Satish Nagarajaiah

Suppressing the ultra-low frequency vibrations of flexible structures is a vital yet challenging task to improve structural safety and resilience. Inspired by latching control developed for wave energy converters, emerging latching-based vibration control devices, such as latched mass dampers (LMDs) and pendulum LMDs (PLMDs), were recently proposed, which have adaptive frequency tuning, limited stroke demand, and minimal power consumption, presenting a promising solution to ultra-low frequency vibration control problems. Given that the performance of these control devices has been illustrated theoretically and numerically in literature, this paper presents the first experimental validations of the vibration control performance of a PLMD installed in a flexible structure. The experimental setup included a PLMD prototype, a flexible bench-scale shear frame, a real-time closed-loop control system, a data acquisition system, and a shake table. The frame was tested under free and forced vibrations, and the experimental results were compared with the numerical simulations. Two different control strategies, the characteristics of effective control force, and the effect of time delay are discussed in this paper. The experimental results verify the advantages of using PLMDs to control ultra-low frequency vibrations and their potential for applications to various flexible structures. The findings of this paper present a major step toward the successful implementations and applications of innovative latching-based vibration control devices.

ABSTRACT This paper proposes a nonstationary stochastic seismic optimization framework and investigates the performance enhancement of base‐isolated nuclear power plants (NPPs) equipped with negative stiffness amplifying dampers (NSADs), while explicitly accounting for dynamic soil–structure‐interaction (SSI). A practical configuration for the application of NSADs in base‐isolated NPP is presented, and the governing equations of motion incorporating NSAD and SSI effects are formulated. By integrating nonstationary seismic excitation with the dynamic representation of the isolated NPP, a time‐variant augmented system is established. A multi‐objective optimization framework is then developed to address the trade‐off between minimizing superstructure acceleration and base isolator drift. Numerical results for a 1000 MW pressurized water reactor demonstrate that the proposed NSAD system effectively reduces superstructure accelerations compared to the conventional base isolation (BI) design, owing to its negative stiffness feature. Meanwhile, base isolator drift is effectively controlled through the damping amplification mechanism of the NSAD, even with a significantly smaller damping demand. For the NPP model considered in this paper, SSI has pronounced influence on superstructure‐dominated mode particularly for containment structure of non‐isolated, base‐isolated, and NSAD‐enhanced NPPs, whereas its effect on the isolation‐dominated mode is negligible.

Liquid storage structures, typically designed with thin walls, are widely used in civil engineering, aerospace, and related fields, such as large LNG tanks, oil transport vessels, and fuel tanks. Liquid sloshing constitutes a critical determinant in the structural integrity assessment of liquid storage tanks, having been the main subject of extensive research in fluid‐structure interaction studies. To better understand the liquid sloshing characteristics of the storage tank, water pressure and wave height sensors were implemented in a shaking table test for quantitative hydrodynamic monitoring. The results show that liquid sloshing exhibits varying responses at different heights, with distinct differences in the time‐history curves of dynamic water pressure and frequency spectra between the bottom and top regions under different seismic wave excitations. The region near the upper liquid layer just below the free surface exhibits the maximum standard deviation and spectral amplitude of dynamic water pressure along the liquid height in the primary vibration direction, particularly under El Centro and Tianjin wave excitations. This indicates that the upper liquid near the free surface experiences larger fluctuations and intensified spectral energy accumulation under seismic excitations. Intense liquid sloshing poses a considerable risk to the stability and safety of these structures, particularly when subjected to external excitations. Such disturbances can lead to fluctuations in sloshing height and dynamic water pressure, which eventually jeopardize structural integrity. To address this challenge, a novel composite partition structure incorporating wire mesh is proposed to reduce these effects. Comparative analysis of parameters of dynamic water pressure, wavenumber, and standard deviation is conducted to compare following the implementation of the measures. Experimental results reveal that the partitions and wire mesh suppress liquid sloshing, effectively reducing the maximum wave height by 20%–50%. As the mesh spacing decreases, the damping effect is further enhanced, leading to greater sloshing suppression efficiency, but it amplifies localized high‐frequency disturbances. In addition, the results of the original tank were in excellent agreement with those from the three‐dimensional numerical simulation analysis.

