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Development of cementitious materials with low carbon footprint is critical for greenhouse gas mitigation. Coal fly ash (CFA) is an attractive diluent additive in cement due to its widespread availability and ultralow cost, but the heavy metals in CFA could leach out over time. Traditional acid washing processes for heavy metal removal suffer from high chemical consumption and high-volume wastewater streams. Here, we report a rapid and water-free process based on flash Joule heating (FJH) for heavy metals removal from CFA. The FJH process ramps the temperature to ~3000 °C within one second by an electric pulse, enabling the evaporative removal of heavy metals with efficiencies of 70–90% for arsenic, cadmium, cobalt, nickel, and lead. The purified CFA is partially substituted in Portland cement, showing enhanced strength and less heavy metal leakage under acid leaching. Techno-economic analysis shows that the process is energy-efficient with the cost of ~$21 ton−1 in electrical energy. Life cycle analysis reveals the reuse of CFA in cement reduces greenhouse gas emissions by ~30% and heavy metal emissions by ~41%, while the energy consumption is balanced, when compared to landfilling. The FJH strategy also works for decontamination of other industrial wastes such as bauxite residue.

Stay-cables in the cable-stayed bridge are the most vital components as they carry the bridge deck’s load and transmit the force to the bridge pylons. However, dynamics loads due to vortex-induced vibration, ambient wind excitation, and even vehicular vibration cause fatigue in the stay-cable. Hence continuous real-time performance monitoring of such cables is necessary for maintenance to avoid any kind of damage to the cable. Wireless sensors are contact-based sensor that provide accurate measurement, and it does not involve any wiring cost like conventional wired sensors. Monitoring cable health using such wireless sensors is a good choice provided packet loss (which occurs while transmitting the measured data to the base station) that invariably occurs is addressed by data processing. Such discontinuity in data (due to packet loss) may interrupt the real-time/online cable health monitoring process - depending on the window length of the data loss. In general, online health monitoring using multiple sensors reduces the estimation errors. In this paper, we propose a framework that takes the wireless sensor data as the input, then reconstructs the packet lost samples (if any), and finally, provides a real-time tension estimation as an output. The novel framework, first adopts compressive sensing algorithm to reconstruct the data due to packet loss. Subsequently, we synthesize the reconstructed responses from multiple sensors to estimate the real-time frequency variation using Blind Source Separation (BSS) Technique. As the cable response due to ambient vibration contains a large number of modes, the dominant modal response or the corresponding dominant frequency is estimated from very few measurements using a variant of the BSS technique named Sparse Component Analysis (SCA). Finally, real-time cable tension is estimated from the frequency variation using the taut-string theory. The proposed technique is applied to a real full-scale cable-stayed bridge. The mean tension obtained from the framework is comparable with the cable’s actual design tension. The accurate estimation of real-time stay-cable tension by the proposed algorithm shows great potential in the field of structural health monitoring.

Jointed structures are ubiquitous constituents of engineering systems; however, their dynamic properties (e.g., natural frequencies and damping ratios) are challenging to identify correctly due to the complex, nonlinear nature of interfaces. This research seeks to extend the efficacy of traditional experimental methods for linear system identification (such as impact testing, shaker ringdown testing, random excitation, and force or amplitude-control stepped sine testing) on nonlinear jointed systems, e.g., the half Brake–Reußbeam, by augmenting them with full-field data collected by high-speed videography. The full-field response is acquired using high-speed cameras combined with Digital Image Correlation (DIC), which enables studying the spatial–temporal dynamic characteristics of the system. As this is a video-based experiment, additional constraints are attached to the beam at the node points to remove the rigid body motion, which ensures that the beam is in the view of the camera during the entire test. The use of a video-based method introduces new sources of experimental error, such as noise from the high-speed camera's fan and electrical noise, and so the measurement accuracy of DIC is validated using accelerometer data. After validating the DIC data, the measurements are recorded for several types of excitation, including hammer testing, shaker ringdown testing, fixed sine testing, and stepped sine testing. Using the DIC data to augment standard nonlinear system identification techniques, modal coupling and the mode shapes’ evolution are investigated. The suitability of videography methods for nonlinear system identification of nonlinear beams is explored for the first time in this paper, and recommendations for techniques to facilitate this process are made. This article focuses on developing an accurate data collection methodology as well as recommendations for nonlinear testing with DIC, which paves the way for video-based investigation of nonlinear system identification. In Part-II (Jin et al., 2021) of this work, the same data set is used for a rigorous assessment of nonlinear system identification with full-field DIC data. ISSN:0888-3270 ISSN:1096-1216

