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The holy grail of structural health monitoring is the quantitative linkage between data and decisions. While structural health monitoring has shown continued growth over the past several decades, there is a persistent chasm between structural health monitoring and the ability of structure owners to make asset management decisions based on structural health monitoring data. This is in part due to the historical structural health monitoring paradigm cast as a problem of estimating structural state and detecting damage by monitoring changes in structural properties (namely, reduced stiffness). For most operational structures, deterioration does not necessarily correspond to changes in structural properties with structures operating in their elastic regimes even when deteriorated. For structures like bridges, upkeep decisions are based on federally mandated condition ratings assigned during visual inspection. Since condition ratings are widely accepted in practice, the authors propose that condition ratings serve as lower limit states (i.e. limit states below yielding) with long-term monitoring data used to quantify these lower limit states in terms of the reliability index. This article presents a method to quantify the reliability index values corresponding to the lower limit states described by existing condition ratings. Once the reliability index thresholds are established, the data-driven reliability index of the in-service asset can be monitored continuously and explicitly mapped to a condition rating at any time. As an illustrative example, the proposed framework for tracking structural performance is implemented with long-term monitoring data collected on a pin-and-hanger assembly on the Telegraph Road Bridge, which is a highway bridge located in Monroe, MI. The successful implementation of the proposed method on the Telegraph Road Bridge results in a human-independent and truly data-driven decision-making strategy that is synergistic with the state of practice, eliminates risks associated with infrequent visual inspections, and expands condition ratings to encompass the entire measurable domain of damage that may exist in an asset.

Fatigue is a primary concern for railroad bridge owners because railroad bridges typically have high live load to dead load ratios and high stress cycle frequencies. However, existing inspection and post-inspection analysis methods are unable to accurately consider the full influence of bridge behavior on the fatigue life of bridge components. Reliability-based fatigue analysis methods have emerged to account for uncertainties in analysis parameters such as environmental and mechanical properties. While existing literature proposes probabilistic fatigue assessment of bridge components, this body of work relies on train parameter estimates, finite element model simulations, or controlled loading tests to augment monitoring data. This article presents a probabilistic fatigue assessment of monitored railroad bridge components using only continuous, long-term response data in a purely data-driven reliability framework that is compatible with existing inspection methods. As an illustrative example, this work quantifies the safety profile of a fracture-critical assembly comprising of six parallel eyebars on the Harahan Bridge (Memphis, TN). The monitored eyebars are susceptible to accelerated fatigue damage because changes in the boundary conditions cause some eyebars to carry a greater proportion of the total assembly load than assumed during design and analysis; existing manual inspection practices aim to maintain an equal loading distribution across the eyebars. Consequently, the limit state function derived in this article accounts for the coupled behavior between fatigue and relative tautness of the parallel eyebars. The reliability index values for both the element (i.e. individual eyebars) and system (i.e. full eyebar assembly) reliability problems are assessed and indicate that under the conservative assumption that progressive failure is brittle, first failure within the parallel eyebar system is generally equivalent to system failure. The proposed method also serves as an intervention strategy that can quantify the influence of eyebar realignment on the future evolution of the reliability index.

This article proposes the deployment of adaptive boosting (AdaBoost) for combining damage feature decisions and improving the detection accuracy of structural health monitoring algorithms. In structural health monitoring applications, damage-sensitive features are combined with classifiers to define decision boundaries and provide information about the structural state. Boosting algorithms combine multiple classifiers aiming at the improvement of their performance. In this study, AdaBoost is deployed on the realizations of a modular structural health monitoring framework, which consists of three tiers: data normalization based on environmental and operational conditions; extraction of damage features, also referred to as condition parameters; and hypothesis testing. Each condition parameter–hypothesis testing pair composes a classifier which is used in AdaBoost as a weak classifier. The integration of AdaBoost with the structural health monitoring framework is validated using experimental data of a 3-kW wind turbine located at the Los Alamos National Laboratory and data generated from a mechanical model of the same structure. The AdaBoost classifier is evaluated with respect to the error rate as well as the true positive and false positive rates, which are typically used in receiver operating characteristic curves. The AdaBoost classifier outperforms the framework classifiers in many cases, improving drastically the detection performance. However, it is shown that the boosting performance depends on the relative location of the condition parameter values on the condition parameter space. The overlaps between the condition parameter values to be combined are quantified using the Bhattacharyya coefficient, which provides a metric for assessing the boosting potential. Finally, omitting condition parameter values corresponding to specific environmental and operational conditions from the boosting process is proposed for obtaining optimum boosting results.

The implementation of unmanned aerial vehicles (UAVs) in civil engineering has primarily focused on remote sensing using optical cameras, light detection and ranging (LiDAR), and communicat...

