Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study.

Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert-Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was experimentally established as the case study subjected to white noise excitations. To this end, four key features of the intrinsic mode function (IMF), including energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF), are extracted to assess the presence, severity, and location of the damage. By analyzing the experimental results through different damage indices defined based on the extracted features, the capabilities of the CEEMDAN-HT-ANN model in detecting, addressing the location and classifying the severity of damage are efficiently concluded. In addition, the energy-based damage index demonstrates a more effective approach in detecting the damage compared to those based on IA and unwrapped phase parameters.

Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy.

The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

Application of the restoring force method for identification of lumbar spine flexion-extension motion under flexion-extension moment.

The restoring force method (RFM), a nonparametric identification technique established in applied mechanics, was used to maximize the information obtained from moment-rotation hysteresis curves under pure moment flexion-extension testing of human lumbar spines. Data from a previous study in which functional spine units were tested intact, following simulated disk injury, and following implantation with an interspinous process spacer device were used. The RFM was used to estimate a surface map to characterize the dependence of the flexion-extension rotation on applied moment and the resulting axial displacement. This described each spine response as a compact, reduced-order model of the complex underlying nonlinear biomechanical characteristics of the tested specimens. The RFM was applied to two datasets, and successfully estimated the flexion-extension rotation, with error ranging from 3 to 23%. First, one specimen, tested in the intact, injured, and implanted conditions, was analyzed to assess the differences between the three specimen conditions. Second, intact specimens (N = 12) were analyzed to determine the specimen variability under equivalent testing conditions. Due to the complexity and nonlinearity of the hysteretic responses, the mathematical fit of each surface was defined in terms of 16 coefficients, or a bicubic fit, to minimize the identified (estimated) surface fit error. The results of the first analysis indicated large differences in the coefficients for each of the three testing conditions. For example, the coefficient corresponding to the linear stiffness (a01) had varied magnitude among the three conditions. In the second analysis of the 12 intact specimens, there was a large variability in the 12 unique sets of coefficients. Four coefficients, including two interaction terms comprised of both axial displacement and moment, were different from zero (p < 0.05), and provided necessary quantitative information to describe the hysteresis in three dimensions. The results suggest that further work in this area has the potential to supplement typical biomechanical parameters, such as range of motion, stiffness, and neutral zone, and provide a useful tool in diagnostic applications for the reliable detection and quantification of abnormal conditions of the spine.

Magnetoactive Acoustic Topological Transistors.

Topological field-effect transistor is a revolutionary concept that physical fields are used to switch on and off quantum topological states of the condensed matter. Although this emerging concept has been explored in electronics, how to realize it in the acoustic realm remains elusive. In this work, a class of magnetoactive acoustic topological transistors capable of on-demand switching on and off topological states and reconfiguring topological edges with external magnetic fields is presented. The key mechanism is to harness magnetic fields to tune air-cavity volumes within acoustic chambers, thus breaking or preserving the inversion symmetry to manifest or conceal the quantum valley Hall effect. To switch the topological transport beyond the in-plane routes, a magneto-tuned non-topological band gap to allow or forbid the wave transport out-of-plane is harnessed. With the reversible magnetic control, on-demand switching of topological routes to realize topological field-effect waveguides and wave regulators is demonstrated. Analogous to the impact of semiconductor transistors on modern electronics, this work may expand the scope of topological acoustics by achieving unprecedented functions in acoustic modulation.

Constructive adaptation of 3D-printable polymers in response to typically destructive aquatic environments.

In response to environmental stressors, biological systems exhibit extraordinary adaptive capacity by turning destructive environmental stressors into constructive factors; however, the traditional engineering materials weaken and fail. Take the response of polymers to an aquatic environment as an example: Water molecules typically compromise the mechanical properties of the polymer network in the bulk and on the interface through swelling and lubrication, respectively. Here, we report a class of 3D-printable synthetic polymers that constructively strengthen their bulk and interfacial mechanical properties in response to the aquatic environment. The mechanism relies on a water-assisted additional cross-linking reaction in the polymer matrix and on the interface. As such, the typically destructive water can constructively enhance the polymer's bulk mechanical properties such as stiffness, tensile strength, and fracture toughness by factors of 746% to 790%, and the interfacial bonding by a factor of 1,000%. We show that the invented polymers can be used for soft robotics that self-strengthen matrix and self-heal cracks after training in water and water-healable packaging materials for flexible electronics. This work opens the door for the design of synthetic materials to imitate the constructive adaptation of biological systems in response to environmental stressors, for applications such as artificial muscles, soft robotics, and flexible electronics.

An alternative measurement tool for the identification of hysteretic responses in biological joints.

In structural engineering, sophisticated multi-dimensional analysis techniques, such as the Restoring Force Method (RFM), have been established for complex, nonlinear hysteretic systems. The purpose of the present study was to apply the RFM to quantify nonlinear spine hysteresis responses under applied moments. First, synthetic hysteretic spine responses (n=50) were generated based on representative results from pure moment flexion-extension loading of a human cadaveric lumbar spine segment. Then, the RFM was applied to each hysteresis response to describe the flexion-extension rotation as a function of applied moment and simulated axial displacement using a set of 16 unique coefficients. Range of motion, neutral zone, elastic zone, and stiffness were also measured. The RFM coefficient corresponding to the 1st-order linear dependence of rotation on applied moment was dominant, and paralleled changes in elastic zone. The remaining RFM coefficients were not captured from the traditional biomechanical analysis. Therefore, the RFM may potentially supplement the traditional analysis to develop a more comprehensive, quantitative description of spine hysteresis. The results suggest the potential for more thorough and specific characterization of spine kinematics, and may lead to future applications of such techniques in characterizing biological structures.