This paper exploits a new direction of structural identification by means of Neural Ordinary Differential Equations (Neural ODEs), particularly constrained by domain knowledge, such as structural dynamics, thus forming Physics-informed Neural ODEs, aiming at governing equations discovery/approximation. Structural identification problems often entail complex setups featuring high-dimensionality, or stiff ODEs, which pose difficulties in the training and learning of conventional data-driven algorithms who seek to unveil the governing dynamics of a system of interest. In this work, Neural ODEs are re-casted as a two-level representation involving a physics-informed term, that stems from possible prior knowledge of a dynamical system, and a discrepancy term, captured by means of a feed-forward neural network. The re-casted format is highly adaptive and flexible to structural monitoring problems, such as linear/nonlinear structural identification, model updating, structural damage detection, driving force identification, etc. As an added step, for inferring an explainable model, we propose the adoption of sparse identification of nonlinear dynamical systems as an additional tool to distill closed-form expressions for the trained nets, that embed a more straightforward engineering interpretation. We demonstrate the framework on a series of numerical and experimental examples, with the latter pertaining to a structural system featuring highly nonlinear behavior, which is successfully learned by the proposed framework. The proposed structural identification with Physics-informed Neural ODEs comes with the benefits of direct approximation of the governing dynamics, and a versatile and flexible framework for discrepancy modeling in structural identification problems., Journal of Sound and Vibration, 508, ISSN:0022-460X, ISSN:1095-8568

An accurate and reliable identification of structural damage is of prime importance to evaluate the structural integrity of civil infrastructure systems. However, the adverse effect of normal fluctuations in the environment on the effectiveness of damage detection techniques remains a continuing challenge in structural health monitoring applications. In this paper, we present the application of principal component analysis (PCA) to temporal damage detection in continuous beam bridge structures subjected to changing environmental effects. For this purpose, we show that sudden discontinuities in the principal components occur at the onset of damage, and that these discontinuities are observed in the projections of the vibration data on the principal components space. The magnitude of the discontinuity is used to define a damage index for damage quantification. A comprehensive numerical study is used to validate the approach on a continuous beam model of highway bridge structures. In particular a sensitivity analysis is conducted to study the effect of both temperature-dependent boundary conditions and material properties on the principal components for multiple damage scenarios. The numerical results show that the approach is robust to mild nonlinearities caused by the effect of temperature on material properties of composite steel-concrete sections and boundary conditions. Furthermore, the approach is experimentally validated using data of the Z24 bridge in Switzerland measured during a period of one year. It is shown that the approach has the capability of tracking the temporal evolution of various damage states induced on the bridge during the testing program.

The unscented Kalman filter (UKF) has proven to be an effective approach for the identification of nonlinear systems from limited output measurements. However, the conventional UKF requires that measurements of the input excitations are available to successfully perform nonlinear system identification, which limits its application in cases where it is difficult or impractical to measure the inputs. In this paper a novel unscented Kalman filter with unknown input (UKF-UI) is proposed for the simultaneous identification of nonlinear structural systems and external excitations. Based on the estimation-based procedures of the conventional UKF, the analytical recursive solutions of the proposed UKF-UI are derived in an analogous fashion resulting in a recursive nonlinear least-squares problem for the unknown input. Moreover, data fusion of partially measured acceleration and displacement responses is used to alleviate the drifts typically observed in the estimated inputs and displacements. Numerical and experimental validation examples are used to demonstrate the effectiveness of the proposed UKF-UI algorithm for the simultaneous identification of nonlinear parameters and unknown external excitations using data fusion of partially measured system responses.

