1,721,052 research outputs found
Statistical vibration-based damage localization for the S101 bridge, Flyover Reibersdorf, Austria
No full textThis paper is focused on the description and application of an extended version of the Interpolation Method for the damage localization where a statistical characterization of the indicator is proposed. The damage indicator is defined in terms of the probability of exceedance of a detection threshold corresponding to the accepted probability of false alarm. The verification of the accuracy of the novel indicator is carried out with reference to a real case study of a typical example of a European highway pre-stressed concrete road bridge, the S101 Flyover Reibersdorf Bridge built in Austria in the early 1960. Responses to ambient vibration have been recorded on the bridge both in the undamaged and in several different damage scenarios artificially inflicted. In the paper data recorded on the S101 have been used to check the accuracy of damage indicator for the several damage scenarios and its sensitivity to the statistic model assumed to describe the variability of the damage feature
S2HM Must Be Real-Time or Not?
No full textSeismic structural health monitoring (S2HM) has advanced significantly in the last three decades. However, currently there is no consensus on the need for real-time processing of data acquired during an earthquake. Numerous applications exist whereby S2HM-equipped systems record valuable seismic response data. A delayed use of the seismic data prohibits timely discovery of hidden damages in a structure which, in turn, possibly increases its vulnerability during events to follow – with increased risk to occupants. Such risks are of particular concern when, for example, there are long-distance/long period effects e.g. for tall buildings and long-span bridges that are significantly affected by events that originate at far distances. These phenomena necessitate near real-time monitored data to make timely data-based informed decisions on the health or performance of the affected structure. The paper discusses criteria for functionality and occupiability thresholds in actual applications
Determination of Sample Size on Surrogate Model-Based Parameter Inverse Analysis of a Super-High Arch Dam
Full text linkValue of information-driven innovation in Gerber saddles monitoring
Full text linkInnovation plays a crucial role in shaping technological, economic, and social progress in modern societies. In the realm of bridge integrity management, the development and diffusion of technologies to acquire information can significantly enhance industries' safety and functionality capabilities. Among the most widely diffused bridge types in Europe and North America, Gerber bridges are particularly susceptible to deterioration over time. Gerber saddles are typically not instrumented and are checked only through visual inspections. This paper introduces the metric of the Value of Information for Innovation to estimate the benefit associated with introducing an established technology in a new market of application. Herein, the operational value of implementing microelectromechanical inclinometers in the integrity management of Gerber saddles is quantified for the specific case of a bridge in northern Italy. Microelectromechanical systems companies may use these results to optimally select the technology price, investigate diverse market strategies, and optimize sensor arrangement
Structural Management and Value of Information Analysis Accounting for Sensor Data Quality
No full textStructural health monitoring (SHM) can be used to assess the state of health of civil structures and infrastructures and acquire information that can support maintenance-related activities and post-disaster emergency management. Nevertheless, SHM outcomes may be susceptible to errors due to malfunctioning of the sensing system. The long-term benefit of SHM systems against the initial investment in sensing instrumentation is often quantified without considering the eventuality of faulty sensors. Inaccurate or missing sensor data, not accounted for when information from the SHM system is used to support decisions, can lead to the choice of sub-optimal maintenance actions, and associated economic losses. In the last two decades, Sensor Validation Tools (SVTs) have been proposed, which assess data quality before the SHM information is extracted to isolate and discard abnormal measurements. Nevertheless, automatic SVTs are still rarely implemented in real applications. Recently, a framework based on Bayesian decision theory has been proposed to quantify the benefit of using an SVT before it is implemented. The novel approach extends the traditional VoI to consider multiple モfunctioningヤ states of the SHM system with the final goal of quantifying the additional benefit obtained from SVTs. In this paper, this framework is demonstrated using a general example representative of different real situations. Uncertainties in the SVT results are accounted for to show that the adoption of an SVT enhances the overall benefit provided by an SHM system
The value of seismic structural health monitoring for post-earthquake building evacuation
Full text linkIn the aftermath of a seismic event, decision-makers have to decide quickly among alternative management actions with limited knowledge on the actual health condition of buildings. Each choice entails different direct and indirect consequences. For example, if a building sustains low damage in the mainshock but people are not evacuated, casualties may occur if aftershocks lead the structure to fail. On the other hand, the evacuation of a structurally sound building could lead to unnecessary financial losses due to business and occupancy interruption. A monitoring system can provide information about the condition of the building after an earthquake that can support the choice between several competing alternatives, targeting the minimization of consequences. This paper proposes a framework for quantifying the benefit of installing a permanent seismic structural health monitoring (S2HM) system to support building evacuation operations after a seismic event. Decision-makers can use this procedure to preventively evaluate the benefit of an SHM system and decide about the worthiness of its installation
Multi-zone parametric inverse analysis of super high arch dams using deep learning networks based on measured displacements
Full text linkParametric inverse analysis/identification provides significant information for structural damage detection and construction in dam engineering. The main challenge in inverse analysis is to enhance the computational accuracy and efficiency for complex structures, especially for super high arch dams with many zone parameters. This study developed a high-precision deep learning-based surrogate model for rapid inverse analysis of concrete arch dams. The relationship between mechanical parameters and multi-point displacement response is interpreted by convolutional neural networks (CNN)-based surrogate model. The proposed model is integrated with the Latin hypercube sampling and a meta-heuristic optimization algorithm for rapid inverse analysis strategy. The objective function is defined as the distance between the displacement predicted by the surrogate model and the measured displacement. The proposed approach is tested on an actual super high concrete arch dam. Results show that the proposed approach can achieve high accuracy and improve the computational efficiency by 95.83 % compared with the direct finite element method
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