VTechWorks Repository :: Browsing by Author "Sarlo, Rodrigo"

Browsing by Author "Sarlo, Rodrigo"

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Active Force Correction of Off-Nominal Structures Using Intelligent Scaffolding
Everson, Holly Kathleen (Virginia Tech, 2024-10-17)
The culmination of this research focuses on the area of structural support and stability as it relates to the field of large space structures. Fitting into the branch of in-space assembly, servicing, and manufacturing (ISAM), this topic covers essential subject matter areas of robotic manipulation, repair, state estimation, and structural health. As the next generation of space structures includes increased areas of modularity, the nature of structures built in-space lends itself significantly to repair efforts. With plans for these repair efforts in place, the lifetime of damaged structures can be greatly extended leading to a greater chance of mission success. By considering how repair efforts factor into the assembly scope, critical failures in large trusses, especially those involving single-point structural failures, can be mitigated. To do this, external forces are applied to the damaged structure utilizing an intelligent scaffolding formulation. This methodology employs robots to strategically apply loads to re-route abnormal stress and strain paths, correct for resulting deflections, and stabilize the structure itself. These tasks are vital to the safety of the structure and must take place before any repair efforts are considered in an effort to prevent cascading damage. The following research explores this damage simulation and correction paradigm through a variety of truss initial conditions, which allow for a suite of deflection responses. Utilizing these deflection responses a safe path for applying loads incrementally through generated waypoints is created with the help of the finite element modeler Ansys and a Python script. The ability for this system to successfully realign the wide scope of truss cases showcases that it is a truly adaptive system. Although this work is primarily proven within a simulation space, efforts to contextualize in a physical system and explore the elements needed to implement this method are also described. Finally, although this research is presented within the scope of damage repair, the final chapter looks to apply this method to other similarly unsupported structures by examining how critical it can be during assembly scenarios.
Airflow sensing with arrays of hydrogel supported artificial hair cells
Sarlo, Rodrigo (Virginia Tech, 2015-01-19)
Arrays of fully hydrogel-supported, artificial hair cell (AHC) sensors based on bilayer membrane mechanotransduction are designed and characterized to determine sensitivity to multiple stimuli. The work draws upon key engineering design principles inspired by the characteristics of biological hair cells, primarily the use of slender hair-like structures as flow measurement elements. Many hair cell microelectromechanical (MEMS) devices to sense fluid flow have already been built based on this principle. However, recent developments in lipid bilayer applications, namely physically encapsulated bilayers and hydrogel interface bilayers, have facilitated the development of AHCs made primarily from biomolecular materials. The most current research in this field of "membrane based AHCs," shows promise, yet still lacks the modularity to create large sensor arrays similar to those in nature. This paper presents a novel bilayer based AHC platform, developed for array implementation by applying some of the core design principles of biological hair cells. These principles are translated into key design, fabrication and material considerations toward improved sensor sensitivity and modularity. Single hair cell responses to base excitation and short air pulses are to investigate the dynamic coupling between hair and bilayer membrane transducer. In addition, a spectral analysis of the AHC system under varying voltages and air flow velocities helps to build simple, predictive models for the sensitivity properties of the AHC. And finally, based on these results, we implement a spatial sensing strategy that involves mapping frequency content to stimulus location by "tuning" linear, three-unit arrays of AHCs. Individual AHC sensors characterization results demonstrate peak current outputs in the nanoamp range and measure flow velocities as high as 72 m/s. Characterization of the AHC response to base excitation and air pulses show that membrane current oscillates with the first three bending modes of the hair. Output magnitudes reflect of vibrations near the base of the hair. A 2 degree-of-freedom Rayleigh-Ritz approximation of the system dynamics yields estimates of 19 N/m and 0.0011 Nm/rad for the equivalent linear and torsional stiffness of the hair's hydrogel base, although double modes suggest non-symmetry in the gel's linear stiffness. The sensor output scales linearly with applied voltage (1.79 pA/V), avoiding a higher-order dependence on electrowetting effects. The free vibration amplitude of the sensor also increases in a linear fashion with applied airflow pressure (3.39 pA/m s??). Array sensing tests show that the bilayers' consistent spectral responses allow for an accurate localization of the airflow source. However, temporal variations in bilayer size affect sensitivity properties and make airflow magnitude estimation difficult. The overall successful implementation of the array sensing method validates the sensory capability of the bilayer based AHC.
