1,721,043 research outputs found
Shared micromobility-driven modal identification of urban bridges
Full text linkRecent research in Indirect Structural Health Monitoring (ISHM) uses the dynamic response of instrumented vehicles to carry out “drive-by” monitoring of bridges. These vehicles are generally cars or trucks instrumented with different types of sensors. However, some urban bridges are inaccessible to regular vehicles. Also, cars and trucks have non-negligible weight and suspension systems that may affect the collected vibration data. Stiff, light, and standardized shared micromobility vehicles, such as bicycles and electric kick scooters, have never been explored for ISHM purposes. This paper proposes an innovative and automatic ISHM strategy based on the data collected by smartphones temporarily installed on shared micromobility vehicles. An identification procedure suitable for cloud computing is proposed to extract the dynamic parameters of bridges without needing any sensor deployment, becoming particularly appealing for monitoring a densely built environment at a territorial scale. The methodology is applied to a real footbridge in Bologna (Italy)
Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks
Full text linkOne of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures
The value of monitoring a structural health monitoring system
Full text linkStructural Health Monitoring (SHM) systems are adopted to acquire timely and continuous data on the state of civil structures, aerospace vehicles, and industrial machines, which deteriorate due to slow processes, such as corrosion and fatigue, and shock events, including natural and man-made disasters. The components of SHM systems themselves are exposed to deterioration after their installation; thereby, they might provide altered information to decision-makers. To account for this issue, Sensor Validation Tools (SVTs) have been developed to give insight into the actual condition of the SHM systems. In the last decade, researchers have exploited the Value of Information (VoI) from Bayesian decision theory to quantify the benefit of the information provided by an SHM system, implicitly assuming that it is working correctly when interrogated. The benefit of the information provided by SVTs on the state of an SHM system has never been investigated. This paper addresses this topic and extends the classical VoI framework to quantify the additional benefit brought by the information on the state of the SHM system to the decision problems it is meant to support. A numerical analysis, as well as a methodology demonstration on a real bridge, are presented to discuss the framework
Structural integrity assessment of railway bridges during the passage of trains
No full textTraditional algorithms for the identification of dynamic parameters based on vibrational structural response generally involve strict assumptions about stationarity and may not be suitable for time-varying systems. Therefore, there is a tendency to discard the measurements of the structural response collected during short-time events, such as train passages on bridges. The response to a strong event may however include valuable information about structural features that cannot be observed in low-excitation scenarios. Moreover, a strong excitation typically generates a higher-amplitude structural response, which is particularly convenient when using low-cost instrumentation for data collection that is usually characterized by low sensitivity. In this paper, dynamic parameters of bridges during the passage of trains are identified by fitting the structural response to a simplified analytical model specialized for railway applications. The efficacy of a damage index based on the fitting residuals is investigated using a numerical study, simulating the use of an event-triggered smart sensing system. Applying the proposed method, damage detection can be achieved using a single low-cost sensing node
A method to assess the value of monitoring an SHM system
No full textAging structural components, together with the increasing transportation needs and limited budgets, are challenging aspects that typically concern decision-makers and infrastructure owners. Although Structural Health Monitoring (SHM) has been a powerful tool to optimize maintenance-related activities and post-disaster emergency management, the sensor readout and, therefore, the outcome of the monitoring system is susceptible to errors due to malfunctioning. For years, the Value of Information (VoI) has been studied to quantify the long-term benefit of SHM systems against the initial investment in sensing instrumentation without considering the eventuality of faulty sensing nodes. However, these are very common in field applications. This paper proposes a new framework to calculate the benefit of using Sensor Validation Tools (SVTs) before calculating the damage-sensitive features that drive the SHM process. The novel approach extends the traditional VoI to consider multiple “health” states of the SHM system, associate the outcome of the SHM system with the state of both the structure and the SHM system, and quantify the additional value obtained from SVTs
Impact of Decision Scenarios on the Value of Seismic Structural Health Monitoring
No full textThe limited knowledge that decision-makers have on the actual condition of civil structures and infrastructures complicates the management of seismic emergencies in urbanized areas. In this respect, Seismic Structural Health Monitoring (S2HM) can support decision-makers by providing real-time information on the structural condition. Nevertheless, S2HM information comes with a cost, and decision-makers have to decide if installing this type of system is worthy before the information is collected. In this paper, the benefit of S2HM in post-earthquake emergency management is assessed through the Value of Information (VoI) from Bayesian decision analysis. The VoI can be intended as the expected reduction in management costs resulting from monitoring information. If the VoI is higher than the cost of the monitoring system, the manager should install it. The methodology is applied to an exemplary building in a seismic area. It is demonstrated and discussed in the paper that the value of S2HM is strongly influenced by the decision scenario considered by the decision-maker. Specifically, it is shown that the VoI is particularly high when the S2HM information prevents unnecessary building evacuation and related losses of functionality
