The response of the San Pietro monumental bell-tower located in Perugia, Italy, to the 2016 Central Italy seismic sequence is investigated, taking advantage of the availability of field data recorded ...by a vibration-based SHM system installed in December 2014 to detect earthquake-induced damages. The tower is located about 85 km in the NW direction from the epicenter of the first major shock of the sequence, the Accumoli Mw6.0 earthquake of August 24th, resulting in a small local PGA of about 30 cm/s
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, whereby near-field PGA was measured as 915.97 cm/s
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(E–W component) and 445.59 cm/s
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(N–S component). Similar PGA values also characterized the two other major shocks of the sequence (Ussita Mw5.9 and Norcia Mw6.5 earthquakes of October 26th and 30th, respectively). Despite the relatively low intensity of such earthquakes in Perugia, the analysis of long-term monitoring data clearly highlights that small permanent changes in the structural behavior of the bell-tower have occurred after the earthquakes, with decreases in all identified natural frequencies. Such natural frequency decays are fully consistent with what predicted by non-linear finite element simulations and, in particular, with the development of microcracks at the base of the columns of the belfry. Microcracks in these regions, and in the rest of tower, are however hardly distinguishable from pre-existing ones and from the physiological cracking of a masonry structure, what validates the effectiveness of the SHM system in detecting earthquake-induced damage at a stage where this is not yet detectable by visual inspections.
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► An automated modal identification procedure is presented. ► Tuning a few parameters does not limit the automated character of the procedure. ► Accuracy of the procedure is shown by ...application to data collected on two bridges. ► The automated procedure identifies closely-spaced and weakly-excited modes. ► Results are in excellent agreement with those obtained by manual techniques.
The increasing diffusion of long term dynamic monitoring systems for structural condition assessment is currently driving a strong interest towards automated procedures of output-only modal identification. Different approaches have been recently developed in the literature for this purpose, often based on Stochastic Subspace Identification (SSI) methods. Such procedures usually rely on heuristic decisional criteria, hence demanding for independent checks with validation purposes.
The aim of this paper is to propose an automated modal identification procedure, belonging to the class of SSI techniques and based on the popular tool of clustering analysis, and to exemplify its application in the operational modal analysis of two bridges, with different levels of complexity from the system identification viewpoint: a historic iron arch bridge and a long-span footbridge. In order to address reliability and accuracy of the procedure, the modal estimates automatically extracted from the data recorded on the two bridges were compared to those independently obtained by using well-established manual techniques. The results demonstrated very good accuracy and robust performance of the developed procedure, also in the case of weakly excited and closely spaced modes, so that its application to process the data collected in continuous monitoring systems seems highly promising.
Multifunctional structural materials are very promising in the field of engineering. Particularly, their strain sensing ability draws much attention for structural health monitoring applications. ...Generally, strain sensing materials are produced by adding a certain amount of conductive fillers, around the so-called "percolation threshold", to the cement or composite matrix. Recently, graphite has been found to be a suitable filler for strain sensing. However, graphite requires high amounts of doping to reach percolation threshold. In order to decrease the amount of inclusions, this paper proposes cementitious materials doped with new hybrid carbon inclusions, i.e., graphite and carbon microfibers. Carbon microfibers having higher aspect ratio than graphite accelerate the percolation threshold of the graphite particles without incurring into dispersion issues. The resistivity and strain sensitivity of different fibers' compositions are investigated. The electromechanical tests reveal that, when combined, carbon microfibers and graphite hybrid fillers reach to percolation faster and exhibit higher gauge factors and enhanced linearity.
Smart multifunctional composites exhibit enhanced physical and mechanical properties and can provide structures with new capabilities. The authors have recently initiated a research program aimed at ...developing new strain-sensing pavement materials enabling roadway-integrated weigh-in motion (WIM) sensing. The goal is to achieve an accurate WIM for infrastructure monitoring at lower costs and with enhanced durability compared to off-the-shelf solutions. Previous work was devoted to formulating a signal processing algorithm for estimating the axle number and weights, along with the vehicle speed based on the outputs of a piezoresistive pavement material deployed within a bridge deck. This work proposes and characterizes a suitable low-cost and highly scalable cement-based composite with strain-sensing capabilities and sufficient sensitivity to meet WIM signal requirements. Graphite cement-based smart composites are presented, and their electromechanical properties are investigated in view of their application to WIM. These composites are engineered for scalability owing to the ease of dispersion of the graphite powder in the cement matrix, and can thus be used to build smart sections of road pavements. The research presented in this paper consists of electromechanical tests performed on samples of different amounts of graphite for the identification of the optimal mix in terms of signal sensitivity. An optimum inclusion level of 20% by weight of cement is obtained and selected for the fabrication of a plate of 30 × 15 × 5 cm3. Results from load identification tests conducted on the plate show that the proposed technology is capable of WIM.
