detect the presence of damages in over 88% of the cases. Finally, we showcase how the proposed method can enhance self-awareness through the use of a simulation, where the proposed monitoring method was able to extend the battery life of the sensor node by over 59%, without impacting the node’s ability to swiftly detect damages in the structure.

This paper reports a novel investigation in applying commercial Ultra High-Frequency RFID tags (UHF-RFID tags), which are widely used in logistics as sensing elements in civil engineering structures, particularly for monitoring out-of-plane displacements of brick masonry walls. Both laboratory tests and in situ experimental tests assessed the feasibility of the proposed application. Laboratory tests showed a very satisfactory response while the in situ experiments showed a weaker response. Nevertheless, the potential reliability of the proposed technique can be stated. The authors traced back the causes of the performance decrease to environmental interference, mainly due to the extensive presence of a rigid steel frame surrounding the out-of-plane loaded panels. Measurements of displacements, in fact, are obtained indirectly from the phase of UHF-RFID signals that strongly suffer from multipath generated by metallic surfaces. Despite some limitations, the proposed measurement technique permits a reliable and sustainable approach to the monitoring of structures. The use of commercial UHF-RFID wireless tags, in fact, assures easy and fast installation operations and assures the possibility of placing a large number of sensors over the structure with very low maintenance costs with respect to the more traditional monitoring techniques. Moreover, using very thin and small commercial UHR-RFID tags on cultural heritage structures can represent an opportunity for sustainable long-time monitoring with reduced costs. Overall, the results of this study are sufficiently satisfactory to be considered as the opening of new possible scenarios in wireless structural monitoring in the civil engineering field. The authors propose as future work to use UHF-RFID tags for the real-time monitoring of an existing masonry facade that, not being characterized by the presence of a steel frame, can potentially assure an adequate response and properly transmit the electromagnetic signal.

In this study, a structural health monitoring system for aircraft wings based on a fiber-optic grating sensing network was designed, and a three-dimensional position correction algorithm based on fiber Bragg grating (FBG) array test data was developed. Solving the deformation parameters using mathematical models of strain-position offset introduces errors, leading to inaccurate measurements and affecting the preciseness of three-dimensional-reconstructed surfaces. Two fitting algorithms (bitone and spline with linear interpolations) are proposed to improve this method in analyzing errors at the measured points of the wing structure. The relative error of each measurement point of the reconstructed wing structure using the bitone and spline interpolation methods was lower than 4.96%. Furthermore, the absolute error was within 0.014 mm, the average relative error was lower than 3.47%, and the root-mean-square error was lower than 0.01 mm. Compared to linear interpolation, the average relative error decreased by up to 5.84%. The experimental results show that the measurement accuracy of bitone and spline interpolation was significantly higher than that of linear interpolation. FBG array measurements using bitone and spline interpolations for the local structural health monitoring system of aircraft improve the fitting reconstruction effect for the wing structure and stabilize the compensation effect.

Digital image correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of the contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for the precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the effectiveness of the DIC methodology has been validated as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

The monumental complex consisting of the cathedral, tower and main square of Modena (Northern Italy) has been a UNESCO World Heritage Site since 1997. The visible tilting of the Ghirlandina tower has always generated concerns for its preservation, even more after the recent seismic events. This paper highlights the role of structural monitoring for the conservation of the site, with particular reference to the data acquired during the recent Emilia earthquakes. Unexpectedly, the inclination of this leaning tower decreased after the major seismic event. Some considerations are presented regarding the difficulty of modelling this kind of effects on buildings with complex historical stratifications and the application of the observational method, derived from geotechnical engineering, is proposed. The importance of structural monitoring is once again recalled, in order to preserve world heritage and comply with the principle of minimum intervention.