To address the damping limitations of conventional damped outrigger (CDO) systems in tall buildings and reduce the damping force demand of viscous damper installed at the outrigger end, a novel negative stiffness damped outrigger with damping amplification (NSDO-DA) has been developed, fabricated, and dynamically tested. The results demonstrated its ability to generate an adaptive negative stiffness effect and enhanced damping performance. This paper presents a hierarchical numerical simulation of the NSDO-DA system, spanning from its key component to its application in tall buildings, using a detailed finite element model. Specifically, the key components, including disk spring pairs and disk spring brace (DSB), were modeled and validated against cyclic test results. Furthermore, numerical finite-element models of three outrigger systems—pure negative stiffness mechanism (NS), negative stiffness damped outrigger (NSDO), and NSDO-DA—were developed using three-dimensional (3D) solid elements. These models were validated through dynamic test results and employed for a parametric study to overcome the limitations of testing facilities and theoretical assumptions. Finally, the seismic performance of tall buildings incorporating the NSDO-DA system was assessed through simulations of a 50-story tall building example.

Sparse system identification of nonlinear dynamic systems is still challenging, especially for stiff and high-order differential equations for noisy measurement data. The use of highly correlated functions makes distinguishing between true and false functions difficult, which limits the choice of functions. In this study, an equation discovery method has been proposed to tackle these problems. The key elements include a) use of B-splines for data fitting to get analytical derivatives superior to numerical derivatives, b) sequentially regularized derivatives for denoising (SRDD) algorithm, highly effective in removing noise from signal without system information loss, c) uncorrelated component analysis (UCA) algorithm that identifies and eliminates highly correlated functions while retaining the true functions, and d) physics-informed spline fitting (PISF) where the spline fitting is updated gradually while satisfying the governing equation with a dictionary of candidate functions to converge to the correct equation sequentially. The complete framework is built on a unified deep-learning architecture that eases the optimization process. The proposed method is demonstrated to discover various differential equations at various noise levels, including three-dimensional, fourth-order, and stiff equations. The parameter estimation converges accurately to the true values with a small coefficient of variation, suggesting robustness to the noise.

In this study, a negative stiffness brace (NSB) device composed of a series of precompressed springs, gap-spring assemblies, and links to achieve the desired negative stiffness behavior suitable for application in structural systems is investigated analytically and experimentally. The NSB achieves a high force magnification by using the geometry, harnessing the spring force at both the ends, and through the usage of multiple precompressed springs and links in series. The series arrangement significantly reduces the stiffness requirements of the precompressed springs. The NSB has a compact arrangement of components, which reduces space consumption as well as allows easy installation. In this study, the analytical model describing the behavior of the NSB is presented and the effect of various parameters on the behavior is investigated. A scaled NSB device is tested, and experimental results of the behavior of the NSB are presented and used to validate the analytical model. Further, the analytical model of a frame connected to the NSB–gap-spring assembly (GSA) system is developed and validated. The efficacy of the NSB in reducing structural response was studied numerically by considering a single-degree-of-freedom system. Negative stiffness devices and supplemental damping devices have been recently studied extensively for the protection of structural systems subjected to wind and seismic excitations because the combination can significantly reduce accelerations, interstory drifts, and base shears. NSB with supplemental dampers has significant potential for application in structural systems.

Abstract The second-order blind identification (SOBI) and its variants have been extensively explored for output-only modal identification of civil structures under varied excitations. At the core of these methods is the matrix joint approximate diagonalization (JAD) technique, while their efficiency and accuracy are largely determined by how the target-matrices for JAD are constructed from multi-channel structural responses. This study first formulates the JAD framework for structural identification, where different techniques in formulating the target-matrices are summarized and mathematical tools to conduct JAD are also presented. Then two novel ways stemming from conventional identification methods are presented as alternatives to construct the target-matrices for ambient identification, to maintain a low-order formulation and even avoiding the formation of covariance matrix. Subsequently, in view of the large number of candidate target-matrices which are analytically usable, a guiding principle is proposed for selecting reliable target-matrices, where the closeness of the eigenvectors of the target-matrices are compared beforehand, therefore eliminating of distorted target-matrices and also improving the efficiency of the subsequent JAD. The proposed techniques are applied to modal identification of the Donghai Bridge from monitoring data and the proposed JAD-based methods are compared in this context. The results suggest the effectiveness of the proposed techniques and also provide a performance evaluation of these methods.