A new passive device named negative stiffness amplifying damper (NSAD) is proposed in this paper by introducing a negative stiffness (NS) spring into to the flexibly-supported-viscous-damper systems (represented by classical Maxwell damping element, MDE).. The NS spring is combined with the dashpot of the MDE, amplifying the stroke of the dashpot; therefore, lead to significant damping magnification effect. The proposed NSAD not only achieves significant damping magnification effect, but also preserves the property of negative stiffness. This feature is attractive for reducing both displacement and structural acceleration when subjected to earthquakes. The closed-form expressions of optimal parameters for an undamped SDOF system with a NSAD is also proposed by modifying the ‘fixed point’ method of tuned mass damper. Then, the performance of NSAD is investigated and evaluated under stochastic excitations, pulse excitations, and real earthquakes. Result shows that the optimal NSAD can substantially reduce displacement and acceleration responses simultaneously. For instance, even using the same small additional damping ratio (i.e., 2.8%), the optimal NSAD reduces the resonance response of MDE by 76.9%. Also with that small additional damping, the NSAD improves the energy dissipation capability by 5–16 times, causing 40–60% of seismic response reduction for most structural period range. Moreover, the NSAD is also effective for both far-field and near-fault earthquakes. Especially for near-fault pulse-like earthquakes which may potentially cause larger seismic responses for long-period structures, the NSAD provides an extra improvement of 15–20% in energy dissipation capability for long-period structures.

In this paper a Bayesian framework is employed to estimate the seismic strong-motion response and the state of structural integrity of a full-scale structure. The approach is applied in the context of a full-scale section of a seven-story shear wall building designed for southern California and tested at the George E. Brown Jr. Network for Earthquake Engineering Simulation shake table site at the University of California San Diego. The test structure was subjected to earthquake records of increasing amplitude until severe damage was observed, and the dynamic response was measured during the experimental program using an array of sensors. The proposed approach provides an estimate of the nonlinear dynamic response at unmeasured degrees of freedom by combining a potentially inaccurate structural model with sparse acceleration measurements using an unscented Kalman filter. Although the method also provides an optimal estimate of the parameters that define the mathematical models, joint state-parameter estimation is not the main purpose of this study. To assess the modeling robustness of the approach two model classes are considered: a linear time-varying cantilever beam model that accounts only for flexural deformations, and a coupled nonlinear chain-cantilever model that accounts for both flexural and shear deformations. It is shown that the approach has the capability to accurately estimate the time-history response and engineering demands of interest, such as floor displacements and accelerations, inter-story drifts, base shear, and overturning moment induced by strong base excitations where the structure experienced considerable nonlinear excursions. The estimated demands are subsequently used to compute damage measures to perform a quantitative assessment of the state of structural integrity.

The present paper proposes a novel adaptive passive stiffness (APS) device which can produce variable stiffness in an adaptive fashion. This APS device consists of four springs arranged in a rhombus configuration and a guidance template. The stiffness of the device is controlled through varying the angle of the rhombus configuration. The function of the guidance template is to relate the angle of the rhombus to the targeted system response such that the stiffness can be varied in an adaptive manner, without the need for feedback or external energy. Two analytical models are developed to capture the behavior of the APS device which shows good agreement with the experimental data. Experimental result indicates that the proposed APS device can produce adaptive stiffness as expected. A cubic nonlinearity is produced and deployed in a single-degree-of-freedom (SDOF) system to form a hardening Duffing oscillator. Dynamic tests show that the experimental characteristics of the Duffing system agrees well with the numerical results, thereby demonstrating that the APS device is effective in producing smoothly and continuously adaptive stiffness in an adaptive fashion. In addition, a fourth order even function is used as the guidance template to produce a softening-hardening adaptive stiffness. The performance of this adaptive stiffness is numerically evaluated for seismic protection. It is found that the structural responses can be reduced effectively when using this softening-hardening adaptive stiffness. The key value of this APS device is that the guidance template can be easily changed to any desired continuous function such that the targeted adaptive stiffness can be obtained. This provides possibility for controlling the vibrations of structures in a passively adaptive manner.