Large-scale structures are more prone to risk than small-scale structures due to their increased weight, stiffness and ductility parameters. The main focus of this investigative study is on the structural monitoring of a building using the non-destructive method of non-linear ultrasonic pulse velocity. A single-bay seven-storeyed frame model subjected to reverse cyclic loading of varying amplitudes is monitored by non-linear ultrasonic technique based on third harmonic wave generation. The critical nodes are identified in two different ways: the points of possible plastic hinge formation (PPPHF) through the theoretical background of plastic hinge analysis and the highly sensitive regions in structures (HSR) through numerical analysis. The common nodes of PPPHF and HSR are considered to monitor the structure. The invisible primary structural damage in the initial stage and the visible structural damage up to ultimate load are observed using non-linear ultrasonic waves. The fundamental frequency value and the third harmonic frequency value are observed and processed in MATLAB in the form of a Fast Fourier Transform (FFT) algorithm. The relationship between the condition of the structure and the ratio of third harmonic parameter, load and displacement is compared. In the initial stage of loading, only slight change or no variation in third harmonic ratio is observed at the observation points indicating material disintegration. This varies consistently up to the development of visible cracks and the same varies inconsistently on further loading after the appearance of visual cracks.

Modern infrastructure heavily relies on robust concrete structures, underscoring the critical need for effective monitoring to ensure their safety and durability. This paper addresses this imperative issue by introducing an innovative automated and wireless system for continuous structural monitoring. By employing embedded electrical resistivity sensors coupled with a wireless-based data transmission mechanism, real-time data collection becomes feasible. We provide a general description of the system’s architecture and its application in a pilot study covering the effects of the devices on concrete properties and data transmission. The dielectric properties of concrete specimens were investigated under natural and accelerated curing/degradation and the results were used in the final design of the antenna device. Furthermore, a pilot test comprising four reinforced concrete columns was used to investigate the range of data transmission from inside to outside of the concrete, the effects of the hardware device on the compressive strength and concrete distribution in the columns, and the data transmission quality in real time under realistic exposure conditions.

During the dynamic development of modern technologies based on advanced algorithmic and instrumental solutions, it is essential to integrate geospatial data efficiently. Such an approach is applied in all geo-information services, especially mobile ones, and is helpful in, for example, precise navigation or effective risk management. One leading application is deformation monitoring (structural monitoring) and displacement control surveying. In addition, spatial data integration methods are used in modern accessibility analysis, Smart City ideas, tracing utility networks, and building information modelling (BIM). The last aforementioned technology plays a crucial role in architectural design and construction. In this context, it is crucial to develop efficient and accurate algorithms supporting data fusion, which do not strain the computing resources and operate efficiently online. This paper proposes an algorithm for real-time adjustment of integrated satellite GNSS (global navigation satellite system), total station, and Earth Gravitational Model (EGM) vertical direction data in a geocentric coordinate system based on a statistical general linear mixed model. A numerical example shows that the proposed algorithm of the online adjustment works correctly. The results of the online adjustment are the same as those of the offline adjustment. It is also shown that the GNSS measurements are necessary only at the total station points in the spatial total station traverse. There is no need to add additional merging points of the total station positions because the differences between the results of the online adjustment, including and excluding the merging points, are very small (around 1–2 mm in standard deviation).

Concrete is a widely used construction material, demanding strict quality control to maintain its integrity. The durability and lifespan of concrete structures rely heavily, amongst other factors, on the characteristics of fresh and early age concrete, which are strongly dependent on the curing process. To ensure long-term durability, it is crucial to assess concrete properties throughout construction and verify compliance with design specifications. Currently, electrical resistivity-based sensors are available and used for quality control and monitoring, however, these sensors tend to be costly or only measure at a single location within the concrete cover. This study introduces a printed circuit board (PCB)-based array of electrodes capable of measuring concrete resistivity profiles across the concrete cover, from its fresh state to early age development. In this work, the feasibility of such resistivity PCB-sensors, novel for concrete, is evaluated under laboratory conditions. The sensors exhibit a promising performance in monitoring the efficiency of concrete curing under various conditions. Additionally, they successfully evaluate the effectiveness of internal curing (in our study, promoted by superabsorbent polymers) during the initial stages of hardening. This sensor array provides a valuable tool for monitoring the curing of concrete at early age, and showcases a preliminary solution that could be further developed to ensure long-term performance of concrete infrastructure.