ABSTRACT This paper proposed a novel negative stiffness damped outrigger with damping amplification (NSDO‐DA) through a smart combination of an L‐shape lever, a precompressed disc spring brace (DSB), and a viscous damper. A theoretical model of the NSDO‐DA considering nonlinear adaptive negative stiffness, damping amplification of nonlinear viscous dampers, and frictions were established. Cyclic tests of disc spring pairs and DSB were presented to verify the feasibility of using DSB as the precompression component of the NSDO‐DA. Subsequently, large‐scale dynamic tests of five different outrigger systems were conducted involving (i) conventional damped outrigger (CDO), (ii) amplified damped outrigger (ADO), (iii) purely negative stiffness mechanism, (iv) negative stiffness damped outrigger (NSDO), and (v) NSDO‐DA. Then, discussions on the dynamic test results and validations of the NSDO‐DA model were provided. The major contributions of this paper are the proposal of the NSDO‐DA and the experimental validation of its two special features: (i) producing negative stiffness force in the parallel direction of the precompression force rather than in the perpendicular direction, making the physical configuration more concise and condensed for outrigger application; (ii) sharing the amplification mechanism of the L‐shape lever for both the viscous damper and the negative stiffness mechanism, leading to an additional damping amplification mechanism for the damper. Moreover, the adaptive stiffness behavior and the frequency‐independent feature of the proposed negative stiffness mechanism were successfully validated by the dynamic tests.

Due to its simple and beautiful architectural appearance, the stress‐ribbon bridge (SRB) has been gradually built around the world as a pedestrian or traffic bridge. However, as characterized by low bending stiffness and low damping ratio features, SRB is prone to the dynamic effects of external excitations, such as pedestrians, vehicles, and/or winds. To control the vertical vibration of the SRB, a rail‐damper system is proposed in this study. In the proposed scheme, the rotation of the handrails triggered by the flexural deformation of the SRB is utilized to drive the viscous dampers installed between the adjacent handrails. The governing equations of the proposed control system are established. The key design parameters and their influences on the dynamic properties of the control system are systematically investigated. The control performances of the proposed rail‐damper system are further investigated through an SRB numerical model subjected to pedestrian excitations. It is discovered that the rail‐damper system can offer considerable supplemental damping to the structural modes through reasonable design, achieving satisfactory control performances. To gain the excellent effect of the proposed rail‐damper system in real applications, a nondimensional rail stiffness of no less than 1000 is recommended, and the stiffness of the damper should be controlled as small as possible.

Abstract Cement is one of the most widely used building materials due to its strength and durability. However, conventional cement has a very high setting time, which makes it less attractive for applications requiring quick-setting behavior, such as rapid construction, emergency repairs, underwater construction, and 3D printing. The present study proposes hexagonal boron nitride (hBN) as a potential accelerant to impart quick-setting behavior to conventional cement. hBN is a two-dimensional material renowned for its exceptional thermal conductivity, chemical stability, and mechanical strength. Our study investigates the incorporation of hBN nanoparticles into class G Portland cement to enhance its mechanical, thermal, and rheological properties. Our experimental investigation demonstrates that hBN acts as an excellent accelerant in cement by reducing the dormancy period by up to 2 h and enhancing the overall setting kinetics. This makes hBN a promising candidate for quick-setting cement applications. Further thermal analysis reveals an improved heat dissipation capability, with lower surface temperatures and enhanced structural integrity due to reduced porosity and microcrack formation. Mechanical testing demonstrates substantial improvements in compressive strength (up to 29%), compressive modulus (up to 45%), and energy absorption capacity (up to 31%) for 1% hBN-reinforced cement compared to neat cement. Moreover, hBN-reinforced 3D-printed cement structures exhibit a 72% increase in compressive strength. The hBN-reinforced cement ink also demonstrates enhanced printability, characterized by superior flow stability, better structural recovery, and reliable shape retention, making it ideal for 3D printing applications.