Application of Smartphones in Pavement Profile Estimation Using SDOF Model-Based Noisy Deconvolution
Moghadam, Amin; Sarlo, Rodrigo (Hindawi, 2021-03-24)
The new generation of smartphones, equipped with various sensors, such as a three-axis accelerometer, has shown potential as an intelligent, low-cost monitoring platform over the past few years. This paper reports the results of an analytical and experimental study on a proposed SDOF model-based noisy deconvolution (SMND) coupled with a deechoing technique to estimate pavement profiles and to modify their geometry using a smartphone inside a vehicle. In the analytical study, the acceleration response of the car was obtained, where the input was a road profile with an arbitrary pattern. Two different methods, classical band-pass filter and wavelet-denoising technique, were used for denoising the acceleration response. In a 2-step deconvolution process coupled with a deechoing technique, the pavement profile was extracted and compared with the original pavement profile, demonstrating good agreement. In the next step, a parametric study was performed to evaluate the effect of vehicle characteristics and speeds. Then, a case study was conducted in Blacksburg, VA, to evaluate the capability of the proposed method in identifying profile types such as potholes and speed bumps. The acceleration-versus-time responses in vertical direction were recorded using smartphone accelerometers located in a moving vehicle. Then, the proposed approach was applied to remove the echo and vehicle dynamics effects to obtain the pavement profiles and to modify their geometry. The results showed that the proposed approach can remove the echo and vehicle dynamics effect from the response to obtain the pavement profile even if the vehicle characteristics and speed are changed.
Applications of Vibration-Based Occupant Inference in Frailty Diagnosis through Passive, In-Situ Gait Monitoring
Goncalves, Rafael dos Santos (Virginia Tech, 2021-08-30)
This work demonstrates an application of Vibration-Based Occupant Inference (VBOI) in frailty analysis. The rise of both Internet-of-Things (IoT) and VBOI provide new techniques to perform gait analysis via footstep-induced vibration which can be analyzed for early detection of human frailty. Thus, this work provides an application of VBOI to passively track gait parameters (e.g., gait speed) using floor-mounted accelerometers as opposed to using a manual chronometer as it is commonly performed by healthcare professionals. The first part of this thesis describes the techniques used for footstep detection by measuring the power of the footstep-generated vibration waves. The extraction of temporal gait parameters from consecutive footsteps can then be used to estimate temporal features such as cadence and stride time variation. VBOI provides many algorithms to accurately detect when a human-induced vibration event happened, however, spatial information is also needed for many gait parameters used in frailty diagnosis. Detecting where an event happened is a complicated problem because footsteps waves travel and decay in different ways according to the medium (floor system), the number of people walking, and even the walking speed. Therefore, the second part of this work will utilize an energy-based approach of footstep localization in which it is assumed that footstep waves decay exponentially as they travel across the medium. The results from this approach are then used to calculate spatial and tempo-spatial parameters. The main goal of this study is to understand the applicability of VBOI algorithms in gait analysis for frailty detection in a healthcare setting.
Characterizing Building Digital Twins for Facilities Management
Kinani, Toufa (Virginia Tech, 2023-01-30)
Digital twins (DT) describe the integration of the physical and digital worlds with the aim of optimizing real world operations and functions. The digital twin concept has gained increasing attention across industries in the past decade including the building sector. However digital twins remain ambiguous with various existing definitions and characteristics. While DTs include all life cycle phases, ultimately their goal is optimization of operations during the use phase. Of the building life cycle phases, building facilities management (FM) is responsible for considerable costs and energy consumption and has potential for improvement through DT implementation. Along with increased building information modeling (BIM) implementation, recent advances in data driven technologies have encouraged the exploration of DT in the building sector. BIM has been coupled with technologies such as internet of things (IoT), data analytics, and cloud computing to optimize various FM functions often resembling DT. This study has reviewed existing literature on digital twins in facilities management using a structured literature review and characterized similar characteristics and definitions by different authors. Additionally, DT implementation in different FM application areas was quantified and analyzed. Results show that DT implementation in FM is still at nascent stages with major challenges surrounding standardization and data integration.
Data-driven Infrastructure Inspection
Bianchi, Eric Loran (Virginia Tech, 2022-01-18)
Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material segmentation, corrosion condition state segmentation, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel annotation guidelines and benefitted from a novel semi-automated annotation process for both object detection and pixel-level detection models. Using the data obtained from our collected sources, task-appropriate deep learning models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a GAN-Inversion model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not wide-spread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes.