This study proposes a novel hybrid simulation technique for analyzing structural deformation and stress using light detection and ranging (LiDAR)-scanned point cloud data (PCD) and polynomial regression processing. The method estimates the edge and corner points of the deformed structure from the PCD. It transforms into a Dirichlet boundary condition for the numerical simulation using the particle difference method (PDM), which utilizes nodes only based on the strong formulation, and it is advantageous for handling essential boundaries and nodal rearrangement, including node generation and deletion between analysis steps. Unlike previous studies, which relied on digital images with attached targets, this research uses PCD acquired through LiDAR scanning during the loading process without any target. Essential boundary condition implementation naturally builds a boundary value problem for the PDM simulation. The developed hybrid simulation technique was validated through an elastic beam problem and a three-point bending test on a rubber beam. The results were compared with those of ANSYS analysis, showing that the technique accurately approximates the deformed edge shape leading to accurate stress calculations. The accuracy improved when using a linear strain model and increasing the number of PDM model nodes. Additionally, the error that occurred during PCD processing and edge point extraction was affected by the order of polynomial regression equation. The simulation technique offers advantages in cases where linking numerical analysis with digital images is challenging and when direct mechanical gauge measurement is difficult. In addition, it has potential applications in structural health monitoring and smart construction involving machine leading techniques.

The paper is devoted to the measurement and to the processing of load spectra of forces and moments acting at the wheel hub of a motorcycle. Smart wheels (SWs) have been specifically developed for the scope. Throughout the paper, the extreme case of a race motorcycle is considered. Accurate load spectra were measured in two race circuits. Standardized load spectra are derived by processing measured data. A way to easily generalize the measured load spectra is proposed for the first time for motorcycles. Several loading conditions, related to the motorcycle straight line motion, cornering, curb hit and gear shift, are identified and extracted from the experimental measures. For each loading condition, by means of simple semi-analytical models (SAMs), a relationship is found between the vertical force on the wheel, the tilt angle of the motorcycle and the remaining forces and moments acting at the wheel hub. Such relationships are nothing else than the standardized load spectra. Additionally, a simple and efficient method based on smart wheels for real-time structural monitoring is proposed. Standardized load spectra prove to provide consistent results even when compared to real-time structural monitoring data. By means of the presented smart wheels, advanced lightweight motorcycle construction is enabled by derivation of standardized load spectra or real time estimation of the damage of structural components.

Dams are one of the most important engineering works of the current human society, and it is crucial to monitor and obtain analytical data to log their conditions, predict their behavior and, eventually, receive early warnings for planning interventions and maintenance activities. In this context, GNSS-based point displacement monitoring is nowadays a consolidated technique that is able to provide daily millimeter level accuracy, even with less sophisticated and less expensive single-frequency equipment. If properly designed, daily records of such monitoring systems produce time series that, when long enough, allow for an accurate reconstruction of the geometrical deformation of the structure, thus guiding semi-automatic early warning systems. This paper focuses on the procedure for the GNSS time series processing with a statistical approach. In particular, real-world times series collected from a dam monitoring test case are processed as an example of data filtering. A remove–restore technique based on a collocation approach is applied here. Basically, it consists of an initial deterministic modeling by polynomials and periodical components through least squares adjustment and Fourier transform, respectively, followed by a stochastic modeling based on empirical covariance estimation and a collocation approach. Filtered time series are interpreted by autoregressive models based on environmental factors such as air or water temperature and reservoir water level. Spatial analysis is finally performed by computing correlations between displacements of the monitored points, as well as by visualizing the overall structure deformation in time. Results positively validate the proposed data processing workflow, providing useful hints for the implementation of automatic early warning systems in the framework of structural monitoring based on continuous displacement measurements.

Research in the field of structural monitoring of structures, buildings and structures is not abating. A key link in a modern wireless monitoring system is a sensor built using wireless technologies. Undoubtedly, wireless sensors are gradually replacing wired systems that are difficult to maintain, connect and costly. However, we should not forget about wired systems, wireless sensor networks are a new stage in the development of structural monitoring. The level of development of monitoring systems and wireless sensors for monitoring tasks has not yet been fully investigated for their universal application in various applications. There are also software restrictions associated with the creation and configuration of sensor networks. The importance of using automatic monitoring systems lies in their application in smart homes as monitoring system for the condition of a building and as a human security system. According to this study, it aims to provide a comprehensive overview of structural health monitoring over the years. In particular, this article reviewed and analyzed the main components of wireless communication, such as: hardware of smart wireless sensors, wireless protocol, network architecture, operating systems. This review also presents the scope of both test benches and real deployments of such systems.