Displacement plays a crucial role in structural health monitoring (SHM) and damage detection of structural systems subjected to dynamic loads. However, due to the inconvenience associated with the direct measurement of displacement during dynamic loading and the high cost of displacement sensors, the use of displacement measurements often gets restricted. In recent years, indirect estimation of displacement from acceleration and strain data has gained popularity. Several researchers have developed data fusion techniques to estimate displacement from acceleration and strain data. However, existing data fusion techniques mostly rely on system properties like mode shapes or finite element models and require accurate knowledge about the system for successful implementation. Hence, they have the inherent limitation of their applicability being restricted to relatively simple structures where such information is easily available. In this article, B-spline basis functions have been used to formulate a Kalman filter-based algorithm for acceleration and strain data fusion using only elementary information about the system, such as the geometry and boundary conditions, which is the major advantage of this method. Also, the proposed algorithm enables us to monitor the full-field displacement of the system online with only a limited number of sensors. The method has been validated on a numerically generated dataset from the finite element model of a tapered beam subjected to dynamic excitation. Later, the proposed data fusion technique was applied to an experimental benchmark test of a wind turbine blade under dynamic load to estimate the displacement time history. In both cases, the reconstructed displacement from strain and acceleration was found to match well with the response from the FE model.

Machine learning for scientific discovery is increasingly becoming popular because of its ability to extract and recognize the nonlinear characteristics from the data. The black-box nature of deep learning methods poses difficulties in interpreting the identified model. There is a dire need to interpret the machine learning models to develop a physical understanding of dynamic systems. An interpretable form of neural network called Kolmogorov-Arnold networks (KAN) or Multiplicative KAN (MultKAN) offers critical features that help recognize the nonlinearities in the governing ordinary/partial differential equations (ODE/PDE) of various dynamic systems and find their equation structures. In this study, an equation discovery framework is proposed that includes i) sequentially regularized derivatives for denoising (SRDD) algorithm to denoise the measure data to obtain accurate derivatives, ii) KAN to identify the equation structure and suggest relevant nonlinear functions that are used to create a small overcomplete library of functions, and iii) physics-informed spline fitting (PISF) algorithm to filter the excess functions from the library and converge to the correct equation. The framework was tested on the forced Duffing oscillator, Van der Pol oscillator (stiff ODE), Burger's equation, and Bouc-Wen model (coupled ODE). The proposed method converged to the true equation for the first three systems. It provided an approximate model for the Bouc-Wen model that could acceptably capture the hysteresis response. Using KAN maintains low complexity, which helps the user interpret the results throughout the process and avoid the black-box-type nature of machine learning methods.

Shake table tests of single degree of freedom elastic/inelastic structures have confirmed that by adding a negative stiffness device (NSD), capable of exhibiting nonlinear elastic negative stiffness, in conjunction with a viscous damper, the acceleration, inter‐story drifts, and base shear can be reduced significantly. However, little is known about how the presence of NSD/damper in the first story influences the response of higher stories of a multistory structure. In this paper, shake table test results are presented that demonstrate the advantages of NSD/damper in the first story of a multidegree of freedom, three‐story, fixed‐base structure (MDOF‐3SFS). Results confirm that deployment of NSD/damper at the first story leads to significant reductions of acceleration as well as base shear and inter‐story deformations in the MDOF‐3SFS. Essentially, an NSD and a damper in the first floor prevents the transmission of the input energy from the ground motion to the second and third story of the multistory structure by deflecting and dissipating energy. If the first‐story displacements become excessive, NSD stiffens and prevents collapse. The efficacy of the NSD/damper system is further demonstrated by comparing it with the performance of a structure with passive viscous dampers deployed in the first story. In addition to the reduction of base shear and maximum accelerations, the NSD/damper system also restricts large deformations to the first story only, leading to minimal damage to the whole structure. Finally, an NSD‐based bracing system is introduced that can be deployed in new and existing structures.