Nanomodified smart cement-based sensors are an emerging self-sensing technology for the structural health monitoring (SHM) of reinforced concrete (RC) structures. To date, several literature works ...demonstrated their strain-sensing capabilities, which make them suited for damage detection and localization. Despite the most recent technological improvements, a tailored measurement technique allowing feasible field implementations of smart cement-based sensors to concrete structures is still missing. In this regard, this paper proposes a multichannel measurement technique for retrieving strains from smart cement-based sensors embedded in RC structures using a distributed biphasic input. The experiments performed for its validation include the investigation on an RC beam with seven embedded sensors subjected to different types of static loading and a long-term monitoring application on an RC plate. Results demonstrate that the proposed technique is effective for retrieving time-stable simultaneous strain measurements from smart cement-based sensors, as well as for aiding the identification of the changes in their electrical outputs due to the influence of environmental effects variable over time. Accordingly, the proposed multichannel strain measurement technique represents a promising approach for performing feasible field implementations of smart cement-based sensors to concrete structures.
This paper presents a novel method for rapidly addressing the earthquake-induced damage identification task in historic masonry towers. The proposed method, termed DORI, combines operational modal ...analysis (OMA), FE modeling, rapid surrogate modeling (SM) and non-linear Incremental dynamic analysis (IDA). While OMA-based Structural Health Monitoring methods using statistical pattern recognition are known to allow the detection of small structural damages due to earthquakes, even far-field ones of moderate intensity, the combination of SM and IDA-based methods for damage localization and quantification is here proposed. The monumental bell tower of the Basilica of San Pietro located in Perugia, Italy, is considered for the validation of the method. While being continuously monitored since 2014, the bell tower experienced the main shocks of the 2016 Central Italy seismic sequence and the on-site vibration-based monitoring system detected changes in global dynamic behavior after the earthquakes. In the paper, experimental vibration data (continuous and seismic records), FE models and surrogate models of the structure are used for post-earthquake damage localization and quantification exploiting an ideal subdivision of the structure into meaningful macroelements. Results of linear and non-linear numerical modeling (SM and IDA, respectively) are successfully combined to this aim and the continuous exchange of information between the physical reality (monitoring data) and the virtual models (FE models and surrogate models) effectively enforces the Digital Twin paradigm. The earthquake-induced damage identified by both data-driven and model-based strategies is finally confirmed by in-situ visual inspections.
Structural health monitoring allows the automated condition assessment of civil infrastructure, leading to a cost-effective management of maintenance activities. However, there is still a debate in ...the literature about the effectiveness of available signal processing strategies to timely assess the health state of a structure. This paper is a contribution to this debate, by presenting the application of different vibration-based damage detection methods using up-to-date multivariate statistical analysis techniques applied to data acquired from a permanently monitored long-span arch bridge. Techniques based on dynamic regression models, linear and local principal component analysis, as well as on their combinations, including, in particular, the newly proposed method based on the combination of dynamic multiple linear regressions and local principal component analysis, and, finally, a method based on the recently proposed approach of cointegration, are considered. A first effort is made to formulate these methods within a unique mathematical framework, highlighting, in particular, the relevant parameters affecting their results and proposing objective criteria for their appropriate tuning and for choosing the length of the training period. Then, the considered damage detection methods are implemented and applied to field data, seeking for damage-sensitive features in the presence of variable environmental and operational conditions. The considered techniques are applied to time histories of identified modal frequencies of the bridge and their capability to reveal structural damage of varying severity is assessed using control charts. The case of an artificially imposed non-linear correlation between the features is also considered. The results provide, for the first time in the literature, an estimation of the minimum level of damage that can be realistically detected in the bridge using dynamic signatures and up-to-date signal processing algorithms, thus contributing to a more aware use of monitoring data and reliance over related health state assessment information.
Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time ...maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens.
Concrete constructions need widespread monitoring for the control of their state of integrity during their service life. In particular, after critical events such as earthquakes, this type of ...structure may experience the formation and development of cracks and damage. A quick and affordable assessment of structural behavior is indicated to identify conditions of danger for users and the incipient collapse of structural elements. This work presents investigations on multifunctional concretes with self-sensing capabilities to carry out static and dynamic monitoring. The materials were produced by the addition of conductive carbon microfibers to the concrete matrix. Electrical and sensing tests were carried out on samples with small-, medium-, and full-scale dimensions. The tests demonstrated the good electrical and electromechanical properties of the proposed smart concrete sensors, which appear promising for their use in civil elements or structures. In particular, tests on real-scale beams demonstrated the capability of the material to monitor the dynamic behavior of full-scale structural elements.
This paper presents an enhanced version and the validation of a recently proposed methodology for earthquake-induced damage detection and localization in masonry towers by using long-term vibration ...monitoring data. The proposed enhanced method is based on continuous operational modal analysis through stochastic subspace identification, dynamic multiple linear regressive analysis to remove the effects of changing environmental conditions and finite element (FE) model updating. Any anomalous frequency deviation from normal conditions resulting from an earthquake triggers the damage localization process. This task is performed by solving an inverse FE model calibration problem, where equivalent elastic properties of macrostructural elements are identified by minimizing an objective function considering experimentally identified and numerically predicted damage-induced decays in natural frequencies and changes in eigenvector components. To minimize the computational effort of this calibration procedure, a quadratic surrogate model is constructed using a tuned numerical FE model of the structure. The methodology is validated through application to the “Sciri Tower”, an historic civic masonry tower located in Perugia, Italy, that has been continuously monitored by the authors for more than 1 year. The validation is carried out by using simulated damage scenarios and a set of real far-field earthquake data and is based on a FE model of the tower including surrounding buildings, calibrated on the basis of the measured data from ambient vibration tests. The results demonstrate that the proposed procedure is capable of correctly detecting and localizing earthquake-induced damages.
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