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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]

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]

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]

• Curvature is identified from dynamic and quasi-static acceleration response. • Filtering and empirical mode decomposition provide curvature influence lines. • Sparse modal parameters and dense influence lines are fused using Kalman filter. • The system state represents dense and robust curvature estimate. • Monitoring the state evolution allows high-resolution damage localization. Curvature is one of the most popular damage-sensitive features in vibration-based structural health monitoring applications, typically calculated from identified modal features. While the relevant strategic or historical importance of bridges may justify dense sensor networks, a limited budget is generally assigned to monitor "minor" viaducts, thus involving inexpensive devices or extremely sparse sensing solutions. Modal parameters can only be obtained at instrumented locations. Thereby, damage assessment methods based on identified features typically have a low spatial resolution, especially when using low-cost monitoring setups with a modest number of sensing devices. This paper proposes an original identification method for the curvature of bridges based on sparse acceleration measurements that can be collected using standard accelerometers. The raw acceleration signal is processed through a particular filter bank that extracts dynamic and quasi-static signal components. The first components are employed to identify modal parameters, from which sparse yet robust estimates of the structural curvature are retrieved. On the other hand, the quasi-static acceleration generated by the structural deflection induced by traffic load is used to identify the curvature influence lines of the bridge, which are fused with modal estimates using a Kalman filter. The state variable of the analyzed system, representing a dense curvature profile of the structure subjected to concentrated loads, can be used as a damage-sensitive feature for high-resolution damage localization. The method is applied to a steel truss bridge subject to different damage configurations. [ABSTRACT FROM AUTHOR]