Lightweight vehicles in indirect structural health monitoring: Current advances and future prospects

Recent 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

Structure-type classification and flexibility-based detection of earthquake-induced damage in full-scale RC buildings

Detecting early damage in civil structures is highly desirable. In the area of vibration-based damage detection, modal flexibility (MF)-based methods have proven to be promising tools for promptly identifying changes in the global structural behavior. Many of these methods have been developed for specific types of structures, giving rise to different approaches and damage-sensitive features (DSFs). Although structural type classification is an important part of the damage detection process, this part of the process has received little attention in most literature and often relies on the use of a-priori engineering knowledge. Moreover, in general, experimental validations are only performed on small-scale laboratory structures with controlled artificial damage (e.g., imposed stiffness reductions). This paper proposes data-driven criteria for structure-type classification usable in the framework of MF-based damage identification methods to select the most appropriate algorithms and DSFs for detecting and localizing structural anomalies. This paper also tests the applicability of the proposed classification criteria and the damage identification methods on full-scale reinforced concrete (RC) structures that have experienced earthquake-induced damage. The considered structures are a seven-story RC wall building and a five-story RC frame building, which were both tested on the large-scale University of California, San Diego-Network for Earthquake Engineering Simulation (UCSD-NEES) shaking table

Statistical approach for vibration-based damage localization in civil infrastructures using smart sensor networks

One 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

Automatic identification of dense damage-sensitive features in civil infrastructure using sparse sensor networks

Widespread monitoring of bridges is yet rarely employed at a territorial level due to the high costs of monitoring systems. However, the aging of civil infrastructures, combined with the growing traffic demand, poses the need for a simple and automatic tool that helps emergency management. In this paper, an integrated algorithm for the identification of dynamic and dense quasi-static structural features exploiting moving vehicles is proposed. Filtering raw acceleration structural responses, triggered by passing vehicles, enables the identification of modal parameters and curvature influence lines. The procedure can be implemented efficiently as its main computational core consists of convolutions. The definition of a curvature-based damage index leads to accurate localization and quantification of structural anomalies using few sensors. The proposed procedure is tested on three viaducts of the Italian A24 motorway. Moreover, a numerical model is studied to evaluate the potentialities of the strategy for damage localization

Modal assurance distribution of multivariate signals for modal identification of time-varying dynamic systems

Most time\u2013frequency representations (TFRs) and signal analysis methods used for the identification of dynamic systems through non-parametric techniques are based on univariate signals. However, combining the information obtained from different sensors to investigate the overall behavior of the monitored structure is not trivial, as different recordings may show different features. Moreover, methods based upon the analysis of the energy density distribution in the time\u2013frequency plane generally suffer from problems related to crossing and closely-spaced modes. In this paper, a new time\u2013frequency representation of multivariate and multicomponent signals based on the modal assurance criterion (MAC) is presented. The analysis of the modal assurance distribution (MAD) thus obtained enables the extraction of decoupled modal responses, which can then be used to evaluate the instantaneous modal parameters of time-varying systems. To this end, a decomposition algorithm based on modal assurance (DAMA) is proposed, employing the watershed segmentation of the MAD. The results for two case studies, a finite element model and a full-scale experimental benchmark, are shown, considering both the original MAD and two enhanced versions, here proposed to improve its readability. The results are compared with those obtained from modern and widely used techniques, showing the promising efficacy of the proposed method for signals with time-varying frequency and amplitude, even in the presence of narrow-band disturbances and white noise, as well as with vanishing modes

Bridge Monitoring Using Vehicle-Induced Vibration

Due to growing traffic demand, aging civil infrastructure raises the need for reliable tools to monitor structural health conditions, usable to plan informed maintenance and emergency management. Several structures with historical and monumental importance are instrumented with structural health monitoring (SHM) systems nowadays. However, even the failure of "minor" viaducts could endanger the safety of travelers and goods. Lately, dense wireless sensor networks (WSNs) based on MEMS devices are used to cut costs and simplify the deployment of SHM systems while collecting as much information as possible. However, dense WNSs are affected by data management, synchronization, and battery replacement issues, which make them unappealing for widespread use. This study presents an original damage identification algorithm based on sparse sensor networks. Traveling vehicles are exploited to obtain spatial information and accurately identify the location of structural anomalies. The curvature influence line of the monitored bridge can be calculated by processing the acceleration response measured at a given instrumented location through a low-pass filter. In this procedure, sensors operate individually, not needing energy-consuming synchronization. The proposed identification algorithm is verified on real data collected on a steel truss bridge subject to artificially induced damage

Impact of Decision Scenarios on the Value of Seismic Structural Health Monitoring

The 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

A method to assess the value of monitoring an SHM system

Aging 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 Vol 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

Shared micromobility-driven modal identification of urban bridges

Recent 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)