A system identification technique suitable for single degree of freedom (SDOF) and multiple degree of freedom (MDOF) structural systems with either nonlinear elastic or inelastic/hysteretic behavior is proposed in this paper. The method is a parametric modeling technique based on sparse regularization. The proposed framework is capable of discovering the underlying governing equations of the system of interest from input-output data. We build on the work of Brunton et al. (2016) by including functions that allow the discovery of significant nonlinearities, and hystertic or inelastic behavior with permanent deformation. We also present model selection using sparse regularization and cross validation using Akaike criteria. We demonstrate through experimental validation that the technique presented in this paper is applicable to a significantly broader class of problems. The effectiveness of the proposed method is evaluated through numerical examples of a 2-story nonlinear or inelastic building with a adjustable stiffness device. We also present experimental validation using a unique nonlinear structural system that consists of a MDOF structural system and a nonlinear negative stiffness device (NSD) to illustrate the significant ability of the proposed framework. We successfully identify the following structural systems from experimental data: a SDOF yielding frame without NSD; a SDOF yielding frame with NSD; and a 3-DOF frame with NSD. The extracted sparse model also shows potential for generalization.

A Kalman filtering based framework for structural damage assessment under changing environmental conditions is presented. The approach is based on the well-known property that the filtering residual is a realization of a white stochastic process when the filter is operating under optimal conditions. To decouple structural damage and environmental effects two additional properties of the filtering residual are employed: i) under global changes in the structure caused by environmental variations the residual remains a white process, and thus its spectral density is approximately constant; ii) local changes caused by structural damage induce peaks in the residual spectral density at the affected vibration frequencies, and thus the residual is a colored process. A Bayesian whiteness test is employed to discriminate between the two situations under finite length data conditions (damage detection), while a normalized damage measure based on the spectral moments of the residual spectral density is proposed as a quantitative damage-sensitive feature (damage quantification). The proposed approach is numerically verified in a continuous beam model of a bridge under different operating conditions, including a robustness assessment for non-uniform temperature fields. It is shown that the approach has the capability to decouple physical changes caused by structural damage and varying environmental conditions, providing robust damage measures for structural health monitoring applications.

The data deluge in Structural Health Monitoring (SHM) and the need for automated online damage detection systems necessitates a move away from traditional model-based approaches. To that end, we propose sparsity-based algorithms for damage detection in plates. Instead of high-fidelity models, our proposed algorithms use dictionaries, consisting of response signals acquired directly from the system of interest, as the key feature to both detect and localize damages. We address the damage detection problem both when the damage is located on or off a grid of possible damage coordinates defined by the dictionary. This gives rise to two classes of problems, namely, on the grid and off the grid problems. In our sparsity-based on the grid damage detection (SDD-ON) platform, we solve a LASSO regression problem, where, the unknown vector is a pointer for existence of damage at the various locations defined on the grid used for dictionary construction. In our proposed off the grid damage detection (SDD-OFF) platform, we use a penalized regression algorithm to extend the dictionary of measured damage signals to points off-the-grid by linear interpolation. We evaluate the performance of both SDD frameworks, in detecting damages on plates, using finite element simulations as well as laboratory experiments involving a pitch-catch setup using a single actuator-sensor pair. Our results suggest that the proposed algorithms perform damage detection in plates efficiently. We obtain area under receiver operating characteristic (ROC) curves of 0.997 and 0.8314 for SDD-ON and SDD-OFF, respectively.

It is well known that each semiconducting species of single-wall carbon nanotube (SWCNT) gives distinct near-infrared fluorescence following excitation with visible light. Fundamental band gap modulation makes the peak emission wavelengths shift predictably when the nanotubes are deformed by axial stretching or compression. This effect forms the basis for a new method of mechanical strain measurement in which nanotubes are dilutely dispersed in thin polymer films applied to surfaces of interest. When the substrate is subsequently strained, load transfer through the film to the nanotubes causes small spectral shifts that are monitored through non-contact spectral measurements. We have demonstrated this method through successful strain mapping on specimens of aluminum, copper, steel, acrylic, polycarbonate, cement, and concrete, using point-by-point scanning. To improve the speed and convenience of SWCNT-based strain mapping, we are adapting the method to camera-based measurements. A macroscopic region of a coated specimen surface is illuminated with an excitation laser and the resulting SWCNT emission is imaged from a stand-off distance of ca. 50 cm by a near-IR sensitive camera. Multiple images are acquired through a tunable spectral filter to span the emission band of interest. The image data are then analyzed by spectrally fitting intensities at each pixel to find the local peak emission wavelength, from which the local strain value is deduced. The set of local strains is converted into a color-coded strain map showing detailed deformations of the specimen. We expect that the introduction of camera-based spectral strain measurement technology will lead to an important industrial application of SWCNTs.