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Equivalent viscous damping plays a central role in displacement-based design procedures. In this paper, approaches for estimating the equivalent viscous damping of RC frame buildings are proposed. At first, the analytical formulation of Blandon and Priestley was analysed, and then a calibration of the coefficients of this formulation was performed. Compared with the work of Blandon and Priestley, a larger set of synthetic accelerograms, related to different types of soil and different intensities, and a wider range of the effective periods were considered. In particular, two different sets of parameters are proposed: the first is usable in the case of spectra obtained numerically (approach 1), and the second is usable in the case of code-based spectra and damping modification factor (approach 2). To test the performed calibration and to compare the considered formulations (i.e., the proposed and literature equations), the direct displacement-based design procedure has been applied to three case studies of reinforced concrete frame structures, and then pushover and nonlinear time-history analyses have been performed. The results show that the use of the calibrated parameters (for both the considered approaches) has determined more conservative results, in terms of design base shear and maximum drift from NLTH. Moreover, the average displacement profiles and the inter-storey drifts obtained from time-history analyses for the frames designed with the calibrated parameters match better the design profile. [ABSTRACT FROM AUTHOR]

This paper proposes a novel damage index for railway bridges based on synchronous strain and displacement data collected at the passage of trains. The approach identifies a transformation operator that converts strains into displacements in a data‐driven fashion without prior structural knowledge and with no parameter selection. The displacement prediction error is proposed as a robust damage index, insensitive to the vehicle loads and temperature effects. Numerical simulations and data generated through the calibrated finite element model of a steel truss bridge showcase the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Information on the presence and location of cracks in civil structures can be precious to support operators in making decisions related to structural management and scheduling informed maintenance. This paper investigates the efficacy of supervised machine learning to solve the inverse electrical impedance tomography problem and to reconstruct the conductivity distribution of a piezoresistive sensing film. This film consists of a conductive paint applied onto structural components, and operators can use its conductivity distribution to identify crack sizes and locations in the underlying structure. A deep neural network is employed to reconstruct a dense conductivity distribution within the painted area by using only voltage measurements collected at sparse boundary locations. Since one of the most challenging aspects of using supervised learning tools for real-world applications is generating a representative training dataset, this paper presents a new approach to test the suitability of synthetic datasets built using a finite element model of the sensing film. Results are reported for four sensing specimens fabricated with two different techniques (i.e., using carbon nanotubes and graphene nanosheets, respectively). Crack-like damage is induced to the substrate of the sensing film and identified using the proposed machine learning technique. Promising results are obtained as compared to conventional methods. [ABSTRACT FROM AUTHOR]

Sensing skins and electrical impedance tomography constitute a convenient and inexpensive alternative to dense sensor networks for distributed sensing in civil structures. However, their performance can deteriorate with the aging of the sensing film. Guaranteeing high identification performance after minor lesions is crucial to improving their ability to identify structural damage. In this paper, electrical resistance tomography is used to identify the crack locations in nanocomposite paint sprayed onto structural components. The main novelty consists of using crack annotations collected during visual inspections to improve the crack identification performance of deep neural networks trained using simulated datasets through transfer learning. Transfer component analysis is employed for simulation‐to‐real information transfer and applied at a population level, extracting low‐dimensional domain‐invariant features shared by simulated models and structures with similar geometry. The results show that the proposed method outperforms traditional approaches for crack localization in complex damage patterns. [ABSTRACT FROM AUTHOR]

In the last few decades, structural health monitoring (SHM) has proven a helpful tool to support the maintenance and management of civil infrastructure. However, typical measurement networks are expensive and require considerable initial efforts. The user-friendliness and interpretability of the outcome of SHM systems are crucial factors in motivating infrastructure owners and decision-makers to sustain their costs. For this reason, simple algorithms that provide structural parameters with direct physical interpretability for professionals familiar with the typical quantities involved in structural engineering are still the most used in field applications. This paper proposes an original method to identify curvature influence lines of bridges and viaducts only using the structural acceleration response induced by vehicular loads. Acceleration time histories collected at sparse locations through standard accelerometers are employed. In contrast to SHM approaches based on modal parameters, the proposed method does not need strict synchronization, thus being suitable for wireless and low-cost monitoring solutions. Identified influence lines are used to define a spatially dense damage indicator for accurate localization of structural anomalies with a clear physical meaning. Experimental results obtained for a steel truss bridge analyzed in different damage conditions prove the efficacy of the proposed method for situations where modal-based approaches may fail. [ABSTRACT FROM AUTHOR]