Construction works, especially in the case of large road or tunnel investments, require the active participation of surveyors. It applies to classic geodetic procedures such as staking out or conducting control measurements and continuously monitoring displacements and deformations. Negative factors at surveying sites may hinder the implementation or directly affect the instruments, especially total stations. Heavy dust and rainfall containing harmful and destructive substances can be mentioned here. Contemporary measuring instruments are technologically complex and expensive devices, often exposed to the sun, wind, and frost. To mainly protect robotic total stations, various preventing housings are used. Practice shows, however, that these are half-hearted solutions, often negatively influencing the accuracy of the surveys. Based on the authors’ professional and scientific experience, a technological line using the modeling and 3D printing of protective housings for robotic electronic total stations has been developed. The workflow includes dedicated online modeling and printing unique instrument covers that best suit certain sites. The prototype was shielded with an additional hydrophobic layer and tested in a geodetic laboratory. The validation results confirm the high usability of the technological line while maintaining the appropriate millimeter level positioning accuracy and effectiveness of the geodetic monitoring.

Structural health monitoring is a crucial issue in areas with different hazard sources, such as Italy. Among non-invasive monitoring techniques, remote sensing provides useful information in supporting the management process and safety evaluations, reducing the impact of disturbances on the functionality of construction systems. The ground displacement time-series based on the analysis of Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements, as well as the information about the geology of the area and the geometry of the construction under monitoring, provides useful data for the built environment’s structural assessment. This paper focuses on the structural monitoring and damage assessment of constructions based on the GIS integration of DInSAR measurements, geological investigation, historical surveys and 3D modeling. The methodology is applied to the residential area of Valco San Paolo in the city of Rome (Italy). Once the geological interpretation has confirmed the results of the DInSAR measurements, a quick damage assessment that considers all the possible conditions of the pre-existing damage at the time zero of the monitoring is shown for a damaged manufact in the area. The presented results highlight how the strategy to correlate the DInSAR-monitored ground settlements with the damage scales allows potentially to monitor continuous construction systems.

The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a built environment is presented, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence (AI) techniques. The satellite measurements are represented by the displacement time series obtained through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as full resolution Small BAseline Subset (SBAS) approach, while the exploited AI technique is represented by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology. The DBSCAN technique is applied to the SBAS-DInSAR products relevant to the achieved Persistent Scatterers (PSs), to identify clusters of pixels corresponding to buildings within the investigated area. The analysis of the deformation evolution of each building cluster is performed in terms of velocity rates and statistics on the DInSAR measurements. Synthetic deformation maps of the areas are then retrieved to identify critical buildings. The proposed methodology is applied to three areas within the city of Rome (Italy), imaged by the COSMO-SkyMed SAR satellite constellation from ascending and descending orbits (in the time interval 2011–2019). Starting from the DInSAR measurements, the DBSCAN algorithm provides the automatic clustering of buildings within the three selected areas. Exploiting the derived deformation maps of each study area, a preliminary identification and ranking of critical buildings is achieved, thus confirming the validity of the proposed approach.

Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam’s structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow.

Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently present a deep learning model to be trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. This could help wind farm operators to reduce maintenance costs by planning cleaning and repair activities more efficiently. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients’ curves. The proposed model was used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we computed the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response was chosen as a defining output as this relates to the output variable that is most commonly monitored under a structural health monitoring (SHM) regime. In this context, we further proposed an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modified for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We performed multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results revealed Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process.

Large amount of data is obtained during bridge monitoring using sensors. Interpreting this data in order to obtain useful information about the condition of the bridge is not straight forward. This paper describes a case study of a railway bridge in India and explains how multi-dimensional visualization tools were used to extract relevant information from data. Parallel axis plots were used to visually examine the data. Trends and patterns in data were observed, which were used for more detailed investigation. The case study shows the complexity in data interpretation even in the case of simple bridge configurations.