Design of One-Story Hollow Structural Section (HSS) Columns Subjected to Large Seismic Drift
Kong, Hye-Eun (Virginia Tech, 2019-09-24)
During an earthquake, columns in a one-story building must support vertical gravity loads while undergoing large lateral drifts associated with deflections of the vertical seismic force resisting system and deflections of the flexible roof diaphragm. Analyzing the behavior of these gravity columns is complex since not only is there an interaction between compression and bending, but also the boundary conditions are not perfectly pinned or fixed. In this research, the behavior of steel columns that are square hollow structural sections (HSS) is investigated for stability using three design methods: elastic design, plastic hinge design, and pinned base design. First, for elastic design, the compression and flexural strength of the HSS columns are calculated according to the AISC specifications, and the story drift ratio that causes the interaction equation to be violated for varying axial force demands is examined. Then, a simplified design procedure is proposed; this procedure includes a modified interaction equation applicable to HSS column design based on a parameter, Pnh/Mn, and a set of design charts are provided. Second, a plastic hinge design is grounded in the concept that a stable plastic hinge makes the column continue to resist the gravity load while undergoing large drifts. Based on the available test data and the analytical results from finite element models, three limits on the width to thickness ratios are developed for steel square HSS columns. Lastly, for pinned base design, the detailing of a column base connection is schematically described. Using FE modeling, it is shown that it is possible to create rotational stiffness below a limit such that negligible moment develops at the column base. All the design methods are demonstrated with a design example
Dual-purpose procedure for bridge health monitoring and weigh-in-motion used for multiple-vehicle events
Moghadam, Amin; AlHamaydeh, Mohammad; Sarlo, Rodrigo (Elsevier, 2023-04)
Current literature on integrating structural health monitoring (SHM) systems with Bridge-Weigh-in-Motion (BWIM) does not extend to multiple-vehicle events. Also, in most of the SHM-BWIM systems, the trucks’ transverse position change is not appropriately considered as a potential source of false alarms. In this study, a multiple-presence dual-purpose (MPDP) SHM approach is proposed to monitor bridge integrity using the existing BWIM system sensors in single and multiple-truck events. This approach centers on the influence line (IL) change and applies a recently developed multiple-presence IL (MP-IL) technique to the SHM domain for the first time in the literature. This can effectively handle the multiple presence issue to make the integrated system more practical. Also, the proposed procedure studies the impact of transverse position changes to provide a more realistic bridge health monitoring approach. To show the approach applicability, an existing long-span concrete-box-girder bridge was modeled and validated against a set of experimental data using known large events. Eleven damage scenarios were simulated to evaluate the MPDP approach under single and multiple truck events. Based on the results, the MPDP SHM procedure coupled with the novel MP-IL could effectively detect the damage scenarios in both single and multiple-truck events, even when the transverse position changed.
Effective Prestress Evaluation of the Varina-Enon Bridge Using a Long-Term Monitoring System and Finite Element Model
Brodsky, Rachel Amanda (Virginia Tech, 2020-07-22)
The Varina-Enon Bridge is a cable-stayed, precast, segmental, post-tensioned box girder bridge located in Richmond, Virginia. Inspectors noticed flexural cracking in July of 2012 that prompted concerns regarding long-term prestress losses in the structure. Prestress losses could impact the future performance, serviceability, and flexural strength of the bridge. Accurately quantifying prestress losses is critical for understanding and maintaining the structure during its remaining service life. Long-term prestress losses are estimated in the Varina-Enon Bridge using two methods. The first utilizes a time-dependent staged-construction analysis in a finite element model of the full structure to obtain predicted prestress losses using the CEB-FIP '90 code expressions for creep and shrinkage. The second method involves collecting data from instrumentation installed in the bridge that is used to back-calculate the effective prestress force. The prestress losses predicted by the finite element model were 44.9 ksi in Span 5, 47.8 ksi in Span 6, and 45.3 ksi in Span 9. The prestress losses estimated from field data were 50.0 ksi in Span 5, 48.0 ksi in Span 6, and 46.7 ksi in Span 9. The field data estimates were consistently greater than the finite element model predictions, but the discrepancies are relatively small. Therefore, the methods used to estimate the effective prestress from field data are validated. In addition, long-term prestress losses in the Varina-Enon Bridge are not significantly greater than expected.
Effects of Surface Condition on the Fatigue Behavior of CFRP-to-Steel Joints
Carrera Loza, Bernardo Jose (Virginia Tech, 2023-01-23)
The strengthening of steel bridges using CFRP laminates has become a commonly used technique because of its numerous advantages compared to conventional repairs which involve welding or bolting of new steel plates. These structures will experience some sort of irregular cyclic loading during their lifetime and to analyze these complex loading cases, small scale testing is used to evaluate the fatigue performance between the steel substrate, adhesive layer and the CFRP laminate. In this research, double-strap joints (DSJ) were fabricated consisting of two high-modulus CFRP laminates and ASTM A36 steel plates bonded using a two-part epoxy adhesive. Two types of steel surface conditions were considered to evaluate the fatigue behavior under constant force amplitudes. Roughness on the steel substrate was achieved by ½ in (13 mm) diameter pits approximately 1/8 in (3.18 mm) deep to simulate an irregular surface. The results show that the surface condition has marginal influence on the total life of the specimens. To assess the damage accumulation in the DSJ, phenomenological methods like the nonlinear strength wearout Model (NLSW) and stiffness degradation were used. It was found that residual strength and stiffness decreased in a non-linear fashion. A non-linear model was used that agrees well with the experimental results and can be used to predict the residual strength of the specimens under variable amplitude fatigue (VAF).
