Search

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]

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]