The integration of DInSAR satellite data and in-situ sensors significantly improve long-term structural monitoring, contributing to reduce potential risk of damages or failures. Collecting and sharing monitoring data through an IoT infrastructure ensures a constant and secure access to the information by professional users’ networks that are often urged to take decisions and activate further investigations. By implementing such integrated systems, a more effective and widespread monitoring of existing building stocks can be achieved

The integration of DInSAR satellite data and in-situ sensors significantly improve long-term structural monitoring, contributing to reduce potential risk of damages or failures. Collecting and sharing monitoring data through an IoT infrastructure ensures a constant and secure access to the information by professional users' networks that are often urged to take decisions and activate further investigations. By implementing such integrated systems, a more effective and widespread monitoring of existing building stocks can be achieved

In the process of using a long-span converter station steel structure, engineering disasters can easily occur. Structural monitoring is an important method to reduce hoisting risk. In previous engineering cases, the structural monitoring of long-span converter station steel structure hoisting is rare. Thus, no relevant hoisting experience can be referenced. Traditional monitoring methods have a small scope of application, making it difficult to coordinate monitoring and construction control. In the monitoring process, many problems arise, such as complicated installation processes, large-scale data processing, and large-scale installation errors. With a real-time structural monitoring system, the mechanical changes in the long-span converter station steel structure during the hoisting process can be monitored in real-time in order to achieve real-time warning of engineering disasters, timely identification of engineering issues, and allow for rapid decision-making, thus avoiding the occurrence of engineering disasters. Based on this concept, automatic monitoring and manual measurement of the mechanical changes in the longest long-span converter station steel structure in the world is carried out, and the monitoring results were compared with the corresponding numerical simulation results in order to develop a real-time structural monitoring system for the whole long-span converter station steel structure’s multi-point lifting process. This approach collects the monitoring data and outputs the deflection, stress, strain, wind force, and temperature of the long-span converter station steel structure in real-time, enabling real-time monitoring to ensure the safety of the lifting process. This research offers a new method and basis for the structural monitoring of the multi-point hoisting of a long-span converter station steel structure.

A long-gauge fiber Bragg grating (FBG) strain sensor with enhanced strain sensitivity is proposed, which is encapsulated with two T-shaped metal blocks. Its fabrication method is described briefly, and the strain sensitivity can be flexibly adjusted through changing its packaging method. A series of experiments are carried out to study the packaging and its sensing properties. The experimental results show that the strain and temperature sensitivity coefficient of the sensor are three times larger than the common FBG sensors. The linearity coefficients of the FBG sensor are larger than 0.999, and the relative error of the repeatability of all sensor samples is less than 1%. Through the stability test on the actual bridge, it is revealed that the long-term stability of the sensor is excellent, and the maximum error is less than 1.5%. In addition, the proposed FBG strain sensors are used to conduct a shear strengthening experiment on a reinforced concrete (RC) beam to verify its working performance. The experimental results show that the strain change and crack propagation of the RC beam are well monitored by the sensors during the loading process.

Theodolites are fundamental geodetic measuring instruments for all practical geodetic tasks, as well as for experimental geodetic scientific purposes. Their development has a long history. Photo and video theodolites represent the advanced development of classic theodolites. Development started in 19th century, but only in the last 15 years has commercial application been achieved in the geodetic profession. The latest development, called image-assisted total stations (IATS), is a theodolite which consists of a classic robotic total station (RTS) with integrated image sensors. It was introduced in the early 2000s. With the development of theodolites, their application became much wider; today, they can be used for structural and geo-monitoring, i.e., for the determination of static and dynamic displacements and deformations of civil engineering structures such as bridges, dams, wind turbines, and high buildings, as well as natural structures, such as mountain slopes. They can be implemented in geodetic monitoring systems, which are an integral part of engineering structural diagnosis, and they provide essential information about the current condition of the structure. This paper describes the technological development of photo and video theodolites divided into phases according to the main innovations in their development. The application of modern video theodolites (i.e., IATS) is presented through several experimental studies that were performed. The procedure of conducting measurements with this kind of instrument, as well as the analysis of acquired data and achieved results, is elaborated. Lastly, the authors conclude, according to the achieved results, that IATS can today be used for determination of quasi-static and dynamic displacements with small and high amplitudes.