Evaluating Trust in AI-Assisted Bridge Inspection through VR
Pathak, Jignasu Yagnesh (Virginia Tech, 2024-01-29)
The integration of Artificial Intelligence (AI) in collaborative tasks has gained momentum, with particular implications for critical infrastructure maintenance. This study examines the assurance goals of AI—security, explainability, and trustworthiness—within Virtual Reality (VR) environments for bridge maintenance. Adopting a within-subjects design approach, this research leverages VR environments to simulate real-world bridge maintenance scenarios and gauge user interactions with AI tools. With the industry transitioning from paper-based to digital bridge maintenance, this investigation underscores the imperative roles of security and trust in adopting AI-assisted methodologies. Recent advancements in AI assurance within critical infrastructure highlight its monumental role in ensuring safe, explainable, and trustworthy AI-driven solutions.
An Exploration of Nonlinear Locally Resonant Metamaterials with Electromechanical and Topological elements
Malla, Arun Lee (Virginia Tech, 2024-07-02)
In recent years, the study of metamaterials has been a subject of much interest, with acoustic metamaterials being applied to a wide range of applications. This utility is in part due to the incorporation of various elements in their design. The addition of local resonators provides greater versatility in controlling vibrations. Nonlinear elements introduce features such as discrete breathers and frequency shift. Electromechanical metamaterials have been established to have great potential for use in simultaneous energy harvesting in addition to vibration control. Furthermore, metamaterials with quasiperiodic patterning have been shown to possess useful properties such as edge-localized modes. However, no works investigate the interaction between all these elements, especially in the nonlinear regime. In this work, we investigate a unique metamaterial with local resonators, nonlinearity, electromechanical elements, and quasiperiodicity. The proposed metamaterial is examined using both analytical and numerical techniques in order to firmly establish the effects of each element. First, a nonlinear metamaterial with electromechanical local resonators is studied using the perturbation method of multiple scales, wavepacket excitation and direct integration, and specto-spatial processing techniques. The effect of the electromechanical local resonators is established for both the linear and nonlinear regimes, notably including the addition of new bandgaps and pass bands. The influence of electrical parameters on the system dynamics is explored through parametric analysis, demonstrating their use in tuning the system response. It is also shown that nonlinear phenomena such as localized solitons and frequency shift are present in the voltage response of the electromechanical metamaterial. Next, a nonlinear metamaterial with local resonators and quasiperiodicity is investigated using the method of multiple scales as well as numerical solution of the method of harmonic balance. Topological features stemming from quasiperiodicity are observed in the linear and nonlinear regimes. The presence of local resonators is shown to result in an additional, topologically trivial bandgap. The influence of quasiperiodic parameters and the source of quasiperiodicity on the system's band structure and mode shapes are established in both the linear and nonlinear regimes. Nonlinearity is also shown to affect topological features such as edge modes, resulting in amplitude dependence that can affect the localization of these modes in the nonlinear regime. Finally, a metamaterial with nonlinearity, electromechanical local resonators, and quasiperiodic patterning is modeled and investigated. Multiple configurations are examined, including different shunt circuits coupled to the electromechanical resonators and different sources of quasiperiodic patterning. It is shown that electromechanical local resonators produce two topologically trivial bandgaps, compared to the single trivial bandgap of the purely mechanical resonator. The influence of mechanical, electrical, and quasiperiodic parameters is explored to establish the effects of these parameters on bandgap formation in the linear regime. The behavior of the metamaterial in the nonlinear regime was found to be consistent with a purely mechanical system, with no adverse effects from the presence of electromechanical elements. The impact of nonlinear and quasiperiodic phenomena on energy harvesting is also investigated. Through exploration of this unique metamaterial, it is shown that beneficial features from all elements can be present at once, resulting in a versatile metamaterial with great potential for numerous applications.
Flexural Behavior of Cold-Formed and Hot-Rolled Steel Sheet Piling Subjected to Simulated Soil Pressure
Ritthiruth, Pawin (Virginia Tech, 2021-01-11)
Hot-rolled sheet piling has long-been believed to have a better flexural performance than cold-formed sheet piling based on a test conducted by Hartman Engineering twenty years ago. However, cold-formed steel can have similar strength to the hot-rolled steel This experimental program studied the flexural behavior of hot-rolled and cold-formed steel sheet pilings. This program quantified the influence of transverse stresses from soil pressures on the longitudinal flexural strength. Four cross-sections with two pairs of equivalent sectional modulus were investigated. Sheet-piling specimens were subjected to simulated soil pressure from an air bladder loaded transversely to their longitudinal axis. The span lengths were varied, while the loading area remains unchanged to examine the effect of different transverse stresses. Lateral bracings were provided at discrete locations to establish a sheet piling wall behavior and allow the development of transverse stresses. Load-pressure, load-deflection, load-strain, and moment-deflection responses were plotted to demonstrate the behavior of each specimen. The moment-deflection curves were then normalized to the corresponding yield stress from tensile coupon tests to make a meaningful comparison. The results indicate that transverse stresses influence the flexural capacity of the sheet pilings. The longer span length has less amount of transverse strains, resulting in a higher moment capacity. The hot-rolled sheet pilings have better flexural performance also because of less transverse strains.