Bridges are critical components of highways ensuring traffic can efficiently travel over obstructions such as bodies of water, valleys, and other roads. Ensuring bridges are in sound structural condition is essential for safe and efficient highway operations. Structural health monitoring (SHM) systems designed to measure bridge responses have been developed to quantitatively track the health of bridges. More recently, SHM systems have also begun to integrate measurement of vehicular loads that create the responses measured. However, precise correlation of traffic loads to bridge responses remains a costly and technically difficult strategy. To address existing technical limitations, a cyber-physical system (CPS) framework is proposed to track truck loads in a highway corridor, to trigger SHM systems to record bridge responses, and to automate the linking of bridge response data with truck weights collected by weigh-in-motion (WIM) stations installed along the corridor but not collocated with the bridges. To link truck weights to bridge responses, computer vision methods based on convolutional neural networks (CNN) are used to automate the detection and reidentification of trucks using traffic cameras. The single-stage CNN object detector YOLO is trained using a customized dataset to identify trucks from camera images at each instrumentation site; high precision is obtained with the YOLO detector exceeding 95% average precision (AP) for an intersection over union (IOU) threshold of 0.75. To reidentify the same truck at different locations in the corridor, this study adopts a CNN-based encoder trained via a triplet network and a mutual nearest neighbor strategy using feature vectors extracted from images at each measurement location. The proposed reidentification method is implemented in the CPS cloud environment and obtains a F1-score of 0.97. The study also explores the triggering of bridge monitoring systems based on visual detection of trucks by a traffic camera installed upstream to the bridges. The triggering strategy proves to be highly efficient with 99% of the triggered data collection cycles capturing truck events at each bridge. To validate, the CPS architecture is implemented on a 20-mile highway corridor that has a WIM station already installed; four traffic cameras and two bridge SHM systems are installed along the corridor and integrated with a CPS architecture hosted on the cloud. In total, over 10,000 trucks are observed at all measurement locations over one year allowing peak bridge responses to be correlated to both measured truck weights and to one another.

To address the grand challenges brought about by urbanization, “smart city” has become a ubiquitous concept for sustaining urban and economic growthwhile addressing the environmental and social issues created by that growth. While numerous articles and reports have been written about smart cities, a universal definition of what constitutes a smart city has yet to emerge with widespread consensus. A common theme to most definitions is the utilization of information and communication technologies (ICT) as an enabler to support city development, enhance urban services, and increase stakeholder access to information. Using this theme as a defining element, smart cities are quickly emerging around the world with city-wide investments in ICT to drive technological innovation, to support the development of new industries, to spur a stronger economy, to maintain a sustainable environment, and to enhance the quality of life of citizens.

Effective real-time execution of nonlinear model predictive control (NMPC) on embedded systems is significantly dependent on the controller formulation. This paper studies the effect of model structure and cost functions on the computation time of scalar bilinear NMPC using variational methods, with hydronic cooling applications. Two algebraically equivalent nonlinear model structures common in literature are primarily considered: a linear state equation with state-dependent control constraints and a bilinear state equation with time-invariant rectangular control constraints. Additionally, the effects of three cost function formulations are also considered: minimum-time, quadratic regulation, and efficient state constraints. High-fidelity computer simulations, hardware-in-the-loop testing, and experiments on a bench-scale hydronic cooling system are used to study sources of computational complexity, rates of convergence, initialization techniques, and overall effectiveness of the different models and costs. These results suggest that NMPC with bilinear state equations, minimizing pump power and a one-sided quadratic state cost, converges sufficiently fast and reliably. This presents an attractive alternative to the traditionally constrained linear quadratic regulator-based NMPC on embedded systems.

Many benefits can be derived from in situ monitoring of the growth, load response, and condition of human bone. In particular, bone monitoring offers opportunity to advance understanding and designing of osseointegrated mechanical components fixated into bones such as artificial joints and more recently osseointegrated prosthetic limbs. In this study, a bio-compatible wireless inductive strain-sensing system is proposed, which is designed to monitor the growth and strain response of bone-hosting implants. Thin-film circuit fabrication methods based on lithography are adopted to develop a conformable wireless strain sensor designed as a passive resistive–inductive–capacitive circuit. Two forms of strain sensing are designed into the thin-film sensor. First, parallel-plate capacitors fabricated from metal electrodes and a polyimide dielectric layer are introduced to modulate bone strain onto a resonant frequency of the thin-film sensor. A second resonant frequency is introduced in the sensor design to measure circumferential bone growth using a highly nonlinear titanium-resistive element, whose resistance exponentially increases well after 1000 µε under monotonic increasing hoop strain. To ensure the possibility for implantation in animal subjects in future study, the thin-film sensing system is fabricated using mainly bio-compatible polymers (e.g. polyimide) and metals (e.g. titanium and gold). Fabricated prototypes inductively coupled using an impedance analyzer are experimentally tested. Results reveal linear response of the first resonant frequency to low levels of strain with a sensitivity of 4.555 Hz per unit microstrain. The second resonant frequency is sensitive to the resistive fuse with nonlinear fuse behavior initiated above 1000 µε and impedance phase increasing exponentially thereafter.