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

Multivariate techniques are used in this study to analyze the monitoring data displacements of a concrete dam, measured by means of pendulums, extensometer bases and multiple rod extensometers, taking into account the action of environmental conditions, bounded by the surface temperature of the concrete at ambient temperature and the tank water level. The canonical correlation analysis is used to evaluate the influence of environmental variables in the displacement of structures and dam foundations. The factor analysis is used to identify data sources of variability and order the sensors according to the action of factors. The dates of the measurements are grouped according to similarities in the present observations, by applying the cluster analysis. Then the discriminant analysis is used to assess the groups as to their homogeneity. The results demonstrate that the techniques used for distinguishing the dam responses and identify the effects of changes in environmental conditions on the displacements of the structures and dam foundations.

At present, most of the research on low velocity impact of composite laminates focuses on load location and damage assessment. To provide further early warnings about structural impact damage, impact energy can be monitored and identified. For high strength composite laminates with variable thickness, in order to further accurately evaluate the impact energy, it is necessary to adopt more suitable dynamic load signal analysis and impact energy identification methods. Therefore, a new low velocity impact monitoring and identification method for composite plates with variable thickness is proposed. All impact sample signals collected by optical fiber sensor network are decomposed by whitening Empirical Mode Decomposition (EMD); the energy feature set is established according to the impact energy eigenvalue of sample signal; according to the first order component of signal decomposition, the thickness coefficient is determined and the energy feature set is modified to evaluate the actual impact energy. Meanwhile, combined with optical fiber sensing and signal processing technology, an impact energy monitoring system has been established, and the low velocity impact monitoring and identification experiments of composite laminates with variable thickness were carried out. The proposed energy identification method successfully identified 1–3 J impact energy with an average error of 4.82%, and the average error of large thickness area with low sensitivity was significantly reduced from 13.25% to 5.67%. The results show that the thickness coefficient correction method based on whitening EMD can evaluate the low velocity impact energy more accurately, and the thickness coefficient correction step significantly improves the recognition performance.

Steel-truss-arch bridges have been applied in high-speed railway bridges due to their excellent dynamic and static structural performance. Under the action of high-speed trains, the steel connections between hangers and decks suffer from repeated stresses, inducing potential fatigue problems or even fatigue failure. In this study, a multiaxial fatigue evaluation method was first created and established based on critical damage-plane methodology, following which the fatigue evaluation procedure was also created and recommended. The methodology was applied to real-life strain data from a high-speed railway bridge from which an assessment of fatigue damage and predicted fatigue life was estimated. The connection between the shortest hanger and deck on the downstream side was selected as the target due to its relatively high stress. A multiscale finite-element model of this bridge was created according to the design profile and monitoring results of traffic flow, where the finite-element model was calibrated and validated by comparing the calculation results with the monitoring data. Influence analysis was then carried out to investigate two factors—i.e., the total traffic flow and compositions of freight trains—having effects on the fatigue life of the steel connection. The results indicate that the applied multiaxial fatigue method is suitable for online fatigue evaluation of actual bridges. In addition, by using the multiaxial fatigue method, the fatigue-damage accumulation rate can be nearly 60 times that obtained by the uniaxial fatigue method. If freighting is taken into consideration, the fatigue damage will increase rapidly, and for the case 10% of proportion traffic as freighting, the actual fatigue life is estimated to be shorter than the design life.

Contactless structural monitoring has in recent years seen a growing number of applications in civil engineering. Indeed, the elimination of physical installations of sensors is very attractive, especially for structures that might not be easily or safely accessible, yet requiring the experimental evaluation of their conditions, for example following extreme events such as strong earthquakes, explosions, and floods. Among contactless technologies, vision-based monitoring is possibly the solution that has attracted most of the interest of civil engineers, given that the advantages of contactless monitoring can be potentially obtained thorough simple and low-cost consumer-grade instrumentations. The objective of this review article is to provide an introductory discussion of the latest applications of vision-based vibration monitoring of structures and infrastructures through an overview of the results achieved in full-scale field tests, as documented in the published technical literature. In this way, engineers new to vision-based monitoring and stakeholders interested in the possibilities of contactless monitoring in civil engineering could have an outline of up-to-date achievements to support a first evaluation of the feasibility and convenience for future monitoring tasks.