High-Resolution, High-Frequency Modal Analysis for Instrumented Buildings
Sarlo, Rodrigo (Virginia Tech, 2018-08-02)
Civil infrastructure failure is hard to predict, both in terms of occurrence and impact. This is due to combination of many factors, including highly variable environmental and operational conditions, complex construction and materials, and the sheer size of these structures. Often, the mitigation strategy is visual inspection and regular maintenance, which can be time-consuming and may not address root causes of failure. One potential solution to anticipating infrastructure failure and mitigating its consequences is the use of distributed sensors to monitor the physical state of a structure, an area of research known commonly as structural health monitoring, or SHM. This approach can be applied in a variety of contexts: safety during and after natural disasters, evaluation of building construction quality and life-cycle assessment for performance based design frameworks. In one way or another, SHM methods always require a ``baseline,'' a set of physical features which describes the behavior of a healthy structure. Often, the baseline is defined in terms of modal parameters: natural frequencies, damping ratios, and mode shapes. Although changes in modal parameters are indicative of structural damage, they are also indicative of a slew of non-damage factors, such as signal-to-noise ratio, environmental conditions, and the characteristics of forces exciting the structure. In many cases, the degree of observed modal parameter changes due to non-damage factors can be much greater than that due to damage itself. This is especially true of low-frequency modal parameters. For example, the fundamental frequency of a building is more sensitive to global influences like temperature than local structural changes like a cracked column. It has been proposed that extracting modal parameters at higher frequencies may be the key to improving the damage-sensitivity of SHM methods. However, for now, modal analysis of civil structures has been limited to low frequency ambient excitation and sparse sensor networks, due to practical limitations. Two key components for high-frequency modal analysis have yet to be studied: 1) Sufficient excitation at high frequencies and 2) high-resolution (high sensor density) measurements. The unifying goal of this work is to expand modal analysis in these two areas by applying novel instrumentation and experimental methods to two full-scale buildings, Goodwin Hall and Ernest Cockrell Jr. Hall. This enables realistic, practical insights into the limitations and benefits of the high-frequency SHM approach. Throughout, analyses are supported through the novel integration of uncertainty quantification techniques which so far has been under-utilized in the field. This work is divided into three experimental areas, with approaches centering on the identification of modal parameters. The first area is the application of high spacial resolution sensor networks in combination to ambient vibration testing. The second is the implementation of a robust automation and monitoring strategy for complex dynamic structures. The third is the testing of a novel method for performing experimental modal analysis on buildings emph{in situ}. The combination of results from these experiments emphasizes key challenges in establishing reliable high-frequency, high-resolution modal parameter ``baselines'' for structural health monitoring (SHM) of civil infrastructure. The first study presented in this work involved the identification of modal parameters from a five-story building, Goodwin Hall, using operational modal analysis (OMA) on ambient vibration data. The analysis began with a high spacial density network of 98 accelerometers, later expanding this number to 117. A second extensional study then used this data as reference to implement a novel automation method, enabling the identification of long-term patterns in the building's response behavior. Three dominant sources of ambient excitation were identified for Goodwin Hall: wind, human-induced loading, and consistent low-level vibrations from machinery, etc. It was observed that the amplitude of excitation, regardless of source, had significant effects on the estimated natural frequencies and damping ratios. Namely, increased excitation translated to lower natural frequencies and higher damping. In addition, the sources had different characteristics in terms of excitation direction and bandwidth, which contributed to significantly different results depending on the ambient excitation employed. This has significant implications for ambient-based methods that assume that all ambient vibrations are broadband random noise. The third and final study demonstrated the viability of emph{in situ} seismic testing for controlled excitation of full-scale civil structures, also known as experimental modal analysis (EMA). The study was performed by exciting Ernest Cockrell Jr. Hall in Austin, Texas with both vertical and lateral ground waves from seismic shaker truck, T-Rex. The EMA results were compared to a standard operational modal analysis (OMA) procedure which relies on passive ambient vibrations. The study focused on a frequency bandwidth from 0 to 11 Hz, which was deemed high frequency for such a massive structure. In cases were coherence was good, the confidence comparable or better than OMA, with the added advantage that the EMA tests took only a fraction of the time. The ability to control excitation direction in EMA enabled the identification of new structural information that was not observed OMA. It is proposed that the combination of high spacial resolution instrumentation and emph{in situ} excitation have the potential to achieve reliable high-frequency characterization, which are not only more sensitive to local damage but also, in some cases, less sensitive to variations in the excitation conditions.
