Determination of tensile behavior of hot-pressed Mg-TiO2 and Mg-ZrO2 nanocomposites using indentation test and a holistic inverse modeling technique

The present study aims to implement a non-destructive approach to determine the tensile properties of magnesium-based nanocomposites reinforced with ZrO2 and TiO2 nanoparticles. Micron-sized magnesium particles were blended with 0, 1.5, 3, and 5 volume percentage of ZrO2 and TiO2 nanoparticles and hot-pressed at 450 °C under the pressure of 600 MPa. Next, the spherical indentation test was performed on the produced composites to obtain the load–penetration curves. A finite element model of the indentation test was then developed using the Hollomon material model with randomly chosen materials constants. At the next stage, load–penetration curves were obtained for each composite using simulations. A Levenberg–Marquardt neural network was then trained and utilized to find the correct material constants by minimizing the differences between the experimental and simulated load–penetration curves. The results indicated that there is a linear relationship between the tensile strength and content of the reinforcement phase, while it is inversely proportional to the size of the reinforcing particles. Magnesium composites reinforced with 5 volume percentage of ZrO2 and TiO2 nanoparticles showed tensile strengths 2.5 and 2.1 times greater than that of unreinforced magnesium, respectively. It was shown that the proposed method is able to calculate the tensile properties of magnesium-based composites in an accurate and inexpensive manner

Rigid Pavement Backcalculation Using Differential Evolution

The backcalculation of pavement layer moduli from Falling Weight Deflectometer (FWD) measured surface deflections is a challenging task. It can also be formulated as a global optimization problem with the objective of finding the optimal pavement layer moduli values that minimize the error between measured and computed surface deflections. Over the years, several backcalculation methodologies have been developed including the use of soft computing techniques such as Neural Networks (NNs), Genetic Algorithms (GAs), etc. In this paper, Differential Evolution (DE), a stochastic parallel direct search evolution strategy optimization method is integrated with rapid surrogate mapping of Finite Element (FE) solutions through Neural Networks (NNs) in developing an automated rigid pavement backcalculation toolbox

A Review of Structural Health Monitoring Techniques as Applied to Composite Structures.

Structural Health Monitoring (SHM) is the process of collecting, interpreting, and analysing data from structures in order to determine its health status and the remaining life span. Composite materials have been extensively use in recent years in several industries with the aim at reducing the total weight of structures while improving their mechanical properties. However, composite materials are prone to develop damage when subjected to low to medium impacts (ie 1 – 10 m/s and 11 – 30 m/s respectively). Hence, the need to use SHM techniques to detect damage at the incipient initiation in composite materials is of high importance. Despite the availability of several SHM methods for the damage identification in composite structures, no single technique has proven suitable for all circumstances. Therefore, this paper offers some updated guidelines for the users of composites on some of the recent advances in SHM applied to composite structures; also, most of the studies reported in the literature seem to have concentrated on the flat composite plates and reinforced with synthetic fibre. There are relatively fewer stories on other structural configurations such as single or double curve structures and hybridised composites reinforced with natural and synthetic fibres as regards SHM

Aging concrete structures: a review of mechanics and concepts

The safe and cost-efficient management of our built infrastructure is a challenging task considering the expected service life of at least 50 years. In spite of time-dependent changes in material properties, deterioration processes and changing demand by society, the structures need to satisfy many technical requirements related to serviceability, durability, sustainability and bearing capacity. This review paper summarizes the challenges associated with the safe design and maintenance of aging concrete structures and gives an overview of some concepts and approaches that are being developed to address these challenges

Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning

The success of contrastive learning is well known to be dependent on data augmentation. Although the degree of data augmentations has been well controlled by utilizing pre-defined techniques in some domains like vision, time-series data augmentation is less explored and remains a challenging problem due to the complexity of the data generation mechanism, such as the intricate mechanism involved in the cardiovascular system. Moreover, there is no widely recognized and general time-series augmentation method that can be applied across different tasks. In this paper, we propose a novel data augmentation method for quasi-periodic time-series tasks that aims to connect intra-class samples together, and thereby find order in the latent space. Our method builds upon the well-known mixup technique by incorporating a novel approach that accounts for the periodic nature of non-stationary time-series. Also, by controlling the degree of chaos created by data augmentation, our method leads to improved feature representations and performance on downstream tasks. We evaluate our proposed method on three time-series tasks, including heart rate estimation, human activity recognition, and cardiovascular disease detection. Extensive experiments against state-of-the-art methods show that the proposed approach outperforms prior works on optimal data generation and known data augmentation techniques in the three tasks, reflecting the effectiveness of the presented method. Source code: https://github.com/eth-siplab/Finding_Order_in_ChaosComment: To be published in the Proceedings of NeurIPS 202

Active thermography for the investigation of corrosion in steel surfaces

The present work aims at developing an experimental methodology for the analysis of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in reflexion configuration (RC). The peculiarity of this AT approach consists in exciting by means of a laser source the sound surface of the specimens and acquiring the thermal signal on the same surface, instead of the corroded one: the thermal signal is then composed by the reflection of the thermal wave reflected by the corroded surface. This procedure aims at investigating internal corroded surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to improve the experimental set up. Surface thermal profiles were acquired by an IR thermocamera and means of salt spray testing; at set time intervals the specimens were investigated by means of AT. Each duration corresponded to a surface damage entity and to a variation in the thermal response. Thermal responses of corroded specimens were related to the corresponding corrosion level, referring to a reference specimen without corrosion. The entity of corrosion was also verified by a metallographic optical microscope to measure the thickness variation of the specimens