This paper describes the development of a distributed sensing system that can be disseminated in an environment of interest to monitor the vibration of a structure. This low-cost system consists of several sensor nodes and a central receiving node. All nodes are built using off-the-shelf electronic components. Each of the sensor nodes is battery-powered and equipped with a triaxial MEMS accelerometer, a wireless Long Range (LoRa) transceiver module for data transmission, a GPS module used for synchronization, and a microcontroller. The operation of the sensor node is validated by controlled laboratory tests where it is compared to a commercial reference accelerometer. Furthermore, the feasibility and potential benefits of the application of the proposed system to a structure in an archaeological site is investigated. Results show that the proposed sensor node could successfully monitor the vibration at several locations within the site. Therefore, it may be employed to detect the most relevant stresses to the structure, allowing for the identification of risks.

Rotating structures are important and commonly used in the transportation and energy generation fields, where a better understanding of the deformations these structures endure is essential for both the design and maintenance phases. This work presents a novel image sensing methodology for measuring the displacements of rotating parts in operation due to dynamic loading. This methodology employs 3D digital image correlation combined with a custom stroboscopic lighting solution to achieve apparent stillness of the target while it rotates and then processes the acquired data to remove small imprecisions and align it to the rotor’s intrinsic coordinate system. It was applied to an RC helicopter, whose blade deformation was measured and compared with a computational model, using fluid–structure interaction between computational fluid dynamics (CFD) and finite element analysis (FEA). Using live measurement techniques, it was possible to obtain the actual behaviour of the blades, which can be used to validate and tune computational models. The proposed methodology complements the methods available in the literature, which were centred around relative out-of-plane displacements, by enabling the comparison of absolute out-of-plane and in-plane ones.

Historical churches, tall ancient masonry buildings, and bell towers are structures subjected to high risks due to their age, elevation, and small base-area-to-height ratio. In this paper, the results of an innovative monitoring technique for structural integrity assessment applied to a historical bell tower are reported. The emblematic case study of the monitoring of the Turin Cathedral bell tower (northwest Italy) is herein presented. First of all, the damage evolution in a portion of the structure localized in the lower levels of the tall masonry building is described by the evaluation of the cumulative number of acoustic emissions (AEs) and by different parameters able to predict the time dependence of the damage development, in addition to the 3D localization of the AE sources. The b-value analysis shows a decreasing trend down to values compatible with the growth of localized micro and macro-cracks in the portion of the structure close to the base of the tower. These results seem to be in good agreement with the static and dynamic analysis performed numerically by an accurate FEM (finite element model). Similar results were also obtained during the application of the AE monitoring to the wooden frame sustaining the bells in the tower cell. Finally, a statistical analysis based on the average values of the b-value are carried out at the scale of the monument and at the seismic regional scale. In particular, according to recent studies, a comparison between the b-value obtained by AE signal analysis and the regional activity is proposed in order to correlate the AE detected on the structure to the seismic activity, discriminating foreshock, and aftershock intervals in the analyzed time series.

In the last decade, some researchers and professionals have been engaged in the study of methods and techniques that can build high wind turbines while containing construction costs within the limits of economic convenience. Among the most promising solutions is that of using innovative devices to mitigate the structural demand for the towers. The reduction in the stress demand in the foundation makes the strategy particularly interesting for the repowering of existing plants, where it is convenient not to demolish and rebuild the foundation, but rather to reuse the existing one for the new plant. A semi-active vibration control strategy, based on the adoption of controllable dissipative devices, is presented herein. The proposed technique requires the tower to be equipped with a measurement system suitable for the real time monitoring of structural response. Performing reliable high-frequency measurements of the horizontal displacement of points located at heights of tens of meters is not simple. With the purpose of assessing the efficiency and feasibility of Global Navigation Satellite System (GNSS)-based systems for the control of wind turbine structures, the proposed paper tries to investigate the characteristics and data processing techniques that are able to make the GNSS useful for such applications. Several numerical simulations were carried out with reference to a case-study wind turbine to quantitatively assess how the performance of the control system changes as the features of the monitoring system worsen, and finally to draw conclusions and suggestions for the minimum performance that monitoring devices must have for an effective reduction in structural demand.