Human Computer Interaction Design for Assisted Bridge Inspections via Augmented Reality
Smith, Alan Glynn (Virginia Tech, 2024-06-03)
To address some of the challenges associated with aging bridge infrastructure, this dissertation explores the development and evaluation of a novel tool for bridge inspections leveraging Augmented Reality (AR) and computer vision (CV) technologies to facilitate measurements. Named the Wearable Inspection Report Management System (WIRMS), the system supports various data entry methods and an adaptable automation workflow for defect measurements, showcasing AR's potential to improve bridge inspection efficiency and accuracy. Within this context, the work's main research goal is to understand the difference in performance between traditional field data collection methods (i.e. pen and paper) and automated methods like spoken data entry and CV-based structural defect measurements. In case of CV assistance, emphasis was placed on human-computer interaction (HCI) to understand whether partial, collaborative automation could address some of the limitations of fully automated inspection methods. The project began with comprehensive data collection through interviews, surveys, and observations at bridge sites, which informed the creation of a Virtual Reality (VR) prototype. An initial user study tested the feasibility of using voice commands for data entry in the AR environment but found it impractical. A second user study focused on optimizing interaction methods for virtual concrete crack measurements by testing different degrees of automated CV assistance. As part of this effort, major technical contributions were made to back-end technologies and CV algorithms to improve human-machine collaboration and ensure the accuracy of measurements. Results were mixed, with larger degrees of automation resulting in significant reductions in inspection time and perceived workload, but also significant increases in the amount of measurement error. The latter result is strongly associated with a lack of field robustness of CV methods, which can under-perform if conditions are not ideal. In general, hybrid techniques which allow the user to correct CV results were seen as the most favorable. Field validations with bridge inspectors showed promising potential for practical field implementation, though further refinement is needed for broader deployment. Overall, the research establishes a viable path for making AR a central component to future inspection practices, including digital data collection, automation, data analytics, and other technologies currently in development.
Integration of Traffic and Structural Health Monitoring Systems Using A Novel Nothing-On-Road (NOR) Bridge-Weigh-In-Motion (BWIM) System
Moghadam, Amin (Virginia Tech, 2022-07-27)
Bridges are vital components of the U.S. transportation network. However, every year, the transportation agencies report a large number of aging bridges that are structurally damaged. Also, evolving traffic and particularly the overloaded traversing traffic can threaten the bridges' integrity and safety further. Bridge weight-in-motion (BWIM) is a system that takes the instrumented bridges as a scale and uses the structure response to compute the trucks' weights with no interruption in the traffic. In a particular type of BWIM, called nothing-on-road BWIM (NOR-BWIM), only a few weighing sensors should be installed under the bridge top slab. Since nothing will be installed on the road surface, NOR-BWIM addresses some of the main challenges of pavement-based WIM and traditional BWIM systems. These include lane closure, interruption to the traveling traffic, and sensitivity to daily tire impacts and harsh weather conditions. It also provides a portable solution with a less labor-intense installation process. Additionally, previous studies have shown that BWIM systems are versatile candidates for overcoming the critical challenges of structural health monitoring (SHM) across various types of bridges. The integration of the two systems is more cost-effective with improved performance; thus, it is more attractive to practitioners. However, the current BWIMs have serious shortcomings that make the integrated SHM-BWIM systems impractical in real-world long-span bridges. In the first two phases of this study, these shortcomings are addressed and a novel BWIM system is proposed. Then, the novel BWIM system is used for SHM in the third phase of the study. These shortcomings are explained as follows. Most studies are performed on short/medium-span T-beam and slab-on-girder bridges. However, longer span lengths, construction methods, different slab properties (e.g., stiffness), etc., can affect the efficacy of the NOR-BWIM. Thus, there is a need to further evaluate this technique on other bridges, such as concrete-box-girder bridges with longer spans, in an effort to ascertain whether or not NOR-BWIM systems would still work effectively on such bridges. Thus, the first phase presents an experimental investigation conducted for a long-span concrete-box-girder bridge (144 m span) called the Smart Road bridge. A total of 18 experimental tests were