Sparse system identification of nonlinear dynamic systems is still challenging, especially for stiff and high-order differential equations for noisy measurement data. The use of highly correlated functions makes distinguishing between true and false functions difficult, which limits the choice of functions. In this study, an equation discovery method has been proposed to tackle these problems. The key elements include a) use of B-splines for data fitting to get analytical derivatives superior to numerical derivatives, b) sequentially regularized derivatives for denoising (SRDD) algorithm, highly effective in removing noise from signal without system information loss, c) uncorrelated component analysis (UCA) algorithm that identifies and eliminates highly correlated functions while retaining the true functions, and d) physics-informed spline fitting (PISF) where the spline fitting is updated gradually while satisfying the governing equation with a dictionary of candidate functions to converge to the correct equation sequentially. The complete framework is built on a unified deep-learning architecture that eases the optimization process. The proposed method is demonstrated to discover various differential equations at various noise levels, including three-dimensional, fourth-order, and stiff equations. The parameter estimation converges accurately to the true values with a small coefficient of variation, suggesting robustness to the noise.

Non-contact and full-field strain mapping captures strain across an entire surface, providing a complete two-dimensional (2D) strain distribution without attachment to sensors. It is an essential technique with wide-ranging applications across various industries, significantly contributing to experimental mechanics and structural health monitoring. Although there have been reviews that focus on specific methods, such as interferometric techniques or carbon nanotube-based strain sensors, a comprehensive comparison that evaluates these diverse methods together is lacking. This paper addresses this gap by focusing on strain mapping techniques specifically used in experimental mechanics and structural health monitoring. The fundamental principles of each method are illustrated with specific applications. Their performance characteristics are compared and analyzed to highlight strengths and limitations. The review concludes by discussing future challenges in strain mapping, providing insights into potential advancements and developments in this critical field.

Abstract Targeting the great demand for adding non‐structural elevators to old residential buildings, this article proposes an updated configuration of tuned mass damper inerter (U‐TMDI) applied in external non‐structural elevators. An analog 2‐DOF system is established to describe the residential building controlled by an inerter elevator. Then, three closed‐form optimal solutions for designing the U‐TMDI are derived via the fixed‐point method, equal modal damping criterion, and the infinity damping assumption. Subsequently, these optimal solutions are compared and discussed involving the expressions of tuning frequency ratio and the optimal damping parameters, root locus diagram and supplemental damping ratios, transfer function, and robustness to frequency variation, respectively. Finally, a residential building example is adopted to validate the feasibility of the proposed retrofitting strategy and the closed‐form optimal design solutions. It is demonstrated that the optimal tuning frequency ratios derived by the fixed‐point method and equal modal damping criterion are different for U‐TMDI due to the influence of elevator stiffness ratio , while its degradation forms for tuned mass damper (TMD) are identical, recognizing the importance of the elevator stiffness. Moreover, the proposed retrofitting strategy of using an inerter elevator can significantly mitigate the main structure displacement by about 18%∼23% for both far‐field and near‐fault earthquakes.

Measurements of mechanical strain are widely used in heavy industry to design and test new structures and to ensure the safety of installed infrastructure. However, the few practical methods for strain measurement all have serious limitations. To complement these methods, we have developed a new strain technology, called S 4 for “strain-sensing smart skin,” which uses single-wall carbon nanotubes (SWCNTs) as microscopic sensors. Nanotubes dilutely embedded in a polymer are applied as a thin film to specimen surfaces of interest. Subsequent strains in the specimen are transmitted by load transfer through the film to the nanotubes, inducing axial compression or extension. Those small structural deformations of the SWCNTs alter the semiconducting band gaps in predictable ways, causing proportional shifts in the peak wavelengths of their near-infrared fluorescence emission. Shifts are quantified by optically exciting the specimen surface and spectrally analyzing the resulting fluorescence to deduce strain values at the probed locations. The S 4 method has recently been implemented using hyperspectral imaging. We demonstrate measurements in less than one minute of strain maps containing hundreds of thousands of pixels with 50 microstrain noise levels and 0.2 mm spatial resolution. This technology has promising potential to become a large-scale commercialized application of carbon nanotechnology.