The paper deals with theoretical and experimental studies for the development of a self-powered structural health monitoring (SHM) system using macro-fiber composite (MFC) patches. The basic idea is to integrate the actuation, sensing, and energy harvesting capabilities of the MFC patches in a SHM system operating in different regimes. As an example, during flight, under the effects of normal structural vibrations, the patches can work as energy harvesters by maintaining or restoring the battery charge of the stand-by SHM electronic board; on the other hand, if relevant/abnormal loadings are applied, or if local faults produce a noticeable stiffness variation of the monitored component, the patches can act as sensors for the power-up SHM board. During maintenance, the patches can then work as actuators, to stress the structure with pre-defined load profiles, as well as sensors, to monitor the structural response. In this paper, the investigation, based on the electromechanical impedance technique, is carried out on a system prototype made of a cantilevered composite laminate with six MFC patches. A high-fidelity nonlinear model of the system, including the piezoelectric hysteresis of the patches and three vibration modes of the laminate beam, is presented and validated with experiments. The results support the potential feasibility of the system, pointing out that the energy storage can be used for recharging a 3V-65mAh Li-ion battery, suitable for low-power electronic boards. The model is finally used to characterize a condition-monitoring algorithm in terms of false alarms rejection and vulnerability to dormant faults, by simulating built-in tests to be performed during maintenance.

This paper presents a simplified model using coupled Bernoulli and shear beams supported by Winkler-type springs for system identification of tunnels subjected to earthquake-induced ground motions. The model formulation is introduced and closed-form solutions to the modal characteristic equation and mode shapes are derived. The latter are verified against the results of numerical models. Then, the system identification algorithm is presented, showcasing its ability to recover model parameters when recorded acceleration time histories along the tunnel are compromised by noise and sensor locations are varied. The presented framework can be used to initially and, simply, recover tunnel response when monitoring data is available, or to plan for monitoring campaigns in newly constructed or existing tunnels.

In this contribution a new method for evaluating seismic risk in regional areas based on the acoustic emission (AE) technique is proposed. Most earthquakes have precursors, i.e., phenomena of changes in the Earth’s physical-chemical properties that take place prior to an earthquake. Acoustic emissions in materials and earthquakes in the Earth’s crust, despite the fact that they take place on very different scales, are very similar phenomena; both are caused by a release of elastic energy from a source located in a medium. For the AE monitoring, two important constructions of Italian cultural heritage are considered: the chapel of the “Sacred Mountain of Varallo” and the “Asinelli Tower” of Bologna. They were monitored during earthquake sequences in their relative areas. By using the Grassberger-Procaccia algorithm, a statistical method of analysis was developed that detects AEs as earthquake precursors or aftershocks. Under certain conditions it was observed that AEs precede earthquakes. These considerations reinforce the idea that the AE monitoring can be considered an effective tool for earthquake risk evaluation.

Greenhouse cultivation has gained a special importance in recent years and become the basis of the economy in south-eastern Spain. The structures used are light and, due to weather events, often collapse completely or partially, which has generated interest in the study of these unique buildings. This study presents a load and displacement monitoring system that was designed, and full scale tested, in an Almería-type greenhouse with a tensioned wire structure. The loads and displacements measured under real load conditions were recorded for multiple time periods. The traction force on the roof cables decreased up to 22% for a temperature increase of 30 °C, and the compression force decreased up to 16.1% on the columns or pillars for a temperature and wind speed increase of 25.8 °C and 1.9 m/s respectively. The results show that the structure is susceptible to daily temperature changes and, to a lesser extent, wind throughout the test. The monitoring system, which uses load cells to measure loads and machine vision techniques to measure displacements, is appropriate for use in different types of greenhouses.

In this work, we present and discuss on the deflection estimation of a bi-dimensional panel by using Fiber Bragg Gratings (FBGs) as strain sensors embedded in the structure and a method based on the classical beam theory. The existing difficulties in the direct measure of the deflection are overcome thanks to the proposed technique and a real-time indirect structural monitoring is possible both on small and large structure. In many tests the estimated deflection with the proposed method has been compared with direct deflection measurements obtained with a mechanical comparator showing good agreement. A resolution of few tens of microns over a surface of the order of 1 m2 has been reached.