performed on the bridge. Moreover, a cost-effective sensor placement was developed. It was found that the number of axles is detectable with an accuracy of 100%. Moreover, the estimated mean-absolute-error for axle spacing, vehicle speed, and gross vehicle weight were 4.6%, 2.6%, and 4.6%, respectively. Lastly, it was also demonstrated that the developed cost-effective NOR-BWIM system is capable of lane identification and truck position detection. The second main issue with the existing BWIM approaches is their limited suitability for simultaneous multiple-vehicle cases on multiple-lane bridges. To address this limitation, in the second phase of this study, a novel BWIM approach is proposed. The approach is built around the removal of the non-localized portion of the strain response. Keeping the localized portion of the strain response, which is not sensitive to nearby loads, allowing for enhanced detection. The superiority of this approach stems from its capability to handle multiple-vehicle cases. These may present with an arbitrary number of trucks and light-weight vehicles, simultaneously passing the bridge in any arbitrary pattern or configuration. To show the applicability of the approach, a finite element (FE) model of a long-span concrete-box-girder bridge was simulated. The model was validated against the experimental data collected under known large events. The FE model was then used to consider single-truck events (for proof-of-concept) as well as complex multiple-truck traffic cases. These included in-one-row trucks, zigzag patterns, side-by-side trucks, and a combination of several trucks with several light-weight vehicles present. The results demonstrated that the proposed BWIM approach is capable of detecting the axle weights and gross vehicle weight (GVW) of the traversing trucks. Based on all complex multiple-truck cases, the overall mean absolute errors for GVW and axle weight estimations were 4.5% and 11.3%, respectively. In the last phase, a multiple-presence dual-purpose (MPDP) SHM approach was proposed to monitor the integrity of bridges using the BWIM system existing sensors. This approach centers on the influence line (IL) change and uses a developed multiple-presence IL (MP-IL) technique (in the second phase) for SHM application. This can effectively handle the multiple presence issue of the current integrated SHM-BWIM systems to make them more practical. Also, unlike many SHM-BWIM studies, noise and transverse position change (defined as false damage indicators) were included in the proposed procedure to provide a more realistic bridge health monitoring approach. To show the applicability of the approach, a similar FE model simulated in the second phase was used. The model was then used to evaluate the MPDP approach under single and multiple truck events. Eleven damage scenarios were simulated, and three SHM trucks (a 3-axle, a 4-axle, and a 5-axle) were used to improve the SHM accuracy. Also, an updated sensor placement was proposed to effectively work for both BWIM and SHM applications in both single and multiple-truck events. According to the results, the MPDP SHM procedure coupled with the novel MP-IL and the proposed sensor placement could effectively detect the damage scenarios in both single and multiple-truck events. Also, it was shown that using several independent SHM trucks can make the monitoring process more effective.
Long-Term Monitoring and Evaluation of the Varina-Enon Bridge
Dahiya, Ankuj (Virginia Tech, 2021-03-30)
To make sound decisions about the remaining life of a structure, the precise calculation of the prestress losses is very important. In post-tensioned structures, the prestress losses due to creep and shrinkage can cause serviceability issues and can reduce flexural capacity. The Varina-Enon Bridge is a cable-stayed, precast, segmental, post-tensioned box girder bridge located in Richmond, Virginia. Observation of flexural cracks in the bridge by inspectors promoted a study regarding long-term prestress losses in the structure. For understanding and sustaining the structure throughout its remaining service life, accurately quantifying prestress losses is important. Two approaches are used to predict long-term prestress losses on the Varina-Enon Bridge. The first approach involves a finite element computer model of the bridge which run a timedependent staged-construction analysis to obtain predicted prestress losses using the CEB-FIP '90 code expressions for creep and shrinkage. The second approach involves the compilation of data from instrumentation mounted in the bridge to back calculate the effective prestress force. The analysis using the computer model predicted the prestress losses as 44.6 ksi in Span 5, 47.9 ksi in Span 6, 45.3 ksi in Span 9, and 45.9 ksi in Span 11. The prestress losses estimated from field data were 50.0 ksi in Span 5, 48.0 ksi in Span 6, 46.7 ksi in Span 9, and 49.1 ksi in Span 11. It can be seen that relative to the results of field data estimations, the finite element analyses underestimated prestress loss, but given the degree of uncertainty in each form of estimation, the results are considered to fit well.