Clump interpolation error for the identification of damage using decentralized sensor networks
No full textRecent developments in the field of smart sensing systems enable performing simple onboard operations which are increasingly used for the decentralization of complex procedures in the context of vibration-based structural health monitoring (SHM). Vibration data collected by multiple sensors are traditionally used to identify damage-sensitive features (DSFs) in a centralized topology. However, dealing with large infrastructures and wireless systems may be challenging due to their limited transmission range and to the energy consumption that increases with the complexity of the sensing network. Local DSFs based on data collected in the vicinity of inspection locations are the key to overcome geometric limits and easily design scalable wireless sensing systems. Furthermore, the onboard pre-processing of the raw data is necessary to reduce the transmission rate and improve the overall efficiency of the network. In this study, an effective method for real-time modal identification is used together with a local approximation of a damage feature, the interpolation error, to detect and localize damage due to a loss of stiffness. The DSF is evaluated using the responses recorded at small groups of sensors organized in a decentralized topology. This enables the onboard damage identification in real time thereby reducing computational effort and memory allocation requirements. Experimental tests conducted using real data confirm the robustness of the proposed method and the potential of its implementation onboard decentralized sensor networks
Value of Information analysis accounting for data quality
No full textStructural Health Monitoring (SHM) can provide valuable information for maintenance-related activities and post-disaster emergency management. However, as with any technological system, SHM systems can be susceptible to errors due to malfunctioning. Therefore, it is essential to assess the potential for malfunctions and the associated costs of maintenance and repair when evaluating the long-term benefits of SHM systems. In the last two decades, sensor validation tools (SVTs) have been proposed to support decisions by isolating and discarding abnormal data. Recently, the authors of this paper have proposed a framework based on the Value of Information (VoI) from Bayesian decision analysis to account for different states of an SHM system and assess the benefit of SVT information. By quantifying the additional value obtained from SVTs, decision-makers can make more informed decisions about investing in these systems. This framework is here demonstrated on a real case study, namely the S101 bridge in Austria, which has been artificially damaged for research purposes. The benefit of collecting SHM and SVT information is quantified by considering a simple decision problem related to the management of the bridge in the aftermath of a damaging event. Overall, the study highlights the potential benefits of using SVTs to improve the reliability of SHM data and inform decision-making in the management of structures
Lightweight vehicles in indirect structural health monitoring: Current advances and future prospects
No full textRecent research has explored the potential of using the dynamic response of passing vehicles to conduct Structural Health Monitoring (SHM) efficiently. Various types of vehicles, including cars, vans, trucks, and even manually propelled carts, have been employed in this approach, with different configurations of exciters and receivers. A noteworthy development in this field involves the inclusion of lightweight vehicles like bicycles and scooters. Lightweight vehicles offer several advantages, including their affordability, sustainability, and minimal environmental impact. These vehicles have a negligible impact on the dynamic behavior of structures due to their low speeds and negligible mass, making them ideal for monitoring structures that are challenging to access, such as footbridges. This paper provides a comprehensive review of recent literature on the application of lightweight vehicles in SHM of urban bridges. It emphasizes the potential benefits and current challenges associated with these applications while offering insights into future research directions
Vibration Response-Based Damage Detection
No full textThis chapter aimed to present different data driven Vibration-Based Methods (VBMs) for Structural Health Monitoring (SHM). This family of methods, widely used for engineering applications, present several advantages for damage identification applications. First, VBMs provide continuous information on the health state of the structure at a global level without the need to access the damaged elements and to know their location. Furthermore, damage can be identified using the dynamic response of the structure measured by sensors non-necessarily located in the proximity of damage and without any prior knowledge about the damage location. By principle, VBMs can identify damage related to changes in the dynamic properties of structures, such as stiffness variations due to modifications in the connections between structural elements, or changes in geometric and material properties. A classification of different VBMs was presented in this chapter. Furthermore, several case studies were presented to demonstrate the potential of these methods
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