Origami structures have been receiving a lot of attention from engineering and scientific researchers owing to their unique properties such as deployability, multi-stability, negative stiffness, etc. However, dynamic properties of origami structures have not been explored much due to a lack of validated analytical dynamic modeling approaches. Given the range of interesting properties and applications of origami structures, it is important to study the dynamic behavior of origami structures. In this study, a dynamic modeling approach for origami structures is presented considering distributed mass modeling, which has the potential to be a generalizable approach. In the proposed approach, stiffness is modeled using the bar and hinge modeling approach while the mass is modeled using the mass distribution approach. Various candidate mass distribution approaches were investigated by comparing their responses to the finite element method responses for various geometric conditions, loading and boundary conditions, and deformation modes. It was observed that a dynamic modeling approach with triangle circumcenter mass distribution was able to capture most of the dynamics satisfactorily consistently. Subsequently, a Miura-ori specimen was manufactured and its free vibration response was determined experimentally and then compared to the prediction of the analytical model. The comparison demonstrated that the analytical model was able to capture most of the dynamics in the longitudinal direction.

Each year, the growth of cities across developing economies in Asia, Africa, and Latin America drives demand for concrete to house and serve their burgeoning populations. Since 1950, the number of people living in urban areas has quadrupled to 4.2 billion, with another predicted 2.5 billion expected to join them in the next three decades. The largest component of concrete by volume is aggregates, such as sand and rocks, with sand as the most mined material in the world. However, the extraction rate of sand currently exceeds its natural replenishment rate, meaning that a global concrete-suitable sand shortage is extremely likely. As such, replacements for fine aggregates, such as sand, are in demand. Here, flash Joule heating (FJH) is used to convert coal-derived metallurgical coke (MC) into flash graphene aggregate (FGA), a blend of MC-derived flash graphene (MCFG), which mimics a natural aggregate (NA) in size. While graphene and graphene oxide have previously been used as reinforcing additives to concrete, in this contribution, FGA is used as a total aggregate replacement for NA, resulting in 25% lighter concrete with increases in toughness, peak strain, and specific compressive strength of 32, 33, and 21%, respectively, with a small reduction in specific Young's modulus of 11%. FJH can potentially enable the replacement of fine NA with FGA, resulting in lighter, stronger concrete.

Abstract CO 2 emissions have become a significant environmental problem over the last few decades, often stemming from combustion of fossil fuels. Production and disposal of waste plastic also contribute greatly to greenhouse gas emissions, due to combustion of fossil fuels during manufacture and incineration or pyrolysis of the waste materials. Hence, researchers have begun developing technologies geared toward the capture, sequestration, and utilization of CO 2 . Several methods are shown to be useful for conversion of gaseous CO 2 into solid carbon feedstocks, such as molten carbonate electrolysis. At the same time, flash Joule heating can rapidly and inexpensively convert carbon‐rich feedstocks into flash graphene (FG). Here, amorphous carbon derived from molten carbonate electrolysis of carbon dioxide is converted into FG, sometimes in combination with waste plastic, and demonstrated for use as a reinforcing additive in composite applications. FG can be used in epoxy and vinyl ester resins with a maximum increase in Young's modulus and hardness of 73% and 73%, respectively. Life cycle assessment also shows that adding 5 wt% 25:75 amorphous carbon‐derived FG to the epoxy results in 7.7%, 5%, and 2.7% decreases in CO 2 emissions, water consumption, and energy consumption, respectively.