On the Effectiveness of Dimensionality Reduction for Unsupervised Structural Health Monitoring Anomaly Detection
Soleimani-Babakamali, Mohammad Hesam (Virginia Tech, 2022-04-19)
Dimensionality reduction techniques (DR) enhance data interpretability and reduce space complexity, though at the cost of information loss. Such methods have been prevalent in the Structural Health Monitoring (SHM) anomaly detection literature. While DR is favorable in supervised anomaly detection, where possible novelties are known a priori, the efficacy is less clear in unsupervised detection. In this work, we perform a detailed assessment of the DR performance trade-offs to determine whether the information loss imposed by DR can impact SHM performance for previously unseen novelties. As a basis for our analysis, we rely on an SHM anomaly detection method operating on input signals' fast Fourier transform (FFT). FFT is regarded as a raw, frequency-domain feature that allows studying various DR techniques. We design extensive experiments comparing various DR techniques, including neural autoencoder models, to capture the impact on two SHM benchmark datasets exclusively. Results imply the loss of information to be more detrimental, reducing the novelty detection accuracy by up to 60\% with autoencoder-based DR. Regularization can alleviate some of the challenges though unpredictable. Dimensions of substantial vibrational information mostly survive DR; thus, the regularization impact suggests that these dimensions are not reliable damage-sensitive features regarding unseen faults. Consequently, we argue that designing new SHM anomaly detection methods that can work with high-dimensional raw features is a necessary research direction and present open challenges and future directions.
A Physically Informed Data-Driven Approach to Analyze Human Induced Vibration in Civil Structures
Kessler, Ellis Carl (Virginia Tech, 2021-06-24)
With the rise of the Internet of Things (IoT) and smart buildings, new algorithms are being developed to understand how occupants are interacting with buildings via structural vibration measurements. These vibration-based occupant inference algorithms (VBOI) have been developed to localize footsteps within a building, to classify occupants, and to monitor occupant health. This dissertation will present a three-stage journey proposing a path forward for VBOI research based on physically informed data-driven models of structural dynamical systems. The first part of this dissertation presents a method for extracting temporal gait parameters via underfloor accelerometers. The time between an occupant's consecutive steps can be measured with only structural vibration measurements with a similar accuracy to current gait analysis tools such as force plates and in-shoe pressure sensors. The benefit of this, and other VBOI gait analysis algorithms, is in their ease of use. Gait analysis is currently limited to a clinical setting with specialized measurement systems, however VBOI gait analysis provides the ability to bring gait analysis to any building. VBOI algorithms often make some simplifying assumptions about the dynamics of the building in which they operate. Through a calibration procedure, many VBOI algorithms can learn some system parameters. However, as demonstrated in the second part of this dissertation, some commonly made assumptions oversimplify phenomena present in civil structures such as: attenuation, reflections, and dispersion. A series of experimental and theoretical investigations show that three common assumptions made in VBOI algorithms are unable to account for at least one of these phenomena, leading to algorithms which are more accurate under certain conditions. The final part of this dissertation introduces a physically informed data-driven modelling technique which could be used in VBOI to create a more complete model of a building. Continuous residue interpolation (CRI) takes FRF measurements at a discrete number of testing locations, and creates a predictive model with continuous spatial resolution. The fitted CRI model can be used to simulate the response at any location to an input at any other location. An example of using CRI for VBOI localization is shown.
Predictive Simulations of the Impedance-Matched Multi-Axis Test Method Using Data-Driven Modeling
Moreno, Kevin Joel (Virginia Tech, 2020-10-02)
Environmental testing is essential to certify systems to withstand the harsh dynamic loads they may experience in their service environment or during transport. For example, satel- lites are subjected to large vibration and acoustic loads when transported into orbit and need to be certified with tests that are representative of the anticipated loads. However, tra- ditional certification testing specifications can consist of sequential uniaxial vibration tests, which have been found to severely over- and under-test systems needing certification. The recently developed Impedance-Matched Multi-Axis Test (IMMAT) has been shown in the literature to improve upon traditional environmental testing practices through the use of multi-input multi-output testing and impedance matching. Additionally, with the use of numerical models, predictive simulations can be performed to determine optimal testing pa- rameters. Developing an accurate numerical model, however, requires precise knowledge of the system's dynamic characteristics, such as boundary conditions or material properties. These characteristics are not always available and would also require additional testing for verification. Furthermore, some systems may be extremely difficult to model using numerical methods because they contain millions of finite elements requiring impractical times scales to simulate or because they were fabricated before mainstream use of computer aided drafting and finite element analysis but are still in service. An alternative to numerical modeling is data-driven modeling, which does not require knowledge of a system's dynamic characteris- tics. The Continuous Residue Interpolation (CRI) method has been recently developed as a novel approach for building data-driven models of dynamical systems. CRI builds data- driven models by fitting smooth, continuous basis functions to a subset of frequency response function (FRF) measurements from a dynamical system. The resulting fitted basis functions can be sampled at any geometric location to approximate the expected FRF at that location. The research presented in this thesis explores the use of CRI-derived data-driven models in predictive simulations for the IMMAT performed on a Euler-Bernoulli beam. The results of the simulations reveal that CRI-derived data-driven models of a Euler-Bernoulli beam achieve similar performance when compared to a finite element model and make similar decisions when deciding the excitation locations in an IMMAT.

Browsing by Author "Sarlo, Rodrigo"

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