Natural frequencies have always been one of the most intuitive and widely used features for damage identification in civil structures. Even with the recent rapid technological and theoretical developments, frequency-based identification methods are of great interest for applications through low-cost sensing systems. Although most techniques for frequency identification assume a linear structural behavior, in real applications, variations in the amplitude of input excitation can lead to short-term frequency fluctuations due to the inherent nonlinearities of civil structures. This paper proposes a procedure for damage detection in nonlinear systems based on instantaneous resonant frequency and amplitude estimates. A statistical model was fitted to identified data, and a synthetic indicator was proposed to obtain robust damage detection, even when frequency shifts due to variations in the input excitation are comparable to those due to actual damage. The proposed method was applied to a dataset recorded from a reinforced concrete building with strongly nonlinear behavior. [ABSTRACT FROM AUTHOR]

Smart devices for structural health monitoring provide edge computing capabilities to reduce wireless transmission and, thus, power consumption. Although effective algorithms have been proposed in the last few decades, traditional microcontrollers require heavy data flow between the memory and the central processing unit that involves a considerable fraction of the total energy consumption. Phase change memory has recently emerged as an attractive solution in the field of resistive nonvolatile memory for analog in-memory computing, which is a valid approach to avoid data being conveyed among distinct elaboration units. However, it has never been envisaged in structural health monitoring applications. As this technology is still in an embryonic state, several challenges related to nonlinearities and nonidealities of the memory elements and the energy expenditure related to the memory reprogramming process may undermine its usage. In this paper, the application of a novel identification approach for civil infrastructures is investigated using phase change memories. The main computational core of the presented algorithm, consisting of one-dimensional convolutions, is particularly suitable for implementations involving analog in-memory computing, thus showing the great potential of this technology for structural health monitoring applications. The test unit is an embedded phase change memory provided by STMicroelectronics and designed in 90-nm smart power bipolar complementary metal-oxide-semiconductor (CMOS)-double-diffused metal-oxide-semiconductor (DMOS) technology with a Ge-rich Ge-Sb-Te alloy for automotive applications. Experimental results obtained for a viaduct of an Italian motorway support the efficacy of the method. Moreover, the influence of nonidealities on the outcomes of damage identification based on both dynamic and quasi-static structural parameters is examined. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a new strategy for vibration‐based structural health monitoring is proposed, specifically designed for smart sensors with edge computing capabilities organized in a line topology. This solution is aimed at maximizing resource optimization and enables the identification of modal parameters even for large or densely instrumented structures, where star‐topology monitoring systems are typically unsuitable. In particular, an efficient data management procedure is proposed to reduce data transmission, thus improving efficiency and minimizing maintenance interventions for battery replacement in wireless applications. The maximum volume of transmitted data can be selected by the user, based on the specific requirements of the network. Although the considerable reduction of data size, the proposed approach enables accurate estimation of the structural parameters in challenging scenarios where other techniques generally fail. Modal parameters are identified in an online fashion, enabling near real‐time detection and localization of early damage. Applications to a real case study instrumented with a dense sensor network show the effectiveness of the proposed approach and the possibility of localizing structural defects in slightly damaged civil structures. [ABSTRACT FROM AUTHOR]

Thanks to emerging techniques in the field of signal processing and due to improvements in smart sensing systems which enable the event‐triggered acquisition of high‐fidelity data at the occurrence of strong ground motion events, seismic structural health monitoring has grown considerably in the last few decades. In this paper, the modal assurance distribution, an alternative time‐frequency representation of the modal features of multivariate and multicomponent signals, is extended for application to short‐term nonstationary vibrational structural responses in which the system may manifest its nonlinear behavior. A general procedure for the extraction of the decoupled normal modes is presented, which allows the identification of instantaneous modal parameters in order to investigate in detail the structural behavior during earthquakes. Valuable information that cannot be recovered by means of traditional criteria can thus be exploited for accurate damage assessment. The results obtained for two case studies consisting of a numerical model with softening nonlinear behavior and a full‐scale experimental reinforced concrete benchmark show the potential and applicability of the method proposed for the integrity assessment of civil structures. [ABSTRACT FROM AUTHOR]

The main purpose of this work is to investigate the usability of easily obtainable parameters instead of the modal traditional ones, in the context of a flexibility-based damage detection procedure, under the assumption of unknown structural masses. To this aim, a comparison is made between two different approaches: the first involves the calculation of the flexibility matrix by using traditional modal parameters, such as natural frequencies and modal vectors, normalized to unitary values, while the second involves the use of singular vectors, obtained through a simple matrix factorization. The modal parameters and the singular vectors necessary for the implementation of the damage detection procedure are evaluated through two different techniques: the Eigensystem Realization Algorithm and a wavelet-based procedure, for which a variant is proposed by introducing the energy reassignment concept into the original algorithm. Through the latter approach, in particular, it is possible to obtain a high number of singular vectors even in the case of reduced availability of sensors. The study is performed under the assumption of nonstationary excitation, in order to achieve general results, and the effectiveness of the procedures is evaluated through simulated tests regarding different structural schemes. [ABSTRACT FROM AUTHOR]