Accurate determination of the mechanical properties of asphalt concrete is very important in Road Engineering. The traditional method to calculate these properties is to run experiments using a hydraulic/pneumatic actuator and strain gauges to apply stress and measure the strain. However, in the last decade optical measurement techniques have become popular for strain calculation on the surface of the specimen and detecting the cracks on the surface. In this study, digital image correlation is used to estimate the strain map on the surface of an asphalt specimen, predict the location of crack initiation, and investigate the healing phenomenon in asphalt concrete.

A shape-adaptable Carbon Fiber Reinforced Composite (CFRC) is proposed to derive a material with tunable mechanical properties in order to optimize its response to external excitations. The composite is bi-stable thanks to internal stresses arising in the manufacturing process and is characterized by a built-in heating system that can control the temperature of the material. This approach allows to gradually change the actual curvature of the material as well as tuning its natural frequencies and damping properties

Direct current resistivity measurements are performed at Reykjanes geothermal reservoir in southwest Iceland to investigate the possibility of using steel casings as electrodes to transfer electric current deep into the ground during cross-well resistivity surveys. Various wells are studied including well IDDP-2 that has been deepened by the Iceland Deep Drilling Project (IDDP) to a depth of 4.7 km. Electrical resistance measured between two well casings is compared to the resistance between a well casing and an electrode on the surface. The results indicate that the current travels deeper into the ground and through water channels from one casing to another when using the well casings as electrodes instead of traveling closer to the surface as when surface electrodes are used. Steel casings provide good conduction into the ground in resistivity studies and cross-well resistivity measurements can be used to gain information about the subsurface such as the fracture connectivity between wells.

We have investigated several full-field contactless techniques, such as thermography and shearography, with several excitation methods for inspecting hybrid composite-metal sandwich structures. The latter are made of a core with epoxy reinforced by carbon and glass fibers and skins of titanium. Several calibrated defects are incorporated at different places in depth and are made of air gaps and inserts.

The wind energy is one of the most important alternatives for renewable and clean electricity generation. During the last decade the number of wind farms has largely increased in South America. Qollpana is only one case of an on-shore wind farm but it is located at 2900 meters above sea level over a complex terrain. Due to high altitude, the air density is reduced by 40 % compared with sea level and the topographic characteristics induce a high level of turbulence. Qollpana wind farm has two 1.5 MW and eight 3 MW wind turbines reaching 27 MW of installed capacity. October is the month of highest wind average velocity and February with the lowest one. This work analyses the capacity factor of the wind farm, also the air density and the turbulence effects on wind turbine efficiency. The main results show that monthly capacity factor varies between 0.08 and 0.67 in the wind farm. Moreover, the results have shown a considerable effect of the turbulence intensity on the turbines efficiency.

Early-stage damage detection could provide better reliability and performance and
a longer lifetime of materials while reducing maintenance time of a variety of structures and systems.
We investigate the early-stage damage formation and damage evolution in advanced multifunctional
laminated aerospace composites embedded with a very small amount of carbon nanotubes
(CNTs) in the matrix material and short carbon fibers along the Z-direction to reinforce the
interlaminar interfaces. The three-dimensional (3-D) conductive network formed by the CNTs and
the flocked carbon fibers allows for sensitive in-situ damage detection in materials in addition to
providing improved mechanical properties such as superior fracture toughness for damage tolerance.
We optimize several parameters such as fiber length, diameter, and density to generate an effective
3-D electrical conductive network, and characterize the responses of these composites under
mechanical loading to investigate damage formation and evolution, advancing science and
technology towards superior damage-tolerant and zero-maintenance structural materials.
Keywords:

This work aims at developing a machine learning-based model to detect cracks on concrete surfaces. Such model is intended to increase the level of automation on concrete infrastructure inspection when combined to unmanned aerial vehicles (UAV). The developed crack detection model relies on a deep learning convolutional neural network (CNN) image classification algorithm. Provided a relatively heterogeneous dataset, the use of deep learning enables the development of a concrete cracks detection system that can account for several conditions, e.g. different light, surface finish and humidity that a concrete surface might exhibit. These conditions are a limiting factor when working with computer vision systems based on conventional digital image processing methods. For this work, a dataset with 3500 images of concrete surfaces balanced between images with and without cracks was used. This dataset was divided into training and testing data at an 80/20 ratio. Since our dataset is rather small to enable a robust training of a complete deep learning model, a transfer-learning methodology was applied; in particular, the open-source model VGG16 was used as the basis for the development of the model. The influence of the model’s parameters such as learning rate, number of nodes in the last fully connected layer and training dataset size were investigated. In each experiment, the model’s accuracy was recorded to identify the best result. For the dataset used in this work, the best experiment yielded a model with an accuracy of 92.2%, showcasing the potential of using deep learning for concrete crack detection.

Through the life cycle of civil infrastructures, quality assessments shall be implemented when construction, in-service, before/ after repair and so forth; however, there are no decisive techniques to evaluate inside of structures non-destructively. The authors have developed an advanced measurement method using tomographic approaches. With these advanced technologies, internal damage or defects can be visualized as a distribution of elastic wave parameters such as velocities so that damage identification consisting of locations and damage degree would be possible. In the paper, fracture processes of concrete decks are visualized by the acoustic approaches. Specifically, RC slabs with/ without water supply subject to wheel loads are cyclically damaged with monitoring acoustic approaches. As a result, depending on the water condition, different pattern of fracture progress can be confirmed.

A wide range of bulk materials with different physical properties are nowadays handled in the packaging industry using different material conveying techniques. Nevertheless, experimental methodologies to characterise flowability of granular materials in actual handling conditions are still under development. This paper presents a new fully instrumented device for flowability assessment by granular column collapse of bulk materials. The generated granular flow is monitored by load cells that register the flow heights and by a high-speed video camera that captures the bulk flow kinematics through particle image velocimetry analysis. The 3D surface morphology of the final condition is determined with a 2D laser profile scanner. Results show the effect of varying the initial column aspect ratio on flow response.

Auxetic materials represent a relatively new class of materials that are characterized by a cellular structure and a negative Poisson coefficient. Auxetics are extremely useful for morphing applications thanks to their synclastic deformation capability. Most of these materials have been developed with macro-scaled cellular units. However there are some applications (e.g. micro-air vehicles or biomedical applications) for which polymeric morphing materials need to be applied in relatively small areas. In these cases, a material scale reduction that leads to lightweight auxetic films with a miniaturized cellular structure could be of great interest. With this in mind, an experimental study was conducted to analyze the response of films that are characterized by a miniaturized cellular structure. The unit cells in this study were made of an aggregation of microwires and micronodes that were strategically interconnected to form auxetic expansions and contractions. The reduction in scale of the cellular units has a significant impact on the material characterization and properties. The response of polymeric micro-scaled cells is in fact here demonstrated to be strongly influenced by surface forces and dramatic changes in gaseous or liquid environment. This represents the most critical aspect and key variation when comparing these films with standard macro-auxetics. The extremely challenging (at this length-scale) fabrication and testing processes were optimized. Single cells, were thus successfully tested in different environments with programmed and digital micro-stages while being monitored under a microscope with a video camera. Digital image correlation techniques were used to highlight the deformation. The expansion/contraction process was found to be fully reversible after several cycles and at different deformation speeds.