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

Instrumental advances in near-IR fluorescence spectroscopy are enabling new types of measurements involving single-wall carbon nanotubes (SWCNTs). Two unique systems will be described. The first is a two-dimensional fluorescence-detected circular dichroism (FDCD) spectrometer. In this, SWCNT samples are excited by a spectrally selected supercontinuum laser beam that is switched between left- and right-circular polarization in an electro-optic modulator. Near-infrared sample fluorescence emitted in the backward direction is captured and directed to a scanning monochromator with a cooled InGaAs single-channel detector. After amplification and high precision digitization, the modulated signal component is extracted by computer-based phase sensitive detection. The system can measure a sample’s E 22 circular dichroism in four spectral modes: 1) conventional FDCD, with scanned visible excitation wavelength and spectrally integrated (zero-order grating) emission detection; 2) Emission-specific FDCD, with scanned visible excitation wavelengths and selected emission wavelength; 3) Emission-scanned FDCD, with selected visible excitation wavelength and scanned emission wavelengths; 4) Excitation-Emission FDCD, with excitation and emission wavelengths both scanned to give two-dimensional data sets. This instrument can spectroscopically resolve enantiomer signals from a single ( n , m ) species in a racemic SWCNT sample. In a parallel project, developments in SWCNT fluorescence spectrometry are advancing nanotube-based strain measurement technology toward commercialization. Because SWCNT emission wavelengths vary systematically with axial strain, nanotubes in a thin coating on a specimen can serve as optically interrogated strain gauges. We apply this effect to measure strain maps through hyperspectral imaging of SWCNT fluorescence. A rotated band pass filter is used to capture a set of images in multiple spectral slices, from which a custom computer program deduces strain at each of ~10 5 image pixels and compiles strain maps. We will describe how this apparatus has evolved from a lab prototype into a compact portable system that can make measurements in industrial settings.

Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network’s capability to apply to materials exhibiting a similar stress–strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.

This paper proposed a novel amplifying damping transfer system (ADTS) as a new damping enhancement solution for high-rise structures like wind turbines. The proposed ADTS can transfer the upper rotation of turbine tower to its bottom with damping amplification mechanism. Hence, viscous damper can be installed on wind turbines in a very convenient and efficient way. The dynamic characteristics of wind turbines equipped with ADTS were parametrically investigated concerning the influence of the damping, stiffness, and position of the ADTS based on complex frequency analysis. It was found that each mode has a maximum damping ratio, which is affected by the ADTS stiffness and position. The optimal ADTS position of the first mode is about 0.7 H (turbine height), and the optimal positions of the second mode are at 0.3 H and 0.86 H. The proposed ADTS considerably attenuated both drift and acceleration responses of wind turbines caused by winds and earthquakes. For example, as compared to the optimized tuned mass damper, ADTS further decreases the displacement (acceleration) of wind turbine tower by about 22% (38%).

Full-field measurements of the continuous spatiotemporal response of the physical processes such as structural vibration or fluid flow generate large datasets. In many scientific fields, such continuous spatiotemporal dynamic models are represented by partial differential equations (PDEs). In the past, attempts have been made to identify the PDE models from the measured response by inferring its parameters by the use of either regression or deep learning-based techniques. But the previously presented regression-based methods fail to estimate the parameters of the higher-order PDE models in the presence of moderate noise. Likewise, the deep learning-based methods lack the much-needed property of repeatability and robustness in the identification of PDE models from the measured response. The authors introduced the method of S imulta N eous Basis Function A pproximation and P arameter E stimation ( SNAPE ) in a recent paper which addresses such drawbacks by fitting basis functions to the measured response and simultaneously infer the parameters of the PDE model. In this paper the theory and formulation of SNAPE is presented to perform physics-guided identification of the Euler–Bernoulli beam PDE model which is widely applied in the modeling of large scale infrastructures to nanoscale structures .The domain knowledge of the physics is used as a constraint in the formulation of the optimization framework. The alternating direction method of multipliers (ADMM) algorithm is used to simultaneously optimize the loss function over the parameter space of the PDE model and coefficient space of the basis functions. The proposed method not only infers the parameters but also estimates a continuous function that approximates the solution to the PDE model. The efficacy of the method is both numerically and experimentally validated on noise corrupted full-field vibration response. The method neither requires the knowledge of the initial or boundary conditions of the beam nor comprises of model discretization error as in the case of finite element model updating. SNAPE demonstrates its applicability on various homogeneous and time-varying nonhomogeneous boundary conditions.